VIIRS flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) and the International Charter Program for rescue and relief efforts. However, some water bodies, like irrigated or flooded paddy rice fields, and water in seasonal wetlands, are detected as floodwater instead of permanent, seasonal, controlled, or normal water in the VIIRS flood products. Because these kinds of floodwater don’t cause any severe disasters, they should be delineated as non-hazard floods to differentiate from real hazardous floods, the latter of which generally pose a significant risk and damage to human life, property, and the environment. Hazardous floods are usually caused by intensive rainfall, rapid snowmelt, or ice jams, they appear accidentally and tend to be short-term events. Non-hazard floodwaters generally result from tides, wetlands, agricultural irrigation, and other human activities, and thus they appear regularly and often stand for a longer time. Therefore, non-hazard floodwaters are some kind of standing water and usually have much larger long-time flood frequency or probability than hazardous floods. Based on this feature, it’s possible to combine long-time flood frequency calculated as the ratio of total flooding days over total clear days, including both flooding and no-flooding days, from VIIRS flood products with some additional information, such as agriculture statistics and land cover and land use map, to identify non-hazard floodwater in paddy rice fields and wetland. In this study, non-hazard floodwater bodies are classified into four types: paddy rice fields, wetlands, riparian and coastal tidal flooding, and other non-hazard floods, including possible fishing ponds. In this study, we selected several examples, such as the 2023 California flood event and some large paddy rice fields in China, to demonstrate how to identify water in paddy rice fields. The Florida flood event due to Hurricane Ian in 2022 is used to demonstrate how to separate wetlands from real hazardous floods. In addition to non-hazard floodwaters in wetlands, some coastal tidal flooding in Florida is also detected. The non-hazard floodwaters extracted from the proposed methods are compared with change detection analysis and good agreement is found. The paddy rice fields obtained from this study are compared and evaluated with the rice distribution data from the USDA. The quantitative evaluation results from the confusion matrix calculation demonstrate that the accuracy is 99.96%, recall is 83.69%, precision is 61.57%, and the F1-score is 70.95%. The high accuracy indicates that the method developed in this study for paddy rice field extraction from the VIIRS flood products is feasible.

Long-term flood mapping datasets can be invaluable for historic flood investigation, flood potential or probability estimate, time series analysis and modelling, and climate change studies. With the developed flood detection algorithm and software for JPSS/VIIRS (Visible Infrared Imaging Radiometer Suite) (Li et al., 2017), in this study, VIIRS historic data since 2012 has been reprocessed from JPSS (Joint Polar Satellite System) series including Suomi-NPP (Suomi National Polar-orbiting Partnership) and NOAA-20. VIIRS global flood time series datasets have been generated and distributed by NOAA through Amazon Web Services (AWS). The derived dataset includes granule flood product, daily and 5-day composited flood products in netCDF4, geotiff and shapefile formats from 2012 to 2020. The dataset not only provides data records of historic flood events, but also shows potential in flood analysis and modelling. With the dataset, a simple application using annual composition is performed to analyze the annual change of flood extent globally and in each continent. The analysis has shown a slightly increasing trend in flood extent at a global scale, but varying in different regions.

To provide global coverage for the hyperspectral infrared (IR) and microwave (MW) sounders, the lowEarth-orbiting (LEO) satellite constellation is in operation in three temporally well-spaced sun-synchronous orbits. However, the satellite program can be altered as a result of aging satellites needing to deorbit and/or termination of the legacy program, resulting in less spatiotemporal coverage. In this study, to stress the contribution of IR and MW sounder observations from the LEO satellite constellation on numerical weather prediction (NWP) system performance, the change of the analysis impact is assessed under two assumptions: 1) the loss of the IR and MW sounder observations in each of three sun-synchronous orbits and 2) the loss of the secondary LEO satellite in two orbits, using a 2017 version of the National Centers for Environmental Prediction Global Forecast System (GFS). In the analysis verification, it is found that the analysis field is degraded due to the loss of the IR and MW sounders in each of the three primary orbits. In particular, the satellites in the afternoon orbit significantly contribute to improving the analysis as compared with the satellites in the other two orbits. In addition, the loss of the secondary satellite results in significant degradation of the analysis, resulting from reduced spatial coverage by the IR and MW sounders. These results suggest that the LEO satellite constellation, consisting of the LEO satellites in three primary sun-synchronous orbits, should be maintained in terms of the contribution to the NWP.

Methane (CH4) is the second most significant contributor to climate change after carbon dioxide (CO2), accounting for approximately 20% of the contributions from all well-mixed greenhouse gases. Understanding the spatiotemporal distributions and the relevant long-term trends is crucial to identifying the sources, sinks, and impacts on climate. Hyperspectral thermal infrared (TIR) sounders, including the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI), have been used to measure global CH4 concentrations since 2002. This study analyzed nearly 20 years of data from AIRS and CrIS and confirmed a significant increase in CH4 concentrations in the mid-upper troposphere (around 400 hPa) from 2003 to 2020, with a total increase of approximately 85 ppb, representing a +4.8% increase in 18 years. The rate of increase was derived using global satellite TIR measurements, which are consistent with in situ measurements, indicating a steady increase starting in 2007 and becoming stronger in 2014. The study also compared CH4 concentrations derived from the AIRS and CrIS against ground-based measurements from NOAA Global Monitoring Laboratory (GML) and found phase shifts in the seasonal cycles in the middle to high latitudes of the northern hemisphere, which is attributed to the influence of stratospheric CH4 that varies at different latitudes. These findings provide insights into the global budget of atmospheric composition and the understanding of satellite measurement sensitivity to CH4.

Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring.

As one of the costliest and most frequent natural disasters, flood is a major threat to human lives and property. Flood forecasting, simulation, and monitoring plays an important role in disaster relief and mitigation. With the support from the JPSS (Joint Polar Satellite System) Proving Ground and Risk Reduction (PGRR) Program, the VIIRS (Visible Infrared Imaging Radiometer Suite) global 375-m flood products in near real-time, daily composition, and 5-day composition have been developed and derived flood extent represented in water fractions from Suomi-NPP and NOAA-20. The products have shown good quality for the spatial distribution of floodwater. However, the moderate spatial resolution is a limiting factor when obtaining inundation detail. Moreover, no vertical information on the floodwater is included in the products. Based on water’s self-leveling nature, it is feasible to construct the vertical structure of floodwater using the VIIRS 375-m water fractions and high-resolution Digital Elevation Model (DEM) such as the 30-m Shuttle Radar Topography Mission (SRTM)/DEM through a downscaling process (Li et al., 2013). With impact factors including topography, land cover, tree cover, river network and watershed, the downscaling process has been integrated into a model called the Downscaling Model. By using the 30-m SRTM/DEM, the model can downscale the VIIRS 375-m floodwater fraction products into 30-m flood extent and water depth products. These products include flood information in both horizontal and vertical directions and thus are called 3-D flood products in this study. This paper presents a comprehensive introduction to the model with the results evaluated against high-resolution satellite images from Landsat-8 Operational Land Imager (OLI), Sentinel-2, and GeoEye, aerial photos, and river gauge observations. Evaluation results indicate reliable quality and promising performance of the model and the downscaled flood products. A routine system has been set up at the Cooperative Institute for Satellite Studies (CIMSS) at University of Wisconsin-Madison to generate experimental VIIRS downscaled flood extent and water depth products over the Continental USA (CONUS). The routinely generated VIIRS 30-m floodwater depth maps are available at: https://floods.ssec.wisc.edu/?products=VIIRS-3Dflood.

Near real-time satellite-derived flood maps are invaluable to river forecasters and decision-makers for disaster monitoring and relief efforts. Combining utilization of the LEO (low earth orbiting) and GEO (geostationary) satellite imagery shows great advantages in flood mapping. With the support from NOAA/NASA JPSS (Joint Polar Satellite System) Program and GOES-R program, the flood mapping system has been developed to derive flood maps from Suomi-NPP (Suomi National Polar-orbiting Partnership) & NOAA-20/VIIRS (Visible Infrared Imaging Radiometer Suite) imagery, GOES-16&17/ABI (Advanced Baseline Imager) imagery, and Himawari-8/AHI imagery. These flood maps are generated on a routine base at Space Science and Engineering Center at University of Wisconsin-Madison and have been applied in flood operations. Initial feedback from river forecasters on the product accuracy and performance has been largely positive. Evaluation efforts including visual inspection and quantitative validation using Landsat-8/OLI and Sentinel-2 imagery, and aerial photos have demonstrated steady performance of these products, which indicates a high feasibility of these flood maps to be produced at the product level.

Near-real-time flood maps derived from satellite data are invaluable to river forecasters and decision makers for disaster monitoring and relief efforts. Combining utilization of the low earth orbiting (LEO) and geostationary (GEO) satellite imagery shows great advantages in flood mapping. Under clear-sky coverage, the floodwater detected in LEO satellite imagery shows rich inundation detail; while under nonclear-sky conditions, composition from multiple GEO satellite images provides more clear-sky coverage for flood detection and the clear-sky information can be used to fill the gaps of clouds and cloud shadows in LEO imagery. With support from the National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration Joint Polar Satellite System and Geostationary Operational Environmental Satellites (GOES) R programs, the flood mapping software has been developed to derive flood maps from Suomi National Polar-orbiting Partnership and NOAA-20/Visible Infrared Imaging Radiometer Suite imagery, and GOES-16 and 17/Advanced Baseline Imager imagery. These flood maps are distributed via the Unidata Local Data Manager, reviewed by river forecasters in the second generation of the Advanced Weather Interactive Processing System and applied in flood operations. Initial feedback from operational forecasters on the product accuracy and performance has been largely positive. Offline evaluation efforts include visual inspection, an intercomparison with the Moderate-Resolution Imaging Spectroradiometer automatic flood products, and a quantitative validation using Landsat imagery. The steady performance indicates encouraging feasibility of these flood maps to be provided at the product level.

To maximize the contribution of the Cross-track Infrared Sounder (CrIS) measurements to the global weather forecasting, we attempt to choose the CrIS channels to be assimilated in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). From pre-selected 431 CrIS channels, 207 channels are newly selected using a one-dimensional variational (1D-Var) approach where the channel score index (CSI) is used as a figure of merit. Newly selected 207 channels consist of 85 temperature, 49 water vapor, and 73 surface channels, respectively. In addition, to examine how the channels are selected if the forecast error covariance is differently defined depending on the latitudinal regions (i.e., Northern and Southern Hemispheres, and tropics), the same selection process is carried out repeatedly using three regional forecast error covariances. From three regional channel sets, two-channel sets are made for the global data assimilation. One channel set is made with 134 channels overlapped between three regional channel sets. Another channel set consists of 277 channels that is the sum of three regional channel sets. In the global trial experiments, the global CrIS 207 channels have a significant positive forecast impact in terms of the improvement of GFS global forecasting, as compared with the forecasts with the operational 100 channels as well as the overlapped 134 and the union 277 channel sets. The improved forecast is mainly due to the additional temperature/water vapor channels of the global CrIS 207 channels that are selected optimally based on the global forecast error of operational GFS.

A summary of the Scanning High-resolution Interferometer Sounder (S-HIS) observations from the FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality) field campaign is presented.

Weather satellites are vital tools for monitoring the global environment. They provide data that is used to deliver essential predictions and warnings, and to save lives. Space agencies have for years recognized the importance of sharing remotely sensed data for weather analysis and forecasting, climate analysis, and monitoring hazards worldwide. As a result, they operate under a policy of freely shared data. The U.S. Joint Polar Satellite System (JPSS), a contributor to the global observing system, provides key observables that are crucial to obtaining continuity, global coverage, and filling data gaps. JPSS collaborates with national and international partners through the WMO and engages with multilateral organizations such as CGMS, GEO, and CEOS to develop requirements, establish best practices for combining measurements from multiple satellite sensors, and develop capabilities that enable communities worldwide to develop local solutions to address the challenges related to global atmospheric processes and their complex interactions.

Among all the natural hazards throughout the world, floods occur most frequently. They occur in high latitude regions, such as: 82% of the area of North America; most of Russia; Norway, Finland, and Sweden in North Europe; China and Japan in Asia. River flooding due to ice jams may happen during the spring breakup season. The Northeast and North Central region, and some areas of the western United States, are especially harmed by floods due to ice jams and snowmelt. In this study, observations from operational satellites are used to map and monitor floods due to ice jams and snowmelt. For a coarse-to-moderate resolution sensor on board the operational satellites, like the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the National Polar-orbiting Partnership (NPP) and the Joint Polar Satellite System (JPSS) series, and the Advanced Baseline Imager (ABI) on board the GOES-R series, a pixel is usually composed of a mix of water and land. Water fraction can provide more information and can be estimated through mixed-pixel decomposition. The flood map can be derived from the water fraction difference after and before flooding. In high latitude areas, while conventional observations are usually sparse, multiple observations can be available from polar-orbiting satellites during a single day, and river forecasters can observe ice movement, snowmelt status and flood water evolution from satellite-based flood maps, which is very helpful in ice jam determination and flood prediction. The high temporal resolution of geostationary satellite imagery, like that of the ABI, can provide the greatest extent of flood signals, and multi-day composite flood products from higher spatial resolution imagery, such as VIIRS, can pinpoint areas of interest to uncover more details. One unique feature of our JPSS and GOES-R flood products is that they include not only normal flood type, but also a special flood type as the supra-snow/ice flood, and moreover, snow and ice masks. Following the demonstrations in this study, it is expected that the JPSS and GOES-R flood products, with ice and snow information, can allow dynamic monitoring and prediction of floods due to ice jams and snowmelt for wide-end users.

The launch of the National Oceanic and Atmospheric Administration (NOAA)/ National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and its follow-on NOAA Joint Polar Satellite Systems (JPSS) satellites marks the beginning of a new era of operational satellite observations of the Earth and atmosphere for environmental applications with high spatial resolution and sampling rate. The S-NPP and JPSS are equipped with five instruments, each with advanced design in Earth sampling, including the Advanced Technology Microwave Sounder (ATMS), the Cross-track Infrared Sounder (CrIS), the Ozone Mapping and Profiler Suite (OMPS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Clouds and the Earth’s Radiant Energy System (CERES). Among them, the ATMS is the new generation of microwave sounder measuring temperature profiles from the surface to the upper stratosphere and moisture profiles from the surface to the upper troposphere, while CrIS is the first of a series of advanced operational hyperspectral sounders providing more accurate atmospheric and moisture sounding observations with higher vertical resolution for weather and climate applications. The OMPS instrument measures solar backscattered ultraviolet to provide information on the concentrations of ozone in the Earth’s atmosphere, and VIIRS provides global observations of a variety of essential environmental variables over the land, atmosphere, cryosphere, and ocean with visible and infrared imagery. The CERES instrument measures the solar energy reflected by the Earth, the longwave radiative emission from the Earth, and the role of cloud processes in the Earth’s energy balance. Presently, observations from several instruments on S-NPP and JPSS-1 (re-named NOAA-20 after launch) provide near real-time monitoring of the environmental changes and improve weather forecasting by assimilation into numerical weather prediction models. Envisioning the need for consistencies in satellite retrievals, improving climate reanalyses, development of climate data records, and improving numerical weather forecasting, the NOAA/Center for Satellite Applications and Research (STAR) has been reprocessing the S-NPP observations for ATMS, CrIS, OMPS, and VIIRS through their life cycle. This article provides a summary of the instrument observing principles, data characteristics, reprocessing approaches, calibration algorithms, and validation results of the reprocessed sensor data records. The reprocessing generated consistent Level-1 sensor data records using unified and consistent calibration algorithms for each instrument that removed artificial jumps in data owing to operational changes, instrument anomalies, contaminations by anomaly views of the environment or spacecraft, and other causes. The reprocessed sensor data records were compared with and validated against other observations for a consistency check whenever such data were available. The reprocessed data will be archived in the NOAA data center with the same format as the operational data and technical support for data requests. Such a reprocessing is expected to improve the efficiency of the use of the S-NPP and JPSS satellite data and the accuracy of the observed essential environmental variables through either consistent satellite retrievals or use of the reprocessed data in numerical data assimilations.

Since 2 June 2020, unusual heavy and continuous rainfall from the Asian summer monsoon rainy season caused widespread catastrophic floods in many Asian countries, including primarily the two most populated countries, China and India. To detect and monitor the floods and estimate the potentially affected population, data from sensors aboard the operational polar-orbiting satellites Suomi National Polar-Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 were used. The Visible Infrared Imaging Radiometer Suite (VIIRS) with a spatial resolution of 375 m available twice per day aboard these two satellites can observe floodwaters over large spatial regions. The flood maps derived from the VIIRS imagery provide a big picture over the entire flooding regions, and demonstrate that, in July, in China, floods mainly occurred across the Yangtze River, Hui River and their tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed (PPE) to flooding. We report here on the procedure to combine such data using the Zonal Statistic Function from the ArcGIS Spatial Analyst toolbox. Based on the flood extend for July 2020 along with the population density dataset, the Jiangxi and Anhui provinces were the most affected regions with more than 10 million people in Jingdezhen and Shangrao in Jiangxi province, and Fuyang and Luan in Anhui province, and it is estimated that about 55 million people in China might have been affected by the floodwaters. In addition to China, several other countries, including India, Bangladesh, and Myanmar, were also severely impacted. In India, the worst inundated states include Utter Pradesh, Bihar, Assam, and West Bengal, and it is estimated that about 40 million people might have been affected by severe floods, mainly in the northern states of Bihar, Assam, and West Bengal. The most affected country was Bangladesh, where one third of the country was underwater, and the estimated population potentially exposed to floods is about 30 million in Bangladesh.

The Direct Broadcast Network (DBNet) provides near-real-time delivery of low-earth-orbiting (LEO) meteorological satellites to operational numerical weather prediction (NWP) systems that need short data cut-off times to allow for the assimilation of the most recent satellite measurements. The NWP model requires timely delivery of observations including atmospheric temperature, humidity, and surface wind vectors. The World Meteorological Organization (WMO) Space Program (WSP) recommends the data latency of no more than 20 min for the satellite measurements. Currently, not all DBNet stations are delivering satellite data within the 20-min time frame. In this study, the forecast impact of the observations of LEO satellite sounders with data latency of 20 min or less was evaluated using the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Reducing the data latency up to 5 min increases the number of LEO infrared (IR) and microwave (MW) sounder observations delivered to the NCEP GFS data assimilation system by more than 20%. Overall, this study demonstrates a positive impact on the global weather forecasts when the IR and MW sounder data are delivered by 20 min anywhere in the world. Additional forecast benefits are not obvious for shorter data latency. Results from this study support the WSP recommendation of 20–minute data latency.

In this paper, we describe how researchers and weather forecasters work together to make satellite sounding data sets more useful in severe weather forecasting applications through participation in National Oceanic and Atmospheric Administration (NOAA)’s Hazardous Weather Testbed (HWT) and JPSS Proving Ground and Risk Reduction (PGRR) program. The HWT provides a forum for collaboration to improve products ahead of widespread operational deployment. We found that the utilization of the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) soundings was improved when the product developer and forecaster directly communicated to overcome misunderstandings and to refine user requirements. Here we share our adaptive strategy for (1) assessing when and where NUCAPS soundings improved operational forecasts by using real, convective case studies and (2) working to increase NUCAPS utilization by improving existing products through direct, face-to-face interaction. Our goal is to discuss the lessons we learned and to share both our successes and challenges working with the weather forecasting community in designing, refining, and promoting novel products. We foresee that our experience in the NUCAPS product development life cycle may be relevant to other communities who can then build on these strategies to transition their products from research to operations (and operations back to research) within the satellite meteorological community.

We compared two fast radiative transfer models, Community Radiative Transfer Model (CRTM) and Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV), with the LBL model Atmospheric Radiative Transfer Simulator (ARTS). We used the measurements from Advanced Technology Microwave Sounder (ATMS) and the Global Precipitation Measurement Microwave Imager (GMI) for evaluation of the radiative transfer models. The models in comparison with the observations and each other performed very well with a mean difference less than 0.5 K for the temperature sounding channels operating near the oxygen absorption band at 60 GHz. There was a difference of up to 1 K among the models as well as compared with the observations for humidity sounding channels operating around water vapor absorption line at 183 GHz. The mean difference between the simulations and observations was up to 6 K for surface sensitive channels. Water vapor and surface sensitive channels also showed to be more sensitive than the temperature sounding channels to the spectroscopy models used to calculate the absorption coefficients. There was a small difference, less than 0.1 K, between brightness temperatures calculated using traditional boxcar and actual Sensor or Spectral Response Functions, except for a difference of 0.25 K for ATMS Channel 6. Double difference technique showed about 1 K difference between water vapor channels from ATMS instruments onboard N20 and National Polar‐orbiting Partnership (NPP). However, comparison of a new version of ATMS/NPP observations recently generated using an enhanced calibration algorithm with ATMS/N20 observations showed that the differences between the two instruments are less than 0.5 K after improving the ATMS/NPP calibration.

Hyperspectral infrared (IR) sounders provide high vertical resolution atmospheric sounding information that can improve the forecast accuracy of numerical weather prediction (NWP) models. Due to the challenges of assimilating cloudy radiances, NWP centers usually assimilate only radiances that are not affected by clouds. An imager based cloud‐clearing technique provides an alternative and effective way to remove the cloud effects from a partially cloudy field‐of‐view and derive the equivalent clear sky radiances or the cloud‐cleared radiances (CCRs) for assimilation in NWP. Since the observation error is amplified in the cloud‐clearing, or noise amplification process, it is necessary to inflate the observation errors appropriately in order to achieve the optimal value‐added impact from assimilating CCRs. The estimation of observation error inflation is established and discussed. Hurricane Harvey (2017) and Hurricane Maria (2017) are used to simulate and understand the impacts of observation error inflation on the assimilation of Cross‐track Infrared Sounder CCRs for hurricane forecast improvement. Both the precipitation location and intensity forecasts are improved when assimilating CCRs with an inflated observation error for Hurricane Harvey (2017). Assimilating CCRs with an inflated observation error adjusts the temperature and geopotential height fields and further affects the hurricane structures to improve the hurricane track forecasts, thereby demonstrating the importance of using hyperspectral IR measurements in partially cloudy skies for simulating the hurricane structure and improving its forecast. This method can be applied to other imager/sounder combined observations for improving sounder radiance assimilation in cloudy skies and has potential for operational applications.

The Joint Polar Satellite System (JPSS) consists of a suite of five instruments: advanced microwave and infrared sounders critical for short and medium range weather forecasting; an advanced visible and infrared imager needed for environmental assessments such as snow/ice cover, droughts, volcanic ash, forest fires and surface temperature; ozone sensor primarily used for global monitoring of ozone and input to weather and climate models; and an earth radiation budget sensor for monitoring the Earth's energy budget. JPSS is implemented through a partnership between NOAA and the US National Aeronautics and Space Administration (NASA). NOAA is responsible for overall funding; maintaining the high-level requirements; establishing international and interagency partnerships; developing the science and algorithms, and user engagement; NOAA also provides product data distribution and archiving of JPSS data. NASA's role is to serve as acquisition Center of Excellence, providing acquisition of instruments, spacecraft and the multi-mission ground system, and early mission implementation through turnover to NOAA for operations (Goldberg et al., 2013). The observatory Nadir deck incorporates: the Ozone Mapping and Profiler Suite (OMPS) instrument built by BATC, Boulder, Colorado; Advance Technology Microwave Sounder (ATMS) built by Northrop Grumman Electronic Systems (NGES) in Azusa, California; Cross- Track Infrared Sounder (CrIS), built by Harris in Ft. Wayne, Indiana; Clouds and Earth's Radiant Energy Sensor (CERES) built by Northrop Grumman Aerospace Systems (NGAS) in El Segundo, California; and the Visible-Infrared Imaging Suite (VIIRS), built by Raytheon Aerospace Systems in El Segundo, California. The JPSS is now being demonstrated by the Suomi National Polar-orbiting Partnership (SNPP), which essentially has the same instrumentation as JPSS-l. JPSS-l which is now NOAA-20 was successfully launched on November 18,2017, followed by JPSS-2 in 2022, JPSS-3 in 2026, and JPSS-4 in 2032. SNPP was launched in 2011.