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.

The Community Satellite Processing Package (CSPP) was developed in 2011 with its first release in 2012 (http://cimss.ssec.wisc.edu/cspp/history.shtml). Since then, as of January 2022, over 70 new and updated releases of software packages to process the satellite direct broadcast raw data into sensor data records (SDRs) and produce various Environmental Data Records (EDRs). CSPP supports the Direct Broadcast (DB) meteorological and environmental satellite community through the packaging and distribution of open-source science software when possible. CSPP supports DB users of both polar-orbiting and geostationary satellite data processing and regional real-time applications through the distribution of free open source software and training in local product applications. So far comprehensive multiple sensors (A VHRR, MODIS, AIRS, AMSU, VIIRS, CrIS, ATMS, IASI, MHS, ABI, and AHI) onboard multiple international satellite platforms (NOAA-17, NOAA-20, S-NPP, Terra, Aqua, METOP-A/B, GOES 16/17, and Himawari-8) are making global visible, infrared, and microwave imaging and sounding spectral measurements and producing environment products (composite color images, cloud property, aerosol, dust, fire, flood, drought, temperature/water vapor profile, total precipitable water, precipitation, trace gases, ocean/land surface property, and many others).

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.

The Community Satellite Processing Package (CSPP) was developed in 2011 with its first release in 2012(http://cimss.ssec.wisc.edu/cspp/history.shtml). Since then, as of January 2021, over 62 new and updated releases of software packages to process the satellite direct broadcast raw data into sensor data records (SDRs) and produce various Environmental Data Records (EDRs). CSPP supports the Direct Broadcast (DB) meteorological and environmental satellite community through the packaging and distribution of open-source science software when possible. CSPP supports DB users of both polar-orbiting and geostationary satellite data processing and regional realtime applications through the distribution of free open source software and training in local product applications. So far comprehensive multiple sensors sensors (AVHRR, MODIS, AIRS, AMSU, VIIRS, CrIS, ATMS, IASI, MHS, ABI, and AHI) onboard multiple international satellite platforms (NOAA-17, NOAA-20, S-NPP, Terra, Aqua, METOP-A/B, GOES 16/17, and Himawari-8) are making global visible, infrared, and microwave imaging and sounding spectral measurements and producing environment products (composite color images, cloud property, aerosol, dust, fire, flood, drought, temperature/water vapor profile, total precipitable water, precipitation, trace gases, ocean/land surface property, and many others).

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.

In this presentation, we present details on ways that the JPSS Program uses its investments in environmental satellites to benefit not only the NOAA mission but other countries as well. The data, products, and capabilities from the NOAA-20 satellite has been well integrated into the global observation network following its launch on November 18, 2017. The Suomi National Polar-orbiting Partnership (Suomi NPP) continues to provide critical data as its sensors maintain their health well past their expected life-time.

The Community Satellite Processing Package (CSPP) was developed since 2011 with its first release on 2012 ( http://cimss.ssec.wisc.edu/cspp/history.shtml ). Since then, as of January 2019, over 60 new and updated releases of software packages to process the satellite direct broadcast raw data into sensor data records (SDRs) and produce various Environmental Data Records (EDRs). CSPP supports the Direct Broadcast (DB) meteorological and environmental satellite community through the packaging and distribution of open source science software when possible. CSPP supports DB users of both polar orbiting and geostationary satellite data processing and regional real-time applications through the distribution of free open source software and through training in local product applications. So far comprehensive multiple sensors (AVHRR, MODIS, AIRS, AMSU, VIIRS, CrIS, ATMS, IASI, MHS, ABI, and AHI) onboard multiple international satellite platforms (NOAA-17, NOAA-20, S-NPP, Terra, Aqua, METOP-A/B, GOES 16/17, and Himawari-8) are making global visible, infrared, and microwave imaging and sounding spectral measurements and producing environment products (composite color images, cloud property, aerosol, dust, fire, flood, drought, temperature/water vapor profile, total precipitable water, precipitation, trace gases, ocean/land surface property, and many others).

In this presentation, we present details on various newsworthy storms that have received support from the data and products from the Joint Polar Satellite System (JPSS) satellites. With the launch of the NOAA-20 satellite on November 18, 2017 and the continued technical health of the Suomi NPP satellite, multiple sources of data and products are available for decisionmakers as they face weather disasters around the world. This value of these satellites is continually reinforced as the JPSS Program is frequently called on to provide tailored products in response to these disasters. The operational application of JPSS data and products comes about in three primary ways.

The successful launch of the Joint Polar Satellite System (JPSS) satellite -1 (JPSS-1, now named as NOAA-20) is providing an array of atmospheric, land, and ocean data products from four major instruments: the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS). These instruments are similar to the instruments currently operating on the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The JPSS program at the center for Satellite Applications and Research (JSTAR) led the efforts for development of algorithms to generate Sensor Data Records (SDRs) and Environmental Data Records (EDRs), as well as the Calibration and Validation (Cal/Val) of the SDRs and EDRs. By now the Cal/Val of NOAA-20 products have been performed as planned, all the NOAA-20 Key Performance Parameters (KPPs) have reached Validated Maturity and in operation. This paper will present an overview of JPSS Cal Val efforts. We will provide an update of the Cal/Val maturity status for the NOAA-20 data products. In addition, the operational implementation status of JPSS enterprise science algorithms for product generation and science product reprocessing efforts for the S-NPP mission will be discussed.

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.

With the launch of NOAA first JPSS Low Earth Orbit (LEO) satellite (now NOAA 20) on December 2017 into the afternoon orbit to join the existing NOAA/NASA Suomi-NPP satellite each carries five advanced instruments - (1) Visible Infrared Imaging Radiometer Suite (VIIRS), (2) Cross-Track Infrared Sounder (CrIS), (3) Advanced Technology Microwave Sounder (ATMS), (4) OMPS, and (5) CERES. To facilitate easy access to real-time S-NPP and NOAA-20 CrIS/ATMS/VIIRS data, NOAA JPSS program office has established direct broadcast (DB) partners with antenna network (DBNet) located in Alaska, California, Florida, Guam, Hawaii, Maryland, Puerto Rico, and Wisconsin (fig 1).

As part of the the Joint Polar Satellite System (JPSS) satellite-1 (JPSS-1, referred hereafter as NOAA-20) post-launch operations, the JPSS program at the Center for Satellite Applications and Research (JSTAR) has laid out the plans for calibration and validation (Cal/Val) of JPSS-1 sensor and environmental data records (SDRs, EDRs). The SDRs, imagery EDRs, and some of the other EDR products prioritized as Key Performance Parameters are in operations and are going through Beta, Provisional, and Validated maturity reviews. The JSTAR teams have also initiated the operational implementation and execution of cal/val plans and schedules for a vast number of EDR products for atmosphere, land, ocean, and cryosphere applications. Quality monitoring tools such as the Integrated Calibration and Validation System and the EDR Long Term Monitoring have been augmented for JPSS-1 to provide improved science trending and analysis for anomaly resolution. With the availability of six years of high quality data from products from the Suomi National Polar-orbiting Partnership (S- NPP) satellite (launched in October 2011), and the continuity of products from the JPSS-1 satellite, the JSTAR science teams are now preparing towards the generation of consistent long-term science data products using enterprise algorithms and reprocessing efforts. This paper presents an insight into these processes with examples of science data products operationally available from the S- NPP satellite and the first light products from JPSS-1 suite of instruments.

In this presentation, we present a new smoke modeling system High Resolution Rapid Refresh (HRRR-Smoke) to simulate biomass burning (BB) emissions, plume rise and smoke transport in real time. The HRRR model (without smoke) is run as an operational numerical weather prediction system at the National Weather Service. HRRR is NOAA Earth System Research Laboratory's version of the Weather Research and Forecasting (WRF) model. Here we make use of WRF-Chem (the WRF model coupled with chemistry) and simulate fine particulate matter (smoke) emissions emitted by BB as well as anthropogenic sources. The model includes an aerosol aware double moment Thompson microphysics scheme [1], which allows to simulate smoke feedback on microphysics in a computationally efficient manner.

As the costliest natural disaster over the globe, floods are predicted to become more frequent in most climate change forecasts. In high latitudes, floods are caused by ice jam and snow melt almost every break-up season. Floods caused by intense rainfall also threaten lives and social infrastructures. Near real-time satellite-derived flood maps are invaluable to river forecasters and decision-makers for disaster monitoring and relief efforts.

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-1. JPSS-1 is scheduled for launch in later 2017, followed by JPSS-2 in 2022. SNPP was launched in 2011.

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument which is onboard the Suomi National Polar-orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) satellites has transitioned much of the capability of the experimental MODerate Resolution Imaging Spectroradiometer (MODIS) instruments into the operational domain. VIIRS provides a continuation of global environment monitoring for Land, Ocean, Cloud, and Atmosphere. The high quality observations and derived products generated from VIIRS have been used to improve operational environmental forecast skills and enhance our understanding of climate change processes. Since S-NPP successfully launched in October 2011, NOAA STAR science teams have been focused on maintaining, development, calibration/validation, and upgrade of the VIIRS algorithms. By far, most of the S-NPP VIIRS sensor and environmental data products have been fully validated and characterized through rigorous cal/val review process. The validated science algorithms are now used for reprocessing of the entire S-NPP mission lifetime of the VIIRS data product records. This paper presents an insight into these processes with examples of science data products operationally available from the S-NPP VIIRS Instrument. In addition, plans and preparations towards JPSS-1 launch and pre-operational evaluation JPSS-1 algorithms and product performance using proxy and Thermal Vacuum measurements are presented.

The JPSS Proving Ground Program has been a huge success. The innovative use of S-NPP data and the full integration of the user community ensured thorough evaluation of capabilities and rapid transition from research to operations. The JPSS Program's multi-year commitment to this program allows project teams to define a successful path to assist the user community in fully integrating new JPSS capabilities.

Space Science and Engineering Center (SSEC) and its Cooperative Institute for Meteorological Satellite Studies (CIMSS) have supported the international Direct Broadcast/Readout (DB/DR) user community since 1985 through the distribution of the International TOVS and ATOVS Processing Packages (ITPP, IAPP) for NOAA Polar Orbiting Environmental Satellite (POES), and since 2000 via the International MODIS/AIRS Processing Package (IMAPP) for NASA Terra and Aqua. Since 2007, SSEC/CIMSS has also participated in the development of regional versions of software for generating Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) Sensor Data Records (SDRs), and for Visible Infrared Imaging Radiometer Suite (VIIRS) atmosphere and cloud Environmental Data Records (EDRs). Currently SSEC/CIMSS is supported by the NOAA JPSS program scientist and NASA to continue facilitating the use of polar orbiter satellite data through the initial development of a newly conceived Community Satellite Processing Package (CSPP) that will support the Suomi-NPP/JPSS and, subsequently, build up over time to support GOES-R with CSPP Geosynchronous Earth Orbit (GEO) component, as well as other international polar orbiting and geostationary meteorological and environmental satellites and their regional user communities.

Satellite data assimilation for societal benefits is demonstrated using high quality ATMS and CrIS radiance data for improving forecast skill of landfalling hurricanes and typhoons. A comprehensive SDR (CSDR) from ATMS, CrIS and VIIRS is created and allows for maximizing the benefits to numerical weather prediction data assimilation. The success of satellite data assimilation requires various quality control techniques that are readily achieved with the new JPSS CSDR data streams.

The Global Space-based Inter-Calibration System (GSICS) is an international program formulated by the World Meteorological Organization (WMO) and the Coordination Group for Meteorological Satellites (CGMS) since 2005. It will inter-calibrate the instruments of the international constellation of operational low-earth-orbiting (LEO) and geostationary (GEO) environmental satellites and tie these to common reference standards to assure the comparability of satellite measurements taken at different times and locations by different instruments operated by different satellite agencies. Upon its 10th anniversary the GSICS community is gradually encompassing all CGMS members. CGMS members are collaborating in the framework of GSICS to develop and apply “best practices” for state-of-the-art and homogeneous calibration. GSICS benefits to satellite operators through sharing of resources and best practices, and to satellite data users through improved calibration, assessments, and traceability to common references. The practical value of GSICS was demonstrated in the role played to facilitate the commissioning operations of several satellite programmes in the most recent years.

Today's rapidly increasing environment satellite observations brought unprecedented opportunities for monitoring the earth environments with remote sensing measurement. The Joint Polar Satellite System (JPSS) is the National Oceanic and Atmospheric Administration's (NOAA) next generation polar satellite program that provides global environmental remote sensing of weather, climate and other environmental applications. The Suomi National Polar-orbiting Partnership (S-NPP) satellite launched in October 2011 was the first satellite designed to bridge into the future JPSS constellation. The data products produced from the S-NPP/JPSS satellites provide critical observations for accurate weather forecasting, reliable severe storm outlooks, and global measurements of atmospheric and oceanic conditions such as sea surface temperatures, ocean color, ozone and other trace gases, aerosol, clouds, temperature and moisture profiles, wind speeds, land surface properties, snow and ice covers, etc. S-NPP/JPSS products provides critical support to NOAA's ecosystems, climate, weather, and water mission goals through numerous operational applications, including weather and climate modeling and prediction, fisheries management; ecosystem monitoring and management; and understanding ocean dynamics and climate variability. This paper will provide insight into the Suomi National Polar-orbiting Partnership (S-NPP) data products and their applications for environment monitoring. Specifically, the presentation will be focused on the advanced data products derived from the Cross-track Infrared Sounder (CrIS) measurements, Cal/Val status, products performance, and their applications for monitoring the atmospheric variability.

We perform a demonstration experiment using the National Oceanic Atmospheric Administration Unique Cross-track Infrared Sounder (CrIS)/Advanced Technology Microwave Sounder Processing System to assess the improvement on trace gas retrievals upon switching to high spectral resolution CrIS radiance measurements (0.625 cm-1). The focus of this study is carbon monoxide retrievals. The experimental high spectral resolution CO retrievals show a remarkable improvement, of almost up to one order of magnitude in the degree of freedom of the signal, with respect to the low-resolution mode. Furthermore, high-resolution CO retrievals show similar skill with respect to existing CO operational products from the Atmospheric InfraRed Sounder, Atmospheric Sounder Interferometer, and Measurements of Pollution In The Troposphere instruments, both in terms of spatial variability and degrees of freedom. The results of this research provide evidence to support the need for high spectral resolution CrIS measurements. This is a fundamental prerequisite in guaranteeing continuity to the CO afternoon orbit monitoring as part of a multisatellite uniformly integrated long-term data record of atmospheric trace gases.

The Weather Research and Forecasting (WRF) model has been widely employed for weather prediction and atmospheric simulation with dual purposes in forecasting and research. Land-surface models (LSMs) are parts of the WRF model, which is used to provide information of heat and moisture fluxes over land and sea-ice points. The 5-layer thermal diffusion simulation is an LSM based on the MM5 soil temperature model with an energy budget made up of sensible, latent, and radiative heat fluxes. Owing to the feature of no interactions among horizontal grid points, the LSMs are very favorable for massively parallel processing. The study presented in this article demonstrates the parallel computing efforts on the WRF 5-layer thermal diffusion scheme using Graphics Processing Unit (GPU). Since this scheme is only one intermediate module of the entire WRF model, the involvement of the I/O transfer does not occur in the intermediate process. By employing one NVIDIA GTX 680 GPU in the case without I/O transfer, our optimization efforts on the GPU-based 5-layer thermal diffusion scheme can reach a speedup as high as 247.5x with respect to one CPU core, whereas the speedup for one CPU socket with respect to one CPU core is only 3.1x. We can even boost the speedup to 332x with respect to one CPU core when three GPUs are applied.

A quantitative method is developed for deriving water fraction from coarse- to medium-resolution satellite data with visible to short-wave infrared (SWIR) channels based on the linear mixture theory. The method uses a SWIR channel (1.64 μm) by assuming that the water-surface-leaving radiance in this channel is insignificant and is thus less affected by water types and water depth than near-infrared (NIR) channels for inland water bodies. For a mixed water pixel, a dynamic nearest neighbor searching (DNNS) method is used to find the nearby land pixels to determine the average land reflectance. The nearby pure water pixels with a similar water type to the subpixel water portion of the mixed water pixel are found dynamically to derive the average water reflectance. The average reflectance in the SWIR channel from both pure land pixels and water pixels is used to calculate the water fraction from a linear mixture model. The developed method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data and shows promising results. High-resolution satellite data from the Thematic Mapper (TM) are used to evaluate the water fraction derived from MODIS. During pixel-to-pixel water fraction evaluation, TM data are spatially aggregated to MODIS resolution. When evaluated against the high-resolution TM observations, water fractions derived from MODIS using the DNNS method with the SWIR channel show a bias of -0.021 with a standard deviation of 0.0338. Comparing lake areas between TM and MODIS data also shows consistent results with the pixel-to-pixel water fraction comparison. The DNNS method is also compared to the traditional histogram method both with SWIR channel and NIR channel. The results show that the DNNS method is more accurate than the histogram method and that the SWIR channel is better than the NIR channel to derive highly accurate water fraction from coarse- to medium-resolution satellite data.

More precise and accurate geostationary measurements are highly needed for satellite applications. It was well known that the Geostationary Operational Environmental Satellite (GOES)-12 imager was susceptible to water-ice contamination, and thus, several decontamination efforts were carried out to remove built-up ice on the instrument during operation. The intercalibration results of GOES-12 with the Atmospheric Infrared (IR) Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) indicate that the calibration accuracy of GOES-12 was impacted by the decontamination procedures. Relative to the AIRS and the IASI, the GOES-12 imager radiances or brightness temperatures increased in the CO2sounding channel (channel 6, 13.3 μm) and decreased in the water-vapor absorption channel (channel 3, 6.5 μm) but was less changed in the window channel (channel 4, 10.7 μm). A simple conceptual model is then proposed to give a physical explanation on the different behaviors of three IR channels in response to the ice-removal procedures.

The current Geostationary Operational Environmental Satellite (GOES) Imager infrared (IR) channels experience a midnight effect that can result in erroneous instrument responsivity around satellite midnight. An empirical method named the Midnight Blackbody Calibration Correction (MBCC) was developed and implemented in the GOES Imager IR operational calibration, aiming to correct the midnight calibration errors. The main objective of this study is to evaluate the MBCC performance for the GOES-11/-12 Imager IR channels by examining the diurnal variation of the mean brightness temperature (Tb) bias with respect to reference instruments. Two well-calibrated hyperspectral radiometers on low Earth orbits (LEOs), the Atmospheric Infrared Sounder on the Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) on the Metop-A satellite, are used as the reference instruments in this study. However, as the timing of the collocated geostationary–LEO intercalibration data is related to the GOES scan angle, it is then necessary to assess the GOES scan angle calibration variations, which becomes the second objective of this study. Our results show that the applications and performance of the MBCC method varies greatly between the different channels and different times. While it is usually applied with high frequency for about 8 h around satellite midnight for the short-wave channels (Ch2), it may only be intensively used right after satellite midnight or even barely used for the other IR channels. The MBCC method, if applied with high frequency, can reduce the mean day/night calibration difference to less than 0.15 K in almost all the GOES IR channels studied in this paper except for Ch4 (10.7$\mu\hbox{m}$). The uncertainty of the nighttime GOES and IASI Tb difference for different scan angles is less than 0.1 K in each IR channel, indicating that there is no apparent systematic variation with the scan angle, and therefore, the estimated diurnal cycles of GOES Imager calibration is not prone to the systematic effects due to scan angle.

Nakanishi and Niino proposed an improved Mellor-Yamada (M-Y) Level-3 model (MYNN) surface for three-dimensional simulation of advection fog. The model is on the basis of large-eddy simulation and is numerically stable. The model predicts vertical profiles of mean quantities such as temperature that are in good agreement with those obtained from large-eddy simulation of a radiation fog. In this paper, we accelerate Nakanishi and Niino PBL's Surface Layer scheme in a highly parallel environment, using NVIDIA Graphics Processing Units (GPU). This GPU implementation efficiently utilizes the fine grained parallelism exhibited by the MYNN PBL scheme. The algorithm is accelerated on a low-cost personal supercomputer with over 500 CUDA cores running on a GPU. This implementation achieves a high speedup of 160×.

Atmospheric Vertical Temperature Profile (AVTP) and Atmospheric Vertical Moisture Profile (AVMP) retrievals produced by the Cross-track Infrared Sounder and the Advanced Technology Microwave Sounder suite (CrIMSS) official algorithm were evaluated with global European Center for Medium Range Weather Forecast (ECMWF) analysis fields, radiosonde (RAOB) measurements, and Aqua-Atmospheric Infrared Sounder (AIRS) heritage algorithm retrievals. The operational CrIMSS AVTP and AVMP product statistics with truth data sets are quite comparable to the AIRS heritage algorithm statistics. Planned updates and improvements to the CrIMSS algorithm will alleviate many issues observed with `day-one' focus-day results and show promise in meeting the Key Performance Parameter (KPP) specifications.

As a part of the Joint Polar Satellite System (JPSS) and the Suomi National Polar-orbiting Partnership (NPP), the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) instruments make up the Cross-track Infrared and Microwave Sounder Suite (CrIMSS). CrIMSS primarily provides globally-referenced calibrated radiances and vertical profiles of temperature, moisture, and pressure. The CrIMSS operational code has been ported to various LINUX systems and retrievals are performed using both proxy and real ATMS/CrIS data. The high quality proxy data generated from the IASI instrument provided useful testing for the CrIMSS EDR algorithm prior to the launch of the SUOMI NPP satellite. The experience learned from processing the proxy data helped us to handle the SUOMI NPP CrIS/ATMS data as soon as they became available to the CAL/VAL team. In this paper, encouraging preliminary results of applying the ported CrIMSS EDR algorithm to the SUOMI NPP CrIS/ATMS data are presented.

The Weather Research and Forecast (WRF) Double Moment 5-class (WDM5) mixed ice microphysics scheme predicts mixing ratio of hydrometeors and their number concentrations for warm rain species including clouds and rain. WDM5 can be computed in parallel in the horizontal domain using a many-core GPU. In order to obtain better GPU performance, we manually rewrote the original WDM5 Fortran module into a highly parallel CUDA C program. The GPU-based implementation of WDM5 microphysics scheme on 1 GTX590 GPU achieves a significant speedup of 147× over its CPU-based single-threaded counterpart when we use asynchronous data transfer and non-coalesced memory access. More importantly, the speedup excluding the host-device data transfer time is 206× when using coalesced memory access. Since the WDM5 microphysics scheme is only an intermediate module of the entire WRF model, its input data should be already available in the GPU global memory from previous modules and its output data should reside at the GPU global memory for later usage by other modules.

The Global Space-based Inter-Calibration System (GSICS), initiated by World Meteorological Organization (WMO) and Coordination Group for Meteorological Satellites (CGMS) in 2005, is an international collaborative effect with the mission to produce consistent and accurate measurements from the constellation of operational meteorological satellites by inter calibration between a variety of different instruments. NOAA GSICS Processing and Research Center (GPRC), located at NOAA/NESDIS, has been conducted the GSICS-related processing and research to ensure the quality of radiance measured with NOAA satellites, as well as the international satellite instruments. In this paper, we summarized most recent activities conducted at NOAA GSICS GPRC in monitoring and improving the Geostationary (GEO) infrared (IR) calibration accuracy, especially the GOES Imager and Sounder instruments.

Outgoing longwave radiation (OLR) at the top of atmosphere (TOA) derived from the Atmospheric Infrared Sounder (AIRS) radiance observations is compared with the Earth's Radiant Energy System (CERES) OLR in monthly and daily time scales. The global monthly mean OLR differences between AIRS and CERES and the spatial standard deviations of the OLR differences are about 1 Wm- 2and 6 W-2, respectively. The regional daily mean differences between AIRS and CERES OLR from 60°N to 60°S are within 1 Wm-2. These results suggest that AIRS and the Cross-track Infrared Sounder (CrIS) could be used to monitor the performance of CERES and used as a backup in case of CERES failure.

Modern reanalysis datasets provide us with the best available four-dimensional, homogeneous datasets for studying the climate and weather systems and for possible validation of climate models. In order to have confidence in the use of reanalysis data for the above-mentioned studies, it is important to see how they compare with a common dataset with high data quality and to understand any discrepancies among them. In this study, the radiance measurements from the Atmospheric Infrared Sounder (AIRS) on NASA Aqua are used to assess the data quality of reanalysis datasets, including NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA), European Centre for Medium-Range Weather Forecast's ERA-Interim Reanalysis, Japanese 25-year Reanalysis (JRA-25). Particularly, we focus on the spectral signature regions that are sensitive to stratosphere temperature and upper troposphere water vapor. The preliminary results on are presented.

The Geostationary Operational Environmental Satellite (GOES) program is developing the Advanced Baseline Imager (ABI), a new generation sensor to be carried onboard the GEOS-R satellite (launch expected in 2014). Compared to the current GOES Imager, ABI will have significant advantages for retrieving land surface temperature (LST) as well as providing qualitative and quantitative data for a wide range of applications. The infrared bands of the ABI sensor are designed to achieve a spatial resolution of 2 km at nadir and a noise equivalent temperature of 0.1 K. These improve the imager specifications and compare well with those of polar-orbiting sensors (e.g., Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer). In this paper, we discuss the development of a split window LST algorithm for the ABI sensor. First, we simulated ABI sensor data using the MODTRAN radiative transfer model and NOAA88 atmospheric profiles. To model land conditions, we developed emissivity data for 78 virtual surface types using the surface emissivity library from SnyderUsing the simulation results, we performed regression analyses with the candidate LST algorithms. Algorithm coefficients were stratified for dry and moist atmospheres as well as for daytime and nighttime conditions. We estimated the accuracy and sensitivity of each algorithm for different sun-view geometries, emissivity errors, and atmospheric assessments. Finally, we evaluated the most promising algorithm using real data from the GOES-8 Imager and SURFace RADiation Network. The results indicate that the optimized LST algorithm meets the required accuracy (2.3 K) of the GOES-R mission.

The operational Atmospheric Infrared Sounder (AIRS) emissivity retrieval uses a National Oceanic and Atmospheric Administration (NOAA) regression emissivity product as a first guess for its retrieval over land. The NOAA approach is based on clear radiances that are simulated from the European Centre for Medium-Range Weather Forecasting forecast and a surface emissivity training data set. The same approach has also been applied to simulated Infrared Atmospheric Sounding Interferometer (IASI) data. Resulted emissivity spectra and maps derived from AIRS and IASI will be presented and discussed.

This paper presents the standard and research products from Atmospheric Infrared Sounder (AIRS) and their current accuracies as demonstrated through validation efforts. It also summarizes ongoing research using AIRS data for weather prediction and improving climate models.

Summary form only given. The research is undertaken by NOAA/NESDIS, for its GOES-R Earth observation satellite series, to be launched in the 2013 time frame, to enable greater distribution of its scientific data, both within the US and internationally. We have developed a new lossless algorithm for compression of the signals from NOAA's environmental satellites using current spacecraft to simulate data from the upcoming GOES-R instrument, and focusing on Aqua Spacecraft's AIRS (atmospheric infrared sounder) instrument in our case study. The AIRS is a high resolution instrument which measures infrared radiances at 2378 wavelengths ranging from 3.74-15.4 /spl mu/m. The AIRS takes 90 measurements as it scans 48.95 degrees perpendicular to the satellite's orbit every 2.667 seconds. We use Level 1A digital count data granules, which represent 6 minutes (or 135 scans) of measurements. Therefore, our data set consists of a 90/spl times/135/spl times/1502 cube of integers ranging from 12-14 bits. Our compression algorithm consists of the following steps: 1) channel partitioning; 2) adaptive clustering; 3) projection onto principal directions; 4) entropy coding of the residuals.

There is an increasing demand for intercalibrating the polar-orbiting radiometers on different satellites to achieve the consistency and traceability required for long-term climate studies with the 25+ years of NOAA satellite data. Also, the calibration of current operational radiometers needs to be linked to those of the next generation NPOESS (National Polar-orbiting Operational Environmental Satellite System) satellites. The simultaneous nadir overpasses (SNOs)/simultaneous conical overpass (SCO) method developed at NOAA/NESDIS has been applied to the intersatellite calibration of radiometers in the infrared, visible/near-infrared, and microwave with excellent results. In this paper, the SNO/SCO methodology is introduced. Preliminary intersatellite calibration results for AVHRR, MODIS, SSM/I, AIRS, HIRS, and AMSU on operational and historical satellites are presented. The plans for establishing the calibration links between POES (Polar Operational Environmental Satellites) and NPOESS radiometers using the SNO/SCO method are discussed.

The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-/spl mu/m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.

The Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) instrument suite onboard Aqua observes infrared and microwave radiances twice daily over most of the planet. AIRS offers unprecedented radiometric accuracy and signal to noise throughout the thermal infrared. Observations from the combined suite of AIRS, AMSU, and HSB are processed into retrievals of atmospheric parameters such as temperature, water vapor, and trace gases under all but the cloudiest conditions. A more limited retrieval set based on the microwave radiances is obtained under heavy cloud cover. Before measurements and retrievals from AIRS/AMSU/HSB instruments can be fully utilized they must be compared with the best possible in situ and other ancillary "truth" observations. Validation is the process of estimating the measurement and retrieval uncertainties through comparison with a set of correlative data of known uncertainties. The ultimate goal of the validation effort is retrieved product uncertainties constrained to those of radiosondes: tropospheric rms uncertainties of 1.0 degC over a 1-km layer for temperature, and 10% over 2-km layers for water vapor. This paper describes the data sources and approaches to be used for validation of the AIRS/AMSU/HSB instrument suite, including validation of the forward models necessary for calculating observed radiances, validation of the observed radiances themselves, and validation of products retrieved from the observed radiances. Constraint of the AIRS product uncertainties to within the claimed specification of 1 K/1 km over well-instrumented regions is feasible within 12 months of launch, but global validation of all AIRS/AMSU/HSB products may require considerably more time due to the novelty and complexity of this dataset and the sparsity of some types of correlative observations.