Dynamical seasonal prediction, Land Effects, and Modern Reanalysis

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Host: CIRA Presenter: Prof. Jagadish Shukla Location: ATS 101 This seminar has three parts. The first part is a brief scientific and biographical overview frommy memoir – “A Billion Butterflies: A Life in Climate and Chaos Theory” that was publishedin April, and to thank my students, professors and research colleagues at MIT, GSFC/NASA,UMD, COLA, GMU, and other research centers in the world with whom I had the privilege ofworking during the past five decades.The second part of the seminar was inspired by a question frequently asked by students, “Howdoes one get research ideas?” The seminar will give a brief personal retrospective of the originsof ideas for modern reanalysis, dynamical seasonal prediction (DSP), and the importance ofland surface processes for modelling and prediction of weather and climate.During the mid-20th Century, the butterfly effect and the limits of weather predictability were thedominant paradigms, indicating that dynamical seasonal prediction would not be possible. Yetthe demonstration of significant impacts of slowly varying boundary conditions of sea surfacetemperature and soil wetness using fledgling climate models of early 1980s provided a scientificbasis for research on dynamical seasonal prediction (DSP). Within a decade, global coupledOcean-Atmosphere models succeeded in simulation and prediction of sea surface temperature for1-2 seasons, and DSP became operational like NWP.There are several national and international research programs and field experiments formeasurements and parametrizations of land surface processes demonstrating the importance ofland surface processes in variability and predictability of sub-seasonal and seasonal variations ofglobal weather and climate. What were the origins of the ideas and model experiments that led tothis recognition.Reanalysis products have now become an indispensable data source for weather and climateresearch and have recently been important for training Artificial Intelligence (AI)/MachineLearning (ML) models. How reanalysis got started? It was not easy!The third part of the seminar will present some recent results on the variability and predictabilityof Indian summer monsoon rainfall.

Shipboard Water Vapor Observation Using Satellite Positioning

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Host: CIRA Presenter: Sho Hibino Location: CIRA Commons The Japan Meteorological Agency (JMA) began Global Navigation Satellite System (GNSS) water vapor observations at sea using research vessels in 2021, expanding to cargo ships and ferries in 2022. Data analyzed on each vessel is transmitted to JMA headquarters in real time every 10 minutes and utilized in operational forecasting and numerical models in the form of Precipitable Water Vapor (PWV). This presentation introduces GNSS water vapor observations from ships, including an overview of research vessels.

Promoting Research and Forecast Product Development: The Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)

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Host: CIRA Presenter: Naufal Razin Location: CIRA Commons Tropical cyclones revolve around the interactions of processes that occur at various spatial and temporal scales. Studying these processes and their interconnections require the consolidation of various data that capture these processes at their respective scales. Such data consolidation is no easy task, as they involve obtaining, collocating, and intercalibrating data from various sources and products; ensuring data homogeneity over the period of the dataset; and standardizing these various data sources for a straightforward analysis. To advance the understanding around the relationship between the tropical cyclone convective structure and the environment, we develop the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical cyclone-centric 1) intercalibrated, multichannel, multisensor microwave brightness temperature, 2) retrieved rainfall from NASA’s Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites.TC PRIMED consists of over 245,000 overpasses of 3,585 tropical cyclones from 1987 through 2024, providing researchers with an analysis-ready dataset to promote and support research and forecast product development. In this talk, I will summarize the efforts to create TC PRIMED and increase its artificial intelligence readiness, and present the research that were enabled by the development of TC PRIMED.

Tropical Wave Interactions: From Dynamics to Numerical Prediction

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Host: Michael Bell Presenter: Quinton Lawton – NSF National Center for Atmospheric Research  Location: ATS 101 The accurate representation of atmospheric waves in the tropics remains a key operational challenge, in part because these systems can have significant local and downstream impacts. Recent research has also shown that interactions between tropical waves can promote tropical cyclone formation and trigger localized extreme rainfall. However, numerical models often struggle to simulate these waves accurately, and their ability to represent wave interactions is poorly understood. In this talk, I discuss recent advances in our understanding of the dynamics, interactions, and predictability of two types of tropical waves: easterly waves (EWs) and convectively coupled Kelvin waves (CCKWs). I begin by highlighting how object-tracking techniques can be used to quantify the variability and dynamics of these waves, as well as the processes linking their interactions to extreme weather events. I then present ongoing work assessing the fidelity of numerical models in simulating tropical waves and their interactions. These include physics-based operational models, the Artificial Intelligence/Integrated Forecasting System (AIFS), and the Model for Prediction Across Scales – Atmosphere (MPAS-A). Our results demonstrate that the physics-based models produce CCKWs that are weaker and propagate faster than in satellite-based observations, likely due to deficiencies in simulating their convective coupling. In contrast, AIFS shows a remarkable ability to simulate CCKWs and EWs, outperforming the other models. Nevertheless, all of the models routinely fail to represent CCKW–EW interactions. These findings underscore how persistent challenges in tropical wave forecasting may hinder our ability to predict extreme weather events, highlighting a need for additional research and model development. 

Experiences in Forecasting the Weather and Training 

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Host: Bernie Connell Presenter: José Manuel Gálvez Location: In-person at CIRA Commons Being able to work with the CIRA Training team directly brings an opportunity to integrate expertise gained during my 13-year tenure at the WPC International Desks. This presentation provides a summary of key responsibilities at the Desks, including operational weather forecasting, Impact-Based Decision Support Services (IDSS), forecast tool development and implementation, and delivering effective onsite and virtual training to diverse audiences. During those years, we collaborated with CIRA to conduct monthly satellite focused weather briefings and training, as well as workshops engaging the international community. While at CIRA, I aim to introduce the WPC-developed Hail and Severe Weather Algorithm (GR02T for Granizo-V02-Trial) and the Galvez-Davison [stability] Index (GDI). The GDI has been tuned for tropical and subtropical locations. GR02T was tuned for severe weather in the Rio de la Plata Basin in South America, and has potential for application in the central United States. I look forward to exploring, sharing, and adapting training methods to enhance applications of spectral bands and RGB products for diverse users and communicating observations from operations back to researchers.

On an efficient line smoother for the p-multigrid γ-cycle

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Host: JOSÉ PABLO LUCERO LORCA 1,2, DUANE ROSENBERG 1,2, ISIDORA JANKOV 3, CONOR MCCOID 4, AND MARTIN GANDER 5 Presenter: Location: As part of the development of a Poisson solver for the spectral element discretization used in the GeoFluid Object Workbench (GeoFLOW) code, we propose a solver for the linear system arising from a Gauss-Legendre-Lobatto global spectral method. We precondition using a p-multigrid γ-cycle with highly-vectorizable smoothers, that we refer to as line smoothers. Our smoothers are restrictions of spectral and finite element discretizations to low-order one-dimensional problems along lines, that are solved by a reformulation of cyclic reduction as a direct multigrid method. We illustrate our method with numerical experiments showing the apparent boundedness of the iteration count for a fixed residual reduction over a range of moderately deformed domains, right hand sides and Dirichlet boundary conditions.

Spring Cleaning: Updates from the CIRA Carbon Group

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Presented by: Andrew Schuh Research Scientist/Scholar III

This overview presentation will discuss the exciting activities of the CIRA Carbon Group. Chris O’Dell presented an initial overview on Carbon Group activities in September 2023 focusing on retrievals and the remote sensing of greenhouse gases (GHG). In this talk, I’ll focus on how we use those satellite observations, in conjunction with other sources of GHG data such as those collected by our colleagues down the road in Boulder at NOAA-GML, to produce surface maps of estimated CO2 flux. While this source-receptor, or “flux inversion”, problem is largely linear in its basic form, we still deal with a number of hurdles that may be overlooked by NWP practitioners. We will also discuss new efforts to use hyperspectral imagery in the field to improve our knowledge of gross primary production (GPP), the carbon “sink” portion of the CO2 net balance at the surface. Lastly, we will give an overview of the efforts being taking by our community to educate the next generation of GHG/trace gas flux inversion modelers right here at CIRA!

CSU’s Advanced Lasers and Extreme Photonics Center and ATLAS Facility: Ultra-Intense Lasers for Science and Technology

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Presented by: Jorge Rocca Hosted by: Dave Randall

CSU’s Advanced Lasers and Extreme Photonics Center and ATLAS Facility: Ultra-Intense Lasers for Science and Technology Ultra-intense lasers can heat matter to reach conditions found at the center of stars, and drive nuclear fusion in the laboratory. This talk will review the applications of ultra-intense lasers in science and technology and the contributions CSU is making in this area.

Export Controls at CSU

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Presented by: Rich Wright Director of Secure and Global Research

This presentation will cover how export controls increasingly shape the way researchers engage with technology and ways to protect yourself and your lab. I will also cover federal reporting requirements and dual-use technologies.

The simultaneous assimilation of underused radar and satellite observations to improve convection forecasts

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Presented by: Dr. Keenan Eure

Host: Dr. Keenan Eure Presenter: Location: Accurate forecasts of the initiation and evolution of deep, moist convection in convection-allowing models (CAMs) are both a priority and a challenge of the numerical weather prediction and convective-scale data assimilation communities. Underused observations from dual-polarization weather radars and all-sky (clear and cloudy affected radiances) satellites have the potential to improve the forecasts of deep convection in CAM ensembles. Skillful convection initiation (CI) forecasts are dependent on accurate forecasts of the planetary boundary layer (PBL). There are many processes within the PBL that contribute to CI, including moisture, lift, and instability. Radars provide clear-air radial velocities within the PBL as well as novel PBL depth observations, obtained from quasi-vertical profiles of differential reflectivity (ZDR). GOES-16 infrared brightness temperatures provide information on cloud structures and cover. The first part of this talk explores the value of these observations when assimilated jointly and separately using a 40-member Ensemble Kalman Filter (EnKF) for a case study. In addition to the PBL, the internal structures of convection are difficult to model, which can be important for storm mode, intensity, and longevity. One distinct dual-polarization signature in intense convection is the ZDR column, which is a vertical protrusion of positive ZDR values above the environmental melting level. These are significant for characterizing storm updrafts, which can dictate storm mode and severe hazards. For the second portion of the talk, the direct assimilation of ZDR is assessed jointly and separately with all-sky infrared brightness temperatures in another case study. From both sets of experiments, some results may suggest radar alone provides modest benefits relative to satellite data assimilation alone; however, simultaneous radar and satellite data assimilation in these cases provide the most promising results.

CIRA National Satellite Training Activities

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Presented by: Dan Bikos & Jorel Torres

We will discuss satellite training activities at CIRA with a focus on the national audience (NOAA users).  National satellite training efforts began in the late 1990s with a focus on live teletraining and were a collaborative effort between CIRA, CIMSS, NESDIS and the NWS.  CIRA developed training on a wide variety of topics, including GOES Rapid Scan Operations (RSO) which was foundational to the current usage of GOES Mesoscale Domain Sectors.  As time passed, asynchronous types of training gained popularity, particularly with NWS forecasters who work rotating shifts.  CIRA played a key role in the development of the Satellite Foundational Course for GOES-R (SatFC-G) and the Satellite Foundational Course for JPSS (SatFC-J). The courses consist of training modules that update NWS forecasters, the scientific community and others on the latest capabilities from geostationary and polar-orbiting satellites. Additionally, we will discuss various types of asynchronous and synchronous satellite training delivered by CIRA. Some of the satellite training materials and resources encompass satellite blogs that help users discover the utility of GOES and JPSS applications in an operational setting. Numerous GOES and JPSS Quick Guides (1-2 page product reference documents) and Quick Briefs (3-5 minute product application videos) have been produced to assist users in the comprehension of the data that include the applications, limitations, and imagery and product interpretation. Currently, CIRA continues to offer weekly GOES and JPSS product teletraining sessions with NWS users and have also been involved in the development of satellite short course webpages for users to access training materials. In complement with the training activities, satellite liaison interactions with users will also be discussed, highlighting the variety of ways to capture user feedback on datasets to help improve the research-to-operations process.

Deep-learning Structure Analysis for Tropical Cyclones and its Application for Studies of Concentric Eyewalls and Climate Change

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Presented by: Buo-Fu Chen Center for Weather Climate and Disaster Research, National Taiwan University, Taipei, Taiwan

Deep learning (DL) is useful in various regression tasks for tropical cyclone (TC) analysis and forecasting, including regression to the current TC radial wind profile and regression to future TC structure parameters (i.e., statistical forecasts of intensity or size). The first part of the presentation showcases the usefulness of DL for reconstructing homogenized and trustworthy global TC wind profile datasets since 1981, thus facilitating an examination of climate trends of TC structure/energy extremes. By training with uniquely labeled data integrating best tracks and numerical model analysis, our model converts multichannel satellite imagery to a 0-750-km wind profile of axisymmetric surface winds. The model performance is verified to be sufficient for climate studies by comparing it to independent satellite-radar surface winds. Moreover, the integrated kinetic energy (IKE) calculated based on the AI winds has an R2 = 0.99 against aircraft observation. Understanding past TC trends and variability is critical for projecting future TC impacts on human society, considering the changing climate. Based on the new homogenized dataset, the major TC proportion has increased by ~13% in the past four decades. Moreover, the proportion of extremely high-energy (IKE) TCs has increased by ~25%, along with an increasing trend (> one standard deviation of the 40-y variability) of the mean total energy of high-energy TCs. Although the warming ocean favors TC intensification, the TC track migration to higher latitudes and altered environments further affect TC structure and energy. On the other hand, long-lived and short-lived concentric eyewalls (CEs) are accompanied by diverse structural parameters and can affect TCs’ intensity. Therefore, we used the DL TC wind profiles, along with a CE dataset, to examine how CEs affect TC development pathways revealed by IKE-intensity (K-V) diagrams. Results show that short-lived CEs (duration < 20 h) tend to maintain TC intensity and IKE, while long-lived CEs (25% of all CEs) even favor IKE growth, contributing to TCs with extremely large circulation. This study showcases that DL-generated data may help accelerate classical research and enhance our scientific understanding of TCs.  

Exploring dense optical flow-retrieved winds for characterization of convective phenomena and future applications

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Presented by: Dr. Theodore McHardy

Modern optical flow techniques have recently been used to retrieve cloud-top motion around convective updrafts and to compute divergent flow using visible satellite imagery. This study applies these novel methods to multiple convective phenomena, including a supercell thunderstorm, a tropical cyclone, a volcanic eruption, and multiple pyrocumulonimbus (pyroCb) events. Optical-flow-retrieved wind vectors and cloud-top divergence (CTD) are compared in order to provide quantitative context and test the baseline functionality of optical-flow-retrieved parameters as investigative tools for all types of deep convection. Multiple time steps between images, representing the different scan modes of the sensor, are tested as inputs for determining the feasibility of using imager scans with larger spatial coverage, such as full hemispheric view. Emphasis is placed on pyroCb events, which are increasingly recognized for impacts spanning the upper-troposphere to stratosphere, including perturbations in chemistry, cloud nucleation, and climate circulation. CTD captures updraft intensification, as well as differences in convective activity between two pyroCb events and individual updraft pulses occurring within a single event. Optical flow-derived parameters can uniquely provide a top-down analysis of convective phenomena, including individual pyroCbs, in real-time. These wind-retrieval techniques show potential for wide-ranging research and operations applications, such as characterizing pyroCb smoke source inputs for downstream smoke modeling, cloud/aerosol height assignment, data assimilation, or tropical cyclone intensification studies.

Evaluation of Tropical Cyclone Track and Intensity Forecasts from Artificial Intelligence Weather Prediction (AI-WP) Models

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Presented by: Kate Musgrave and Mark DeMaria

Weather prediction (WP) models based on artificial intelligence (AI) have proliferated over just the past few years. This study evaluates the utility of AI-based weather prediction for tropical cyclone track intensity forecasting. Four AI-WP models are evaluated for northern hemisphere 2023 tropical cyclones from May-November using National Hurricane Center verification procedures. Results show that the track forecasts are comparable to those from the best physically based NWP models. However, the intensity forecasts have no skill relative to even the simplest statistical models, due to an extreme low bias in the prediction of the maximum wind. The low intensity bias is explained by consideration of the least-squares minimization of a misplaced idealized vortex between successive forecast times in the AI-WP models. These results show that the utility of current AI-WP models is highly variable, depending on what phenomena are being predicted.

Leveraging Social Science to Understand the Ready in Weather-Ready Nation

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Presented by: Valerie Were

Forecasts have improved significantly over the years but there remain gaps in understanding the nation’s readiness for increasingly severe weather, water, and climate events. In 2021, the National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) formally established a Social, Behavioral, and Economic Sciences (SBES) program in the Office of Science and Technology Integration to identify and begin addressing those gaps. CIRA researchers are embedded in the NWS supporting that program. Beyond that support, CIRA is also interested in further growing SBES integration and application internally. This seminar will provide an overview of how the NWS and CIRA are each leveraging SBES to build a Weather-Ready Nation.   Valerie Were Valerie Were, Ph.D. is a social scientist who supports the Social, Behavioral, and Economic Sciences (SBES) Program at the National Weather Service (NWS) and also works on increasing social science integration at CIRA. Before joining CIRA in August 2021, Valerie was the Social Science Lead at the NOAA Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies. Prior to that, she was a contractor in the NOAA Chief Economist’s Office. Valerie holds a B.S. in Watershed Science from Utah State University and survived the winters to earn an M.S. in Water Resources Science and a Ph.D. in Natural Resources Science and Management from the University of Minnesota-Twin Cities. When she’s not working she’s likely enjoying an outdoor activity, watching sports, reading, or replacing the house plants she can’t seem to keep alive.

Enhancing Research Impact With Intellectual Property and Technology Transfer

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Presented by: Dr. Hoeher CSU STRATA

There are a variety of avenues for research to reach the public and make an impact. This talk will discuss the basics of intellectual property and delve into the beginning stages of tech transfer, exploring valuable strategies to amplify the influence of your research. Whether you’re aiming to commercialize your findings, collaborate with industry partners, or enhance societal impact, this discussion will help you gain insight towards new pathways that can help you meet your research and impact goals.

CIRA’s Involvement in International Training Activities

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Presented by: Bernadette Connell Meteorologist / Satellite Training Specialist, RAMMB- Training Group, CIRA

CIRA has been involved in international training activities on the usage of satellite imagery in the forecast process since the 1990s. With the launch of new satellites like the GOES I-M series, satellite operators across the globe recognized the need to better promote the usage of satellite imagery in the forecast process both in their own countries and in countries with fewer resources. Initially, many of the activities mirrored what was applied in country: in-person 2-week workshops. With tight funding and a growing internet, virtual activities were evaluated to enhance the workshops. When this showed promising results, formal efforts to support the training activities came out of the World Meteorological Organization (WMO) and the Coordination Group for Meteorological Satellites (CGMS) with the establishment of a Virtual Laboratory for Education and Training (VLab) in 2000. One of the initiatives of the group was the establishment of regular monthly virtual Regional Focus Group sessions to continue to engage participants after the workshops. CIRA was a part of this effort starting in 2004 and in March of this year, celebrated 20 years of sessions. We will give an overview of what a Regional Focus Group session is, some of the successes and challenges of the sessions and how they have supported capacity-building activities in the Americas and the Caribbean.

Threats in Motion: Exploring and Implementing Moving Weather Warnings (and Watches)

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Presented by: Kevin Manross CIRA-GSL

Ideally the average person would be able to pull out their phone, choose a location and time and get a solid idea of hazardous weather conditions for the “when and where” they chose. To a degree, this is currently the case, but there are some known gaps in updating hazard weather information as the user approaches “now”. Notably, in the case of severe thunderstorms and tornadoes, the “outlook to watch” and the “watch to warning” time frames could benefit from improved information flow. FACETS aims to address these gaps through the use of probabilities and more continuous updating of location and timing. Probabilistic Hazards Information (PHI) and Threats In Motion (TIM) are two specific implementations of the FACETs paradigm developed for warning on severe thunderstorms and tornadoes. This talk will briefly touch on PHI but focus on TIM, its benefits of more equitable lead time, the variations of TIM and its operational path.

How Do We Make And Improve Satellite Data Products That Enable Breakthrough Science? Journeys Through Solar Backscatter Observations

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Presented by: Dr. Joanna Joiner NASA Goddard Space Flight Center Hosted by: Dan Lindsey

Over the past two decades, satellite solar backscatter instruments have been a cornerstone for monitoring atmospheric composition, including near surface pollutants and climate agents such as ozone, sulfur dioxide, and nitrogen dioxide. Some of these same sensors were also surprisingly able to measure a small signal arising from solar-induced fluorescence inside leaves, global measurements related to the total amount of carbon taken up by plants. All these satellite-derived products require complex physics-based retrieval algorithms that transform spectral measurements of backscattered sunlight into useful geophysical quantities that can be used in scientific studies. In this talk, I will present several examples that illustrate how important advances in retrieval algorithms occur unexpectedly or in a non-linear manner, and how retrieval improvements then lead to important new science. For example, machine learning is now being used not only to speed up processing of the massive amounts of available satellite data, but also to improve products by reducing noise and expanding coverage in cloudy areas. These improvements will continue to yield new science and applications.

Pure AI-based weather forecasting models – Where are we and where should we go?

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Presented by: Imme Ebert-Uphoff and Jacob Radford Hosted by: CIRA, CSU; Machine Learning Group

Over the past 18 months purely AI-driven global weather forecasting models have been emerging that demonstrate increasingly impressive skill.  These models are typically trained on ERA5 data and are completely data-driven – most of them do not include a single physical equation.  Many of these models are orders of magnitude faster than NWP models and can run on modest computational resources enabling repeatable on-demand forecasts competitive with NWP. The low computational cost enables the creation of very large ensembles, which better represent the tails of the forecast distribution, which, if an ensemble is well calibrated, allows for better forecasting of rare and extreme events.   As of right now these models are still in the proof-of-concept stage, but new models emerge roughly monthly with rapidly increasing abilities, raising the question whether AI models might soon compete with NWP models for selected forecasting tasks. While these models have not yet undergone the necessary vetting for transition into forecast operations, we can begin to lay the foundation towards this goal. This includes not just bulk verification, but also familiarizing forecasters with the output, strengths, and weaknesses of AI models and soliciting feedback from forecasters on where they envision AI models benefiting forecast processes. To foster this communication, a group of scientists from CIRA and NOAA-GSL have started to visualize the output of these models and are currently ramping up activities to evaluate these models.  At CIRA we now – thanks to Jacob Radford and Robert DeMaria – run several AI models locally and display 7-day global forecasts on a CIRA webpage.  We are also setting up a multi-year archive of forecasts for scientists to dig into. This presentation aims to bring everyone up to speed on these recent activities and to solicit feedback (and potential collaboration) regarding additional evaluation criteria and methods.

An overview of the P3 property-based bulk microphysics scheme

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Presented by: Jason Milbrandt Environment and Climate Change Canada Hosted by: Yoonjin Lee and Kyle Hilburn

In 2015, the Predicted Particle Properties (P3) bulk microphysics scheme was introduced.  In the original P3 scheme, all ice-phase hydrometeors were represented by a single “free” category with 4 prognostic variables from which various physical properties could be computed.  As a result, the properties could evolve continuously in time and space and the ice category could represent any type of frozen hydrometeor (within the confines of models that try to represent the complexity of ice particles with simple geometric shapes).  As such, the P3 scheme represented a dramatic shift away from the traditional paradigm of representing ice using pre-defined “typical” categories (e.g. “snow”, “graupel”, etc.) with constant parameters to define their physical properties.  Since its inception, there have been several major developments to P3 that have enhanced its capacity to model ice microphysics. Ice is now triple-moment, it has a prognostic liquid fraction, and has a user-specified number of free categories.   In this presentation, an overview of the P3 scheme and its recent developments will be given along with illustrations of how these advances lead to improved realism of simulations for a wide range of weather.  Limitations and plans for future development will also be discussed.  An argument will be made that in order for the modeling community to advance significantly in its capacity to represent cloud microphysics in both research and operational atmospheric models, the commonly-used traditional approach must be abandoned in favor of property-based microphysics schemes.

An Overview of the CIRA Carbon Group

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Presented by: Chris O’Dell CIRA, CSU; Carbon team

This overview presentation will discuss the exciting activities of the CIRA Carbon Group.  Our group primarily focuses on processing and utilizing satellite observations to better understand aspects of the earth’s carbon cycle.  In simpler language, this means studying the current distribution as well as changes to sources and sinks of carbon dioxide and methane to and from the atmosphere by using these atmospheric observations.  In this talk I’ll introduce the still-new-but-slowly-maturing field of space-based remote sensing of greenhouse gases, it’s successes as well as failures in the last decade, and what we hope for the future in the next decade.

Satellite Use at Joint Typhoon Warning Center (JTWC)

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Presented by: James Darlow Technical Services Dept. / JTWC Hosted by: Dr. Galina Chirokova

The Joint Typhoon Warning Center (JTWC) is a joint United States Navy – United States Air Force command in Pearl Harbor, Hawaii. JTWC is responsible for the issuing of tropical cyclone warnings in the North-West Pacific Ocean, South Pacific Ocean, and Indian Ocean for all branches of the U.S. Department of Defense and other U.S. government agencies. Their warnings are intended for the protection of primarily military ships and aircraft as well as military installations jointly operated with other countries around the world. JTWC adheres to the World Meteorological Organization’s (WMO) rules for storm names and acknowledged guidelines for intensity of tropical cyclones, with the exception of using the U.S. standard of measuring sustained winds for 1-min instead of the 10-min span recommended by the WMO (see Saffir-Simpson Hurricane Scale). The JTWC is not one of the WMO designated Regional Specialized Meteorological Centers, nor one of its Tropical cyclone warning centers, as its main mission is to support the United States government agencies. JTWC monitors, analyzes, and forecasts tropical cyclone formation, development, and movement year round. Its area of responsibility covers 89% of the world’s tropical cyclone activity. The center is staffed by about 37 U.S. Air Force and Navy personnel. JTWC uses several satellite systems and sensors, radar, surface and upper level synoptic data as well as atmospheric models to complete its mission.

Multisatellite Water Vapor Products at the Weather/Climate Interface

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Presented by: John Forsythe CIRA, CSU; MetSat team

Water vapor is the fuel for much of what we perceive as weather, including the formation of clouds and precipitation.  Since the primary source of water vapor is evaporation over the oceans, global satellite observations provide the key measurement of this key variable.  Weather forecasters use two CIRA-developed products, Blended Total Precipitable Water (BTPW) and Advected Layer Precipitable Water (ALPW), to track pipelines of moisture which support heavy precipitation.  Each product is driven primarily by passive microwave data, but infrared GOES and surface GPS data are important for BTPW.  ALPW is being transitioned to NOAA operations this year, and science upgrades to BTPW are also currently being transitioned. ALPW allows forecasters to see the origins and transport of upper-level moisture.  When converging and aligned with low-level moisture this can be the difference between an ordinary weather event and an extraordinary one. Recent case studies and applications of ALPW for several different hazardous weather events and floods will be presented. As satellite records begin to cover more decades, products to place the water vapor amounts into historical context become possible.  This is especially important to identify extreme events.  Results from a new percentile ranking of ALPW values which is being evaluated by forecasters this summer will be presented. Experiences on the path from research at a cooperative institute to a product widely used by forecasters will be described.

Long-term variation of TC lifetime maximum intensity location over the northwestern Pacific

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Presented by: Hyeong-Seog Kim Ocean Science and Technology School, Korea Maritime & Ocean University

The annual mean locations of tropical cyclone (TC) lifetime maximum intensity (LMI)have been poleward migration for the last 40 years. In this study, we evaluated the factors affecting the long-term changes in the LMI location using the track pattern classification metrics. We classified the TCs in the western North Pacific into seven clusters by the Fuzzy c-mean clustering. Using the track patterns, we calculated the effects of track pattern change and pure change in the total variation of LMI latitudes. As a result, the long-term poleward migration of the LMI latitudes is associated with the pure change rather than track change. Also, the result indicated that the long-term change in the pure change is strongly associated with the local sea surface temperature warming. The track change has a large interannual variation related to the El Nino/Southern Oscillation while it has a minor effect on the long-term changes in the LMI latitudes.

Tropical cyclone intensity prediction based on satellite cloud feature extraction and machine learning

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Presented by: Myung-Sook Park Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology

Convection intensity and organization are significant contributors to the intensification of tropical cyclones but suffer from the lack of in situ observations over the ocean. This study developed i) the geostationary satellite cloud feature extraction (CFE) algorithm to quantify the dynamic process of Tropical cyclone (TC) rapid intensification (RI) and ii) machine-learning-based TC intensity prediction. In the CFE algorithm, we newly developed satellite indices to quantify the degree of convection organization, such as Largest Patch Index and Effective Mesh Size, in addition to the well-known indices of overall convection intensity and symmetry from the satellite images. Regular observations from geostationary satellites in RI TCs in comparison with slow- and neutral-intensifying TCs in the western North Pacific for 2015-2019 are used to extract the time series of primary convective features related to RI. We constructed a model based on deep convolutional neural networks based on the CFE algorithm and re-analysis data to predict 6, 12, and 24-hour TC intensity. Machine-learning-based TC intensity prediction with multi-scale TC modulating factors is expected to reduce the TC intensity forecast errors.

Use of a U-Net Architecture to Improve Microwave Integrated Retrieval System (MiRS) Precipitation Rates

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Presented by: Shuyan Liu

We report on implementation of a U-Net convolutional neural network architecture to improve operational satellite retrievals of instantaneous precipitation rate from the NOAA Microwave Integrated Retrieval System (MiRS). The U-Net architecture was implemented using NOAA-20/ATMS (Advanced Technology Microwave Sounder) passive microwave retrievals from the MiRS system. Training data consisted of input features that included operational retrievals of precipitation rate, total precipitable water, latitude, and longitude. Training target data (i.e. reference) were hourly precipitation rates from the operational Multi-Radar/Multi-Sensor System (MRMS) over the Conterminous U.S. (CONUS). The U-Net was trained using one year of collocated MiRS and MRMS data over the CONUS during 2021. Independent validation of U-Net was performed using data from 2022. Validation results showed that U-Net predictions were clearly improved relative to the original MiRS retrievals in terms of bias and root mean square error, as well as categorical scores. The improvement mainly stemmed from a much better depiction of light rainfall distribution. Categorical scores such as the probability of detection and Heidke skill score were also significantly improved, as were aggregate error statistics. For instance, Heidke skill score and false alarm rate improved from 0.42 to 0.50, and 0.057 to 0.014, respectively. Bias improved from 0.033 to -0.006 mm/hr. The spatial distribution correlation coefficient of the accumulated precipitation improved from 0.77 to 0.89. Once trained, the extremely low computational requirements of the U-Net model predictions highlight a potentially attractive means of improving operational retrievals of satellite precipitation rates, where latency of product dissemination is an important consideration.

Global daily gap-free ocean color products derived from multi-satellite merged measurements using the DINEOF method

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Presented by: Xiaoming Liu

Satellite ocean color products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A (S3A) and Sentinel-3B (S3B) have been widely used for surveillance of the ocean environment and research on ocean physical, biological, biogeochemical, and ecological processes. However, either VIIRS or OLCI daily ocean color images are often incomplete in spatial coverage due to cloud cover, contamination of high sun glint, narrow swath width, high sensor-zenith angle, high solar-zenith angle, and/or other unfavorable retrieval conditions. Although merging daily ocean color images from multiple satellite sensors can help reduce the number of invalid pixels, gap-filling methods such as the Data Interpolating Empirical Orthogonal Function (DINEOF) are often used to reconstruct invalid pixels and generate gap-free images. In this talk, multi-sensor derived global daily gap-free ocean color data, including chlorophyll-a (Chl-a) concentrations, water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), and suspended particulate matter (SPM) concentrations will be presented. In addition, gap-free data in different spatial resolutions of 9-km, 2-km, 1-km and 0.5-km, will be compared and discussed.

Preliminary Investigation of Ensemble Forecast Sensitivity to Observation Impact with GFS and RRFS

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Presented by: Liao-Fan Lin CIRA, CSU; NOAA/OAR/Global Systems Laboratory

Forecast Sensitivity to Observation Impact (FSOI) helps to quantify the impact of assimilated observations on forecast skills in numerical weather prediction systems. At NOAA, an ensemble FSOI (EFSOI) package is available within the Gridpoint Statistical Interpolation (GSI) and the ensemble Kalman filter (EnKF) data assimilation system (i.e., GSI-EnKF).  This tool has been upgraded and implemented into the Global Forecast System (GFS) v.16 (operational since March 2021) and the Rapid Refresh Forecast System (RRFS) developing version (the successor of the current operational Rapid Refresh and High-Resolution Rapid Refresh systems).  There are differences in the practices of EFSOI between the GFS and RRFS.  One of the obvious differences is the cycling frequency.  The global model cycles and performs the EFSOI calculation every six hours, while we run regional ensemble analyses and EFSOI calculations using shorter frequencies (i.e., 1 or 3 hours).  The EFSOI uses the 24-hour global (and 3- or 6-hour regional) ensemble forecasts as inputs to estimate the observation impact at the analysis time.  We quantify the EFSOI of various types of data (e.g., in-situ, radiosonde, and satellite data), and a preliminary investigation of global and regional cycling ensemble data assimilation experiments will be presented.

CIRA Tropical Cyclone Forecast Uncertainty Product Development for the National Hurricane Center

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Presented by: Dr. Mark DeMaria

The National Hurricane Center (NHC) implemented the Hurricane Strike Probability program in 1983 in recognition of the need to provide uncertainty information to complement their deterministic track and intensity forecasts to aid decision makers in mitigation activities. The Strike Probabilities only provided information about track forecast uncertainty and were replaced by the wind speed probabilities (WSP) in 2006. The WSP model was developed by CIRA in collaboration with NHC and NESDIS/RAMMB, and several modifications have been made since the original implementation. CIRA is also developing the next generation WSP that includes higher spatial resolution and a more accurate treatment of surface wind reduction over land. In the longer term, CIRA is also developing new methods to estimate tropical cyclone track and intensity forecast uncertainty using machine learning methods, which have the potential to further improve the WSP model. This presentation will summarize CIRA’s contributions to NHC’s current operational WSP model and future directions for model improvements.

Untangling the Amazon Carbon Cycle with Satellite Data

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Presented by: Dr. Ian Baker

In this talk, we will present a ‘farm to table’ description of how satellite data is being used to inform carbon cycle processes in Amazonia, which is both poorly sampled at the surface and critical to the global carbon cycle. We will describe how results from statistical ‘top down’ inversion models are used to confront process-based ‘bottom-up’ models of ecophysiological mechanisms to gain fuller understanding. Inversion models assimilate column CO2 from greenhouse gas satellites which, when coupled with aircraft data and process models, allows a description and understanding of regional biophysics that was not previously possible. The Amazon is the largest tropical evergreen forest on the planet and has a significant influence on the global carbon cycle. This influence translates into variability in the airborne fraction of CO2 in response to anthropogenic sources and interannual variability (IAV) forced by modes of climate variability such as ENSO and Tropical North Atlantic indices. This variability will be realized as anomalies around the mean annual cycle of carbon flux for the region. Consensus on this mean flux, in terms of timing, amplitude, and biophysical determinants is elusive, forming an obstruction to our predictive capability. Seasonality is defined by wet and dry seasons, and anomalies in timing, intensity and duration determine IAV. A generation ago, we believed that Amazonian carbon flux was directly coupled to precipitation, with carbon uptake during the wet season and efflux during seasonal drought. Findings from (a few) surface observations flipped the paradigm, with the result that most bottom-up regional estimates now simulate uptake during the dry season and efflux during seasonal rains. Using satellite-based and aircraft data, we now have strong evidence that carbon flux sign is indirectly tied to precipitation seasonality, the amplitude of the seasonal net flux is much smaller, and the determining processes are completely different than previously thought.

Non-Gaussian Data Assimilation Developments at CIRA

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Presented by: Steven Fletcher and Senne Van Loon

The underlying assumption for variational and Kalman filter based data assimilation algorithms is that the associated errors are Gaussian distributed random variables.  Over the last 18 years at CIRA we have worked on relaxing this assumption to allow for lognormally distributed, and recently reverse-lognormally distributed errors.  The first part of this talk will be an overview of the development of the lognormal and the mixed Gaussian-lognormal variational approaches along with a representer, formulation, as well as the recent development of the mixed Gaussian-lognormal based Kalman filter. In the second part, we introduce the reverse-lognormal distribution to be able to include negatively skewed errors. All these ideas can then be combined to develop a mixed version of the maximum likelihood ensemble filter. The main question then remains: how do we decide the underlying distribution of the errors? To answer this question we have developed a basic machine learning algorithm that can help us.

Non-Gaussian Data Assimilation Developments at CIRA

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Presented by: Drs. Steven J. Fletcher and Senne Van Loon

The underlying assumption for variational and Kalman filter based data assimilation algorithms is that the associated errors are Gaussian distributed random variables.  Over the last 18 years at CIRA we have worked on relaxing this assumption to allow for lognormally distributed, and recently reverse-lognormally distributed errors.  The first part of this talk will be an overview of the development of the lognormal and the mixed Gaussian-lognormal variational approaches along with a representer, formulation, as well as the recent development of the mixed Gaussian-lognormal based Kalman filter. In the second part, we introduce the reverse-lognormal distribution to be able to include negatively skewed errors. All these ideas can then be combined to develop a mixed version of the maximum likelihood ensemble filter. The main question then remains: how do we decide the underlying distribution of the errors? To answer this question we have developed a basic machine learning algorithm that can help us.

The USA Does Not Measure Up! The History and Current Status of the Metric System in America

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Presented by: Don Hillger

Abstract: Even though the United States has resisted the worldwide change to the metric system, its adoption is inevitable for Americans!  History tells us that countries have only switched to metric, none the other way (except for temporary reversions).  The US remains in the midst of its metric transition as measurements change in various aspects of business and life.  This information is especially important for teachers/instructors, who should also know the history and current status of the metric system in the US. The US “missed the boat” in the 1970s when the rest of the English-speaking world converted to metric.  That was a time when the metric system was being taught in schools, and metric was intended to replace our former units in most aspects of daily life.  However, the lack of a firm deadline and the voluntary nature of our metric transition has hindered progress towards metric.  Therefore, the US in effect remains the only major industrial nation not using metric as our primary measurement system. The slow (voluntary) path that the US chose to follow is why we are still struggling with metric transition.  Most people are surprised when they learn of the large number of consumer products, services, and standards that already use metric units, most of which are hidden to the average/casual observer.

How and Why Does Tropical Cyclone Precipitation Respond to Climate Change?

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Presented by: Alyssa Stansfield NSF Postdoctoral Fellow Department of Atmospheric Science in Professor Kristen Rasmussen's research group Hosted by: Dr. Steve Miller

Tropical cyclone (TC) precipitation can create dangerous hazards and cause millions of dollars in damages. While previous literature agrees that future TC precipitation will increase due to rising global temperatures, the estimates of how much it will increase vary, ranging from around 3 to 20% per °C of warming, or three times the Clausius-Clapeyron scaling (about 7% per °C). In this talk, various methodologies and datasets are utilized to disentangle the interwoven factors that impact the response of TC precipitation to warming, including TC intensity, outer size, landfall frequency, and increases in atmospheric moisture. Results are first presented for the North Atlantic and eastern United States specifically and then generalized globally using idealized aquaplanet model simulations. The idealized simulations are compared to more realistic global model simulations and satellite observations of TC precipitation. Finally, proposed high-resolution (~1 km) limited-domain idealized simulations with the goal of exploring changes in three-dimensional TC precipitation structures as sea surface temperatures warm are discussed.    Seminar PDF

Quantifying tropical cyclone behavior with open-source tools across spatial and temporal scales

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Presented by: Kimberly M. Wood Department of Geosciences Mississippi State University

The ever-expanding volume of readily-available atmospheric data requires matching improvements in computational power and computing tools. There is a growing ecosystem of geoscience Python packages and increasing federal funding for such efforts, but it can be challenging to identify the most appropriate tools for a given task and then navigate the learning curve required to implement a chosen tool. Recent collaborative work has investigated seasonal tropical cyclone (TC) activity—such as the record-breaking 2020 Atlantic hurricane season—and machine-learning-friendly metrics to quantify TC convective structure with potential for short-term structural prediction. This talk will present research findings and discuss how the research was conducted by providing workflow examples and linking to sample code (http://arashi.geosci.msstate.edu/python/PythonResources.html) to support others in implementing similar approaches. It will also describe these tools’ applicability to real-time analysis by presenting data visualizations generated for the 2022 North Atlantic and eastern North Pacific hurricane seasons.

Air quality and haze across the U.S over the past 30 years: past and current issues

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Presented by: Jenny Hand CIRA-NPS

Over the past 30 years, dramatic changes in particulate matter composition across the U.S. have been evidenced by trends in speciated aerosol data collected by large-scale U.S. monitoring networks, such as the Interagency Monitoring of Protected Visual Environments (IMPROVE) network. In this presentation, I will discuss how these trends point to the success of combined regulatory activities aimed at reducing anthropogenic emissions over the last three decades. However, as regulated sources of precursor emissions decline, the contributions to haze from unregulated sources, such as biomass burning, dust, agricultural activities, and oil and gas extraction, have increased. In addition to trends, I will discuss the current status of aerosol composition and haze across the U.S. in the context of these sources. Reducing haze from unregulated sources will require additional mitigation strategies and resource management plans in order to improve air quality and visibility in the U.S. for future generations.

Use of mixture-model track clustering to interpret tropical cyclone ensemble forecasts

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Presented by: Alex Kowaleski Penn State University Hosted by: Dr. Kate Musgrave, CIRA

Ensemble forecasts of tropical cyclones provide a wealth of data for operational forecasters and researchers, but fully utilizing the data remains a challenge. Regression mixture-model clustering is demonstrated as a method to partition tropical cyclone ensemble forecasts into a small number of groups based on track. Clustering facilitates the exploration of storm evolution and hazards (e.g. storm surge) associated with each track grouping. Mixture-model clustering is first applied to partition an ensemble of 72 simulations of Hurricane Sandy (2012) into six clusters based on storm track. After clustering, the structural evolution of Sandy is examined in the four most populous clusters. Sandy undergoes a warm seclusion extratropical transition in each analyzed cluster, with extratropical transition timing the clearest difference between clusters. Inter-cluster differences are smaller, but still relevant, when extratropical transition is analyzed relative to the landfall time of each simulation. Track clustering is next applied to simulations of Hurricane Irma (2017) to study storm surge hazard. Each of 51 WRF ensemble members, initialized five and two days before Irma’s Florida landfalls, is used to drive a corresponding ADCIRC ocean simulation.  Irma’s tracks in the WRF simulations are then partitioned into clusters; inundation volume and inundation probability from ADCIRC are examined for each cluster. The inundation results among clusters show how track clustering can augment probabilistic hazard forecasts by elucidating hazard scenarios and variability across a dynamical ensemble.

Satellite radiance data assimilation within the NOAA hourly Rapid Refresh and High-Resolution Rapid Refresh

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Presented by: Dr. Haidao Lin CIRA/CSU and NOAA/ESRL/GSD

Satellite radiance data have been shown to have consistent positive impact with statistical significance within the NOAA Rapid Refresh (RAP) hourly updated model system.  RAP version 4 (RAPv4) was implemented operationally at the National Centers for Environmental Prediction (NCEP) in July 2018.  This implementation included a significant radiance upgrade package with greater use of direct broadcast/readout data as well as assimilation of data from new sensors.  The RAP uses the Gridpoint Statistical Interpolation (GSI) hybrid variational/Ensemble Kalman Filter (EnKF) data assimilation system, with ensemble information for the regional assimilation coming from the 80-member global ensemble data assimilation system. The next satellite assimilation upgrade package has been recently finalized for the coming RAP version 5 (RAPv5) NCEP operation upgrade (planned for spring 2020).  This RAPv5 radiance upgrade package includes the assimilation of the GOES-16 Advanced Baseline Imager (ABI) infrared radiance data, the Cross-track Infrared Sounder Full-Spectral-Resolution (CrIS-FSR) data both from S-NPP and NOAA-20, and the Advanced Technology Microwave Sounder (ATMS) data from NOAA-20.  The initial ABI radiance assimilation work started from the code development to read in ABI radiance data into the GSI and single case GSI study to identify the ABI data impact from the three water vapor channels, then ABI O-B bias and standard deviation evaluation for different cloud masks and different surface types within the RAP domain was performed.  Research work associated with ABI error tuning and quality control was conducted through hourly RAP retrospective runs with forecast verification to maximize the ABI data impact.  In addition, a series of RAP retrospective runs were performed to evaluate the forecast impact from these new instruments/data separately and/or combined together. Use of polar orbiter satellite radiance data in rapidly updated regional models has traditionally been limited by data latency issues combined with the very short data cutoff window for these models.  For the hourly RAP mesoscale model system, the data cutoff time is ~ 30 min. resulting in limited data usage for standard data delivery methods.  The availability of direct broadcast data offers the potential for improvements in the percent of total polar orbiter data being assimilated into rapidly updated regional models.  The data impact from assimilation of the direct broadcast/readout radiance data has been evaluated for the RAP model through extensive retrospective experiments with the short-term forecast verification against the traditional radiosonde observations as well as the CrIS observed radiance observations. At the seminar, I will present the overall and individual radiance data impact within the RAPv4, and then will focus on the recent radiance updates for the coming RAPv5, including GOES-16 ABI, NOAA-20 CrIS-FSR/ATMS data assimilating.  The impact from direct broadcast radiance data within RAP will also be shown as well as some preliminary work on radiance assimilation within the High-Resolution Rapid Refresh (HRRR)/Alaska.

Improving Quantitative Precipitation Estimation in Complex Terrain over the San Francisco Bay Area Using Profiler and Gap-filling Radar Observations

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Presented by: Dr. Haonan Chen Radar, Satellite, and Precipitation Research Scientist with CIRA and Physical Sciences Division of NOAA Earth System Research Laboratory

The San Francisco Bay Area is covered by two operational S-band WSR-88D: KMUX and KDAX. However, the KDAX radar beams are partially blocked at low elevation angles due to the mountainous terrain, whereas the KMUX radar is deployed at an elevation of over 1000 m, which can easily overshoot precipitation during the winter storm seasons in Northern California. As a result, these two radars are not sufficient to observe low-level atmospheric conditions and provide detailed precipitation information for quantitative hydrometeorological applications. This study aims to improve operational radar rainfall estimates using auxiliary remote sensing observations. In particular, a number of S-band profilers are deployed to investigate the vertical structure of precipitation at various locations in this complex terrain. The representative vertical profiles of reflectivity (VPR) measured by the profilers, which can better characterize the rainfall microphysical structure during its falling processes, are incorporated in WSR-88D radar data processing and the derivation of improved rainfall products. In addition, NOAA and CIRA are building an Advanced Quantitative Precipitation Information (AQPI) system to improve monitoring and forecasting of precipitation and coastal flooding in the San Francisco Bay Area. As part of the AQPI program, high-frequency (i.e., X-band) high-resolution gap-filling radars are being deployed to improve tracking of incoming storms and provide high-resolution coverage over populated and flood-prone urban areas throughout the Bay region. To date, two X-band radars have been deployed and collected a substantial set of precipitation measurements that contribute to the development of local radar rainfall algorithms. This talk will discuss the applications of vertical-pointing profilers and gap-filling scanning radars in enhancing monitoring and quantitative estimation of precipitation over the Bay Area. Results show that rainfall products derived with the aid of additional remote sensing observations have better performance compared to the operational radar products currently available in this particular domain.

Part 2: Preliminary Analysis of Wind Gusts in Recent Landfalling Hurricanes

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Presented by: John Kaplan NOAA/AOML/Hurricane Research Division

Forecasting the timing and magnitude of tropical cyclone rapid intensification (RI) remains an ongoing forecasting problem.  Although the ability of deterministic tropical cyclone intensity models to forecast such events has improved, their skill remains inadequate. Thus, forecasters have relied on other tools such as the SHIPS suite of probabilistic statistical rapid intensity models to aid with the forecasting of RI. Since the start of the 2016 Hurricane Season, the SHIPS RI model guidance suite has provided operational RI forecasts at multiple lead times (12, 24, 36, 48 and 72-h) rather than the single lead time of 24-h for which it was originally developed. In our upcoming presentation, a verification of those operational multi-lead time forecasts as well as a discussion of preliminary efforts to improve those existing statistical RI models using storm structure information will be provided. Although wind gusts produced by tropical cyclones are important for operational forecasting, building design, and for use in tropical cyclone damage models; explicit operational numerical model forecasts of wind gusts are currently not provided. Thus, forecasters and engineers typically apply gust factors that had been determined in previous studies to forecast wind gusts in real time. In our present study, high resolution surface wind observations obtained from the National Center for Environmental Information are utilized to compute gust factors in recent landfalling hurricanes Harvey (2017), Irma (2017), Florence (2017), and Michael (2017). Preliminary results from the analysis of the distribution of gust factors in those four storms will be discussed in our upcoming presentation.

Part 1: Statistical Rapid Intensity Prediction: A Review of Recent Model Results

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Presented by: John Kaplan NOAA/AOML/Hurricane Research Division

Forecasting the timing and magnitude of tropical cyclone rapid intensification (RI) remains an ongoing forecasting problem.  Although the ability of deterministic tropical cyclone intensity models to forecast such events has improved, their skill remains inadequate. Thus, forecasters have relied on other tools such as the SHIPS suite of probabilistic statistical rapid intensity models to aid with the forecasting of RI. Since the start of the 2016 Hurricane Season, the SHIPS RI model guidance suite has provided operational RI forecasts at multiple lead times (12, 24, 36, 48 and 72-h) rather than the single lead time of 24-h for which it was originally developed. In our upcoming presentation, a verification of those operational multi-lead time forecasts as well as a discussion of preliminary efforts to improve those existing statistical RI models using storm structure information will be provided. Although wind gusts produced by tropical cyclones are important for operational forecasting, building design, and for use in tropical cyclone damage models; explicit operational numerical model forecasts of wind gusts are currently not provided. Thus, forecasters and engineers typically apply gust factors that had been determined in previous studies to forecast wind gusts in real time. In our present study, high resolution surface wind observations obtained from the National Center for Environmental Information are utilized to compute gust factors in recent landfalling hurricanes Harvey (2017), Irma (2017), Florence (2017), and Michael (2017). Preliminary results from the analysis of the distribution of gust factors in those four storms will be discussed in our upcoming presentation.

Overview of the Joint Typhoon Warning Center (JTWC)

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Presented by: Capt. Amanda Nelson of the USAF Hosted by: Dr. Kate Musgrave, CIRA

The Joint Typhoon Warning Center (JTWC) was established in 1959. The mission of JTWC is to provide analysis, forecast and decision support to enable DoD and other decision makers to plan, prepare, and protect against the threat of tropical cyclones, tsunamis and other weather impacts. This presentation will provide a brief history and overview of the organization. Additionally, it will cover some of the products that are distributed and a few of the tools and techniques utilized to assist in the creation of these products.

Effects of Midwinter Arctic Leads on Boundary Layer Clouds

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Presented by: Steven Krueger Visiting from the University of Utah Hosted by: Dave Randall

Leads are quasi-linear openings within the interior of the polar ice pack, where the ocean is exposed directly to the atmosphere. Due to the extreme air-water temperature contrast (20 to 40 K), turbulent and radiative heat fluxes over leads can be two orders of magnitude larger than those over the ice surface in winter and thus dominate the wintertime heat budget of the Arctic boundary layer. Cold-season leads may also produce boundary layer clouds that extend tens of kilometers downwind. These clouds can spatially and temporally extend the impacts of leads on the Arctic surface heat budget. We are using multi-source observations and a 3D cloud-resolving model to understand the impact of leads on the boundary layer clouds. We have used measurements from the ARM cloud radar at Barrow and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on board Aqua to establish statistical associations between large-scale lead fraction and low cloud occurrence. We expected low cloud occurrence frequency to increase with the large-scale lead flux (lead fraction x calculated sensible heat flux per unit area over open leads). However, we found just the opposite. Low cloud occurrence frequency from CloudSat-CALIPSO over a large-scale region also increased with the large-scale lead flux. Motivated by these results, a 3D cloud-resolving model, System for Atmospheric Modeling (SAM), was used to explore the underlying physics. We found that a wide recently frozen lead produces large sensible heat fluxes, but reduced latent heat fluxes, and consequently produces thinner and less extensive low-level clouds. This result provides a plausible explanation for the counterintuitive observational results: The observed high lead fraction must largely consist of newly refrozen leads which produce less low-level cloudiness. Our results emphasize the need to differentiate, in observations and in models, recently refrozen leads from open-water leads and from thicker ice.   Link to colloquia page: https://www.atmos.colostate.edu/colloquia/

Use of Trace Gas Measurements to Quantify Convective Transport Time Scales and Pathways

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Presented by: Z. Johnny Luo Visiting ATS from the City University of New York Hosted by: Sue van den Heever

Discussion will begin at 11:15 a.m. Refreshments will be served at 10:45 a.m. in the weather lab     Convective transport from the marine boundary layer (MBL) to the upper troposphere (UT) is investigated using airborne in situ measurements of chemical species over the tropical western Pacific (TWP). Using 42 trace gas species with photochemical lifetimes ranging from shorter than a day to multiple decades, we derive a transit time spectrum G(t) and the associated modal and mean transit times for the UT air mass sampled over the convectively dominant TWP region. G(t) describes relative contributions of air masses transported from the MBL to the UT via all transport paths with different transit times (see the schematic illustration below). We further demonstrate that the tracer-derived transit time scale is broadly comparable to that estimated from convective mass flux. The observation-based transit time spectrum not only provides insights into convective transport pathways, but also has the potential to serve as an effective diagnostic or metric for evaluating the representation of convective transport in global models.

A Brief History of ESRL/GSD Central Facility Data Services

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Presented by: Bob Lipschutz Hosted by: Bob Lipschutz

Within the NOAA Earth System Research Laboratory (ESRL), the Global Systems Division (GSD) develops weather information systems, weather forecast models, and other applications in support of the National Weather Service, the Federal Aviation Administration, and other agencies. Well-known GSD products include the Rapid Refresh (RAP) and High Resolution Rapid Refresh (HRRR) models, the Meteorological Assimilation Data Ingest System (MADIS), and Science on a Sphere® (SOS). In addition, GSD has had a foundational role in the creation and evolution of NWS’ AWIPS meteorological workstation system.   A common feature of many GSD projects is that they require observational and model data provided by systems running within GSD’s Central Facility (CF). The CF systems are largely developed and maintained by CIRA Associates in the Data Services Group (DSG) of the Information and Technology Services (ITS) branch. These systems currently handle some 4 TBytes of incoming data daily as they acquire, decode, store, transfer and distribute numerous data sets for GSD scientists, developers and collaborators.   While bearing little resemblance to the original PROFS system, fielded nearly 40 years ago by GSD’s progenitor organization, the systems running in today’s Central Facility can be traced directly back to those early days. This talk provides a brief look at the ambitious goals laid out in the original PROFS program documents, and the remarkable progression from primitive capabilities to the systems running now, as viewed by a data guy who witnessed and participated in the development of those systems.

US Air Force Operational Weather Structure and The Meteorological Forecast Process

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Presented by: Kerrin Caldwell, Major, USAF Chief, Air Force Weather Program Requirements & Resources

Abstract: The operational design and strategic construct of weather operations within the Air Force weather architecture is rapidly changing. The motto of “bring the future faster” has allowed for the introduction of unconventional training methods and forecasting techniques being utilized by todays weather operators. New capability requirements upgrades to our weather data assimilation center has restructured the way Air Force weather operations utilizes numerical weather modeling and forecasting.   Bio: Maj Kerrin Caldwell is currently the chief of Weather Requirements and Resources located at Air Combat Command Headquarters at Langley Air Force Base, Virginia. As Chief, she manages and tracks the US Air Force Weather Weapons System capability requirements program. Since graduating and commissioning from Embry-Riddle Aeronautical University, She has served in various operational leadership positions. This includes serving as the Joint METOC Officer to the Commander of the Joint Task Force Migration Operations Team in Guantamo Bay, Cuba, lead meteorologist for multinational weather operations and security cooperation in the U.S. Southern Command area of operations, as well as Chief of the Weather Specialty Team at the Air Operations Center at Osan Air Base, Republic of Korea.

Introduction on South Korean Meteorological Satellite Programs focusing on Geo- KOMPSAT -2A (GK- 2A) applications

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Presented by: Chu-Yong Chung and Eunha Sohn Korea Meteorological Administration (KMA)/National Meteorological Satellite Center (NMSC)

Since 2010, KMA has operated and utilized the Communication, Ocean and Meteorological satellite (COMS), which is expected to be available by 2020. KMA is now preparing for a new era of the Geostationary Korea operational multi-purpose satellite (GeoKompsat-2A or GK-2A; launch scheduled in December 2018). GK-2A has a 16-channel Advanced Meteorological Imager (AMI), which is a similar sensor to Himawari-8/9 AHI and GOES-16/17 ABI. We will present the status of KMA’s meteorological satellite programs and recent improvements of the products in this talk. We will also introduce dust-monitoring applications at KMA/NMSC. In addition to the current Aerosol Index (AI) algorithm using two channels (BTD: 10.8㎛-12㎛) of the COMS, D*-parameter based on the spectral variability of dust emissivity at 8.6㎛, 11㎛ and 12㎛ wavelengths has been tested with Himawari-8 AHI toward applications on the upcoming GK-2A AMI. A study to improve false detection over the source region will be presented.

Wildfire-Driven Thunderstorms Cause a Volcano-Like Stratospheric Injection of Smoke

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Presented by: David A. Peterson Naval Research Laboratory Hosted by: James R. Campbell, Edward J. Hyer, Michael D. Fromm, George P. Kablick, Joshua H. Cossuth, Matthew T. DeLand

Intense heating by wildfires can generate a deep, smoke-infused thunderstorm, known as pyrocumulonimbus (pyroCb). This extreme weather phenomenon can release a large quantity of smoke particles into the upper troposphere and lower stratosphere. The meteorology driving pyroCb occurrence, combined with increasingly active fire seasons, indicates that pyroCb are a significant and endemic summertime feature in several temperate regions worldwide. A single fire season in western North America can include more than 25 intense single or multi-updraft pyroCb events. Since 2000, several stratospheric aerosol layers, first thought to be of volcanic origin, have been reclassified as originating from pyroCb activity. To date, however, the impact of pyroCb on climate has never been systematically explored, and remains almost compl¬etely unquantified. Here we quantify the mass of smoke aerosol injected into the lower stratosphere from five near-simultaneous intense pyroCb observed in western North America on 12 August 2017. We find that the stratospheric aerosol mass injected by this extreme event was comparable to a moderate volcanic eruption, and at least an order of magnitude larger than previous benchmarks for extreme pyroCb activity. The resulting high-altitude smoke layer encircled the Northern Hemisphere over several months. Our results demonstrate that extreme pyroCb activity significantly influences the lower-stratosphere in a manner similar to infrequent volcanic intrusions. We anticipate that this study will establish a foundation for understanding the effects of pyroCb smoke on lower-stratospheric chemistry and dynamic circulation.

A numerical framework for operational coupled fire-atmosphere-fuel moisture-smoke forecasting

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Presented by: CIRA Hosted by: Jan Mandel, University of Colorado Denver (presenting) Adam Kochanski, University of Utah Sher Schranz, CIRA Martin Vejmelka, AVAST

We present an integrated wildland fire model WRF-SFIRE, based on combining a high resolution, multi-scale weather forecasting model WRF, with a semi-empirical fire spread model, a prognostic dead fuel moisture model, and smoke dispersion. Fire-released heat and moisture impact local meteorology. The fuel moisture model is driven by the atmospheric component of the system in order to render the diurnal and spatial fuel moisture variability. The dead fuel moisture is traced in three different fuel classes (1h, 10h and 100h fuel), which are combined to provide the total dead fuel moisture content at the fire model resolution (tens of meters) using fuel properties at the location. The wind and the fuel moisture in turn impact the fire rate of spread. The sub-kilometer model resolution enables detailed representation of complex terrain, and small-scale variability in surface properties. Ingest of infrared fire perimeters is supported by an interpolation of the fire arrival time between the perimeter and a previous one to spin up the atmosphere model. The fire simulations are run in online system WRFx. The simulations are initialized by a web-based control system allowing a user to define a fire as well as basic simulation properties such as simulation length, type of meteorological forcing and resolution, anywhere in CONUS and any time meteorological products are available to initialize the weather model. The data is downloaded automatically, and the system monitors execution on a cluster. The simulation results are processed while the model is running and displayed as animations on a web visualization portal. The portal also provides nationwide nowcasting of fuel moisture, based on the fuel moisture model with assimilation of surface observations of the fuel moisture from RAWS stations.

How much will Earth warm? Looking for clues in Earth’s historical energy budget.

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Presented by: Mark Richardson JPL/Caltech/UCLA JIFRESSE

Abstract: The amount of global warming we expect is controversial in the media. A common metric for this is Equilibrium Climate Sensitivity (ECS), which is the eventual warming following doubled atmospheric CO2. Recently published estimates include “maybe things won’t be that bad” (1.7 °C) and “we could probably destroy most of civilisation” (5.3 °C), but the “consensus” value has stayed stubbornly near 3 °C for decades. This talk aims to share some key techniques and to help listeners interpret results that can seem contradictory. In particular, it investigates “energy budget” approaches which are often lauded for being based on observations. It uses a recent study, Lewis & Curry (2018), as a detailed example.   Bio: Mark works with Graeme Stephens at JPL and mainly retrieves cloud properties with OCO-2. He did his PhD in Reading (UK) before a Caltech postdoc and UCLA JIFRESSE position at JPL. He has also worked on global temperature data, climate sensitivity, carbon budgets and other topics. One paper appeared on John Oliver’s Last Week Tonight and he is a contributing author to the recent IPCC Special Report on Global Warming of 1.5 °C.

Viewing the Earth’s Global Environment from Space: from Scientific Knowledge to Societal Benefits

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Presented by: Jack A. Kaye Associate Director for Research, Earth Science Division NASA HQ – Science Mission Directorate Hosted by: A. R. Ravishankara

The vantage point of space provides a unique opportunity to see all the elements of the global Earth system – atmosphere, ocean, land surface, ice, biosphere – and how they interact with each other. The ability to characterize both natural processes and those caused by humans, as well as the ability to study processes on a range of time scales from days to decades, helps scientists characterize and understand earth system variability and its causes and effects, as well as allowing for improvements in predictive capability. With this information, Earth system scientists can work with partners in other federal and international agencies, academia, industry, and the non-profit sector to help anticipate and respond to both naturally-occurring and humaninduced changes in the Earth system. In this talk, a review of how satellite-derived information, integrated together with complementary data from aircraft and surface based measurements and used in the context of Earth system models, is advancing our knowledge of the Earth and how the resulting information is being used by NASA and its interagency and international partners will be presented. Link to seminar page: https://www.atmos.colostate.edu/colloquia/

Wildfire-Driven Thunderstorms Cause a Volcano-Like Stratospheric Injection of Smoke

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Presented by: David A. Peterson Naval Research Laboratory Hosted by: James R. Campbell, Edward J. Hyer, Michael D. Fromm, George P. Kablick, Joshua H. Cossuth, Matthew T. DeLand

Intense heating by wildfires can generate a deep, smoke-infused thunderstorm, known as pyrocumulonimbus (pyroCb). This extreme weather phenomenon can release a large quantity of smoke particles into the upper troposphere and lower stratosphere. The meteorology driving pyroCb occurrence, combined with increasingly active fire seasons, indicates that pyroCb are a significant and endemic summertime feature in several temperate regions worldwide. A single fire season in western North America can include more than 25 intense single or multi-updraft pyroCb events. Since 2000, several stratospheric aerosol layers, first thought to be of volcanic origin, have been reclassified as originating from pyroCb activity. To date, however, the impact of pyroCb on climate has never been systematically explored, and remains almost completely unquantified. Here we quantify the mass of smoke aerosol injected into the lower stratosphere from five near-simultaneous intense pyroCb observed in western North America on 12 August 2017. We find that the stratospheric aerosol mass injected by this extreme event was comparable to a moderate volcanic eruption, and at least an order of magnitude larger than previous benchmarks for extreme pyroCb activity. The resulting high-altitude smoke layer encircled the Northern Hemisphere over several months. Our results demonstrate that extreme pyroCb activity significantly influences the lower-stratosphere in a manner similar to infrequent volcanic intrusions. We anticipate that this study will establish a foundation for understanding the effects of pyroCb smoke on lower-stratospheric chemistry and dynamic circulation.

Viewing the Earth’s Global Environment from Space: from Scientific Knowledge to Societal Benefits

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Presented by: Jack A. Kaye Associate Director for Research, Earth Science Division NASA HQ – Science Mission Directorate Hosted by: A. R. Ravishankara

The vantage point of space provides a unique opportunity to see all the elements of the global Earth system – atmosphere, ocean, land surface, ice, biosphere – and how they interact with each other. The ability to characterize both natural processes and those caused by humans, as well as the ability to study processes on a range of time scales from days to decades, helps scientists characterize and understand earth system variability and its causes and effects, as well as allowing for improvements in predictive capability. With this information, Earth system scientists can work with partners in other federal and international agencies, academia, industry, and the non-profit sector to help anticipate and respond to both naturally-occurring and human-induced changes in the Earth system. In this talk, a review of how satellite-derived information, integrated together with complementary data from aircraft and surface based measurements and used in the context of Earth system models, is advancing our knowledge of the Earth and how the resulting information is being used by NASA and its interagency and international partners will be presented. Link to seminar page: https://www.atmos.colostate.edu/colloquia/

Climate Monitoring, Climate Research, and Climate Services for Colorado

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Presented by: Colorado Climate Center Staff Hosted by: Russ Schumacher

The Colorado Climate Center, based in the Department of Atmospheric Science at Colorado State University since 1973, has a threefold mission to serve our state. We are responsible for monitoring the state’s weather and climate, including analyzing trends and extremes and operating two of our own weather observing networks; for conducting applied research toward improved understanding and prediction, with a particular focus on drought; and for providing value-added services and interpretation of climate data. In this presentation, the staff of the Colorado Climate Center will introduce and discuss their contributions to these wide-ranging responsibilities. This will include discussion of the CCC’s drought monitoring and early warning activities; an introduction to the CoCoRaHS and CoAgMET observation networks; other projects the CCC is involved with; an overview of our data holdings and resources; and our plans for the future, including potential collaborations with the department and CIRA.

NOAA’s Virtual Laboratory (VLab) Move to Liferay Digital Experience (DXP)

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Presented by: Ken Sperow, Jason Burks and Michael Giebler

The National Oceanic and Atmospheric Administration’s (NOAA) Virtual Laboratory (VLab) has experienced tremendous growth year over year since its inception in 2012. VLab provides collaboration tools (web content management, document library, forums, forms, and blogs), and development tools (project and issue management, revision control, code review, and continuous integration). In order to meet the needs of NOAA the VLab has continued to evolve. This past year the VLab Collaboration Services was migrated to the latest version of Liferay called the Liferay Digital Experience (DXP). Liferay DXP is a modular portal framework using OSGI standards and Elastic Search for its search engine. This new framework enables VLab to scale as the project grows and provide a dynamic, flexible collaboration space. In addition to infrastructure changes, energies are being spent on the look and feel of VLab to make for a more pleasant and enjoyable end-user experience.   VLab is now an essential component in the transition of research to operations (R2O) for the Environmental Modeling Center (EMC) at the National Centers for Environmental Prediction (NCEP) as well as the National Weather Service (NWS) Advanced Weather Interactive Processing System (AWIPS) II system. Both groups make full use of the collaboration and development services within VLab.   An overview and live demo of the VLab will be presented, highlighting tools facilitating R2O as well as the upgrade work from the previous version of Liferay to Liferay DXP.

The Utility of Ensemble – Sensitivity Analysis for Targeted Observing, Ensemble Sub setting , and Diagnosing Environmental Controls on Storm Characteristics

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Presented by: Aaron J. Hill Texas Tech University

Abstract   At Texas Tech University, we are interested in developing and using novel ensemble tools to improve our understanding of severe storm predictability.   Ensemble sensitivity is one such tool that when applied within an ensemble framework reveals atmospheric flow features (e.g. position of a jet streak, or magnitude of a low-level moisture plume) at early forecast times that are related to a chosen forecast response later in the forecast window. Typically, responses are chosen that diagnose forecast features of interest, e.g., accumulated rainfall, maximum updraft helicity, or low-level vertical vorticity. The ESA relationships between forecast responses and earlier-time variables has the potential to inform where additional sampling should occur in order to improve the response forecast. Intelligent methods to target additional observations have been available for decades, which take into account fast growing errors (i.e., singular vectors) and gradients of tangent linear models (i.e., adjoint sensitivity). Unfortunately, less than- ideal results have been realized when ESA has been utilized for targeted observing of mesoscale convection/precipitation forecasts.   Recent literature has noted near-neutral average impacts of additional observations when targeted with ESA. Given the desire to exploit advantages of ESA over other targeting methodologies (e.g., execution time), it is imperative to understand factors, which may include forecast nonlinearity, data assimilation procedures, and model error, that influence the prediction of observation impacts and the impacts after assimilation. A 50-member ensemble is generated over ten cases of severe storms along the dryline with the Advanced Research core of the Weather Research and Forecasting model (WRF) and Data Assimilation Research Testbed (DART) software. The observing system simulation experiment (OSSE) methodology is employed to control for degrees of freedom, including model error. A number of experiment permutations will be discussed, which aim to diagnose relative impacts of target observations on mesoscale convection forecasts.   This presentation will also discuss the utility of ESA to subset ensembles for improved probability forecasts as well as evaluate environmental controls on storm-scale processes. During the Hazardous Weather Testbed Spring Forecast Experiment, we demonstrated the use of ESA to subset ensembles to improve probabilistic forecasts of severe convection. Moreover, the application of ESA for storm-scale simulations (i.e., < 3-km grid spacing), despite linear constraints in the algorithm, reveals environmental heterogeneities and relevant storm-scale features that may influence low-level rotation in organized deep convection.

Precipitation Processes in Cyclones Passing over a Coastal Mountain Range: Recent Results from the Olympic Mountains Experiment (OLYMPEX)

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Presented by: Lynn McMurdie Visiting ATS from the University of Washington Hosted by: Kristen Rasmussen

The Olympic Mountains Experiment (OLYMPEX) was a multi-faceted, international, multi-agency field campaign that took place over the Olympic Mountains in the Pacific Northwest during the fall 2015 and continued through the winter 2016. The goals of OLYMPEX were to provide physical validation and verification of satellite-derived precipitation measurements by the Global Precipitation Measurement (GPM) satellites and to document the precipitation processes in land-falling wintertime cyclones as they approach land and are modified by complex terrain. The data assets of OLYMPEX covered both the windward and lee sides of the Olympic Mountains including an array of rain gauges and disdrometers placed at a variety of elevations, multi-frequency ground-based dual-polarization radars (NASA’s S-band NPOL and Ka/Ku-band D3R, NSF’s X-band DOW, and Environment Canada’s X-band), and three aircraft (NASA’s DC-8 and ER-2 and the University of North Dakota’s Citation). This presentation summarizes a wide variety of results from these OLYMPEX datasets that document the nature of orographic enhancement of precipitation as storms pass over a coastal mountain range. Link to seminar page: https://www.atmos.colostate.edu/colloquia/

Deep Learning and Novel Data Analytics for Climate Science

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Presented by: Karthik Kashinath Visiting ATS from the Lawrence Berkeley National Laboratory Hosted by: Ben Toms and Aryeh Drager

In this talk we discuss how machine learning, deep learning and novel data-driven analytics from applied math and physics can be used for two fundamental challenges in climate and weather sciences: (i) pattern recognition, pattern discovery and pattern tracking in large climate datasets, and (ii) emulation of complex dynamical processes that are critical for modeling Earth’s weather and climate. Part-1: Detecting, classifying and characterizing weather and climate patterns is a fundamental requirement to improve our understanding of extreme events, their formation and how they may change with global warming. These tasks, however, remain challenging across all classes of weather and climate patterns, and especially for extreme events. Deep Learning has revolutionized solutions to pattern recognition problems resulting in tremendous advances in computer vision, speech recognition, robotics and control systems. Topological data analysis is providing new and insightful ways of recognizing and characterizing the “shape” of data. Physics-based unsupervised pattern discovery is the next frontier of learning algorithms for scientific applications. In this presentation, we show how deep learning, applied topology and physics-based unsupervised discovery can be impactful in climate science. We present results on the methodology and lessons learned both on the science and the computations. We also highlight some challenges and opportunities. Part-2: Predictive modeling of complex, nonlinear, high-dimensional dynamical processes such as atmospheric convection are crucial for improving the reliability and accuracy weather and climate models. Deep generative models have recently been successful in learning the underlying statistics of complex physical processes from large amounts of data. However, their success in predicting physical systems have been limited, primarily because they do not require physical consistency. By integrating physics constraints and desirable statistical properties into an emerging class of deep generative model called Generative Adversarial Networks (GANs), we develop a new paradigm for modeling complex dynamical systems. We present preliminary results on the methodology and performance of this model in simple test cases and more complex chaotic processes of relevance to weather and climate modeling.

Ongoing Research into New and Emerging Technologies Aimed at Improving Scientific Research, Results, and Applications

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Presented by: Jebb Stewart Sr. Research Associate

At the Earth System Research Laboratory (ESRL), within the Advanced Technology Outreach (ATO) branch, research is ongoing into new and emerging technologies aimed at improving scientific research, results, and applications. These efforts range from improving data access and visualization through the development of high-performance web services, using machine learning to extract more relevant information from satellite observations, as well as the development of software containers for the ease of application deployment, both internally and externally to cloud service providers, and reducing hurdles for community model development. This presentation will provide an overview of these ongoing activities, the goals, the challenges, and future directions.

How Strong are the Strongest Wind Gusts within Tropical Cyclones?

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Presented by: Daniel Stern University Corporation for Atmospheric Research

The most intense tropical cyclones are characterized by maximum 1-minute mean surface (10-meter) wind speeds of 70-90 ms-1. Dropsondes within the boundary layer occasionally sample gusts (representative of timescales of a few seconds) exceeding 90 ms-1 and (more rarely) 100 ms-1. Such extreme wind gusts are found in nearly every category-5 tropical cyclone, but because of irregular and sparse sampling, it is unclear how frequent these gusts are, or whether they actually represent the upper-limit of wind speed in TCs. In this talk, I will present a large-eddy simulation of a realistic category-5 TC, and show that there are nearly always gusts exceeding 120 ms-1 somewhere within the eyewall. I will then show that realistic sampling with “virtual” dropsondes yields wind gust distributions similar to what has been observed, and that gusts exceeding 110 ms-1 are very rarely sampled. Based on the observed dropsondes and the simulation, I conclude that it is likely that real category-5 TCs are characterized by peak gusts of 120-140 ms-1.

An Operational Update on GOES-16 and Post-Launch Status of GOES-17”

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Presented by: Mike Stringer GOES-R Assistant System Program Director

NOAA’s Geostationary Operational Environmental Satellites (GOES) are a mainstay of weather forecasts and environmental monitoring in the United States. The next generation of GOES satellites, known as the GOES-R Series, represents significant advancements in the near real-time observation of severe weather across the Western Hemisphere. The GOES-R satellite, the first in the series that also includes GOES-S, GOES-T and GOES-U, launched on November 19, 2016. GOES-R became GOES-16 when it reached geostationary orbit and is now operational as GOES-East. The recently launched GOES-S, now known as GOES-17, is undergoing post-launch checkout and validation. GOES-17 will be operational as GOES-West, giving the nation two next-generation GOES to watch over the Western Hemisphere.   GOES-16 is proving to be a game changer for forecasters. The improved resolution, faster coverage and increased number of spectral channels available from the satellite’s Advanced Baseline Imager (ABI) allow for “nowcasting” of severe storms and discernment of atmospheric features not available with the previous GOES imager. The Geostationary Lightning Mapper (GLM), the first operational lightning mapper flown in geostationary orbit, will deliver further benefit for severe storm forecasting. Total lightning (in-cloud and cloud-to-ground) data from GLM, used in combination with radar, ABI data, and surface observations, has great potential to increase lead time for severe thunderstorm and tornado warnings and reduce false alarm rates. GOES-16 also hosts a suite of instruments that provide significantly improved detection of approaching space weather hazards.   This presentation will provide an operational GOES-16 status including imagery and data use in operational forecasts. It also includes an update on the launch of GOES-S (GOES-17) as well as the latest on GOES-T and GOES-U development.

OAWL’s Journey Toward Space: A Doppler Lidar Approach for Global Wind Profiles from Space

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Presented by: Mike Hardesty- CIRES & Sara Tucker- Ball Aerospace

Knowledge of weather patterns a day, three days, or even three weeks from now requires forecasts based on models initialized with accurate data. Vertically resolved wind profile data has been shown to have a high impact on forecast accuracy but measurements remain sparse, especially over areas such as the oceans or the Southern Hemisphere. This lack of wind profile data is a major limiting factor in numerical weather prediction’s ability to provide high-accuracy longer-term weather forecasts, to predicting hurricane storm tracks and intensity, and to understand aerosol transport.   To enable future space-based wind profile measurements, Ball Aerospace and NASA’s Earth Science Division Earth Science Technology Office funded development and demonstrations of Optical Autocovariance Wind Lidar (OAWL) technology. Recently NASA funded the build and demonstration of an airborne, Green-OAWL, or GrOAWL, a project to test a two-line-of-sight (LOS), 532 nm wavelength, aerosol Doppler wind lidar. While the LOS data can be directly assimilated into forecast models, by combining data from two orthogonal looks collected from a single instrument, this lidar can provides continuous, vertically-resolvedprofiles of horizontal wind speed and direction. Performance of the GrOAWL instrument was demonstrated and validated during a series of test flights on NASA’s WB-57 aircraft, during which GrOAWL measurements were compared with winds measured by dropsondes. The GrOAWL instrument was designed to be an airborne demonstrator for ATHENA-OAWL, a mission concept to measure winds from the International Space Station proposed to NASA under the Earth Venture Instrument Program. Science goals of the mission included improving low and mid-latitude weather forecasts and reanalyses, investigating the interactions between aerosol radiative forcing and dynamics in tropical cyclone formation, and improving understanding of the impact of long range aerosol transport on global energy cycles, air quality, and climate. Although classified as selectable and well-reviewed, the mission was not funded. Currently, an upcoming Earth Venture Announcement of Opportunity provides the opportunity to improve and resubmit a proposal based on the ATHENA-OAWL concept.   During our talk we will describe the GrOAWL technique, show results from the airborne validation of the instrument, and discuss the ATHENA-OAWL mission parameters and science objectives.   Bios Dr. Mike Hardesty has worked on development and application of Doppler lidars for measuring winds and atmospheric constituents for more than 30 years. He was a member of the science team for the Laser Atmospheric Wind Sounder, NASA’s original effort to implement space-based Doppler lidar. Since 1994 he has been a member of the Working Group on Space-based Lidar Winds, serving as co-chair since 2009. He has been a US visiting member of the European Space Agency’s Aeolus Mission Advisory Group since 2005 and is coordinating the US Calibration/Validation effort for Aeolus after launch of the instrument in Autumn of 2018.   Dr. Sara Tucker has worked for 18 years in industry and at NOAA designing, building, testing, and developing models and processing algorithms for multiple types of Doppler Wind lidars including two-micron coherent detection systems, a UV double-edge Fabry-Perot system and the dual-wavelength Optical Autocovariance Wind Lidar (OAWL)…. Read more »

History and Results from the Two Decade Quest to Measure the Earth’s Radiation Budget

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Presented by: Thomas H. Vonder Haar ATS Hosted by: Christian Kummerow

The first global measurements of the Earth’s emitted thermal energy and the amount of solar energy it absorbs from the Sun were made from early satellites in the 1960s to the 1980s. They provided a much different Radiation Budget than had been thought in the pre-satellite era. Following the scientific method, two additional satellite missions were used to confirm the first measurements by Vonder Haar and Suomi (1969, 1971). The early instruments and satellites are discussed. The new scientific results and their implications for Earth’s weather and climate system are reviewed. Some lessons learned are noted and suggestions for today’s continuing research are provided.

The 2017 Atlantic Hurricane Season

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Presented by: Michael Bell ATS

The 2017 North Atlantic hurricane season was an extremely active one, with 17 named storms (1981-2010 median is 12.0), 10 hurricanes (median is 6.5), 6 major hurricanes (median is 2.0) and 245% of the 1981-2010 median Accumulated Cyclone Energy occurring. The combination of a hurricane-enhancing large-scale environment and a stronger western Atlantic subtropical high led to one of the most damaging Atlantic hurricane seasons on record. Record-breaking levels of activity occurred during September, and the season was very destructive from a landfall perspective, with Harvey and Irma devastating portions of the continental US, while Irma and Maria brought catastrophic damage to Puerto Rico, Cuba and many smaller Caribbean islands. Continental United States (CONUS) hurricane-related inflation-adjusted damage has increased significantly since 1900, but neither observed CONUS landfalling hurricane frequency nor intensity show significant trends, including the devastating 2017 season. Growth in coastal population and regional wealth are the overwhelming drivers of observed increases in hurricane-related damage. As the population and wealth of the US has increased in coastal locations, it has invariably led to the growth in exposure and vulnerability of coastal property along the US Gulf and East Coasts. Unfortunately, the risks associated with more people and vulnerable exposure came to fruition during the 2017 season following the landfalls of hurricanes Harvey, Irma, and Maria.

Background Error Covariances for convective scale 4D Ensemble-Variational Data Assimilation

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Presented by: Joël Bédard Environment and Climate Change Canada Hosted by: Milija Zupanski

Higher model resolution model implies a higher number of degrees of freedom and a need for dense observation networks (e.g. satellite, radar and surface observations) to constrain the model initial state. Like in many other NWP centers, only a small fraction of the available observations is being used in ECCC operational systems. The horizontal thinning for all assimilated radiances is 150 km; radar observations are not yet assimilated operationally; and the screen level wind observations are not yet operationally assimilated over land. Although data assimilation for convective scale NWP has been the object of intense research lately, the resolution and the quality of background error covariances remain factors limiting the assimilation of dense observations.   The data assimilation component for a new short-term convective-scale numerical weather prediction (NWP) system covering most of Canada at 2.5 km resolution is currently being developed. It is based on a fully cycling deterministic 4DEnVar scheme with analysis increments initially computed at 10 km resolution. Several practical approaches have been evaluated and compared for generating ensembles of short-term forecasts for specifying the required background-error covariances. This includes ensembles from an EnKF and also from much simpler approaches. The new system is evaluated and compared with using Environment and Climate Change Canada’s currently operational regional data assimilation system (with increments computed at 50 km resolution) for initializing forecasts from the identically configured atmospheric model.

History and Results from the Two Decade Quest to Measure the Earth’s Radiation Budget

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Presented by: Thomas H. Vonder Haar ATS Hosted by: Christian Kummerow

The first global measurements of the Earth’s emitted thermal energy and the amount of solar energy it absorbs from the Sun were made from early satellites in the 1960s to the 1980s. They provided a much different Radiation Budget than had been thought in the pre-satellite era. Following the scientific method, two additional satellite missions were used to confirm the first measurements by Vonder Haar and Suomi (1969, 1971). The early instruments and satellites are discussed. The new scientific results and their implications for Earth’s weather and climate system are reviewed. Some lessons learned are noted and suggestions for today’s continuing research are provided.

Part I: Where, When and Why Did It Rain During PECAN? Part II: Overview of NCAR’s Water Vapor DIAL

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Presented by: Tammy Weckwerth NCAR Earth Observing Laboratory Hosted by: Michael Bell

Part I: The 2015 Plains Elevated Convection At Night (PECAN) field campaign, based in Hays, KS, was designed to understand the causes of and improve the predictive skill of the central U.S. nocturnal precipitation maximum. Over 100 instruments were utilized to sample the pre-convective and convective conditions within and around unorganized storms and mesoscale convective systems.   Multiple WSR-88D radars were combined with NCAR’s S-Pol radar to estimate the quantitative precipitation (QPE). As expected, the PECAN precipitation maximum occurred overnight from 03-09 UTC (10-04 CDT). The rainfall came nearly equally from systems that initiated in both the plains and mountains. The convection initiation (CI) events occurred most frequently in the late afternoon in the mountains and early evening in the plains. The top 10% rain-producing storms dropped 91% of the PECAN-observed precipitation. The NCEP/North American Reanalysis fields suggest a substantial difference in moisture and low-level winds between storm and non-storm days.   Part II will present an overview of the EOL and Montana State University micropulse water vapor differential absorption lidar (WV DIAL). This vertical-pointing system offers an exciting new capability in moisture sensing. The WV DIAL has been shown to run autonomously in multiple six-week field campaigns. Intercomparisons with radiosondes, AERI, GPS receivers and microwave radiometers show excellent agreement. Future plans for a 5-unit WV DIAL network and further lidar developments will also be presented.

A Comprehensive Observational Study of Graupel and Hail Properties

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Presented by: Andrew Heymsfield NCAR Hosted by: Christian Kummerow

In this seminar, I will describe the general properties of graupel (rimed particles < 0.5 cm) and hail, based on observations. I will then report on my work that uses novel approaches to estimate the fall characteristics of hail. Three-dimensional volume scans of hailstones of sizes from 2 to 7 cm were printed in 3D models (I’ll show some in my seminar) using ABS plastic, and their terminal velocities were measured in the Mainz vertical wind tunnel. To simulate graupel, some of the hailstone models were printed with dimensions of 0.2-0.5 cm, and their terminal velocities measured. From these experiments, together with earlier observations, I’ve parameterized the properties of graupel and hail for a wide range of particle sizes and heights (pressures) in the atmosphere. The wind tunnel observations, together with the combined total of more than 2800 hailstones for which the mass and cross-sectional area were measured, has been used to develop size-dependent relationships for the terminal velocity, mass flux, and kinetic energy of realistic hailstones.   Also in my seminar, I’ll fill you in on work that I’ve unraveled (going back to data from the mid 1930’s), to try and understand why the insurance and building industries use “outdated” data to estimate and repair hail damage.

The Land-Atmosphere Feedback Experiment

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Presented by: Dave Turner NOAA Hosted by: Sue Van Den Heever

The Land-Atmosphere Feedback Experiment (LAFE) was conducted at the ARM Southern Great Plains (SGP) site in north-central Oklahoma from 1-31 August 2017. This experiment deployed multiple scanning wind, temperature, and humidity lidar systems, additional surface energy balance stations, an Unmanned Aerial System, and a fixed wind aircraft to characterize the role of surface inhomogeneity and its impact on the atmospheric surface layer, the convective boundary layer above it, and the entrainment zone. I will provide some of the motivation for LAFE, our observational approach, and results from an excellent land-atmosphere interaction event: the 2017 solar eclipse.

Assessing United States County-level Exposure to Tropical Storms and Investigating the Association between Tropical Storm Exposure and Community-wide Mortality Risks

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Presented by: Brooke Anderson CSU Department of Enviromental & Radiological Health Services Hosted by: Michael Bell

Hurricanes and tropical storms can cause substantial economic and human health impacts. These impacts occur through a number of hazard pathways, including severe winds, rain, flooding, and tornadoes. In the United States, hurricane impacts are often assessed at the county level, often the level at which health and economic data are available. I will describe results from assessing hurricane exposure in U.S. counties by distance and four hazard-specific metrics to measure how well exposure classification agrees across metrics. Further, I will describe our results from using these exposure metrics to assess community-wide mortality risks associated with tropical storms in the U.S. While risks of accidental deaths from tropical storms (e.g., drowning, carbon monoxide poisoning) have been well-documented, much less is known about risks for more common causes of mortality (e.g., cardiovascular, respiratory). We conducted the first multi-year, multi-state epidemiological study to estimate the relative risks (RRs) of community-wide all-cause, cardiovascular, respiratory, and accidental mortality associated with tropical storm exposure in the United States (US). For each exposure metric, we modeled the association between community-level storm exposure and daily death counts in 78 large eastern US communities, 1988–2005, for a window from two days before to seven days after the storm’s closest approach. Under wind-based exposure metrics, we found substantially elevated risk for all mortality outcomes considered, with highest risk typically on the day the storm was closest. These estimated associations may be dominated by extremely high risks during the few most severe storms (e.g., Andrew [1992], Katrina [2005]), a hypothesis we continue to explore.

WRF-Chem/DART: Introduction, Application, Verification, and Compact Phase Space Retrievals (CPSRs)

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Presented by: Dr. Arthur P. Mizzi Climate and Global Dynamics Laboratory NCAR Hosted by: Ting Chi Wu

Air pollution is linked to lung and heart disease and other human health problems. It is also linked to regional climate change impacts with urban areas bearing the greatest burden of those impacts. In the United States its estimated costs range between $71B – $277B (0.7% – 2.8% of the 2005 GDP) annually. Clearly air pollution is an important social and scientific problem. To address the impacts of air pollution, policymakers and air quality managers rely on cutting-edge science to establish regulations and make management decisions to reduce and control air pollution with cost- effective approaches. WRF-Chem/DART – a regional chemical weather forecasting/ensemble data assimilation system is one such research/forecasting tool used in the United States, China, Mexico, Germany, and Canada to study and forecast air quality. WRF-Chem/DART integrates WRF-Chem (the Weather Research and Forecasting (WRF) model with online chemistry) into DART (the Data Assimilation Research Testbed) and includes the ability to assimilate: meteorology observations; in situ air chemistry observations; and satellite-based air chemistry observations (MOPITT CO full and partial column retrievals, IASI CO and O3 full and partial column retrievals, MODIS AOD total column retrieval, and OMI NO2 total column retrievals). In this talk, I will introduce WRF- Chem/DART and discuss its capabilities; application (FRAPPE, PANDA, and KORUS); and verification. Chemical data assimilation faces a number of challenges: spatially and temporally sparse in situ and satellite-based observations; indirect satellite observations (satellite retrievals); retrieval profile observations with large data-volume, low information density, and significant observation error covariance; emissions with large but unknown uncertainties, and non-Gaussian distributions. In WRF-Chem/DART we introduced Compact Phase Space Retrievals (CPSRs) to address the retrieval profile assimilation challenges. I will discuss CPSRs and their application to full and truncated retrieval profiles. I will close the talk with a discussion of some of the more important/difficult challenges still facing chemical data assimilation.

GeoCollaborate®: A Breakthrough Technology to Accelerate Data Sharing Across Multiple Platforms in a RT Collaborative Environment to Improve Research to Operations (R2O), Operations to Research (O2R), Situational Awareness and Decision Making

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Presented by: Dave Jones Founder & CEO, StormCenter Communications, Inc. Hosted by: CIRA/RAMMB

GeoCollaborate®, developed under the Federal Government’s SBIR program (Small Business Innovation Research), is a new technology that unlocks the burden of isolated data, complex tools and limited interaction by enabling disparate data sources to be accessed, in a real-time collaborative environment, across any platform. GeoCollaborate® places all participants on the same map at the same time. This breakthrough capability connects remote teams right now for rapid, informed decision making. Now, everyone, everywhere can be on the same map at the same time looking at the same data that is delivered to their device.   There are many use cases to put GeoCollaborate® to work and this seminar will describe some of those use cases and hopefully motivate attendees to think about the future of staging data for rapid access. From the active hurricane season of 2017 to the nationwide mobilization of fleet utility vehicles to restore power, to delivering the Eclipse2017 to broadcast meteorologists nationwide and creating an environment to leverage GIS and data holdings to share across state agencies, GeoCollaborate® is delivering a new way to put more data to work for new users and decision makers no matter what platform they are using.   Come and participate in the data sharing experience of GeoCollaborate® as Dave will engage your computers and mobile devices LIVE and deliver datasets right into your communications or computing platform. All you need is a browser and a connection to the Internet. The era of Data Driven Decision Making (3DM) is here.   About the speaker Dave Jones is the founder and CEO of StormCenter Communications, Inc. (www.stormcenter.com) in the Baltimore/Washington corridor. Dubbed an “Applications Futurist” by NASA, and StormCenter is a patent-holder of GeoCollaborate® (www.geocollaborate.com). Dave and his team has spent their careers focusing on applying science data to communications, disaster preparations, response and recovery and Dave has worked at NASA Goddard Space Flight Center, private weather companies, NBC in Washington, DC as a broadcast meteorologist for nearly a decade and founded two companies during his career. StormCenter is focused on applying the vast data resources available from Federal, State, NGOs and private sector organizations to saving lives and property and improving public response before and after disasters.   Dave is also an active member of the ESIP Federation (Federation for Earth Science Information Partners) and a past president of ESIP. He currently serves as co-chair of the Disaster Lifecycle Cluster, working to follow the pathway of trusted datasets for use in decision making environments and is leading the evolution of “Operational Readiness Levels” for trusted data. He is also a member of the All Hazards Consortium and sits on the SISE committee (Sensitive Information Sharing Environment) for trusted data exchange for situational awareness and decision making for the movement of fleet utility vehicles. Dave was also named a Fellow of the American Meteorological Society in 2013 and is co-chair of an annual Summit for broadcast meteorologists in Colorado. The Glen Gerberg Weather & Climate Summit brings together scientists and broadcast meteorologists to… Read more »

Cold-Weather Challenges for Water Infrastructure in the United States

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Presented by: Rob Ettema CSU's Department of Civil and Enviromental Engineering Hosted by: Sonia Kreidenweis

It is well understood that weather affects the design and performance of civil infrastructure. Not so well understood, though, are the cold-weather systems that challenge the design and performance of water-related, civil infrastructure in the United States. This talk describes several cold-weather challenges faced by water-related infrastructure; in the context of water-resource management, inland navigation, flood control, bridges and ports. Weather systems influence the ways whereby ice forms, behaves and breaks up. Challenges to infrastructure occur when ice-related processes disrupt infrastructure performance and at times lead to infrastructure failure.   This talk also describes, somewhat tentatively, certain characteristics of the cold-weather systems that challenge water infrastructure in the U.S. More work is needed to define the characteristics and to develop methods for forecasting and tracking them so as to enhance water-infrastructure performance.

The Relative Roles of Radiative Feedbacks and Poleward Heat Transport in the Spatial Pattern of Climate Change

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Presented by: Kyle Armour University of Washington Hosted by: Elizabeth Barnes

The pattern of greenhouse-gas induced climate change is not spatially uniform. For example, we have observed amplified warming in the Arctic, slow warming in the Southern Ocean and Antarctica, and an enhanced hydrologic that has increased precipitation gradients – all features that are robustly simulated by global climate models. What sets these patterns?   I show that zonal-mean climate change can be largely understood in terms of a moist energy balance model (MEBM) that, given radiative feedbacks and forcing, predicts the patterns of surface warming, poleward heat transport, and hydrologic cycle changes seen in CMIP5 models. The MEBM represents atmospheric heat transport as a simple diffusion of latent and sensible heat – as a down-gradient transport of moist static energy with constant diffusivity, as supported by comprehensive climate models and atmospheric reanalyses. Using the MEBM, I consider the relative roles of feedbacks and poleward heat transport in setting the robust patterns of climate change. Moreover, I consider sources of uncertainty in large-scale climate prediction; while uncertainty in tropical feedbacks induce a global temperature response, the impact of uncertainty in polar feedbacks is predominantly confined to the poles.The pattern of greenhouse-gas induced climate change is not spatially uniform. For example, we have observed amplified warming in the Arctic, slow warming in the Southern Ocean and Antarctica, and an enhanced hydrologic that has increased precipitation gradients – all features that are robustly simulated by global climate models. What sets these patterns?

In-Situ Measurements of the Global Distribution of Aerosol Particles

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Presented by: Christina Williamson NOAA Earth System Research Lab, Boulder & Cooperative Institute for Research in the Enviroment Sciences Hosted by: Jeff Pierce

Atmospheric aerosols affect climate by direct scattering of solar radiation and by altering cloud properties. Current uncertainties in anthropogenic aerosol forcing are one of the largest factors in total uncertainties in predicting climate change. In situ measurements of the properties, origins and climatic relevance of aerosols are needed to constrain global climate models, validate satellite measurements and better understand aerosol sources and processing in the atmosphere. In-situ measurements of aerosol in the remote free troposphere have hitherto been particularly sparse.   The Atmospheric Tomography Mission (ATom) is a unique set of measurements characterizing the remote free troposphere. ATom uses the NASA DC-8 as a flying lab, equipped with gas phase and aerosol measurements, flying over both Pacific and Atlantic Ocean basins, with near pole-to-pole coverage, constantly scanning between 0.2 and 13km altitude. Measurements are conducted in all four seasons to capture seasonal variations. So far three out of four deployments have been completed.

Bernhard Haurwitz Memorial Lecture (2017): Potential Vorticity Aspects of Tropical Dynamics

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Presented by: Professor Emeritus Wayne Schubert Colorado State University Hosted by: Michael Bell

Bernhard Haurwitz (1905-1986) was a member of our faculty for 13 years, teaching atmospheric dynamics and doing research on atmospheric tides. He was a pioneer in the study of tropical cyclone dynamics, writing papers on this subject before satellites, weather radars, and aircraft observations. He is also known for his analysis of “Rossby-Haurwitz waves.”   After a review of some of Bernhard’s contributions, the talk will focus on the potential vorticity aspects of tropical systems, especially tropical cyclones. We will try to understand how a tropical cyclone can become a hollow PV tower with values of PV exceeding 200 PV units. We will also discuss the role of the frictional boundary layer in establishing the eyewall and often a concentric eyewall, and also the role of barotropic instability in the breakdown of eyewall structures.

How Well Can We Explain Why Some Supercells Make Tornadoes and Others Do Not?

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Presented by: Matthew Parker NC State University Hosted by: Russ Schumacher

It has long been known that most significant tornadoes are produced by supercell thunderstorms, and yet the majority of supercells are non-tornadic. The environmental soundings near tornadic vs. non-tornadic supercells from VORTEX2 reveal a number of discrepancies that may be physically meaningful. For example, new idealized simulations of supercells using these tornadic vs. non-tornadic VORTEX2 soundings exhibit rather different evolution. An ensemble of simulated supercells in the tornadic environment produces intense tornado-like vortices in every case. An ensemble of simulated supercells in the non-tornadic environment produces non-tornadic storms in the vast majority of cases, even though this environment would still be viewed as favorable for tornadoes by conventional operational indices. A challenging problem is to explain the physical linkages between the observed environmental differences and the resultant changes to the internal storm processes that might lead to tornadogenesis. This talk will describe the distinctive elements of the composite VORTEX2 tornado environments, the character of the simulated supercells produced within the tornadic vs. non-tornadic composite environments, and a hierarchy of idealized studies designed to address the question: what are the fundamental environmental requirements for producing tornadic surface vertical vorticity in a storm?

Breaking Through the Clouds: Joanne Simpson and the Tropical Atmosphere

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Presented by: Jim Fleming Colby College Hosted by: Jeff Collett & Sue Van Der Heever

Joanne Simpson was a pioneering tropical meteorologist. She earned her Ph.D. in meteorology in 1949, the first US woman to do so. However, her life encompassed much more than that gendered feat. Through a troubled childhood, three marriages, two divorces, the birth of three children, a decade-long affair, struggles with depression and migraines, and sexism in the workplace, Joanne persevered and made fundamental contributions to the field of meteorology. Her work spanned many decades, societal attitudes, and technological advances. While her accomplishments are well known in the meteorological community, her personal life has long been misperceived.

Radiative Regulation of Tropical Convection by Preceding Cirrus Clouds

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Presented by: Hiro Masunaga Nagoya University, Japan Hosted by: Christian Kummerow

Radiative-convective feedbacks are known to constitute a key element of the climate system, whereas the underlying processes have yet to be understood at a fundamental level of the convective dynamics. This work seeks evidence for convective-radiative interactions in satellite measurements, with focus on the variability over the life cycle of tropical convection. To this end, the vertical profiles of cloud cover and radiative heating from the CloudSat-CALIPSO products are sorted into a composite time series around the time of convective occurrence identified by the TRMM PR.   The findings are summarized as follows. Cirrus cloud cover begins to increase, accompanied by a notable reduction of LW cooling, in moist atmospheres even 1-2 days before deep convection is invigorated. In contrast, LW cooling stays efficient and clouds remain shallow where the ambient air is very dry. To separate the radiative effects by the preceding cirri on convection from the direct effects of moisture, the observations with enhanced cirrus cover are isolated from those with suppressed cirrus under a moisture environment being nearly equal. It is found that rain rate is distinctly higher if the upper troposphere is cloudier regardless of moisture, suggesting that the cirrus radiative effects may be linked with the subsequent growth of convection. A possible mechanism to support this observational implication is discussed using a simple conceptual model.

The Role of Upper Tropospheric Cloud Systems in Climate: Building Observational Metrics for Process Evaluation Studies

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Presented by: C. J. Stubenrauch LMD/IPSL, UPMC, Paris, France Hosted by: Sue Van Den Heever

Upper tropospheric clouds, representing about 40% of the Earth’s total cloud cover, play a crucial role in the climate system by modulating the Earth’s energy budget and heat transport. They often form mesoscale systems. Cirrus emerge as outflow of convective and frontal systems or form in cold air supersaturated with water. Their evolution with climate change and their feedback can only be reliably estimated if these cloud systems are adequately represented in climate models. Recently GEWEX initiated working groups on Process Evaluation Studies (PROES) to provide observational based metrics for a better understanding of physical processes. One goal of the PROES working group on ‘Upper Tropospheric Clouds and Convection’ (UTCC) is to gain a better understanding of the role of convection on cloud feedbacks (Stubenrauch and Stephens 2017). Studies on tropical mesoscale convective systems so far concentrated mainly on the thick cirrus anvils, because radar and visible-infrared imagery either miss or misidentify thin cirrus. However, the thinner cirrus are thought to be a part of the anvils that has a significant radiative impact which might regulate convection itself.   Hence we are creating a synergetic data base of UT cloud systems anchored on IR Sounder observations, because these are sensitive to cirrus down to an optical depth of 0.2, day and night. By merging adjacent measurements with similar cloud height, the horizontal extent of these cloud systems has been determined, and convective cores, cirrus anvils and thin cirrus within these systems could be identified using cloud emissivity (Protopapadaki et al. 2017). The A-Train synergy provides information on the vertical structure and precipitation of these systems, essential to determine their heating rates, and helps to derive proxies for convective strength. We will present relationships of anvil properties with respect to convective strength and to their surrounding atmosphere. This observational metrics is being used to evaluate different convection / detrainment / microphysical parameterizations in climate models as well as studies of these processes using modelling at finer scale.

Modeling Refractive Index of Biomass Burning Aerosols

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Presented by: Solomon Bililign North Carolina A& T Hosted by: Jeff Pierce

The refractive index (RI) is one of the most fundamental parameters differentiating aerosol species. It is important to constrain the RI of aerosol components since there is still significant uncertainty regarding the RI of biomass burning aerosols. Experimentally measured extinction cross sections, scattering cross sections and single scattering albedos, for white pine soot under two different burning and sampling conditions were modeled using T-Matrix theory. The refractive indices were extracted from the calculations. Experimental measurements were conducted using a cavity ring-down spectrometer to measure extinction and a nephelometer to measure scattering of size selected aerosols. Soot was obtained by burning white pine using (1) an outdoor burn drum, where the aerosols were collected in distilled water using an impinger and then re-aerosolized after several days, and (2) a tube furnace to directly introduce the soot particles into an indoor smog chamber, where soot particles were then sampled directly. In both cases, filter samples were also collected and electron microscopy images were used to obtain morphology and size information used in T-Matrix calculations. In this talk a brief introduction of major goals and activities (past and present) in the NCA&T Atmospheric Chemistry/physics group will be presented followed by results of our most recent work on modeling of refractive index of biomass burning aerosols from white pine will be presented.

The Global Energy-Carbon Dilemma is Solved!

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Presented by: Alexander E. MacDonald Spire Global, Inc Hosted by: Scott Denning

The United States and other developed countries have underpinned their economic advances around cheap and reliable energy during the last 130 years. In the 21st century there are two more requirements that must be met; energy must also be secure and sustainable. The dangers of climate change are now obvious to everyone except those with a vested interest in the existing system. Wind and solar energy are dropping rapidly in cost, but will never command a large share of the energy market until the variability problem is solved. A recent study at NOAA ESRL in Boulder shows a way to provide low cost and low carbon energy. The key to the idea is the Rossby radius; we must be able to move wind and solar energy over a domain big enough that it has low variability in time. Fortunately, the technology of High Voltage Direct Current transmission of electricity has reached a level of capability that it is ready to do the job. The extreme dependence of modern economies on electricity means that a modernization of the electricity system could be designed to be robust and resilient, while creating a system that provides affordable, reliable, secure and sustainable energy. Why is this idea so hard to sell? I will briefly discuss my many interactions with US officials in the congress and the administration.

Statistical Rapid Intensity Prediction: Implications of Recent Model Results

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Presented by: John Kaplan NOAA/AOML/Hurricane Research Division Hosted by: Galina Chirokova

Despite recent improvements in tropical cyclone (TC) intensity forecasting skill, predicting changes in TC intensity remains problematic particularly the forecasting of episodes of rapid intensification (RI) which the National Hurricane Center (NHC) has declared as one of its highest operational forecasting priorities.   In recent years, a statistical rapid intensification index (SHIPS-RII) that employs environmental data from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) to estimate the probability of RI has been developed based upon linear discriminant analysis. Although the SHIPS-RII has been utilized as an operational forecasting tool by the NHC since 2004, its utility has been somewhat restricted since the original version only provided probabilistic forecasts for the single lead time of 24 h. Thus, additional versions of the SHIPS-RII as well as new logistic regression and bayesian RI models have been recently developed for the added lead times of 12-h, 36-h, 48-h, and 72-h. These new multi-lead time RI models became operational for the first time during the 2017 Hurricane Season.   In our upcoming presentation, a brief description of the new statistical RI models as well as an assessment of their overall level of skill will be provided. An evaluation of the ability of the current operational numerical intensity models to predict RI will also be presented and the implications of the current RI predictive skill of both the statistical and numerical models will be discussed.

Air Pollution Accountability: Assessing Regulatory Impacts on Emissions and Air Quality

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Presented by: Dr. Armistead (Ted) G. Russell Georgia Institute of Technology Hosted by: Sonia Kreidenweis

The United States has seen large improvements in air quality over the last half century with the implementation of regulations designed to reduce air pollutant emissions. Regulatory costs, estimated by the Environmental Protection Agency at tens of billions of dollars per year, motivate air pollution accountability research, which evaluates impacts of air quality regulations on emissions, air quality, exposure/dose, and public health—components of the so-called Accountability Chain. This work conducts a detailed analysis of a range of regulatory actions on electricity generating units and on-road mobile sources promulgated since the 1990s from the action at the federal level, implementation at the state level, the resulting emissions changes and the impacts on air quality and health. Results show that the United States has seen major emissions reductions over this period attributable to regulatory policies, although influences such as fuel costs, demographic shifts, and technological improvements have influenced emissions reductions as well. The resulting emissions reductions have led to air quality and health benefits.

Probing Precipitation, Cloud, and Clear Air Using EOL Remote Sensing Facilities

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Presented by: Wen-Chau Lee National Center for Atmospheric Research/Earth Observing Laboratory Hosted by: Michael Bell

Probing precipitation, cloud, and clear air using remote sensing instruments has enabled and advanced our understanding of mesoscale phenomena and high impact weather. The Remote Sensing Facility (RSF) of the National Center for Atmospheric Research/Earth Observing Laboratory (EOL) has a long history of developing, operating and deploying radars and lidars in the past 40 years to serve the National Science Foundation funded investigators. The rich history of RSF that influenced the radar community and US national radar network (Doppler and dual-polarization) will be briefly reviewed. The main focus of this talk will be on RSF’s current remote sensing capabilities and illustrating scientific achievement from data collected in recent field experiments. The advancement in multiple-frequency measurements in the same (or comparable) sampling volumes opened the doors to retrieve atmospheric moisture profiles and liquid water content within clouds. Developing low-cost water vapor DIAL in RSF permitted a vertical water vapor profile to be obtained every ~5 min. By looking into the future, EOL plans to develop airborne phased array radar with dual-Doppler and dual-polarization capabilities to replace the current airborne tail Doppler radars and explore the possibility to add temperature profiling capability to the existing water vapor DIAL system.

Tropical Atmospheric Madden-Julian Oscillation: Strongly Nonlinear Free Solitary Rossby Wave?

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Presented by: Jun-ichi Yano University of Reading Hosted by: Eric Maloney

The Madden-Julian oscillation (MJO), a planetary-scale eastward propagating coherent structure with periods of 30-60 days, is a prominent manifestation of intraseasonal variability in the tropical atmosphere. It is widely presumed that small-scale moist cumulus convection is a critical part of its dynamics. However, the recent results from high-resolution modeling as well as data analysis suggest that the MJO may be understood by dry dynamics to a leading-order approximation. Simple, further theoretical considerations presented herein suggest that if it is to be understood by dry dynamics, the MJO is most likely a strongly nonlinear solitary Rossby wave. Under a global quasi-geostrophic equivalent-barotropic formulation, modon theory provides such analytic solutions. Stability and the longevity of the modon solutions are investigated with a global shallow water model. The preferred modon solutions with the greatest longevities compare overall well with the observed MJO in scale and phase velocity within the factors.

GOES-16: A New Era in Geostationary Satellite Observations

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Presented by: Dan Lindsey & Steve Miller CIRA Hosted by: Christian Kummerow

After dodging a close-call with Hurricane Matthew, GOES-R was launched from Kennedy Space Center in Florida on 19 November 2016. Upon reaching geostationary orbit a few weeks later, it was officially christened as GOES-16, the first in a series of next-generation satellites operated by NOAA. The primary earth-viewing environmental instruments of GOES-16 are the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM). The ABI provides significantly improved spatial, temporal, and spectral resolution over previous-generation GOES imagers, while the GLM is a first-of-its-kind optical lightning detector in geostationary orbit.   Since the ABI opened its nadir door to the world and began collecting first-light imagery in early January of this year, CIRA has been assisting NOAA with the checkout and initial analysis of data from the ABI and GLM. Imagery from the ABI includes 500-m visible resolution and up to 30-second temporal sampling, allowing unprecedented observations of phenomena such as supercell thunderstorms, tropical cyclones, wildfires and their smoke plumes, and even sea ice (among others). Data from GLM come as real-time streaming lists of flash information, provided at the base granularity of “events”, “groups” of events, and “flashes” comprised of the groups, at roughly 10 km spatial resolution.   This seminar will highlight some of the initial data from the GOES-16 ABI and GLM and discuss potential research areas made possible by these new geostationary observations. Multispectral imagery capabilities from the ABI will be showcased as a way of communicating visually the rich information content. Data from interesting case studies are being saved and combined with data from other sources such as Numerical Weather Prediction (NWP) model fields and surface observations, including radar. These new data are a potential goldmine of information supporting research in the Atmospheric Science Department.

Predicting Pesticide Volatilization, Vapor Drift, and Impacts on Honey Bees

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Presented by: Kimberly J. Hageman University of Otago, New Zealand Hosted by: Jeff Collett

Pesticide vapor drift is the transfer of pesticides as gas-phase molecules from a sprayed field to downwind locations via the atmosphere. Under certain circumstances, vapor drift is an important pathway of pesticide exposure to non-target crops and organisms. In this presentation, I will discuss three projects that contribute to my long-term goal to develop a comprehensive set of models for predicting the transport and impacts of semi-volatile pesticides.

The Geolocated Information Processing System (GeoIPS): A System for Processing Geoscience Data for Research and Operations

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Presented by: Melinda Surratt & Jeremy Solbrig U.S. Naval Research Laboratory Marine Meteorology Division Hosted by: Jeremey Solbrig, CIRA

The Geolocated Information Processing System (GeoIPS) is a Python-based system for processing any data with latitudes and longitudes. It is composed of multiple high-level objects that define standard internal formats for data, description of domains, and construction of imagery and data product recipes. In addition to static sectors, dynamic sectors for following events such as tropical cyclones can be deployed. GeoIPS is currently capable of processing satellite data from a large number of meteorological satellites as well as some Navy models and is easily extendable to accept other data sets. In the near future, it is hoped that GeoIPS will be extended to include capabilities for Data Fusion, and the system is rapidly becoming the operational system for processing of geolocated data at operational Navy centers, is operating in near real-time at CIRA through a software transfer agreement, and its core will soon be open sourced. In addition to providing a simple transition pathway to Navy entities, GeoIPS provides the tools needed for easy data exploration, research, and product development.

Tropical Cyclone Intensification under Moderate Vertical Wind Shear

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Presented by: Rosimar Rios-Berrios University at Albany, SUNY Hosted by: Michael Bell

Deep-layer (200−850 hPa) vertical wind shear is generally an inhibiting factor for tropical cyclone intensification. This negative relationship stems from a number of processes, including: vertical misalignment of the vortex, increased stability, ventilation of the upper-tropospheric warm core, and dry air entrainment. Despite these processes, many tropical cyclones can intensify under moderate vertical wind shear—the range of shear magnitudes that are neither too weak nor too strong (5–10 m s−1). Multiple studies have proposed potential explanations for tropical cyclones intensification amid moderate shear (e.g., vortex re-alignment, downshear reformation); however, the majority of those studies have considered either case studies or simplified idealized numerical experiments. These limitations prevent understanding why some tropical cyclones intensify while most tropical cyclones weaken in sheared environments.   A potential hypothesis to explain intensification under moderate vertical wind shear is that other factors—associated with both the tropical cyclone and its environment—can help offset the effects of wind shear and aid intensification. This hypothesis was tested with three different approaches: (1) two case studies, (2) a climatological analysis, and (3) idealized numerical simulations. These approaches consistently show that the three-dimensional distribution of thermodynamic quantities is key for intensity changes in sheared environments. Tropical cyclones are likely to intensify under moderate shear when surface latent heat fluxes and middle tropospheric moisture are uniformly distributed around the low-level center of circulation. Such conditions, when coupled with storm-relative kinematics, can limit the amount of dry air intrusions and favor symmetric rainfall around sheared tropical cyclones. Altogether, these findings suggest that three-dimensional observations of thermodynamic fields are important for understanding and predicting tropical cyclone intensity.

Understanding Storm Track Shifts Across a Range of Timescales

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Presented by: Tiffany Shaw University of Chicago Hosted by: Thomas Birner

Storm tracks are regions where extratropical cyclones occur most frequently, they control weather and climate in the extratropics. Storm tracks shift latitudinally in response to energetic perturbations across a range of timescales. On seasonal timescales, the Northern Hemisphere storm track shifts poleward between winter and summer and equatorward between summer and winter. On interannual timescales, the storm tracks shift equatorward in response to El Nino minus La Nina conditions. On centennial timescales, climate models project the storm tracks will shift poleward in response to increased CO2 concentration. Here we present an energetic framework that connects energetic perturbations to storm track position and use it to understand storm track shifts across a range of timescales.

Digital Holography of Aerosol Particles

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Presented by: Matthew J. Berg Kansas State University Hosted by: Sonia Kreidenweis

Methods to determine the physical properties of aerosol particles is important in a vast array of scientific and applied contexts. Due in part to the difficulty of collecting such particles, a variety of contact-free techniques have been developed that infer information about the particles in an indirect manner. A popular example is elastic light-scattering where the angular pattern of light scattered from a particle is analyzed to estimate particle properties like shape and size. This approach is often called the inverse problem, as there is generally no way to know if the inferred information is correct. Fundamentally, this is due to the loss of optical phase information in such measurements. An alternative approach is to image the particles using holography. By placing a digital image-sensor in an optical beam containing a particle, the interference pattern produced by the scattered and unscattered light can be easily measured. The pattern constitutes a digital hologram of the particle and useful information can be extracted from it directly since phase information is encoded in the hologram. For example, applying a Fourier-transform operation yields a silhouette-like image of the particle, thus revealing its shape and size without any need for a priori information. The extinction cross section can also be obtained from the hologram. Thus, digital holography “solves” the classic inverse problem. This talk will present our recent work in this area and its future applications, including plans for field measurements of coarse-mode atmospheric aerosols.

Improving geophysical system understanding and modelling by exploring nonlinear data assimilation

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Presented by: Peter Jan Van Leeuwen University of Reading

Geophysical systems can be characterised as complex, nonlinear and high dimensional. All of these provide major challenges to understanding and modelling. To explore observations and existing knowledge encoded in numerical models to their full extent one can try to combine both sources of information. A systematic tool for doing this is data assimilation. It can be used to provide a description of the full nonlinear evolution of the system including its uncertainty using all information we have. This can then be analysed for linear and nonlinear relations between processes that determine the behaviour of the system. Specifically, using and extending existing causality theory on information flow, we can unravel cause, effect, and feedbacks. Data assimilation can also be used to infer errors in the model equations. The estimated errors will contain both random and structural components. By extracting the structural components we can infer and quantify missing physics, which can then be used to develop improved parameterisations. In this presentation I will explore recent advances in nonlinear data assimilation and provide examples of its use on the challenges mentioned above.

Measuring and interpreting faunal responses to climate in the Intermountain West

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Presented by: Erica Fleishman CSU Professor, Department of Fish, Wildlife and Conservation Biology Hosted by: Sonia Kreidenweis

There is considerable research and management interest in whether and how native faunas are responding to climate change. Evaluating whether empirical data support range-shift hypotheses is complicated by variation in climate, differences in response variables and the extent and resolution of analyses, and mismatches between the resolutions at which climate data typically are available and species respond to environmental heterogeneity. Emerging results from analyses of 15 years of data on the distributions of birds and butterflies in the Great Basin suggest many productive opportunities for creative collaboration among atmospheric scientists and ecologists. Results from a partnership among climatologists, ecologists, resource managers, and public health experts in the southwestern United States also highlight potential multidisciplinary nexuses with strong social benefits.   BIO: Erica Fleishman is Director, Center for Environmental Management of Military Lands and Professor, Department of Fish, Wildlife and Conservation Biology at Colorado State University. She received a B.S. (1991) and M.S. (1992) in Biological Sciences from Stanford University and a Ph.D. (1997) in Ecology, Evolution, and Conservation Biology from University of Nevada, Reno. Much of Fleishman’s research focuses on responses of native animals to changes in land use and climate in the Great Basin and California. She also participates in multidisciplinary projects on interactions among climate extremes, natural resources, and public health in the southwestern United States. Additionally, Fleishman has worked with federal agencies and industry on responses of marine mammals to underwater sound, and coauthored curricula on applications of remote sensing to environmental sciences and ecological modeling. Fleishman is past editor in chief of Conservation Biology and currently serves on the editorial boards of three other ecological journals.

Remote Sensing of the Atmosphere from the Troposphere to the Edge of Space

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Presented by: Katrina Bossert GATS, Inc. Boulder Hosted by: Christian Kummerow

Lidar remote sensing enables observations of various atmospheric properties and dynamics from the troposphere to the region of the atmosphere considered the edge of space near ~80-100 km. For some aspects of the atmosphere, studying coupling between different altitudes and regions is important for a more in depth understanding. Gravity waves are one aspect integral to understanding atmospheric coupling, as they strongly influence dynamics within the atmosphere via the transport of energy and momentum from the lower atmosphere to the middle and upper atmosphere. Gravity waves especially have implications on the climatology and circulation within the mesosphere and lower thermosphere. More research is needed to improve our understanding about gravity wave “hot spots” in the lower atmosphere and implications at higher altitudes, secondary gravity wave generation leading to momentum transport above gravity wave breaking regions, multi-scale gravity wave interactions, and small-scale features and instabilities associated with gravity wave dissipation. This talk investigates coupling between different regions of the atmosphere through observations of gravity wave breaking, secondary gravity wave generation, and small-horizontal scale gravity wave propagation environments using Rayleigh lidar, resonance fluorescence lidar, and airglow observations throughout the atmosphere.

Energetic Constraints on Global Climate

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Presented by: Aaron Donohoe University of Washington Hosted by: Christian Kummerow

Spatial variations in the solar heating of the climate system drive the atmospheric and oceanic circulation and set patterns of temperature and precipitation. This presentation explores the processes that determine the absorption of solar radiation in the climate system including latitudinal, vertical and seasonal distributions. It is shown that atmospheric circulations and temperature adjust to the distribution of absorbed solar radiation resulting in two general findings: I) model biases in the distribution of absorbed solar radiation result in climate biases ­– most notably in the strength of the atmospheric circulation and the location of tropical precipitation and ii) changes in the circulation and temperature under global warming follow the spatial pattern of changes in absorbed solar radiation. The latter has much uncertainty due to cloud processes but there is a robust increase in absorbed solar radiation due to atmospheric moistening and Spatial variations in the solar heating of the climate system drive the atmospheric and oceanic circulation and set patterns of temperature and precipitation. This presentation explores the processes that determine the absorption of solar radiation in the climate system including latitudinal, vertical and seasonal distributions. It is shown that atmospheric circulations and temperature adjust to the distribution of absorbed solar radiation resulting in two general findings: I) model biases in the distribution of absorbed solar radiation result in climate biases ­– most notably in the strength of the atmospheric circulation and the location of tropical precipitation and ii) changes in the circulation and temperature under global warming follow the spatial pattern of changes in absorbed solar radiation

Using Remote Sensing Observations to Advance Understanding of Cloud-aerosol-precipitation-radiation Interactions

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Presented by: Christine Chiu University of Reading Hosted by: Christian Kummerow

Currently, the scientific community is unable to identify how characteristics of clouds will alter as the climate warms in response to emissions of greenhouse gases from human activities, and to what extent changes in cloud characteristics will feed back on surface temperature responses. In particular, models disagree substantially in the magnitude of cloud feedback for the regimes of subtropical marine boundary-layer clouds. Common, longstanding model deficiencies in cloud and drizzle properties call for the need of observations with sufficient accuracy, temporal and spatial resolution for understanding cloud-aerosol-precipitation-radiation interactions at process levels. Capitalizing on new scanning cloud radars/lidars and shortwave spectrometers, I will show detailed cloud/drizzle properties and demonstrate how they may help constrain warm rain formation and aerosol impacts on precipitation. Capitalizing on a technology revolution in small satellites and sensor miniaturization, I will also show a novel, viable and sustainable strategy to globally monitor the Earth’s radiation, which will allow us to study how fast evolving phenomena, such as clouds and aerosols, aggregate to affect our climate system.

Mean Precipitation Change from Invariant Radiative Cooling

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Presented by: Nadir Jeevanjee Princeton/Geophysical Fluid Dynamics Laboratory Hosted by: Christian Kummerow

Global warming simulations robustly show that mean precipitation increases at 1-3% per Kelvin, but we do not know what sets these values. Mean precipitation is constrained by radiative cooling, however, and we demonstrate here that radiative cooling profiles exhibit a certain invariance under warming when plotted in temperature coordinates. This invariance can then be leveraged to derive simple analytical equations for precipitation change with warming. These equations are tested against both CRM and GCM output, and in both cases give intuition for why precipitation changes at a rate of 1-3% per Kelvin.

Recent Advances in University of Wyoming King Air Observation Capabilities

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Presented by: Zhien Wang University of Wyoming Hosted by: Sue Van Den Heever

University of Wyoming King Air (UWKA) is a part of NSF-supported Lower Atmosphere Observing Facilities (LAOF). Through multi-year development efforts, UWKA has equipped with integrated observation capabilities for cloud dynamics and microphysics, aerosols, and environment conditions through combining lidar, radar, radiometer and in situ measurements. Approaches were developed to retrieve droplet and ice concentrations in stratiform clouds from combined lidar-radar measurements. The new addition of a Ka-band precipitation radar (KPR) allows us to improve cloud and precipitation characterizations with dual-frequency radar techniques. Meanwhile, dual-Doppler measurements provide 2-D cloud-scale dynamics. The simultaneous measurements of aerosol, temperature, and water vapor from airborne Raman lidars transform UWKA into an efficient platform for atmospheric boundary layer processes study, especially combined with various in situ measurements. Observation examples or case study will be presented to highlight these new observational capabilities. As a part of NSF LAOF, these new observational capabilities are available to the University research community. Information to request UWKA facility can be found at https://www.eol.ucar.edu/node/86 and http://www.atmos.uwyo.edu/uwka/.

Deep Machine Learning for High-Impact Weather Forecasting

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Presented by: David John Gagne NCAR Hosted by: Greg Hermand & Russ Schumacher

The weather forecasting process has grown more complex in recent years with the growing amount of observational data and model output available to weather forecasters and the trend toward providing more impact-based decision support services. In order to assist forecasters and end-users with the task of managing the firehose of data, I have developed and evaluated machine learning forecast guidance systems for different high-impact weather phenomena. Machine learning models have demonstrated the ability to synthesize large, multifaceted datasets into accurate predictions for many different problems. In this presentation, I will discuss my storm-based machine learning hail forecasting model. The machine learning hail model identifies potential storms in convection-allowing model output, associates each forecast storm with an observed hailstorm, and then feeds storm and environmental information into a machine learning model to predict whether hail will occur and what the size distribution of the hail will be. The machine learning hail model has run in real-time on the Center for Analysis and Prediction of Storms and NCAR Convection-Allowing ensembles and has shown increased skill over other hail forecasting methods for predicting severe and significant severe hail. I will also discuss ongoing work on incorporating deep learning models into different weather prediction tasks. Deep learning models can identify multiscale features in gridded spatio-temporal data and use that information to produce better predictions than traditional machine learning approaches. A form of deep learning called generative adversarial networks will be discussed and demonstrated. It has the ability to learn complex feature representations in spatial data without the need of labeled examples. These deep learning methods will be demonstrated against traditional machine learning models on the GEFS Reforecast dataset for the task of predicting 2 m temperature anomalies.

Seasonal Variability of Warm Boundary Layer Cloud and Precipitation Properties in the Southern Ocean as Diagnosed from A-Train and Ship-Based Remote Sensing Data

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Presented by: Jay Mace University of Utah Hosted by: Paul DeMott

The extensive cloudiness and resulting high albedo of the Southern Oceans (SO) are predominantly due to the occurrence of widespread marine boundary layer (MBL) clouds. Recent work finds correlations between biogenically enhanced cloud condensation nuclei concentrations and cloud droplet number concentrations derived from passive satellite data. The active remote sensors in the A-Train have created a unique and long-term record of these clouds that include vertical profiles of radar reflectivity and microwave brightness temperature from CloudSat that can be combined with solar reflectances from MODIS. We examine this data record using a unique algorithm to infer warm-topped cloud and precipitation properties. We find seasonal variations in cloud properties of summer season clouds demonstrating higher cloud droplet number concentrations on average. In addition, a given rain rate requires higher liquid water contents in summer suggesting that the precipitation in summer clouds are more susceptible to changes in droplet number compared to similar clouds during winter.

Sensing Hazards with Operational Unmanned Technology

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Presented by: Andrew Kren NOAA ESRL

A key project within the National Oceanic and Atmospheric Administration (NOAA) Global Observing Systems Analysis (GOSA) group is the Sensing Hazards with Operational Unmanned Technology (SHOUT) project. One of the main objectives of SHOUT is to conduct both Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs) to evaluate the impact of real and simulated Unmanned Aircraft Systems (UAS) data on weather forecasts of tropical cyclones and high-impact weather events over the United States. In 2016, the GOSA SHOUT group participated in NOAA’s El Niño Rapid Response (ENRR) mission, conducted between January and March 2016, as well as in the SHOUT Hurricane Rapid Response (HRR) Mission during the fall of 2016.   We provided targeted observing support to determine areas sensitive to forecast error growth and evaluated the impact of Global Hawk (GH) data on global numerical weather prediction. Both OSE and OSSE experiments were conducted prior to and after the field missions. OSEs were performed for an extratropical storm which hit Alaska in February 2016 during ENRR and for hurricane Matthew in October 2016 during HRR. One of the questions that SHOUT addresses is how much can the GH observations mitigate a possible gap in satellite data. Results here address the impact of GH dropsonde data in addition to the current observing system, as well as under a potential gap in global satellite coverage. Results show that targeted GH dropsondes improve forecast skill over the verification region of interest in both cases. Furthermore, accompanying OSSEs using a realistic nature run are used to validate the Ensemble Transform Sensitivity (ETS) targeting technique to identify data sensitive regions and also to investigate the impact of targeted data collected from different simulated flight tracks. Finally, an objective flight path design that covers sensitive regions identified using the ETS method, as well as subjective flight path designs that also include areas of key meteorological features, such as upper-level jet streaks, frontal systems, and moisture plumes, are studied and will also be discussed during this talk.

Pressure Perturbations in Cumulus Convection

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Presented by: John Peters CSU Postdoctoral Fellow Hosted by: Russ Schumacher

Pressure perturbations are regions of anomalously low or high pressure in deep convection and play key roles in modulating the magnitude and distribution of vertical velocities within cumulus clouds. A cloud’s vertical momentum budget is primarily regulated by two pressure forces: Effective buoyancy pressure acceleration (EBPA), and dynamic pressure acceleration (DPA).   I will first discuss EBPA, which drives upward (downward) acceleration of air parcels if they are anomalously less (more) dense than their surrounding environments. I show that EBPA is dependent on (1) the temperature perturbation within an updraft, (2) the temperature immediately surrounding an updraft, and (3) the updraft’s width-to-height aspect ratio. A consequence of (3) is that wider and/or shallower clouds have weaker vertical velocities than narrower and/or deeper clouds, all else being equal. I will then discuss DPA. Dynamic pressure perturbations arise from spatial gradients in wind velocity and preserve approximate zero mass-flux convergence in the atmosphere. For a general, un-sheared updraft, DPA is oriented upward below the updraft’s level of maximum temperature perturbation, and downward above the updraft’s level of maximum temperature perturbation. This leads to a cloud’s maximum vertical velocity occurring near the middle troposphere, rather than near the tropopause (as parcel theory would suggest).   Are there ways that we can better understand EBPA and DPA and their impact on deep convection? To address this question, I will first discuss potential avenues for improving the representation of EBPA and DPA in cumulus parameterization. I will then address a specific example of how DPA constrains the predictability of supercell behavior in certain atmospheric environments. For supercells in vertical wind shear, DPA causes the supercell’s motion to deviate to the left or right of the mean tropospheric wind (this behavior is called “deviant motion”). I will use a series of sensitivity experiments to show that the character of DPA deviant supercell motion is highly sensitive to lower-tropospheric temperature and model grid spacing.