- Western Regional Air Partnership www.wrapair2.org
- WRAP Technical Support System http://views.cira.colostate.edu/tssv2/
- Intermountain West Data Warehouse http://views.cira.colostate.edu/iwdw/
- WESTAR http://www.westar.org/
- Climate and Weather Processes
- Data Distribution
- Data Assimilation
- Education and Outreach
- Satellite-based greenhouse gas observations (primarily carbon dioxide and methane)
- Carbon flux inverse modeling
- Biospheric/land surface modeling of carbon, water and energy exchange
- Atmospheric transport of trace gases
- A better understanding of the present state of the earth’s carbon cycle, and the contribution of the biosphere to carbon cycling at urban to regional to global scales.
- Observing, understanding, and attributing changes to the earth’s carbon cycle in the era of climate change
- Feasibility of monitoring anthropogenic emissions of greenhouse gases from satellites
- Orbiting Carbon Observatory 2 and 3 (with NASA JPL)
- GeoCarb (with NASA and the University of Oklahoma)
- ACT-America field campaign (with NASA and Penn State)
- Evaluating carbon dynamics across regions and scales (NASA, NOAA, NSF)
- Designing optimized space-based GHG observing systems (with NASA Goddard)
- Utilization of solar induced fluorescence (SIF) towards characterizing dryland agriculture (NASA and USDA)
- Climate and Weather Processes
- Satellite Algorithm Development, Training and Education
- Modeling Systems Research
- Data Assimilation
- CIRA’s ML philosophy, key expertise, core activities and educational resources
- ML related to inferring cloud properties
- Machine Learning for Tropical Cyclones
- Machine Learning related to the VIIRS Day Night Band
- Kevin Manross
- Venita Hagerty
- Paul Hamer
- Dr. Anton Kliewer
- Liaofan Lin
- Ian McGinnis
- Evan Sheehan
- Emily Schlie
- Josh Weber
- Amanda Back
- Ryan Lagerquist
- Dana Uden
- Dr. Ning Wang
- John Schneider
- Evan Polster
- Leigh Cheatwood-Harris
- Jim Frimel
- Yujun Guo
- Patrick Hildreth
- Brian Jamison
- Tom Kent
- Dr. Haidao Lin
- Robert Lipschutz
- Chris MacDermaid
- Jacques Middlecoff
- Glen F. Pankow
- Randy Pierce
- Jennifer Raab
- James Ramer
- Dr. Duane Rosenberg
- Richard Ryan
- Bonny Strong
- Ed Szoke
- Ka Yee Wong
- Amanda Terborg
- Bill Theus
- Dr. Yali Mao
- Alex Korner
- Anders (Mick) Ohrberg
- Dan Vietor
- Nicole Stevens
- Ken Loveday
- Bret Sorensen
- Steve Chance
- Robin Brandeberry
- John (Jack) Lind
- Jared Schadler
- Hsin-Mu Lin
- Understand Place: Studying how the local context affects behaviors, impacts, and outcomes
- Communicate Risk: Developing tools and messages to communicate risk and uncertainty
- Evaluate Products: Improving products and services for all partners, including the public
- Assess Impact: Studying impacts and outcomes of weather hazards
- Creating Opportunities for WCMs to Support Service Equity and Use of Weather and Hydrologic Data Products
- FY 2022 Collaborative Science, Technology, and Applied Research (CSTAR) Projects
- Understanding the Human Response to Water Hazards
- Supporting the Improvement of Tsunami Warning Messages
- Space Weather Advisory Group User Needs Support
- Alan Brammer
- Galina Chirokova
- Greg DeMaria
- Mark DeMaria
- Robert DeMaria
- Jack Dostalek
- Dr. Katherine Haynes
- Dr. John A. Knaff
- Alex Libardoni
- Lixin Lu, Ph.D
- Jonathan Martinez
- Marie McGraw
- Deb Molenar
- Dr. Kate Musgrave
- Naufal Razin
- Jonathan Rogers
- Chris Slocum
- Natalie D. Tourville
- Ben Trabing
- Ray Zehr
- Dr. Yijie Zhu
About the Carbon Team
Carbon Group Working Areas:
Key Science Goals:
T.E. Taylor, C.W. O’Dell, D. Crisp, et. al., An eleven year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm. In prep, to be submitted to Earth System Science Data.
Peiro, H., Crowell, S., Schuh, A., Baker, D. F., O’Dell, C., et al: Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7, Atmos. Chem. Phys., 22, 1097–1130, https://doi.org/10.5194/acp-22-1097-2022, 2022.
Schuh, A.E., et al. On the role of atmospheric model transport uncertainty in estimating the Chinese land carbon sink. Nature 603, E13–E14 (2022). https://doi.org/10.1038/s41586-021-04258-9
Zhang, L., Davis, K. J., Schuh, A. E., et al. (2022). Multi-season evaluation of CO2 weather in OCO-2 MIP models. Journal of Geophysical Research: Atmospheres, 127, e2021JD035457. https://doi.org/10.1029/2021JD035457
Davis, K.J., E.V. Browell, S. Feng, T. Lauvaux, M.D. Obland, S. Pal., B.C. Baier, D.F. Baker, I.T. Baker, Z.R. Barkley, K.W. Bowman, Y. Cui, A.S. Denning,, J.P., DiGangi, J.T. Dobler, A. Fried, T. Gerken, K. Keller, B. Li, A.R. Nehrir, C.P., Normile, C.W. O’Dell, L.E. Ott, A. Roiger, A.E. Schuh, C. Sweeney, Y. Wei, M. Xue, C.W. Williams, 2021: The Atmospheric Carbon and Transport (ACT) America Mission. , Bull. Amer. Meteorol. Soc., E1714-E1734, https://doi.org/10.1175/BAMS-D-20-0300.1.
Schuh, A.E. et al., Far-field biogenic and anthropogenic emissions as a dominant source of variability in local urban carbon budgets: A global high-resolution model study with implications for satellite remote sensing, Remote Sensing of Environment,Volume 262,2021,112473,ISSN 0034-4257, https://doi.org/10.1016/j.rse.2021.112473.
Massie, S. T., Cronk, H., Merrelli, A., Schmidt, K. S., Chen, H., and Baker, D., Analysis of 3D cloud effects in OCO-2 XCO2 retrievals, Atmospheric Measurement Techniques, 14, 1475-1499, 2021. https://doi.org/10.5194/amt-14-1475-2021.
Somkuti, P., O’Dell, C. W., Crowell, S., Köhler, P., McGarragh, G. R., Cronk, H. Q., and Burgh, E. B. Solar-induced chlorophyll fluorescence from the Geostationary Carbon Cycle Observatory (GeoCarb): An extensive simulation study. Remote Sensing of Environment, 263:112565, 2021. https://doi.org/10.1016/j.rse.2021.112565.
Somkuti, P., Bösch, H., Feng, L., Palmer, P. I., Parker, R. J., and Quaife, T. A new space-borne perspective of crop productivity variations over the US Corn Belt. Agricultural and Forest Meteorology, 281:107826, 2020. https://doi.org/10.1016/j.agrformet.2019.107826.
Somkuti, P., Bösch, H., and Parker, R. J. The significance of fast radiative transfer for hyperspectral SWIR XCO2 retrievals. Atmosphere, 11(11):1219, 2020. https://doi.org/10.3390/atmos11111219.
T.E. Taylor, et. al., OCO-3 early mission operations and initial (vEarly) XCO2 and SIF retrievals, Remote Sensing of the Environment, 251, 2020. https://doi.org/10.1016/j.rse.2020.112032.
Baker, I.T., A.S. Denning, D.A. Dazlich, A.B. Harper, M.D. Branson, D.A. Randall, M.C. Phillips, K.D. Haynes, S.M. Gallup (2019). Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest. textitJ. Adv. Mod. Earth Sy., 11(8), 2523-2546, doi:10.1029/2019MS001650.
Haynes, K., Baker, I. T., Denning, S., St ̈ockli, R., Schaefer, K., Lokupitiya, E. Y., Haynes, J. M. (2019). Representing grasslands using dynamic prognostic phenology based on biological growth stages: 1. Implementation in the Simple Biosphere Model (SiB4). J. Adv. Mod. Earth Sy., 11. https://doi.org/10.1029/ 2018MS001540
Haynes, K. D., Baker, I. T., Denning, A. S., Wolf, S., Wohlfahrt, G., Kiely, G., et al. (2019). Representing grasslands using dynamic prognostic phenology based on biological growth stages: 2. Carbon cycling. , J. Adv. Mod. Earth Sy., 11. https:// doi.org/10.1029/2018MS001541
Schuh, A.E. et al, Quantifying the Impact of Atmospheric Transport Uncertainty on CO2 Surface Flux Estimates, Global Biogeochemical Cycles, https://doi.org/10.1029/2018GB006086 2019
Crowell, S., Baker, D., Schuh, A. et al: The 2015–2016 Carbon Cycle As Seen from OCO-2 and the Global In Situ Network, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-87 2019.
A. Eldering, T.E. Taylor, C.W. O’Dell and R. Pavlick, The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data, Atmospheric Measurement Techniques, 12 (4), 2341-2370, 2019, https://doi.org/10.5194/amt-12-2341-2019.
M. Kiel, C.W. O’Dell, et. al., How bias correction goes wrong: measurement of XCO2 affected by erroneous surface pressure estimates, Atmospheric Measurement Techniques, 12, 2241-2259, 2019, https://doi.org/10.5194/amt-12-2241-2019.
C.W. O’Dell, et. al., Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm, Atmospheric Measurements Techniques, 11, 6539-6576, 2018, https://doi.org/10.5194/amt-11-6539-2018.
Baker, I.T., P.J. Sellers, A.S. Denning, I. Medina, P. Kraus, K.D. Haynes, 2017: Closing the scale gap between land surface parameterizations and GCMs with a new scheme, SiB3-Bins. J. Adv. Mod. Earth Sy., 9, doi:10.1002/ 2016MS000764.
Baker, I.T., H.R. da Rocha, N. Restrepo-Coupe, R. St ̈ockli, L.S. Borma, O.M. Cabral, A.O. Manzi, A.D. Nobre, S.C. Wofsy, S.R. Saleska, M.L. Goulden, S.D. Miller, F.L. Cardoso, A.S. Denning, 2013: Surface ecophysiological behavior across vegetation and moisture gradients in Amazonia. Agric. For. Meteor., 182-183, 177-188, doi: http://dx.doi.org/10.1016/j.agformet.2012.11.015.
Berry, J.A., A. Wolf, J.E. Campbell, I. Baker, N. Blake, D.Blake, A.S. Denning, S.R. Kawa, S.A. Montzka, U. Seibt, K. Stimler, D. Yakir, Z. Zhu, 2013: A coupled model of the global cycles of carbonyl sulfide and CO2: A possible new window on the carbon cycle. J. Geophys. Res., doi:10.1002/jgrg.20068.(I add this one because it is the seminal OCS modeling paper and gets cited all the time)
D. O’Brien, I. Polonsky, C.W. O’Dell, A. Kuze, N. Kikuchi, and V. Natraj, 2011: Testing the polarization model for TANSO-FTS on GOSAT against clear-sky observations of sun-glint over the ocean. In prep, to be submitted to IEEE Trans. Geosci. Remote Sens.
D. Hammerling, A. Michalak, C.W. O’Dell, and S.R. Kawa, 2012: Global CO2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT). Geophys. Res. Lett., 39, L08804, doi:10.1029/2012GL051203.
C. Frankenberg, C.W. O’Dell, L. Guanter, and J. McDuffie, 2012: Remote sensing of near-infrared chlorophyll fluorescence from space in scattering atmospheres: implications for its retrieval and interferences with atmospheric CO2 retrievals. Atmos Meas. Tech., 5, 2081-2094.
C. Frankenberg, O. Hasekamp, C.W. O’Dell, S. Sanghavi, A. Butz, and J. Worden, 2012:Aerosol information content analysis of multi-angle high spectral resolution measurements and its benefit for high accuracy greenhouse gas retrievals. Atmos. Meas. Tech., 5, 1809-1821.
S. Oshchepkov, A. Bril, T. Yokota, I. Morino, Y. Yoshida, T. Matsunaga, D. Belikov, D. Wunch, P. O. Wennberg, G. Toon, C. W. O’Dell, A. Butz, S. Guerlet, A. Cogan, H. Boesch, N. Eguchi, D. Griffith, R. Macatangay, J. Notholt, N. Deutscher, R. Sussmann, M. Rettinger, V. Sherlock, J. Robinson, E. Kyrö, P. Heikkinen, D. G. Feist, T. Nagahama, N. Kadygrov, S. Maksyutov, O. Uchino, H. Watanabe, 2012: Effects of atmospheric light scattering in validation of spectroscopic space-based observations of carbon dioxide by ground-based FTS measurements, Part 1. J. Geophys. Res., 117, D12, doi:10.1029/2012JD017505.
D. Crisp, B. M. Fisher, C.W. O’Dell, C. Frankenberg, R. Basilio, H. Bösch, L. R. Brown, R. Castano, B. Connor, N. M. Deutscher, A. Eldering, D. Griffith, M. Gunson, A. Kuze, L. Mandrake, J. McDuffie, J. Messerschmidt, C. E. Miller, I. Morino, V. Natraj, J. Notholt, D. O’Brien, F. Oyafuso, I. Polonsky, J. Robinson, R. Salawitch, V. Sherlock, M. Smyth, H. Suto, T. Taylor, P. O. Wennberg, D. Wunch, and Y. L. Yung, 2012: The ACOS XCO2 retrieval algorithm, Part 2: Global XCO2 data characterization. Atmos Meas. Tech., 5, 687-707.
T.E. Taylor, C.W. O’Dell, D.M. O’Brien, N. Kikuchi, T. Yakota, T. Y. Nakajima, H. Ishida, D. Crisp, and T. Nakajima, Comparison of cloud screening methods applied to GOSAT near-infrared spectra, IEEE Trans. Geosci. Remote Sens, 50 (1), 2012. doi:10.1109/TGRS.2011.2160270.
C.W. O’Dell, B. Connor, H. Bösch, D. O’Brien, C. Frankenberg, R. Castano, M. Christi, D. Crisp, A. Eldering, B. Fisher, M. Gunson, J. McDuffie, C. E. Miller, V. Natraj, F. Oyafuso, I. Polonsky, M. Smyth, T. Taylor, G. C. Toon, P. O. Wennberg, and D. Wunch, 2011: The ACOS CO2 retrieval algorithm, Part 1: Description and validation against synthetic observations. Atmos. Meas. Tech. Discuss., 4, 6097-6158.
D. Wunch, P. O. Wennberg, G. C. Toon, B. J. Connor, B. Fisher, G. B. Osterman, C. Frankenberg, L. Mandrake, C.W. O’Dell, P. Ahonen, S. C. Biraud, R. Castano, N. Cressie, D. Crisp, N. M. Deutscher, A. Eldering, M. L. Fisher, D. W. T. Griffith, M. Gunson, P. Heikkinen, G. Keppel-Aleks, E. Kyrö, R. Lindenmaier, R. Macatangay, J. Mendonca, J. Messerschmidt, C. E. Miller, I. Morino, J. Notholt, F. A. Oyafuso, M. Rettinger, J. Robinson, C. M. Roehl, R. J. Salawitch, V. Sherlock, K. Strong, R. Sussmann, T. Tanaka, D. R. Thompson, O. Uchino, T. Warneke, and S. C. Wofsy, 2011: A method for evaluating bias in global measurements of CO2 total columns from space. Atmos. Chem. Phys. Discuss., 11, 20899-20946.
C.W. O’Dell, J.O. Day, H. Pollock, C. Bruegge, D.M. O’Brien, R. Castano, I. Tkatcheva, C.E. Miller, and D. Crisp, Preflight radiometric calibration of the Orbiting Carbon Observatory, IEEE Trans. Geosci. Remote Sens., 49 (6), 2438-2447, 2011. doi:10.1109/TGRS.2010.2090887.
J.O. Day, C.W. O’Dell, H. Pollock, C. Bruegge, D. Rider, D. Crisp, and C.E. Miller, Preflight spectral calibration of the Orbiting Carbon Observatory, IEEE Trans. Geosci. Remote Sens., 49 (7), 2793-2801, 2011. doi:10.1109/TGRS.2011.2107745 .
Kuze, A., D.M. O’Brien, T.E. Taylor, J.O. Day, C.W. O’Dell, F. Kataoka, M. Yoshida, Y. Mitomi, C. Bruegge, H. Pollock, R. Basilio, M. Helmlinger, T. Matsunaga, S. Kawakami, K. Shiomi, T. Urabe and H. Suto, Vicarious calibration of the GOSAT sensors using the Railroad Valley Desert Playa, IEEE Trans. Geosci. Remote Sens., 49 (5), 1781-1975, 2010. doi:10.1109/TGRS.2010.2089527 .
C.W. O’Dell, Acceleration of multiple-scattering, hyperspectral radiative transfer calculations via low-streams interpolation. J. Geophys. Res., 115, D10206, 2010. https://doi.org/10.1029/2009JD012803.
J. Vidot, J., R. Bennartz, C.W. O’Dell, R. Preusker, R. Lindstrot, and A.K. Heidinger, CO2 retrieval over clouds from the OCO mission: Model simulations and error analysis, J. Atmos. Oceanic Technol., 26 (6), 1090-1104, 2009. https://doi.org/10.1175/2009JTECHA1200.1.
A.K. Heidinger, C.W. O’Dell, T. Greenwald, & R. Bennartz, 2006: The Successive Order of Interaction Radiative Transfer Model, Part I: Model Development. J. Appl. Meteorol. Clim., 45 (10), pp. 1388-1402.
C.W. O’Dell, A.K. Heidinger, T. Greenwald, & R. Bennartz, 2006: The Successive Order of Interaction Radiative Transfer Model, Part II: Model Performance and Applications. J. Appl. Meteorol. Clim., 45 (10), pp. 1403-1413.
CIRA Machine Learning (ML)
CIRA Software Engineering Group (CSEG)
The CIRA Software Engineering Group (CSEG) began as an informal, enthusiastic grassroots group in April of 2014. Since then, it has grown into an active and cross-cutting group with representation from teams at CIRA-Fort Collins, CIRA-Boulder, NOAA MDL, the National Parks Service (NPS), the CSU Department of Atmospheric Science, the CSU Department of Soil and Crop Sciences, and the CSU Center for the Environmental Management of Military Lands (CEMML). CSEG supports the Vision for CIRA by exploring and testing emerging software technologies from outside the atmospheric sciences and incorporating them into our work to support the efficiency and excellence of CIRA’s research themes.
CSEG meets monthly to discuss how new tools and techniques can inform and improve CIRA’s data management, computing, and scientific efforts. CSEG further benefits CIRA’s projects by providing opportunities to discuss common software issues, to collect feedback on ideas, and to brainstorm alternative solutions, as well as assisting in planning future computing infrastructure and collaboratively developing software for general use across CIRA teams. CSEG also offers trainings for software tools that benefit the CIRA community (e.g., CSEG has offered Git training since 2016 and manages a local-network Git repository server).
CSEG is open to all and you need not characterize yourself as a software engineer to participate — all tech junkies are welcome! If you are interested in joining this innovative and vibrant community, please contact firstname.lastname@example.org for more information.
Data Assimilation (DA)
The future state of a dynamical model depends on control parameters such as initial conditions, model errors, empirical parameters of the model, and boundary conditions. Insufficient knowledge of any of the former can lead to prediction uncertainty, which implies a probabilistic nature of the problem. The chaotic nature of nonlinear dynamical systems in weather and climate, and in geosciences in general, confirms the fundamentally probabilistic character of dynamical systems. Information about the dynamical state and its uncertainty is collected from observations. Blending the information from observations with information from dynamical models requires a coordinated effort in several areas of Physics and Mathematics: Probability Theory, Estimation Theory, Control Theory, Nonlinear Dynamics, and Chaos/Information Theory. Since we are primarily interested in geosciences applications to high-dimensional dynamical systems, the computational component of the problem is also of great importance to our objectives. Our research is encompassing all the formerly mentioned disciplines with the goal of developing a general methodology for uncertainty estimation of dynamical systems.
Data Processing Centers (DPC)
CIRA is the home of the Data Processing Center (DPC) for CloudSat, a NASA ESSP satellite launched in April 2006. CloudSat carries a 94 GHz radar measuring the vertical distribution of cloud reflectivity around the world from a low-Earth, sun-synchronous polar orbit. Working with internal and external science partners, the CloudSat DPC processes the observed reflectivity into retrieved parameters that describe cloud microphysics, precipitation, and cloud radiative properties for weather and climate research. The DPC has produced and distributed more than 105 million data files with a total volume of more than 3.7 petabytes to users around the world.
In 2018, CIRA was awarded the contract to design, build, and operate the Data Operations Center (GDOC) for NASA’s GeoCarb mission which will put an instrument on a commercial telecommunications satellite for launch in the early 2020’s. From its vantage point in geostationary orbit over the western hemisphere, GeoCarb’s high spectral resolution radiometer will observe solar radiation reflected by the Earth’s surface. The GDOC will receive data acquired by the instrument and run science applications to retrieve concentrations of carbon dioxide, methane, and carbon monoxide, and measure solar induced fluorescence, an indicator of vegetation health.
The Meteorological Satellite (MetSat) applications team at CIRA focuses on developing cutting-edge satellite products for research-to-operational use, utilizing a variety of satellite platforms covering the full spectrum of satellite observations.
MetSat research substantially supports the RAMMB group, working with NOAA partners on GOES-R and Suomi NPP/NOAA 20 product development, research, and calibration/validation. Our work also supports tropical storm and severe weather research, and training and outreach efforts, and is integral to reporting and engaging with the larger NOAA community through RAMMB research.
MetSat research also engages outside of the RAMMB group within NOAA, partnering with NASA, the Department of Defense, the Department of Energy, and other federal, state, and private organizations, and are designed to support operational forecasting, aviation, marine operations, fire weather, and numerical weather prediction model integration.
MetSat group members support a wide range of projects, from small, experimental programs developing new technologies, to large research-to-operations projects integrating with the National Weather Service and other organizations. The MetSat group also proposes new missions designed to leverage emerging technologies to develop new observations and observational platforms for satellite remote sensing. Data distribution and access are a partnering priority for the MetSat group, as is coordinating with education and training partners to better disseminate the research products created by the group.
NESDIS Environmental Applications Team (NEAT)
Based within NOAA/NESDIS/STAR in College Park, MD, the NESDIS Environmental Applications Team (NEAT) converts satellite observations into actionable ocean, land, and atmospheric parameters and improves forecast model performance. A newer emphasis is on modern visualization, coastal studies, and trend analysis. We are also extending our activities to the cryosphere (polar Cal/Val and sea-ice) by including high-resolution airborne images. These efforts involve substantial national/international collaboration, align with NOAA’s Next-Gen strategic plan, and strengthen mission-critical capabilities.
CIRA scientists located in Miami, FL, work with the National Hurricane Center (NHC) to help to facilitate the Hurricanes and Ocean Testbed (HOT) by coordinating with developers supported by the HOT and assisting with real time product demonstrations and evaluations. They also work with NHC forecasters and technical staff on evaluation of model upgrades, including the National Weather Service global model (Global Forecast System, GFS) and regional hurricane models such as the new Hurricane Analysis and Forecast System (HAFS). CIRA staff at NHC also help to update and improve existing operational guidance models run by NHC including their wind speed probability model and several statistical models for intensity forecasting, with emphasis on increased use of satellite data and more advanced machine learning techniques.
NOAA/Earth System Research Laboratories (ESRL)
Earth System Research Laboratories
In Boulder, Colorado, CIRA collaborates with all four of NOAA’s Earth System Research Labs (ESRL). At ESRL, scientists study atmospheric and other processes that affect air quality, weather, and climate. ESRL researchers monitor the atmosphere, study the physical and chemical processes that comprise the Earth system, and integrate those findings into environmental information products. This work improves critical weather and climate tools for the public and private sectors.
On April 2, 2020, NOAA designated the four divisions of the Earth System Research Laboratory in Boulder as full laboratories within the NOAA Oceanic and Atmospheric Research line office to meet recent shifts in mission-essential priorities. All laboratories retained their core research missions, and continue to collaborate closely with each other and other NOAA Research laboratories to improve understanding and ability to predict changes in Earth’s atmosphere, climate and weather.
The Global Systems Laboratory
The Global Systems Laboratory (GSL) of the Earth System Research Laboratories (ESRL) conducts world-class applied research and directed development resulting in technology transfer of environmental data, models, products, and services that enhance environmental understanding with the outcome of supporting commerce, supporting NWS in protecting life and property, and promoting a scientifically literate public.
Physical Sciences Laboratory
NOAA’s Physical Sciences Division (PSL) of the Earth System Research Laboratories (ESRL) conducts scientific research to observe, understand, model, predict and forecast weather, water and climate extremes and their impacts.
NWS/Aviation Weather Center (AWC)
Our Team Members:
The Aviation Weather Center (AWC) is part of the National Centers for Environmental Prediction (NCEP) under the NOAA/National Weather Service (NWS). The AWC is located in Kansas City, MO and has a long history of providing operational global aviation weather forecasts and warnings for the NWS, the Federal Aviation Administration (FAA), industry, and aviators around the globe. The Aviation Weather Testbed (AWT) is co-located with the AWC and supports the transition of aviation research into operations for the NWS, the FAA, and their partners. The AWC and AWT collaborate with universities, governmental forecast centers and research laboratories, FAA organizations, International Meteorological Watch Offices, and other NOAA and NWS organizations. The AWC maintains 24×7 global forecasting and warning operations, and the AWT supports aviation meteorology hazards training, applied research, and transitioning research-to-operations. The AWC and AWT are also actively engaged in supporting the FAA’s NextGen weather initiative in building a 4-Dimensional Weather Data Cube (4-D Cube) that will improve access and accuracy of weather information to support improvements to aviation services in the NextGen era. Activities within the AWT and AWC will play a significant role in the development, testing, and evaluation of NextGen development. CIRA is a collaborating partner with the AWC on a number of research projects and activities. CIRA professionals assist the AWC/AWT in supporting, developing, testing, and transitioning aviation weather research into NWS operations.
The Aviation Weather Center (AWC) Aviation Support Branch (ASB) is responsible for providing support to the research and operations processes, maintaining server and networking infrastructure, and supporting the www.aviationweather.gov website.
The primary goal of the ASB is to maintain the internal network, servers and workstations at the AWC to ensure continuity of operations. The 24×7 support is critical to AWC forecast and web operations. The ASB collaborates with the other National Center for Environmental Prediction (NCEP) centers and the National Weather Service (NWS) to provide data and research to operations support. The branch supports the research operations at the AWC, headed by a team of Technique Development Meteorologists (TDMs). This includes support for the Testbed (AWT) as well as support for AWRP. The AWRP products include Current and Forecast Icing Products (CIP/FIP), Graphical Turbulence Guidance (GTG), National Ceiling and Visibility Analysis (NCVA), and the National Convective Weather Diagnostic/Forecast (NCWD/F). The ASB also supports the AWC website which includes Aviation Digital Display Service (ADDS), World Area Forecast System (WAFS) Internet File service (WIFS) and the International Flight Folder Program (IFFDP).
NWS/Meteorological Development Laboratory (MDL)
The primary goal in the research partnership between CIRA and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Meteorological Development Laboratory (MDL) in Silver Spring, MD, is to keep abreast of advanced technology and apply it to CIRA and MDL in support of decision support tools and technologies. The partnership is focused upon providing technical expertise to MDL, providing a framework to foster innovation, science sharing, and development of new tools and services within the NWS with the goal of streamlining the transition of research to operations. The MDL develops and implements techniques that generate products and services that enhance the value of NWS forecast products. Prototyping of promising techniques is done to identify those best for implementation. Once developed and vigorously tested, these techniques are implemented in software on NWS operational platforms.
Office of Marine and Aviation Operations (OMAO-Training)
The Office of Marine and Aviation Operations (OMAO) Learning Office oversees employee learning, development, and training programs to support mission readiness. The focus of the Learning Office is to ensure standardized processes, application of best practices, transparency in training programs, equitable opportunity for employees, and compliance with NOAA, Department of Commerce (DOC) and Office of Personnel Management (OPM) guidance across OMAO. The OMAO Chief Learning Officer (CLO)/Learning Office is located at National Weather Service Training Center (NWSTC) in Kansas City, MO as part of a NOAA agreement to share resources and mutually support common training. The OMAO Learning Office is responsible for the development and implementation of OMAO learning policy; and the management and maintenance of OMAO’s LMS and training portal (a Google Site), and providing leadership training to staff. The agreement to share resources includes use of common techniques, hardware and software systems by both line offices, and collaboration on joint use projects.
Regional and Mesoscale Meteorology Branch (RAMMB)
The Regional and Mesoscale Meteorology Branch (RAMMB) of NOAA/NESDIS/Satellite Applications and Research (StAR) conducts research on the use of satellite data to improve analysis, forecasts and warnings for regional and mesoscale meteorological events. RAMMB is co-located with the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University in Fort Collins CO.
Societal Impacts of Weather and Climate
“The importance of advancing the social and behavioral sciences across the Weather Enterprise cannot be overstated in order to optimize weather information services so that they are value-focused and deliver optimally on NOAA’s mission of saving lives, protecting property, and bettering the U.S. economy.”
–Report on Priorities for Weather Research, Section 7.1, NOAA Science Advisory Board, Dec 2021
At CIRA, we recognize the importance of developing meaningful interdisciplinary partnerships to help us apply our research in ways that directly benefit society. As such, the Societal Impacts of Weather and Climate (SIWC) research group was established to address NOAA’s social science research priorities. Through our partnership with CSU, we’ve been able to collaborate with experts in areas such as risk communication, impacts of extreme weather on human health, and economic impacts of weather and climate. In addition, our participation in the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) Risk Communication group has allowed us to develop strong working relationships with social scientists at NCAR and University of Washington. We also have two employees (Dr. Fooks and Dr. Were) embedded within the National Weather Service’s Social, Behavioral, and Economic Sciences (SBES) program.
Tropical cyclone (TC) wind exceedance graphics evaluation: CIRA scientists, in collaboration with NOAA/NWS Miami and the National Hurricane Center, are developing a new version of the Tropical Cyclone Wind Speed Probability (WSP) product with support from the NOAA/WPO JTTI program (FY21-23). This project includes the development of a new graphical TC wind exceedance product, analogous to the storm surge flood inundation map, that will be used to communicate TC wind hazards to NWS forecasters, private and government users, and potentially the public. As such, CIRA is collaborating with Dr. Marilee Long and a PhD student in the CSU Department of Journalism and Media Communication to conduct research examining how different design elements impact users’ understanding of the wind exceedance guidance. This work will provide the information needed to develop clear, understandable, and user-vetted graphics ready for use as soon as the product is implemented in operations. A more general goal of this project is to demonstrate how conducting social science research during the product development phase can potentially streamline and improve the R2O process.
Foundational Research in AI Risk Communication (RC) for Environmental Science (ES) Hazards: CIRA scientists are actively working in the field of artificial intelligence (AI) to develop new satellite algorithms and models for forecasters. As developers adopt new machine learning (ML) methods, it is important to understand how the use of these methods might impact the ability of forecasters to understand, evaluate, and develop trust in new models. To address these concerns, the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) has a research team devoted to conduct foundational research on AI risk communication. CIRA currently has 4 employees on this research team, working on a variety of topics related to ethics and bias, trust and trustworthiness, and transparency and explainability in AI/ML forecast product development.
Extreme weather and human health
Estimating hurricane wind exposure for health impacts studies: Numerous epidemiological studies have found that hurricanes cause severe human health impacts in U.S. communities. These studies typically fall into two categories; case studies that link health outcomes to specific hurricanes (e.g., hospitalizations after Hurricane Katrina) and longer-scale studies that estimate community-wide excess mortality and morbidity associated with hurricanes. These latter studies require meteorological hurricane data that is consistent over multiple years and storms and comparable across communities. CIRA scientists assisted Dr. Brooke Anderson, an Associate Professor of Epidemiology in the CSU Department of Environmental & Radiological Health Sciences, to develop such a dataset and made it available as open source R packages. This model and the dataset have already been adopted by many in the epidemiology, biostatistics, and public health communities.
Hurricane health impacts in varying climatic regimes: CIRA scientists are collaborating with Dr. Brooke Anderson and a PhD student in the CSU Department of CSU Department of Environmental & Radiological Health Sciences to study county-level tropical cyclone wind exposure and projected TC-wind-induced excess mortality under varying climate scenarios. CIRA researchers have provided synthetic TC tracks expected under different climatic regimes such as El Niño Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO). Millenia of synthetic tropical cyclone data from each climate state will be input into a wind model to compute expected county-level wind exposure during each combination of ENSO and AMO. These wind data will then be input into the public health model used by Brooke and Daniel to calculate expected county-level excess mortality during each climate state.
NWS Social, Behavioral, and Economic Sciences (SBES)
CIRA has two social scientists embedded in the National Weather Service’s Social, Behavioral, and Economic Sciences (SBES) program. The program is young and just beginning to drop its anchor into the Office of Science and Technology Integration (OSTI). Dr. Valerie Were and Dr. Jacob Fooks joined CIRA in August and December 2021, respectively, and the SBES program itself was officially established on November 30, 2021. Dr. Fooks and Dr. Were supported the Acting Program Director until Ji Sun Lee, the permanent Director was hired in November 2022. Given the youth of an official program, the major goal and therefore much of Dr. Fooks’ and Dr. Were’s work, is related to building the program. The program is currently finalizing its research priorities for the next three to five years.
All the projects fall under the umbrella of ensuring equitable services to all communities (Strategic Goal 2 in the NOAA FY 2022-26 Strategic Plan and Goal 3.8 in the NWS 2023-2033 Strategic Plan). There are four priorities:
NWS SBES is working on a number of ongoing activities that fall within these research priorities. Examples include:
Through CIRA’s work, the NWS is now better positioned to apply SBES to meet its mission. The outcome of CIRA’s work maximizes the value of the NWS’ investments and actions while ensuring products and services are delivered equitably. Beyond the science and technology, the impact on society will be equitable service delivery and improvements to forecast messaging so people can take life and property-saving actions.
Social science datasets for weather research
Hurricane evacuation order database: Hurricane evacuation order data is needed for various types of socio-economic research. Yet this data is very time-intensive and difficult to collect because it is issued by inconsistent sources via inconsistent methods. To date, there is no comprehensive, consistent, searchable, openly available dataset available for obtaining U.S. evacuation order data – researchers must search government websites, social media, and news archives to find orders and then reconcile any differences these sources provide. To address this need, CIRA scientists developed a methodology for systematically collecting U.S. mainland tropical cyclone evacuation order data. So far, 3 main cases (Laura/Marco 2020, Henri 2021, Ian 2022) have been collected in detail and made available in CSV format via a git repository. All U.S. mainland TC evacuation orders from the 2021 Atlantic hurricane season have been collected and are currently being vetted and quality controlled, and will be added to the repository when ready. In addition to the datasets, CIRA is currently designing a searchable web interface that can be used to easily find the evacuation order data needed. We hope that making this data available in a consistent and searchable/sub-settable way will help facilitate future social science research related to TC impacts.
Economic impacts of weather and climate
Schumacher, A.B., M. DeMaria, A. Brammer, K. Musgrave, P. Santos, Z. Rosen, M. Long, and W. Hogsett, 2023: Taking steps to unify and improve NWS TC wind forecast products. 2023 Tropical Cyclone Operations and Research Forum / Interdepartmental Hurricane Conference, Miami, FL, 7-9 March 2023.
Jacob Fooks, Jeff Adkins, Valerie Were, Jennifer Sprague-Hilderbrand. 2023. Serving the Underserved: Increasing Our Skill. Oral presentation. 103rd American Meteorological Society Annual Meeting, January 8-12, 2023, Denver, Colorado.
Jacob Fooks, Jeff Adkins, Paul Roeber, Jennifer Sprague-Hilderbrand. 2023. Predicting Surprising Outcomes: Agent-Based Models at the intersection of complex human and environmental systems. Oral presentation. 103rd American Meteorological Society Annual Meeting, January 8-12, 2023, Denver, Colorado.
Valerie Were. 2023. Sea Level Rise Risk Communication: Research Findings and Emerging Best Practices to Advance Equity. Oral presentation. 103rd American Meteorological Society Annual Meeting, January 8-12, 2023, Denver, Colorado.
Schumacher, A.B., K. Musgrave, O. Ostwald, and M. Niznik, 2023: A New Evacuation Database for Societal Impacts Research. 103rd AMS Annual Meeting, Denver, CO, 8-12 January 2023.
Schumacher, A.B., K. Musgrave, O. Ostwald, and M. Niznik, 2023: A New Evacuation Database for Societal Impacts Research. 103rd AMS Annual Meeting, Denver, CO, 8-12 January 2023.
Stephen Smith, Cindy Woods, Tyra Brown-Harris, Patricia Brown, John-Michael Bloomquist, and Valerie Were. 2023. Equitable Services for a Weather-Ready Nation:
The NWS Service Equity Action Plan. Oral presentation. 103rd American Meteorological Society Annual Meeting, January 8-12, 2023, Denver, Colorado.
Valerie Were. 2022. To Flee or not to Flee: A Best Practices Guide for the Consistent Depiction of Risk at the National Weather Service. Oral presentation. 2022 NOAA Hurricane Conference, November 29 to December 2, 2022, Virtual Conference.
Schumacher, A. B., 2021: Using hurricane data for health impacts research. International Society for Environmental Epidemiologists – N. American Chapter (ISEE-NAC) Workshop on Climate Change, Hurricanes, and Health. Virtual (https://www.youtube.com/watch?v=Lr8DcDfi7XI), 14 April 2021.
McGovern, A., D.J. Gagne II, C.D. Wirz, I. Ebert-Uphoff, A. Bostrom, Y. Rao, A. Schumacher, M. Flora, R. Chase, A. Mamalakis, M. McGraw, R. Lagerquist, R.J. Redmon, and T. Peterson, In Review: Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-22-0225.1, in press.
Anderson, G. B., A. Schumacher, J. M. Done, and J. Hurrell, 2022: Projecting the impacts of a changing climate: Tropical cyclones & flooding. Current Environmental Health Reports, 9(2), 244-262, https://doi.org/10.1007/s40572-022-00340-0.
Anderson, G. B., A. Schumacher, and J. M. Done, 2022: Exposure assessment for tropical cyclone epidemiology. Current Environmental Health Reports, 9(1), https://doi.org/10.1007/s40572-022-00333-z.
Nethery, R. C., N. Katz-Christy, M. Kioumourtzoglou, R. M. Parks, A. Schumacher, and G. B. Anderson, 2021: Integrated causal-predictive machine learning models for tropical cyclone epidemiology, Biostatistics, https://doi.org/10.1093/biostatistics/kxab047.
Anderson G. B., J. Ferreri, M. Al-Hamdan, W. Crosson, A. Schumacher, S. Guikema, S. Quiring, D. Eddelbuettel, M. Yan, and R. D. Peng, 2020: Assessing United States county-level exposure for research on tropical cyclones and human health. Environmental Health Perspectives. 128(10), https://doi.org/10.1289/EHP6976.
Yan, M., A. Wilson, S. Magzamen, F. Dominici, Y. Yang, M. Al-Hamdan, W. Crosson, A. Schumacher, S. Guikema, R. Peng, and G. B. Anderson, 2020: Tropical cyclone exposures and risks of emergency Medicare hospital admission for cardiorespiratory diseases in 175 United States counties, 1999–2010. Epidemiology. 32(3), 315-326, https://doi.org/10.1097/EDE.0000000000001337.
Anderson G. B., A. Schumacher, S. Guikema, S. Quiring, J. Ferreri, A. Staid, M. Guo, L. Ming, and L. Zhu, 2020: `stormwindmodel`: Model tropical cyclone wind speeds. Version 0.1.4 [Software]. Available from: https://cran.r-project.org/web/packages/stormwindmodel/index.html.
Anderson G. B., M. Yan, J. Ferreri, W. Crosson, M. Al-Hamdan, A. Schumacher, and D. Eddelbuettel, 2020: `hurricaneexposure`: Explore and Map County-Level Hurricane Exposure in the United States. Version 0.1.1 [Software]. Available from: https://cran.r- project.org/web/packages/hurricaneexposure/index.html.
Anderson G. B., A. Schumacher, W. Crosson, M. Al-Hamdan, M. Yan, J. Ferreri, Z. Chen, S. Quiring, and S. Guikema, 2020: `hurricaneexposuredata`: Data Characterizing Exposure to Hurricanes in United States Counties. Version 0.1.0 [Software]. Available from: https://github.com/geanders/hurricaneexposuredata.
Training for Professionals
To help meet satellite training competencies for National Weather and Meteorological Services in the U.S. and globally, CIRA leverages three programs and coordinates activities with NOAA and other US and International partners. The VISIT and SHyMet programs focus on training for the U.S. National Weather Service (NWS) and the WMO VLab program focuses on the global community.
The primary mission of the Virtual Institute for Satellite Integration Training (VISIT) is to accelerate the transfer of research results based on atmospheric remote sensing data into NWS operations. The continuing professional development of NWS forecasters focuses on transferring the latest techniques to integrate remote sensing data, especially from satellite and radar, into the forecast process. The education approach uses distance education techniques (web-based audio/video modules and live teletraining) and is most effective when utilizing the expertise of the Science Operations Officer (SOO) and a satellite/radar focal point at the local forecast offices.
The Satellite Hydrology and Meteorology (SHyMet) Courses offer existing, new, and updated satellite training materials in a series of structured courses. The courses cover basic principles of satellite imaging and sounding, channels and products, identification of atmospheric and surface phenomena, and the integration of meteorological analysis with satellite observations and products into the weather forecasting and warning process. Advanced topics on identification of atmospheric and surface phenomena with associated case examples are also included.
The World Meteorological Organization (WMO) Virtual Laboratory for Training and Education in Satellite Meteorology (VLab) is a global network of specialized training centres and meteorological satellite operators working together to improve the utilization of data and products from meteorological and environmental satellites. It was established by the WMO and the Coordination Group for Meteorological Satellites (CGMS) in 2003. The activities here are sponsored by NOAA, the US satellite operator.
The WMO VLab Regional Focus Group (RFG) of the Americas and the Caribbean meets virtually for monthly weather and climate briefings. Register here to join us for the next session on Wednesday, 21 June 2023 at 15:00 UTC.
The Tropical Cyclone team works closely with NOAA RAMMB. The main objectives of RAMMB and the TC team are to conduct research on hurricanes and tropical cyclones to improve our understanding through an observational approach, to develop and test satellite (and other) products for tropical cyclone analysis and forecasting, and evaluate numerical model output using satellite diagnostics and when such products/methods have demonstrated success, transition them to operational platforms and centers. In this research, we utilize a combination of satellite data and products, numerical model output, aircraft reconnaissance, and tropical cyclone data, and metrics derived from operational advisories and best tracks. We develop satellite products/methods that improve the diagnosis and forecasting of tropical cyclones, conduct research to improve our understanding of atmospheric and oceanic physical processes, and produce techniques and products that help forecasters do their job efficiently.
Realtime TC centric products are available at TC Realtime.