During the Asian summer monsoon rainy season, heavy and continuous rainfall usually causes widespread floods in many Asian countries, including the two most populated countries China and India. Flood mapping datasets from moderate-resolution satellites, such as the JPSS (Joint Polar Satellite System) series including Suomi-NPP (Suomi National Polar-orbiting Partnership) and NOAA-20, can be invaluable for monitoring floods and assessing the affected population. The flood maps derived from the VIIRS (Visible Infrared Imaging Radiometer Suite) imagery can provide a big picture of what’s on the ground over the entire Asia flooding region. The VIIRS 5-day composite flood maps, along with a population density dataset, can be combined to estimate the population potentially exposed (PPE) to flooding. The VIIRS flood maps demonstrate floods in China primarily occurred along the Yangtze River Basin and its tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed to flooding. Here we use the summer of 2020 as an example, based on the flood extent along with the population density map, approximately over 50 million people in the entire China might have been affected by the floodwaters. In addition to China, several other countries including India, Bangladesh, and Myanmar were also affected. In India, the worst inundation and the affected population were located mainly in the northern states of Bihar, Assam, and West Bengal.

Different retrieval algorithms have been developed to process top-of-atmosphere (TOA) spectral radiance data provided by hyperspectral infrared sounder missions. Those algorithms are either optimal estimation method (OEM) based schemes with radiative transfer calculation involved in the retrieval process, or machine learning based methods that allow ultra-efficient data procession but lack of radiometric consistency validation based on the directly measured information. Combining both approaches leverages their respective technical advantages, leading to more accurate results. This study introduces a hyperspectral sounder fingerprinting algorithm to explore this hybrid approach. This approach involves the use of a spectral information-based classification method to identify an reference geophysical state and the corresponding radiative kernel. This enables the efficient retrieval of geophysical variables of interest through a radiative kernel-based linear inversion procedure. The fingerprinting method has been applied to analyze a decadelong hyperspectral sounder data record.

There are a set of satellite land surface products produced to public use at the U.S. NOAA/NESDIS, including (but not limited to) land surface temperature (LST), land surface albedo (LSA) [1], vegetation indices (VIs) [2], green vegetation fraction (GVF) [3] and leaf area index (LAI). These products are of fundamental importance to many aspects of geoscience disciplines, e.g., the net radiation budget at the Earth surface, monitoring state of crops and vegetation, and serving as important indicators of both the greenhouse effect and the physics of land-surface processes at local through global scales [4] [5] [6]. In particular, LST, LSA and LAI are listed as essential climate variables (ECVs) by the Global Climate Observing System of the World Meteorological Organization [7]. LST, LSA, VIs and GVF are baseline products produced for years at NOAA/NESDIS, through observations from the U.S. Joint Polar-orbiting Satellite System (JPSS).In the past two years, significant product improvements and quality assurance have been accomplished on the above land surface products. First, the VI and GVF algorithm package has been optimized which greatly reduced the processing time and improved robustness of the production; meanwhile, an innovative high resolution (up to 20 meters, daily) VI algorithm has been developed. Second, an all-weather LST algorithm has been tested and evaluated; operational all-weather LST production is expected in 2025. Third, a LAI production package has been approved and is in the operational installation process; experimental LAI data production is expected by late 2024. Fourth, a bare soil (background of the vegetation surface) albedo climatology dataset has been developed which improves quality of the albedo data; a BRDF data production algorithm package has been developed and is in the operational installation process. Fifth, a daily data monitoring system has been run in the science team, in addition to production monitoring in the operational unit, which particularly checks scientific significance of the products for LST and LSA; as a result, global LST anomaly report is generated monthly. Finally, in-situ data collection, filtering and analysis process has been improved, which is the base of our product quality. This presentation provides a comprehensive summary of all above-mentioned in detail.

This paper provides the latest updates of the NOAA-21 post-launch product validation status. The new products and enhanced capabilities have been transitioned to operations. The status and plans of the reprocessing data records from more than a decade of the Suomi National Polar-orbiting Partnership (SNPP) observations is also provided.

The National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) retrieves atmospheric temperature, water vapor, and trace gases profiles from hyperspectral infrared sounding and microwave instruments aboard Joint Polar Satellite System (JPSS) satellites, and the Infrared Atmospheric Sounder (IASI) aboard the Meteorological Operational Satellite Program (Metop) satellites. Among all enhancement of the recently released NUCAPS V3.1, a new ozone climatology, a new carbon dioxide climatology, and averaging kernel matrix output are important updates. The comparison between NUCAPS retrieved ozone and ECMWF analysis on 12 focus days in 12 months indicates that the global ozone retrievals are improved in all seasons, especially over Antarctic region during spring to summer transition time. The diagnostic study using averaging kernel analysis shows that NUCAPS retrieved ozone profiles have good agreement with ozonesonde measurements.

Over ten-years, the Integrated Calibration and Validation System (ICVS) Long-Term Monitoring (LTM) System has provided near-real time (NRT) monitoring for Joint Polar Satellite System (JPSS) spacecraft and instruments including their on-orbit status and performance and science data product quality [1] - [4]. The ICVS also harnesses JPSS Sensor Data Record (SDR) data to rapidly (with little latency) visualize radiometric features of severe weather events such as hurricanes and volcanos [5] [6]. This study presents two case studies, one depicting the 3-dimensional (3D) atmospheric warm core structure inside Hurricane Ian from the 2022 North Atlantic Hurricane Season and another showing the 3D temperature structures present during the 2021 Heat Dome event by using JPSS ATMS (and VIIRS for hurricane events) SDR and TDR data. More details and images/animations for hurricane events can be found at https://www.star.nesdis.noaa.gov/smcd/sew/index.php.

Thermal infrared (IR) environmental satellite data assimilation and remote sensing of the surface and lower troposphere depend on the accurate specification of the spectral surface emissivity within clear-sky forward calculations. Over ocean surfaces, accurate modeling of surface-leaving radiances over the sensor scanning swaths is complicated by a quasi-specular bidirectional reflectance distribution function (BRDF). Recent findings at the Joint Center for Satellite Data Assimilation (JCSDA) have also revealed significant zonally varying systematic biases ($\approx |0.5|$K) on a global scale over cold ocean waters; these are the results of temperature dependence in the thermal IR optical constants. This article proposes practical solutions to these problems by modeling thermal IR “effective emissivity” in a manner that accounts for both surface emission and quasi-specular reflectance, along with temperature dependence, while meeting the latency and computational constraints of operational global data assimilation and retrieval systems. We overview the theoretical basis of the model and validate it against ship-based Marine Atmospheric Emitted Radiance Interferometer (MAERI) spectra obtained from cold and warm water ocean campaigns.

Since early 2012, the cross-track infrared sounder (CrIS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has continually provided the hyperspectral infrared observations for profiling atmospheric temperature, moisture, and greenhouse gases. In this study, the CrIS sensor data record (SDR) data are improved for climate applications with its fine-tuning of calibration coefficients in an NOAA reprocessing project. A specific software system was developed to reprocess the CrIS SDR. This software system was updated with a new calibration algorithm, nonlinearity, and geolocation to improve the SDR data quality and long-term consistency. The calibration coefficients are refined with the latest updates, which were used to calibrate the latest operational SDR products and replace those in the engineering packet (EP) in the raw data record (RDR) data stream. The resampling wavelength was updated based on the metrology laser wavelength and resulted in zero sampling error in the spectral calibration. All the historical SDRs (from February 2012 to March 2017) were generated with the same calibration coefficients and same version of the processing software system, resulting in improved accuracy and stability in terms of spectral and radiometric calibration during the CrIS lifetime mission. The quality of the reprocessed CrIS SDR data at nominal spectral resolution (NSR) is assessed in terms of its radiometric and spectral calibration. Comparisons against the operational SDR data are carried out to demonstrate the improved long-term stability of the reprocessed CrIS SDR data. Overall radiometric biases are found to be small and highly stable over the instrument mission, the FOV-to-FOV differences are less than ~10 mK, and much better than that from the operational SDR data. It is shown that the CrIS metrology laser wavelength varies within 4 ppm as measured by the neon calibration system. The reprocessed SDR data have spectral errors less than 0.5 ppm, which is much better than the operational SDR data with about 4 ppm. This baseline version of the reprocessed SNPP CrIS SDR data is suitable for long-term climate monitoring and model assessments and can provide an infrared reference observation to assess other narrow- or broadband infrared instruments’ calibration accuracy.

The NOAA Unique Combined Atmospheric Processing System (NUCAPS) is the official operational hyperspectral enterprise sounding product system that produces vertical profiles of temperature, water vapor, ozone, and a variety of trace gas products (e.g. CO, CH4, and CO2). These products provide continued support of global environmental data from multiple polar-orbiting satellites to monitor atmospheric composition, trace gas concentrations, climate change and Earth system processes. This paper presents an overview of the NUCAPS atmospheric composition products, performance, and utilization for many regional and global applications. Future plans on collaborations, special data needs by user communities, and upgrades to the data products to support near real-time applications are discussed. In addition, continued efforts towards reprocessing to generate mission-long high-quality science products, adapting to enterprise algorithms, and providing calibrated radiances and geophysical data products via direct broadcast networks are discussed for user awareness and international collaborations.

The Ozone Mapping and Profiler Suite (OMPS) Nadir Profilers (NPs) are advanced backscatter ultraviolet (BUV) hyperspectral instruments that measure ozone profiles in the Earth atmosphere. The first NP sensor onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite began its science observations on January 26, 2012, after its aperture door opened. The second OMPS NP, flying on the NOAA-20 satellite, opened its aperture door on January 8, 2018, starting science its data collection. The two NP sensors acquire Earth spectral images along their satellite flight path with a 16.7° wide swath, enabling weekly coverage of vertical ozone distribution in the Earth atmosphere. A successful thorough sensor calibration enables the NP sensors’ data records (SDRs) to meet measurement accuracy requirements. The largest error term in the albedo calibration came from the spectral wavelength calibration. This article provides SDRs accuracy analysis for both NP sensors and discusses important aspects of the SDRs performance in relation to the sensors’ characterization and calibration.

The NOAA Unique Combined Atmospheric Processing System (NUCAPS) is currently the National Oceanic and Atmospheric Administration (NOAA) operational hyper-spectral infrared sounding product system for deriving vertical profiles of temperature, water vapor, ozone, and trace gas products (CO, CH4, and CO2). In pursuance on the use of European Organisation for the Exploitation of Meteorological Satellite Second Generation (EUMETSAT-SG, expected launch in 2022) IASI Next Generation (IASI-NG) hyperspectral observations, NOAA Center for Satellite Applications and Research (STAR) has initiated refinements to the NUCAPS algorithm to produce NOAA unique products from the IASI-NG and accompanying Microwave Sounder (MWS). This paper presents an overview of the NUCAPS Suomi-NPP/NOAA-20 products performance and on-going efforts in setting up a NUCAPS-NG product development system using proxy/synthetic data sets provided by the EUMETSAT and generated at STAR for the IASI-NG instrument.

The latest Arctic Report Card indicated that the Arctic ecosystems and communities are increasingly at risk due to continued warming and declining sea ice [1]. With the high spatial, spectral resolution, and the high temporal resolution over the Arctic regions, the data products derived from Joint Polar Satellite System (JPSS) provide very useful information to monitor the rapid changes of the Arctic Environment. The applications of using the JPSS imaging and sounding data products for Arctic monitoring, such as the status and the changes of the temperature, moisture, trace gases, snow and ice, as well as the outgoing longwave radiation budget will be introduced in this paper.

The Joint Polar Satellite System (JPSS) atmospheric composition products are derived from an array of instruments aboard the JPSS satellites, and provide continued support for atmospheric concentrations of both greenhouse gases and aerosols in the context of air quality, furthering scientific understanding, improving environmental monitoring, and enhancing operational applications. This paper presents an outline of the JPSS atmospheric composition products that are operationally available from the JPSS Suomi National Polar-orbiting Partnership (S-NPP) suite of instruments. Experimental products currently under consideration and exploration, continuity and product upgrades for NOAA-20, future plans on collaborations, special data needs by user communities, and upgrades to the data products in support of near real time applications are included in this paper. Continued efforts towards reprocessing to generate mission-long high quality science products, adapting to enterprise algorithms, and providing calibrated radiances and geophysical data products via direct broadcast networks are discussed for user awareness and international collaborations.

The Advanced Technology Microwave Sounder (ATMS) onboard the Suomi-National Polar-orbiting Partnership (S-NPP) is a cross-track scanning instrument with frequencies ranging from 23 to 183 GHz which allows for probing the atmospheric temperature and moisture. Reprocessed S-NPP ATMS data produced by NOAA/STAR provides consistent high quality data by optimizing algorithms and processing systems. In this study, measurements at 23.8 and 31.4 GHz from reprocessed S-NPP ATMS data are used to retrieve the liquid water path (LWP) in the atmosphere over ocean surfaces. In order to connect with AMSU-A retrieved LWPs, those two ATMS channels are remapped to AMSU-A beam width of 3.3° from their original 5.2° beam width. It's found that LWP retrieved from remapped ATMS is larger than that from original ATMS, and more consistent with LWPs retrieved from AMSU-A onboard NOAA and MetOp satellites. Moreover, diurnal cycle of LWP can be identified among those NOAA and MetOp satellites.

The NOAA-20 Ozone Mapping and Profiler Suite (OMPS) is one of the five instruments in the US Joint Polar Satellite System (JPSS) program. Like the first OMPS instrument [1],[2], the N20 OMPS contains two advanced nadir viewing hyper-spectral instruments, the Nadir Profiler and the Nadir Mapper, to track the health of the ozone layer via measurements the concentration of ozone in the Earth's atmosphere. This paper presents the NOAA-20 OMPS in-flight spectral calibration through solar observations and Earth views. Using a working solar diffuser and a reference solar diffuser, changes in the instruments' wavelength registration are determined through the OMPS solar calibrations. The observed sensor degradation at the shortest wavelengths is less than 0.5% for the sensor optics, but in excess of 1.0% for the OMPS working solar diffuser. The absolute irradiance calibration uncertainties meet system requirement of 7% for most of the channels. The in-flight sensor wavelength variation due to thermal-optical influence is less than 0.02 nm, which could cause about 1.6% error, not compliant with a 2% allocation, so a routine wavelength calibration will take place to accommodate the exceedance.

A physical retrieval system is developed for all sky single field-of-view (FOV) hyperspectral sounding. This system can be used to retrieve atmospheric temperature, moisture, and trace gas profiles, along with cloud height and cloud microphysical properties simultaneously from single FOV radiances measured by hyperspectral sounders. Single FOV retrieval results allow the users to extract horizontal gradient information with a spatial resolution defined by the size of one FOV. Moreover, the system finds solutions by directly fitting observed spectral radiances and therefore can be used to obtain radiative kernels that fulfill the `radiance closure' needed for climate applications. As a comparison, current operational retrieval algorithms find solutions by fitting the `cloud cleared' radiances generated from several adjacent FOVs. Not only the spatial resolution of those results is several times coarser than that defined by a single FOV, but the results cannot be used to build radiometric consistent radiative kernels under cloudy sky conditions. In this paper, some applications of the system are demonstrated to highlight the benefit of the full spatial resolution retrieval and the capability of building radiometric consistent radiative kernels under all sky conditions.

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 ozone mapping and profiler suite (OMPS) is carried onboard the Suomi National Polar-orbiting Partnership satellite that was launched on October 28, 2011. The OMPS mission objectives concern atmospheric ozone concentrations and their variations in the earth atmosphere. A successful thorough on-orbit sensor calibration enabled current validated operational sensor data record (SDR) stage, providing quality sensor data that meet the requirements and users' expectations. The calibration coefficients derived from this paper have been successfully used in a life-cycle SDR reprocessing to improve sensor data quality. In this paper, we present our qualitative analyses and the results of OMPS nadir mapper SDR on-orbit calibration, and address current SDR data quality in relation to instrument detector performance, stray light correction, and wavelength registration. Our OMPS SDR calibration experience sets a reference for the successor instruments for accurate long-term monitoring of ozone total column concentrations.

The Suomi National Polar-orbiting Partnership NPP satellite, which was launched in October 2011 as the predecessor to the recently launched Joint Polar Satellite System (JPSS-1, November 11, 2017) satellite has been extremely successful in operations for the last six years. In order to realize mission-long products of consistent high quality, the JPSS program at the Center for Satellite Applications and Research (STAR) has initiated reprocessing of S-NPP geophysical data products (termed as Environmental Data Records or EDRs) using the most matured enterprise algorithms. The Sensors Data Records (SDRs) needed as inputs to generate the reprocessed EDRs have already been reprocessed for the S-NPP suite of instruments and the EDR science teams are currently assessing improvements in using reprocessed SDRs and EDR enterprise algorithms operating at S-NPP Data exploitation (NDE) at the Office of Satellite Product Operations (OSPO). This paper discusses the implementation of enterprise EDR algorithms and their impact on product performance, and outlines the reprocessing efforts, plans, and preparations to generate missing-long S-NPP EDR products.

The NOAA-20 Ozone Mapping Profiler Suite (OMPS) is the second Ultraviolet (UV) sensor suite in the US Joint Polar Satellite System (JPSS) program. The sensor suite was launched aboard the NOAA-20 spacecraft on Nov. 18, 2017. Like the first OMPS instrument which is flying on the Suomi National Polar-orbiting Partnership spacecraft (S-NPP), the NOAA-20 OMPS contains two advanced nadir viewing hyper-spectral instruments, the Nadir Profiler (NP) and the Nadir Mapper (NM), tracks the health of the ozone layer and measure the concentration of ozone in the Earth's atmosphere. A successful thorough early orbital calibration enabled the current provisional Sensor Data Records (SDRs) stage. We present our analyses and results of the NOAA-20 OMPS early-orbit SDR performance, demonstrate that the sensor smoothly transitioned from ground to orbit by meeting or exceeding sensor level requirements. Our results suggest that a routine calibration of dark current as well as wavelength shifts is critical in the SDR data processing.

The atmospheric temperature and water vapour profile products, derived from the hyperspectral cross-track infrared sounder/advanced technology microwave sounder (CrIS/ATMS) on board the Suomi national polar orbiting partnership (S-NPP) satellite and the Infrared Atmospheric Sounding Interferometer (IASI) on board MetOP-B satellite, were assessed using radiosonde data as the target. This is achieved by leveraging the strengths of satellite synchronized dedicated, Global Reference Upper Air Network (GRUAN), and conventional radiosonde observations (RAOBs) in the analysis of satellite-radiosonde collocation data, compiled through the NOAA sounding Products Validation System (NPROVS). One of the results found in the study is water vapour profiles of both the NOAA Unique Combined Atmospheric Processing System (NUCAPS) and EUMETSAT IASI L2 products are comparable to or even better than numerical model prediction (NWP) outputs, when dedicated ship RAOBs were used as the benchmark.

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.

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.

Accurate representation of currently actual land surface types at regional to global scales is critical for land surface parameterization in numerical weather, climate, hydrological and ecological models. Depending on the time scale, monitoring land surface type changes is also increasingly important for natural disaster assessment and natural resources management. To provide near real time surface type information for downstream data product generation (e.g. land surface temperature) from the Suomi-National Polar-orbiting Partnership (S-NPP) Visible and Infrared Radiometer Suite (VIIRS), numerical weather prediction models, and natural disaster assessment and resources management, the Surface Type (ST) Environmental Data Record (EDR) has been generated since early 2012 from the VIIRS observations. The ST EDR contains a static surface type label as well as current day active fire and snow conditions for each 750m pixel of the whole globe. In addition to the surface type changes caused by active fire and snow, other surface type changes may occur and have implications for land surface parameterization. For example, flooding of a land area will completely change its surface albedo, temperature, and the water, energy and CO2exchange rates. In this study, a near real time surface type change monitoring framework is developed and tested to provide daily surface type information using the S-NPP and future Joint Polar Satellite System (JPSS) VIIRS surface reflectance observations. This near real time surface type information includes burned, flooded, deforested/urbanized, crop-harvested areas in addition to the surface type label described by a static surface type map generated from annual or multi-year satellite observations. In this paper, the current VIIRS surface type EDR and its characteristics are introduced as reference. The design of a surface type change monitoring framework and the surface type change detection methodology are described, followed by three case studies in which this framework is used to detect flooding and burned areas. The potential for making the framework operational for use in operations of numerical weather prediction, water resources and disaster management is also discussed.

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.

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.

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.

In this paper we describe the current maturity status of the Environmental Data Records (EDRs) from the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The maturity status is measured by the performance of the algorithm against the documented requirements as well as the level of validation effort accomplished. Overall, the products have reached performance levels allowing operational evaluation or implementation on operational applications. Further improvements are underway towards meeting the needs of the user community.

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.

This paper reports on the recent status of the validation program for the Suomi National Polar-orbiting Partnership (NPP) Cross-track Infrared Microwave Sounding Suite (CrIMSS), a hyperspectral infrared sounding system designed for providing high resolution atmospheric vertical temperature and moisture profile (AVTP and AVMP) environmental data records (EDRs). CrIMSS EDR validation activities, currently (as of this writing, May 2012) segueing from the Early-Orbit Checkout (EOC) phase to the Intensive Cal/Val (ICV) phase of the program, are briefly highlighted.

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 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.

The assimilation of Atmospheric InfraRed Sounder, Advanced Microwave Sounding Unit-A, and Humidity Sounder for Brazil (AIRS/AMSU/HSB) data by Numerical Weather Prediction (NWP) centers is expected to result in improved forecasts. Specially tailored radiance and retrieval products derived from AIRS/AMSU/HSB data are being prepared for NWP centers. There are two types of products - thinned radiance data and full-resolution retrieval products of atmospheric and surface parameters. The radiances are thinned because of limitations in communication bandwidth and computational resources at NWP centers. There are two types of thinning: (1) spatial and spectral thinning and (2) data compression using principal component analysis (PCA). PCA is also used for quality control and for deriving the retrieval first guess used in the AIRS processing software. Results show that PCA is effective in estimating and filtering instrument noise. The PCA regression retrievals show layer mean temperature (1 km in troposphere, 3 km in stratosphere) accuracies of better than 1 K in most atmospheric regions from simulated AIRS data. Moisture errors are generally less than 15% in 2-km layers, and ozone errors are near 10% over approximately 5-km layers from simulation. The PCA and regression methodologies are described. The radiance products also include clear field-of-view (FOV) indicators. The residual cloud amount, based on simulated data, for FOVs estimated to be clear (free of clouds) is about 0.5% over ocean and 2.5% over land.