doi:10.1029/2008GL035867

doi:10.1029/2007JD009304

doi:10.1029/2008JG000842.

doi:10.1029/2009GB003464

doi:10.1029/2009GL038993

doi:10.1029/2009JD011750

doi:10.1029/2008GL036848

The Suomi-NPP Visible Infrared Imager Radiometer Suite (VIIRS) instrument provides the next generation of visible/infrared imaging including the day/night band (DNB) with nominal bandwidth from 500 to 900 nm. Previous to VIIRS, the Defense Meteorological Satellite Program Operational Linescan System (OLS) measured radiances that spanned over seven orders of magnitude, using an onboard gain adjustment to provide the capability to image atmospheric features across the solar terminator, to observe nighttime light emissions over the globe, and to monitor the global distribution of clouds. The VIIRS DNB detects radiances that span over eight orders of magnitude, and because it has 13-14-b quantization (compared with 6 b for OLS) with three gain stages, the DNB has its full dynamic range at every part of the scan. One process that is applied to the VIIRS DNB radiances is a solar/lunar zenith angle dependent gain adjustment to create near-constant contrast (NCC) imagery. The at-launch NCC algorithm was designed to reproduce the OLS capability and, thus, was constrained to solar and lunar angles from 0° to 105°. This limitation has, in part, lead to suboptimal imagery due to the assumption that DNB radiances fall off exponentially beyond twilight. The VIIRS DNB ultrasensitivity in low-light conditions enables it to detect faint emissions from a phenomenon called airglow, thus invalidating the exponential fall-off assumption. Another complication to the NCC imagery algorithm is the stray light contamination that contaminates the DNB radiances in the astronomical twilight region. We address these issues and develop a solution that leads to high-quality imagery for all solar and lunar conditions.

Operational sea surface temperature (SST) retrieval algorithms are stratified into nighttime and daytime. The nighttime algorithm uses two split-window Visible Infrared Imaging Radiometer Suite (VIIRS) bands—M15 and M16, centered at ~11 and ~12 m, respectively—and a shortwave infrared band—M12, centered at ~3.7 m. The M12 is most transparent and critical for accurate SST retrievals. However, it is not used during the daytime because of contamination by solar radiation, which is reflected by the ocean surface and scattered by atmospheric aerosols. As a result, daytime VIIRS SST and cloud mask products and applications are degraded and inconsistent with their nighttime counterparts. This study proposes a method to remove the solar contamination from the VIIRS M12 based on theoretical radiative transfer model analyses. The method uses either of the two VIIRS shortwave bands, centered at 1.6 m (M10) or 2.25 m (M11), to correct for the effect of solar reflectance in M12. Subsequently, the corrected daytime brightness temperature in M12 can be used as input into nighttime cloud mask and SST algorithms. Preliminary comparisons with the European Centre for Medium-Range Weather Forecasts (ECMWF) SST analysis suggest that the daytime SST products can be improved and potentially reconciled with the nighttime SST product. However, more substantial case studies and assessments using different SST products are required before the transition of this research work into operational products.

Storm-centered infrared (IR) imagery of tropical cyclones (TCs) is related to the 850-hPa mean tangential wind at a radius of 500 km (V500) calculated from 6-hourly global numerical analyses for North Atlantic and eastern North Pacific TCs for 1995–2011. V500 estimates are scaled using the climatological vortex decay rate beyond 500 km to estimate the radius of 5 kt (1 kt = 0.514 m s⁻¹) winds (R5) or TC size. A much larger historical record of TC-centered IR imagery (1978–2011) is then used to estimate TC sizes and form a global TC size climatology. The basin-specific distributions of TC size reveal that, among other things, the eastern North Pacific TC basins have the smallest while western North Pacific have the largest TC size distributions. The life cycle of TC sizes with respect to maximum intensity shows that TC growth characteristics are different among the individual TC basins, with the North Atlantic composites showing continued growth after maximum intensity. Small TCs are generally located at lower latitudes, westward steering, and preferred in seasons when environmental low-level vorticity is suppressed. Large TCs are generally located at higher latitudes, poleward steering, and preferred in enhanced low-level vorticity environments. Postmaximum intensity growth of TCs occurs in regions associated with enhanced baroclinicity and TC recurvature, while those that do not grow much are associated with west movement, erratic storm tracks, and landfall at or near the time of maximum intensity. With respect to climate change, no significant long-term trends are found in the dataset of TC size.

The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aquasatellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.

Previous studies have demonstrated a link between gross moist stability (GMS) and intraseasonal variability in theoretical and reduced-complexity models. In such simplified models, MJO-like moisture modes—convectively coupled tropical disturbances akin to the MJO whose formation and dynamics are linked to moisture perturbations—develop only when GMS is either negative or “effectively” negative when considering additional sources of moist entropy. These simplified models typically use a prescribed, time-independent GMS value. Limited work has been done to assess GMS and its connection to intraseasonal variability in full-physics general circulation models (GCMs).

The time-mean and intraseasonal behavior of normalized GMS (NGMS) are examined in three pairs of GCMs to elucidate the possible importance of NGMS for the MJO. In each GCM pair, one member produces weak intraseasonal variability, while the other produces robust MJOs because of a change in the treatment of deep convection. A strong linear correlation between time-mean NGMS and MJO simulation skill is observed, such that GCMs with less positive NGMS produce improved MJO eastward propagation. The reduction in time-mean NGMS is primarily due to a sharp drop to negative values in the NGMS component related to vertical advection, while the horizontal advection component has a less clear relationship with MJO simulations. Intraseasonal fluctuations of anomalous NGMS modulate the magnitude of background NGMS but generally do not change the sign of background NGMS. NGMS declines ahead of peak MJO rainfall and increases during and after heaviest precipitation. Total NGMS fluctuates during MJO passage but remains positive, suggesting that other sources of moist entropy are required to generate an effectively negative NGMS.

An improved understanding of atmospheric ice nucleating particles (INP), including sources and atmospheric abundance, is needed to advance our understanding of aerosol-cloud-climate interactions. This study examines diverse biomass burning events to better constrain our understanding of how fires impact populations of INP. Sampling of prescribed burns and wildfires in Colorado and Georgia, U.S.A., revealed that biomass burning leads to the release of particles that are active as condensation/immersion freezing INP at temperatures from −32 to −12°C. During prescribed burning of wiregrass, up to 64% of INP collected during smoke-impacted periods were identified as soot particles via electron microscopy analyses. Other carbonaceous types and mineral-like particles dominated INP collected during wildfires of ponderosa pine forest in Colorado. Total measured nINP and the excess nINP associated with smoke-impacted periods were higher during two wildfires compared to the prescribed burns. Interferences from non-smoke sources of INP, including long-range transported mineral dust and local contributions of soils and plant materials lofted from the wildfires themselves, presented challenges in using the observations to develop a smoke-specific nINP parameterization. Nevertheless, these field observations suggest that biomass burning may serve as an important source of INP on a regional scale, particularly during time periods that lack other robust sources of INP such as long-range transported mineral dust.

Two global level 2 sea surface temperature (SST) products are generated at NOAA from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data records (L1) with two independent processing systems, the Joint Polar Satellite System (JPSS) Interface Data Processing Segment (IDPS) and the NOAA heritage Advanced Clear-Sky Processor for Oceans (ACSPO). The two systems use different SST retrieval and cloud masking algorithms. Validation against in situ and L4 analyses has shown suboptimal performance of the IDPS product. In this context, existing operational and proposed SST algorithms have been evaluated for their potential implementation in IDPS. This paper documents the evaluation methodology and results. The performance of SST retrievals is characterized with bias and standard deviation with respect to in situ SSTs and sensitivity to true SST. Given three retrieval metrics, all being variable in space and with observational conditions, an additional integral metric is needed to evaluate the overall performance of SST algorithms. Therefore, we introduce the Quality Retrieval Domain (QRD) as a part of the global ocean, where the retrieval characteristics meet predefined specifications. Based on the QRDs analyses for all tested algorithms over a representative range of specifications for accuracy, precision, and sensitivity, we have selected the algorithms developed at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF) for implementation in IDPS and ACSPO. Testing the OSI-SAF algorithms with ACSPO and IDPS products shows the improved consistency between VIIRS SST and Reynolds L4 daily analysis. Further improvement of the IDPS SST product requires adjustment of the VIIRS cloud and ice masks.

Normalized water-leaving radiance spectra nLw(λ) at the red, near-infrared (NIR), and shortwave infrared (SWIR) are quantified and characterized in highly turbid waters of the western Pacific using 3 yr (2009–2011) observations from the Moderate Resolution Imaging Spectroradiometer on the satellite Aqua. nLw(645; red), nLw(859; NIR), and nLw(1240; SWIR) were higher in the coastal region and river estuaries, with SWIR nLw(1240) reaching up to ∼ 0.2 mW cm−2 µm−1 sr−1 in Hangzhou Bay during winter. The NIR ocean-reflectance spectral shape represented by the ratio of the normalized water-leaving reflectance ρwN(λ) at the two NIR bands ρwN(748) : ρwN(869) is highly dynamic and region-dependent. The NIR spectral feature associated with the sediment source from the Yellow River and Ancient Yellow River is noticeably different from that of the Yangtze River. There are non-negligible SWIR nLw(1240) contributions for waters with the NIR nLw(859) > ∼ 2.5 mW cm−2 µm−1 sr−1. Estimation of the NIR ocean reflectance with iterative approaches might only be accurate for turbid waters with nLw(859) < ∼ 1.5 mW cm−2 µm−1 sr−1. Thus, the SWIR atmospherics correction algorithm for satellite ocean-color data processing is indispensable to derive accurate nLw(λ) for highly turbid waters. Current existing satellite algorithms for chlorophyll a, diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), total suspended matter, and inherent optical properties (IOPs) using nLw(λ) at the red band for coastal waters are limited and can only be applied to turbid waters with nLw(859) < ∼ 1.5 mW cm−2 µm−1 sr−1. Thus, the NIR nLw(λ) measurements are required to characterize water properties for highly turbid waters. Based on the fact that pure water absorption is significantly larger than other absorption components in the NIR wavelengths, we show that it is feasible to analytically derive accurate IOP data for turbid waters with combined satellite-measured visible-NIR nLw(λ) spectra data.

Errors of coupled general circulation models (CGCMs) limit their utility for climate prediction and projection. Origins of and feedback for tropical biases are investigated in the historical climate simulations of 18 CGCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), together with the available Atmospheric Model Intercomparison Project (AMIP) simulations. Based on an intermodel empirical orthogonal function (EOF) analysis of tropical Pacific precipitation, the excessive equatorial Pacific cold tongue and double intertropical convergence zone (ITCZ) stand out as the most prominent errors of the current generation of CGCMs. The comparison of CMIP–AMIP pairs enables us to identify whether a given type of errors originates from atmospheric models. The equatorial Pacific cold tongue bias is associated with deficient precipitation and surface easterly wind biases in the western half of the basin in CGCMs, but these errors are absent in atmosphere-only models, indicating that the errors arise from the interaction with the ocean via Bjerknes feedback. For the double ITCZ problem, excessive precipitation south of the equator correlates well with excessive downward solar radiation in the Southern Hemisphere (SH) midlatitudes, an error traced back to atmospheric model simulations of cloud during austral spring and summer. This extratropical forcing of the ITCZ displacements is mediated by tropical ocean–atmosphere interaction and is consistent with recent studies of ocean–atmospheric energy transport balance.

Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) single-moment 6-class (WSM6) microphysics scheme in the Advanced Research WRF (WRF-ARW) produces less upper-level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite-13 (GOES-13) imagery at 10.7 μm of simulated cloud fields from the 4-km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper-level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the 4-km NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.

The Dynamics of the MJO (DYNAMO) field campaign took place in the Indian Ocean during boreal fall and winter of 2011–2012 to collect observations of Madden-Julian Oscillation (MJO) initiation. Hindcast experiments are conducted with an atmospheric general circulation model with varying values of a dilute CAPE entrainment rate parameter for the first two MJO events of DYNAMO from 1 October 2011 to 15 December 2011. Higher entrainment rates better reproduce MJO precipitation and zonal wind, with RMM skill up to 20 days. Simulations with lower entrainment rapidly diverge from observations with no coherent MJO convective signal after 5 days, and no MJO predictive skill beyond 12 days. Analysis of the tropical Indian Ocean column moist static energy (MSE) budget reveals that the simulations with superior MJO performance exhibit a mean positive MSE tendency by vertical advection; inconsistent with reanalysis that indicates a weak negative tendency. All simulations have weaker mean MSE source tendency and significantly weaker cloud-radiative feedbacks. The vertical gross moist stability (VGMS) is used to interpret these MSE budget results in a normalized framework relevant to moisture mode theory. VGMS in the high entrainment runs is far too low compared to ERAi, indicating that it cannot be used in isolation as a measure of model success in producing a realistic MJO hindcast, contrary to previous studies. However, effective VGMS that includes radiative feedbacks is similar among the high entrainment runs and ERAi. We conclude that the MJO is erroneously improved by increasing the entrainment parameter because moistening by vertical MSE advection compensates for the overly weak cloud-radiative feedbacks.

Gridpoint Statistical Interpolation (GSI) is an assimilation tool that is used at the National Centers for Environmental Prediction (NCEP) in operational weather forecasting in the USA. In this article, we describe implementation of an extension to the GSI for assimilating surface measurements of PM2.5, PM10, and MODIS aerosol optical depth at 550 nm with WRF-Chem (Weather Research and Forecasting model coupled with Chemistry). We also present illustrative results. In the past, the aerosol assimilation system has been employed to issue daily PM2.5 forecasts at NOAA/ESRL (Earth System Research Laboratory) and, we believe, it is well tested and mature enough to be made available for wider use. We provide a package that, in addition to augmented GSI, consists of software for calculating background error covariance statistics and for converting in situ and satellite data to BUFR (Binary Universal Form for the Representation of meteorological data) format, and sample input files for an assimilation exercise. Thanks to flexibility in the GSI and coupled meteorology–chemistry of WRF-Chem, assimilating aerosol observations can be carried out simultaneously with meteorological data assimilation. Both GSI and WRF-Chem are well documented with user guides available online. This article is primarily intended to be a technical note on the implementation of the aerosol assimilation. Its purpose is also to provide guidance for prospective users of the computer code. Scientific aspects of aerosol assimilation are also briefly discussed.

June–October east Pacific warm pool intraseasonal variability (ISV) is assessed in eight atmospheric general circulation simulations. Complex empirical orthogonal function analysis is used to document the leading mode of 30–90-day precipitation variability in the models and Tropical Rainfall Measuring Mission observations. The models exhibit a large spread in amplitude of the leading mode about the observed amplitude. Little relationship is demonstrated between the amplitude of the leading mode and the ability of models to simulate observed north-northeastward propagation.

Several process-oriented diagnostics are explored that attempt to distinguish why some models produce superior ISV. A diagnostic based on the difference in 500–850-hPa averaged relative humidity between the top 5% and the bottom 10% of precipitation events exhibits a significant correlation with leading mode amplitude. Diagnostics based on the vertically integrated moist entropy budget also demonstrate success at discriminating models with strong and weak variability. In particular, the vertical component of gross moist stability exhibits a correlation with amplitude of −0.9, suggesting that models in which convection and associated divergent circulations are less efficient at discharging moisture from the column are better able to sustain strong ISV.

Several other diagnostics are tested that show no significant relationship with leading mode amplitude, including the warm pool mean surface zonal wind, the strength of surface flux feedbacks, and 500–850-hPa averaged relative humidity for the top 1% of rainfall events. Vertical zonal wind shear and 850-hPa zonal wind do not appear to be good predictors of model success at simulating the observed northward propagation pattern.

The mean absolute error of the official tropical cyclone (TC) intensity forecasts from the National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC) shows limited evidence of improvement over the past two decades. This result has sometimes erroneously been used to conclude that little or no progress has been made in the TC intensity guidance models. This article documents statistically significant improvements in operational TC intensity guidance over the past 24 years (1989–2012) in four tropical cyclone basins (Atlantic, eastern North Pacific, western North Pacific, and Southern Hemisphere). Errors from the best available model have decreased at 1%–2% yr−1 at 24–72 h, with faster improvement rates at 96 and 120 h. Although these rates are only about one-third to one-half of the rates of reduction of the track forecast models, most are statistically significant at the 95% level. These error reductions resulted from improvements in statistical–dynamical intensity models and consensus techniques that combine information from statistical–dynamical and dynamical models. The reason that the official NHC and JTWC intensity forecast errors have decreased slower than the guidance errors is because in the first half of the analyzed period, their subjective forecasts were more accurate than any of the available guidance. It is only in the last decade that the objective intensity guidance has become accurate enough to influence the NHC and JTWC forecast errors.

In part III of a three-part study on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) models, the authors examine projections of twenty-first-century climate in the representative concentration pathway 8.5 (RCP8.5) emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. The authors also examine changes in the eastern North Pacific and North Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe. Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the northern United States, and Atlantic and east Pacific tropical cyclone activity.

The Visible Infrared Imaging Radiometer Suite (VIIRS) Raw Data Records (or Level-0 data) are processed using the current standard Algorithm Development Library (ADL) to produce Sensor Data Records (SDR; or Level-1B data). The ocean color Environmental Data Records (EDR), one of the most important product sets derived from VIIRS, are processed from the SDR of the visible and near-infrared moderate resolution (M) bands. As the ocean color EDR are highly sensitive to the quality of the SDR, the bands from which the EDR data arise must be accurately calibrated. These bands are calibrated on-orbit using the onboard Solar Diffuser, and the derived calibration coefficients are called F-factors. The F-factors used in the forward operational process may have large uncertainty due to various reasons, and thus, to obtain high-quality ocean color EDR, the SDR needs to be regularly reprocessed with improved F-factors. The SDR reprocessing, however, requires tremendous computational power and storage space, which is about 27 TB for one year of ocean-color-related SDR data. In this letter, we present an efficient and robust method for reduction of the computational demand and storage requirement. The method is developed based on the linear relationship between the SDR radiance/reflectance and the F-factors. With this linear relationship, the new SDR radiance/reflectance can be calculated from the original SDR radiance/reflectance and the ratio of the updated and the original F-factors at approximately 100th or less of the original central processing unit requirement. The produced SDR with this new approach fully agrees with those generated using the ADL package. This new approach can also be implemented to directly update the SDR in the EDR data processing, which eliminates the hassle of a huge data storage requirement as well as that of intensive computational demand. This approach may also be applied to other remote sensors for data reprocessing from raw instrument data to science data.

This paper presents numerical solutions and idealized analytical solutions of axisymmetric, f-plane models of the tropical cyclone boundary layer. In the numerical model, the boundary layer radial and tangential flow is forced by a specified pressure field, which can also be interpreted as a specified gradient balanced tangential wind field vgr(r) or vorticity field ζgr(r). When the specified ζgr(r) field is changed from one that is radially concentrated in the inner core to one that is radially spread, the quasi-steady-state boundary layer flow transitions from a single eyewall shock-like structure to a double eyewall shock-like structure. To better understand these structures, analytical solutions are presented for two simplified versions of the model. In the simplified analytical models, which do not include horizontal diffusion, the u(∂u/∂r) term in the radial equation of motion and the u[f+(∂v/∂r)+(v/r)] term in the tangential equation of motion produce discontinuities in the radial and tangential wind, with associated singularities in the boundary layer pumping and the boundary layer vorticity. In the numerical model, which does include horizontal diffusion, the radial and tangential wind structures are not true discontinuities, but are shock-like, with wind changes of 20 or 30 m s−1 over a radial distance of a few kilometers. When double shocks form, the outer shock can control the strength of the inner shock, an effect that likely plays an important role in concentric eyewall cycles.

Pyrocumulus clouds above three Colorado wildfires (Hewlett Gulch, High Park, and Waldo Canyon; all during the summer of 2012) electrified and produced localized intracloud discharges whenever the smoke plumes grew above 10 km MSL (approximately −45°C). Vertical development occurred during periods of rapid wildfire growth, as indicated by the shortwave infrared channel on a geostationary satellite, as well as by incident reports. The lightning discharges were detected by a three-dimensional lightning mapping network. Based on Doppler and polarimetric radar observations, they likely were caused by ice-based electrification processes that did not involve significant amounts of high-density graupel. Plumes that did not feature significant amounts of radar-inferred ice at high altitudes did not produce lightning, which means lightning observations may assist in diagnosing pyrocumulus features that could affect the radiative characteristics and chemical composition of the upper troposphere. The lightning was not detected by the National Lightning Detection Network, implying that pyrocumulus lightning may occur more frequently than past studies (which lacked access to detailed intracloud information) might suggest. Given the known spatial and temporal advantages provided by lightning networks over radar and satellite data, the results also indicate a possible new application for lightning data in monitoring wildfire state.

The regional daily-integrated net primary production (NPP) model for the Chesapeake Bay, Chesapeake Bay Production Model (CBPM), has been improved for use with ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. A polynomial regression formula for the photosynthetic parameter (i.e., optimal carbon fixation rate, PoptB) as a function of sea surface temperature (SST) was derived for the Chesapeake Bay. Results show that the CBPM-derived NPP using the new model for PoptB are improved for the Chesapeake Bay. Comparisons of MODIS-Aqua-derived and in situ-measured NPP show that the satellite-derived data correspond reasonably well to in situ measurements, although MODIS-Aqua-derived NPP values may be slightly overestimated for the upper Bay, primarily due to uncertainties in the bio-optical algorithm for satellite ocean color products for that region. We also generated MODIS-Aqua-derived NPP maps using the improved CBPM for the period of 2002 to 2011 to characterize NPP in the Chesapeake Bay. Spatial distributions of MODIS-Aqua-derived NPP products show that higher NPP values are generally found in the southern upper Bay and northern middle Bay (regions around 38.3°N–39.0°N), including the Potomac River, while relatively low NPP values were found in the northern upper Bay, the eastern area of middle Bay, and lower Bay. The temporal pattern of MODIS-Aqua-derived NPP showed lowest values in winter (December to February) over the entire Bay, while high NPP values were in late spring to summer (May to August), depending on location. Furthermore, there is a strong interannual variability in NPP for the Chesapeake Bay, and an apparent increasing trend from 2003 to 2011.

The Suomi National Polar-Orbiting Partnership (S-NPP) satellite was successfully launched on 28 October 2011. It carries five new-generation instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS is a whiskbroom radiometer that scans the surface of the earth using a rotating telescope assembly, a double-sided half-angle mirror, and 16 individual detectors. Substantial efforts are being made to accurately calibrate all detectors in orbit. As of this writing, VIIRS striping is reduced to levels below those seen in corresponding Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands and meets the program specifications and requirements. However, the level 2 SST products derived from level 1 sensor data records (SDRs) thermal emissive bands still show residual striping. These artifacts reduce the accuracy of SST measurements and adversely affect cloud masking and the output of downstream applications, such as thermal front detection. To improve the quality of SST imagery derived from the VIIRS sensor, an adaptive algorithm was developed for operational use within the National Environmental Satellite, Data, and Information Service (NESDIS)’s SST system. The methodology uses a unidirectional quadratic variational model to extract stripe noise from the observed image prior to nonlocal filtering. Evaluation of the algorithm performance over an extended dataset demonstrates a significant improvement in the Advanced Clear-Sky Processor for Oceans (ACSPO) VIIRS SST image quality, with normalized improvement factors (NIF) varying between 5% and 25%.

Process-oriented diagnostics for Madden–Julian oscillation (MJO) simulations are being developed to facilitate improvements in the representation of the MJO in weather and climate models. These process-oriented diagnostics are intended to provide insights into how parameterizations of physical processes in climate models should be improved for a better MJO simulation. This paper proposes one such process-oriented diagnostic, which is designed to represent sensitivity of simulated convection to environmental moisture: composites of a relative humidity (RH) profile based on precipitation percentiles. The ability of the RH composite diagnostic to represent the diversity of MJO simulation skill is demonstrated using a group of climate model simulations participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). A set of scalar process metrics that captures the key physical attributes of the RH diagnostic is derived and their statistical relationship with indices that quantify the fidelity of the MJO simulation is tested. It is found that a process metric that represents the amount of lower-tropospheric humidity increase required for a transition from weak to strong rain regimes has a robust statistical relationship with MJO simulation skill. The results herein suggest that moisture sensitivity of convection is closely related to a GCM’s ability to simulate the MJO.

The Visible Infrared Imaging Radiometer Suite (VIIRS) combines the best aspects of both civilian and military heritage instrumentation. VIIRS has improved capabilities over its predecessors: a wider swath width and much higher spatial resolution at swath edge. The VIIRS day-night band (DNB) is sensitive to very low levels of visible light and is capable of detecting low clouds, land surface features, and sea ice at night, in addition to light emissions from both man-made and natural sources. Imagery from the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite has been in the checkout process since its launch on 28 October 2011. The ongoing evaluation of VIIRS Imagery helped resolve several imagery-related issues, including missing radiance measurements. In particular, near-constant contrast imagery, derived from the DNB, had a large number of issues to overcome, including numerous missing or blank-fill images and a stray light leakage problem that was only recently resolved via software fixes. In spite of various sensor issues, the VIIRS DNB has added tremendous operational and research value to Suomi NPP. Remarkably, it has been discovered to be sensitive enough to identify clouds even in very low light new moon conditions, using reflected light from the Earth’s airglow layer. Impressive examples of the multispectral imaging capabilities are shown to demonstrate its applications for a wide range of operational users. Future members of the Joint Polar Satellite System constellation will also carry and extend the use of VIIRS. Imagery evaluation will continue with these satellites to ensure the quality of imagery for end users.

The increasing use of mobile phones (MPs) equipped with digital cameras and the ability to post images and information to the Internet in real time has significantly improved the ability to report events almost instantaneously. From the perspective of weather forecasters responsible for issuing severe weather warnings, the old adage holds that a picture is indeed worth a thousand words; a single digital image conveys significantly more information than a simple web-submitted text or phone-relayed report. Timely, quality-controlled, and value-added photography allows the forecaster to ascertain the validity and quality of storm reports. The posting of geolocated, time-stamped storm report photographs utilizing an MP application to U.S. National Weather Service (NWS) Weather Forecast Office (WFO) social media pages has generated recent positive feedback from forecasters. This study establishes the conceptual framework, architectural design, and pathway toward implementation of a formalized photo report (PR) system composed of 1) an MP application, 2) a processing and distribution system, and 3) the Advanced Weather Interactive Processing System II (AWIPS II) data plug-in software. The requirements and anticipated appearance of such a PR system are presented, along with considerations for possible additional features and applications that extend the utility of the system beyond the realm of severe weather applications.

A new and improved method for estimating tropical-cyclone (TC) flight-level winds using globally and routinely available TC information and infrared (IR) satellite imagery is presented. The developmental dataset is composed of aircraft reconnaissance (1995–2012) that has been analyzed to a 1 km × 10° polar grid that extends outward 165 km from the TC center. The additional use of an azimuthally average tangential wind at 500 km, based on global model analyses, allows the estimation of winds at larger radii. Analyses are rotated to a direction-relative framework, normalized by dividing the wind field by the observed maximum, and then decomposed into azimuthal wavenumbers in terms of amplitudes and phases. Using a single-field principal component method, the amplitudes and phases of the wind field are then statistically related to principal components of motion-relative IR images and factors related to the climatological radius of maximum winds. The IR principal components allow the wind field to be related to the radial and azimuthal variability of the wind field. Results show that this method, when provided with the storm location, the estimated TC intensity, the TC motion vector, and a single IR image, is able to estimate the azimuthal wavenumber 0 and 1 components of the wind field. The resulting wind field reconstruction significantly improves on the method currently used for satellite-based operational TC wind field estimates. This application has several potential uses that are discussed within.

Satellite-based observations of the diffuse attenuation coefficient for the downwelling spectral irradiance at the wavelength of 490 nm, K_d(490) and the diffuse attenuation coefficient for the downwelling photosynthetically available radiation (PAR), K_d(PAR) in the ocean can play important roles for ocean–atmospheric circulation, biogeochemical, and ecosystem models. Since existing K_d(PAR) models for the satellite ocean color data have wide regional variations, we need to improve the K_d(PAR) algorithm for global ocean applications. In this study, we propose a new blended K_d(PAR) model for both open oceans and turbid coastal waters. The new method has been assessed using in situ optical measurements from the NASA Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-Optical Archive and Storage System (SeaBASS) database. Next, the new method is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) to derive K_d(PAR) products, and is compared with in situ measurements. Results show that there are significant improvements in model-derived K_d(PAR) values using the new approach compared to those from some existing K_d(PAR) algorithms. In addition, matchup comparisons between MODIS-derived and in situ-measured K_d(PAR) data for the global ocean show a good agreement with mean and median ratios of 1.109 and 1.035, respectively. Synoptic maps of MODIS- and VIIRS-derived K_d(PAR) data generated using the new method provide very similar and consistent spatial patterns in the U.S. East Coast region, although there are some slight differences between two satellite-derived K_d(PAR) images (~ 1–5% higher in VIIRS K_d(PAR) compared with those from MODIS-Aqua in the shallow water region), which are possibly due to differences in spectral bands and sensor performance (e.g., calibrations). Monthly maps of VIIRS-derived K_d(PAR) data for the global ocean are also generated using the new K_d(PAR) model, and provide spatial and temporal K_d(PAR) distributions that show consistent results with those from previous studies. Thus, results show that satellite-derived K_d(PAR) data using the new K_d(PAR) model, e.g., from MODIS and VIIRS, can provide more accurate K_d(PAR) data to science communities, in particular, as an important input for ocean–atmospheric circulation, biogeochemical, and ecosystem models.

Maritime ships operating on-board illumination at night appear as point sources of light to highly sensitive low-light imagers on-board environmental satellites. Unlike city lights or lights from offshore gas platforms, whose locations remain stationary from one night to the next, lights from ships typically are ephemeral. Fishing boat lights are most prevalent near coastal cities and along the thermal gradients in the open ocean. Maritime commercial ships also operate lights that can be detected from space. Such observations have been made in a limited way via U.S. Department of Defense satellites since the late 1960s. However, the Suomi National Polar-orbiting Partnership (S-NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers a vastly improved ability for users to observe commercial shipping in remote areas such as the Arctic. Owing to S-NPP’s polar orbit and the DNB’s wide swath (~3040 km), the same location in Polar Regions can be observed for several successive passes via overlapping swaths—offering a limited ability to track ship motion. Here, we demonstrate the DNB’s improved ability to monitor ships from space. Imagery from the DNB is compared with the heritage low-light sensor, the Operational Linescan System (OLS) on board the Defense Meteorological Support Program (DMSP) satellites, and is evaluated in the context of tracking individual ships in the Polar Regions under both moonlit and moonless conditions. In a statistical sense, we show how DNB observations of ship lights in the East China Sea can be correlated with seasonal fishing activity, while also revealing compelling structures related to regional fishery agreements established between various nations.

Hydrologic response at all resolutions is controlled by physical processes. Accurately capturing the physical process at a high-resolution is essential for down scaling many satellite observations at coarse resolutions. In this paper, a four-dimensional process representative soil moisture downscaling model is developed to downscale the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) 25 km resolution soil moisture product. The model is composed of the calculation of an antecedent precipitation accumulation (APA) index to capture soil moisture spatial and temporal variations at the 500 m resolution, and the application of a Geographic Information System (GIS) to simulate physical processes which can regulate soil moisture changes throughout the watersheds. APA index, as a representation of the provisional value of soil moisture, is calculated by adopting an exponential formulation to synthesize the effects of infiltration, soil evaporative efficiency, and vegetation resistance on soil water content following precipitation. Five days of AMSR-E soil moisture derivatives spanning the start of the monsoon and the duration of the storm are selected for downscaling. The results show that soil moisture spatial variation is primarily controlled by the distribution of precipitation and soil properties. Subsequently relative soil moisture, radiation, and vegetation become significant in controlling landsurface fluxes and thus influence soil moisture variation as time progresses. The downscaled soil moisture data (500 m resolution) are assessed using in-situ soil moisture measurements from the National Oceanic and Atmospheric Administration (NOAA) Hydrometeorology Testbed (HMT) and the U.S. Department of Agriculture (USDA) Southwest Watershed Research Center (SWRC) Walnut Gulch Experimental Watershed (WGEW) observing networks. The root mean square error (RMSE) between the disaggregated and in-situ soil moisture is 0.034 vol./vol. with percent bias (PBIAS) 0.85%. The overall R2 value is 0.788.

A hydrostatic global weather prediction model based on an icosahedral horizontal grid and a hybrid terrain-following/isentropic vertical coordinate is described. The model is an extension to three spatial dimensions of a previously developed, icosahedral, shallow-water model featuring user-selectable horizontal resolution and employing indirect addressing techniques. The vertical grid is adaptive to maximize the portion of the atmosphere mapped into the isentropic coordinate subdomain. The model, best described as a stacked shallow-water model, is being tested extensively on real-time medium-range forecasts to ready it for possible inclusion in operational multimodel ensembles for medium-range to seasonal prediction.

Diagnostics obtained as an extension of empirical orthogonal function (EOF) analysis are shown to address many disadvantages of using EOF-based indices to assess the state of the Madden–Julian oscillation (MJO). The real-time multivariate MJO (RMM) index and the filtered MJO OLR (FMO) index are used to demonstrate these diagnostics. General characteristics of the indices, such as the geographical regions that most heavily influence each index, are assessed using the diagnostics. The diagnostics also identify how a given field, at various geographical locations, influences the index value at a given time. Termination (as defined by the RMM index) of the October 2011 MJO event that occurred during the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY) Dynamics of the MJO (DYNAMO) field campaign is shown to have resulted from changes in zonal wind anomalies at 200 hPa over the eastern Pacific Ocean, despite the onset of enhanced convection in the Indian Ocean and the persistence of favorable lower- and upper-level zonal wind anomalies near this region. The diagnostics objectively identify, for each specific geographical location, the index phase where the largest MJO-related anomalies in a given field are likely to be observed. This allows for the geographical variability of anomalous conditions associated with the MJO to be easily assessed throughout its life cycle. In Part II of this study, unique physical insight into the moist static energy and moisture budgets of the MJO is obtained from the application of diagnostics introduced here.

CloudSat (CS) heralded a new era of profiling the planet’s cloud systems and storms with its launch in 2006. This satellite flies the first 94-GHz spaceborne Cloud Profiling Radar, and the data collected have provided a unique perspective on Earth’s cloudiness and processes that affect clouds. CS flies in formation with the afternoon satellite constellation, a collection of active and passive satellite sensors offering near-simultaneous observations of the same cloud phenomena. While passes of the nadir-pointing Cloud Profiling Radar (CPR) antenna occur infrequently over tropical cyclones, they happen enough to provide a detailed compilation of the inner structure of clouds and precipitation of these complex storm systems. Nearly 8,000 vertical profiles of TCs have been collected during the period June 2006–December 2013 and observations continue as CS flies in daylight-only mode. These observations have been assembled into a one-of-a-kind dataset of three-dimensional features revealing precipitation areas, moats, and multilayered clouds. Each unique overpass profiled by CS has been compiled with corresponding A-Train sensors, model data, and storm-specific best-track information. The multisensor components of the CS and A-Train TC dataset together with these other data are summarized and cataloged as a function of radial distance from storm center. Example imagery is provided along with stratified reflectivity profiles detailing changes in storm structures across varying environmental shear conditions. The data reported on in this paper offer an unprecedented view of these major storm types and their inner structure.

The accurate and timely depiction of the state of the atmosphere on multiple scales is critical to enhance forecaster situational awareness and to initialize very short-range numerical forecasts in support of nowcasting activities. The Local Analysis and Prediction System (LAPS) of the Earth System Research Laboratory (ESRL)/Global Systems Division (GSD) is a numerical data assimilation and forecast system designed to serve such very finescale applications. LAPS is used operationally by more than 20 national and international agencies, including the NWS, where it has been operational in the Advanced Weather Interactive Processing System (AWIPS) since 1995.

Using computationally efficient and scientifically advanced methods such as a multigrid technique that adds observational information on progressively finer scales in successive iterations, GSD recently introduced a new, variational version of LAPS (vLAPS). Surface and 3D analyses generated by vLAPS were tested in the Hazardous Weather Testbed (HWT) to gauge their utility in both situational awareness and nowcasting applications. On a number of occasions, forecasters found that the vLAPS analyses and ensuing very short-range forecasts provided useful guidance for the development of severe weather events, including tornadic storms, while in some other cases the guidance was less sufficient.

Processes controlling moisture variations associated with the MJO are investigated using budgets of moist static energy (MSE) and moisture. To first order, precipitation anomalies are maintained by anomalous large-scale vertical moisture advection, which can be understood through application of a weak temperature gradient balance framework to the MSE budget. Intraseasonal variations in longwave radiative cooling play a crucial role in destabilizing the MJO by enhancing intraseasonal variations in large-scale vertical moisture advection. This enhancement allows the effect of intraseasonal variations in large-scale vertical moisture advection to meet or exceed the effect of intraseasonal variations in net condensation, resulting in a positive feedback between the net effect of these processes and moisture anomalies. Intraseasonal variations in surface latent heat flux (SLHF) enhance this positive feedback, but appear to be insufficient to destabilize the MJO in the absence of radiative feedbacks.

The effect an ensemble cloud population has on large-scale moisture is investigated using fields where only high-frequency variability has been removed. During the enhanced phase, approximately 85% of the moisture removed by net condensation is resupplied by the large-scale vertical moisture advection associated with apparent heating by microphysical processes and subgrid-scale vertical fluxes of dry static energy. This suggests that a relatively large increase in net condensation could be supported by a relatively small anomalous moisture source, even in the absence of radiative feedbacks. These results highlight the importance of process-oriented assessment of MJO-like variability within models, and suggest that a weak temperature gradient (WTG) balance framework may be used to identify destabilization mechanisms, thereby distinguishing between MJO-like variability of fundamentally different character.

The National Hurricane Center (NHC) has a long history of forecasting the radial extent of gale force or 34-knot (kt; where 1 kt = 0.51 m s⁻¹) winds for tropical cyclones in their area of responsibility. These are referred to collectively as gale force wind radii forecasts. These forecasts are generated as part of the 6-hourly advisory messages made available to the public. In 2004, NHC began a routine of postanalysis or “best tracking” of gale force wind radii that continues to this day. At approximately the same time, a statistical wind radii forecast, based solely on climatology and persistence, was implemented so that NHC all-wind radii forecasts could be evaluated for skill. This statistical wind radii baseline forecast is also currently used in several applications as a substitute for or to augment NHC wind radii forecasts. This investigation examines the performance of NHC gale force wind radii forecasts in the North Atlantic over the last decade. Results presented within indicate that NHC’s gale force wind radii forecasts have increased in skill relative to the best tracks by several measures, and now significantly outperform statistical wind radii baseline forecasts. These results indicate that it may be time to reinvestigate whether applications that depend on wind radii forecast information can be improved through better use of NHC wind radii forecast information.

Processes associated with the local amplification of easterly waves (EWs) in the east Pacific warm pool are explored. Developing EWs favor convection in the southwest and northeast quadrants of the disturbance. In nascent EWs, convection favors the southwest quadrant. As the EW life cycle progresses, convection in the northeast quadrant becomes increasingly prominent and southwest quadrant convection wanes. The EW moisture budget reveals that anomalous meridional winds acting on the mean meridional moisture gradient of the ITCZ produce moisture anomalies supportive of convection in the southwest quadrant early in the EW life cycle. As EWs mature, moisture anomalies on the poleward side of the EW begin to grow and are supported by the advection of anomalous moisture by the mean zonal wind.

In the southwest and northeast portions of the wave, where convection anomalies are favored, lower-tropospheric vorticity is generated locally through vertical stretching that supports a horizontal tilt of the wave from the southwest to the northeast. EWs with such tilts are then able to draw energy via barotropic conversion from the background cyclonic zonal wind shear present in the east Pacific. Convection anomalies associated with EWs vary strongly with changes in the background intraseasonal state. EWs during westerly and neutral intraseasonal periods are associated with robust convection anomalies. Easterly intraseasonal periods are, at times, associated with very weak EW convection anomalies because of weaker moisture and diluted CAPE variations.

The VIIRS Day/Night Band (DNB) is a visible and near-infrared sensor that is sensitive to a broad range of light intensities ranging from daylight down to airglow at night. The on-board calibration of the DNB allows for the quantification of radiance values over the full range of the instrument’s sensitivity, unlike the heritage Operational Linescan System (OLS). For scenes that span the day/night terminator, observed DNB radiance values may vary by up to eight orders of magnitude. Consequently, it is impractical to display the full range of radiance values in a single digital image. In this work, an algorithm is presented that scales the observed radiance values between expected maximum and minimum values that are a function of solar and lunar zenith angles as well as the fraction of the lunar disc that is illuminated by the Sun. This dynamic scaling algorithm preserves scene contrast over the full range of solar and lunar illumination conditions, similar to the Near Constant Contrast (NCC) imagery product. Unlike the NCC algorithm, however, the ‘erf-dynamic scaling’ algorithm (so-called for its likeness to the Gauss error function) presented here requires no ancillary information outside of what is included in the DNB data distributed according to the Joint Polar Satellite System (JPSS) program file standards. Results indicate that this algorithm has improved performance over simple methods for displaying DNB imagery and, in some instances, may exceed the performance of the NCC product itself. This algorithm is expected to replace many ad hoc methods of displaying DNB imagery and may serve as a substitute for operational users that do not have access to the NCC product.

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NCAR’s AutoNowCaster (ANC) was modified to run over a large domain that encompasses the air traffic management hubs of Chicago, Illinois; New York City, New York; and Atlanta, Georgia. ANC produces nowcasts of convective likelihood (CL), with higher values delineating areas where storms are likely to form and be sustained, and vice versa. This paper presents the results of verifying ANC’s 60-min nowcasts of CL over the study area using data collected from 11 June to 30 September 2012. To reduce the high sensitivity of statistical scores to small errors in location and timing, spatial and temporal relaxation techniques were explored. The results show that, at a spatial scale of roughly 50 km and with no temporal relaxation, a CL value of 0.6 is an optimum threshold for nowcasting the general areas both where new storms may initiate and where existing storms will be sustained. Moreover, at that same spatial scale and with temporal relaxation (45–90 min from the nowcast issuance time), a CL value of 0.7 is a good threshold for nowcasting convective initiation alone.

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The impact of storm size on the forecast of tropical cyclone storm track and intensity is investigated using the 2016 version of the operational GFDL hurricane model. Evaluation was made for 1529 forecasts in the Atlantic, eastern Pacific, and western North Pacific basins, during the 2014 and 2015 seasons. The track and intensity errors were computed from forecasts in which the 34-kt (where 1 kt = 0.514 m s⁻¹) wind radii obtained from the operational TC vitals that are used to initialize TCs in the GFDL model were replaced with wind radii estimates derived using an equally weighted average of six objective estimates. It was found that modifying the radius of 34-kt winds had a significant positive impact on the intensity forecasts in the 1–2 day lead times. For example, at 48 h, the intensity error was reduced 10%, 5%, and 4% in the Atlantic, eastern Pacific, and western North Pacific, respectively. The largest improvements in intensity forecasts were for those tropical cyclones undergoing rapid intensification, with a maximum error reduction in the 1–2 day forecast lead time of 14% and 17% in the eastern and western North Pacific, respectively. The large negative intensity biases in the eastern and western North Pacific were also reduced 25% and 75% in the 12–72-h forecast lead times. Although the overall impact on the average track error was neutral, forecasts of recurving storms were improved and tracks of nonrecurving storms degraded. Results also suggest that objective specification of storm size may impact intensity forecasts in other high-resolution numerical models, particularly for tropical cyclones entering a rapid intensification phase.

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Abstract An X-band radar system was deployed in Santa Clara, CA from February through May 2016 to support the National Weather Service in the event of potential flooding during one of the largest El Niños on record and to provide a better understanding of rainfall processes occurring in the Bay Area. The system was also used to provide high-quality precipitation estimation (quantitative precipitation estimation—QPE) for Santa Clara’s urban hydrologic modeling system. Although the Bay Area has coverage from the Next-Generation Weather Radar (NEXRAD) operational radar network, the combination of topographic influences and proximity to a maritime environment provide unique QPE challenges in this urban region. The X-band radar provided high-quality rainfall estimates that performed better than NEXRAD, demonstrating the added value of the X-band system. High-resolution rainfall monitoring systems in urban regions also provide a host of benefits across different sectors of the economy, including flood damage mitigation, water quality, water supply, and transportation.

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The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.

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Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.

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Forecasts of tropical cyclone (TC) surface wind structure have recently begun to show some skill, but the number of reliable forecast tools, mostly regional hurricane and select global models, remains limited. To provide additional wind structure guidance, this work presents the development of a statistical–dynamical method to predict tropical cyclone wind structure in terms of wind radii, which are defined as the maximum extent of the 34-, 50-, and 64-kt (1 kt = 0.514 m s⁻¹) winds in geographical quadrants about the center of the storm. The basis for TC size variations is developed from an infrared satellite-based record of TC size, which is homogenously calculated from a global sample. The change in TC size is predicted using a statistical–dynamical approach where predictors are based on environmental diagnostics derived from global model forecasts and observed storm conditions. Once the TC size has been predicted, the forecast intensity and track are used along with a parametric wind model to estimate the resulting wind radii. To provide additional guidance for applications and users that require forecasts of central pressure, a wind–pressure relationship that is a function of TC motion, intensity, wind radii (i.e., size), and latitude is then applied to these forecasts. This forecast method compares well with similar wind structure forecasts made by global forecast and regional hurricane models and when these forecasts are used as a member of a simple consensus; its inclusion improves the forecast performance of the consensus.

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The development of an infrared (IR; specifically near 11 μm) eye probability forecast scheme for tropical cyclones is described. The scheme was developed from an eye detection algorithm that used a linear discriminant analysis technique to determine the probability of an eye existing in any given IR image given information about the storm center, motion, and latitude. Logistic regression is used for the model development and predictors were selected from routine information about the current storm (e.g., current intensity), forecast environmental factors (e.g., wind shear, oceanic heat content), and patterns/information (e.g., convective organization, tropical cyclone size) extracted from the current IR image. Forecasts were created for 6-, 12-, 18-, 24-, and 36-h forecast leads. Forecasts were developed using eye existence probabilities from North Atlantic tropical cyclone cases (1996–2014) and a combined North Atlantic and North Pacific (i.e., Northern Hemisphere) sample. The performance of North Atlantic–based forecasts, tested using independent eastern Pacific tropical cyclone cases (1996–2014), shows that the forecasts are skillful versus persistence at 12–36 h, and skillful versus climatology at 6–36 h. Examining the reliability and calibration of those forecasts shows that calibration and reliability of the forecasts is good for 6–18 h, but forecasts become a little overconfident at longer lead times. The forecasts also appear unbiased. The small differences between the Atlantic and Northern Hemisphere formulations are discussed. Finally, and remarkably, there are indications that smaller TCs are more prone to form eye features in all of the TC areas examined.

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The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0–6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRF-Solar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcasting models perform better or worse for particular lead times and weather situations. StatCast performs best in clear skies and for 0–1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators.

Abstract. The relative importance of wildfire smoke for air quality over the western US is expected to increase as the climate warms and anthropogenic emissions decline. We report on in situ measurements of ozone (O3), a suite of volatile organic compounds (VOCs), and reactive oxidized nitrogen species collected during summer 2015 at the Boulder Atmospheric Observatory (BAO) in Erie, CO. Aged wildfire smoke impacted BAO during two distinct time periods during summer 2015: 6–10 July and 16–30 August. The smoke was transported from the Pacific Northwest and Canada across much of the continental US. Carbon monoxide and particulate matter increased during the smoke-impacted periods, along with peroxyacyl nitrates and several VOCs that have atmospheric lifetimes longer than the transport timescale of the smoke. During the August smokeimpacted period, nitrogen dioxide was also elevated during the morning and evening compared to the smoke-free periods. There were nine empirically defined high-O3 days during our study period at BAO, and two of these days were smoke impacted. We examined the relationship between O3 and temperature at BAO and found that for a given temperature, O3 mixing ratios were greater (∼ 10 ppbv) during the smoke-impacted periods. Enhancements in O3 during the August smoke-impacted period were also observed at two long-term monitoring sites in Colorado: Rocky Mountain National Park and the Arapahoe National Wildlife Refuge near Walden, CO. Our data provide a new case study of how aged wildfire smoke can influence atmospheric composition at an urban site, and how smoke can contribute to increased O3 abundances across an urban–rural gradient.

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Knowledge of cloud-base height (CBH) is important to describe cloud radiative feedbacks in numerical models and is of practical relevance to the aviation community. Whereas satellite remote sensing with passive radiometers traditionally has provided a ready means for estimating cloud-top height (CTH) and cloud water path (CWP), assignment of CBH requires heavy assumptions on the distribution of CWP within the cloud profile. An attempt to retrieve CBH has been included as part of the VIIRS environmental data records, produced operationally as part of the Suomi–National Polar-Orbiting Partnership (SNPP) and the forthcoming Joint Polar Satellite System. Through formal validation studies tied to the program, it was found that the operational CBH algorithm failed to meet performance specifications in many cases. This paper presents a new methodology for retrieving CBH of the uppermost cloud layer, developed through statistical analyses relating cloud geometric thickness (CGT) to CTH and CWP. The semiempirical approach, which relates these parameters via piecewise fitting, enlists A-Train satellite data [CloudSat cloud profiling radar (CPR), CALIPSO/CALIOP, and Aqua MODIS]. CBH is provided as the residual difference between CTH and CGT. By eliminating cloud type–dependent assumptions on CWP distribution, artifacts common to the operational algorithm (which contribute to high errors) are reduced. Special accommodations are made for handling optically thin cirrus and deep convection. An application to SNPP VIIRS is demonstrated, and the results are compared against global CloudSat observations. From the VIIRS–CloudSat daytime matchups (September–October 2013 and January–May 2015), the new algorithm outperforms the operational SNPP VIIRS algorithm, particularly when the retrieved CTH is accurate. Best performance is expected for single-layer liquid-phase clouds.

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The Joint Typhoon Warning Center’s (JTWC) forecast improvement goals include reducing 34-kt (1 kt = 0.514 m s⁻¹) wind radii forecast errors, so accurate real-time estimates and postseason analysis of the 34-kt wind radii are critical to reaching this goal. Accurate real-time 34-kt wind radii estimates are also critical for decisions regarding base preparedness and asset protection, but still represent a significant operational challenge at JTWC for several reasons. These reasons include a paucity of observations, the timeliness and availability of guidance, a lack of analysis tools, and a perceived shortage of personnel to perform the analysis; however, the number of available objective wind radii estimates is expanding, and the topic of estimating 34-kt wind radii warrants revisiting. In this work an equally weighted mean of real-time 34-kt wind radii objective estimates that provides real-time, routine operational guidance is described. This objective method is also used to retrospectively produce a 2-yr (2014–15) 34-kt wind radii objective analysis, the results of which compare favorably to the postseason National Hurricane Center data (i.e., the best tracks), and a newly created best-track dataset for the western North Pacific seasons. This equally weighted mean, when compared with the individual 34-kt wind radii estimate methods, is shown to have among the lowest mean absolute errors and smallest biases. In an ancillary finding, the western North Pacific basin average 34-kt wind radii calculated from the 2014–15 seasons are estimated to be 134 n mi (1 n mi = 1.852 km), which is larger than the estimates for storms in either the Atlantic (95 n mi) or eastern North Pacific (82 n mi) basins for the same years.