Satellite Applications And Research Research Articles

Satellite Oceanography in NOAA: Research, Development, Applications, and Services Enabling Societal Benefits from Operational and Experimental Missions

The National Oceanic and Atmospheric Administration’s (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, applications, tools, and services, and curating, translating, and integrating diverse data products into information that supports informed decision making. STAR research, development, and application efforts span from passive visible, infrared, and microwave observations to active altimetry, scatterometry, and synthetic aperture radar (SAR) observations. These efforts directly support NOAA’s operational geostationary (GEO) and low Earth orbit (LEO) missions with calibration/validation and retrieval algorithm development, implementation, maintenance, and anomaly resolution, as well as leverage the broader international constellation of environmental satellites for NOAA’s benefit. STAR’s satellite data products and services enable research, assessments, applications, and, ultimately, decision making for understanding, predicting, managing, and protecting ocean and coastal resources, as well as assessing impacts of change on the environment, ecosystems, and climate. STAR leads the NOAA Coral Reef Watch and CoastWatch/OceanWatch/PolarWatch Programs, helping people access and utilize global and regional satellite data for ocean, coastal, and ecosystem applications.

Mid‐Tropospheric Layer Temperature Record Derived From Satellite Microwave Sounder Observations With Backward Merging Approach

AbstractWe present a new version (v5.0) of the NOAA Center for Satellite Applications and Research (STAR) mid‐tropospheric temperature (TMT) time series. This data set uses a backward‐merging approach to intercalibrate 16 satellite‐based microwave sounding records. The instrument observations included those from the Microwave Sounding Unit (MSU) during 1979–2004, Advanced Microwave Sounding Unit‐A (AMSU‐A) during 1998–2017, and Advanced Technology Microwave Sounder (ATMS) from 2011 to present. A TMT time series during 2002–present based on satellite microwave observations in stable sun‐synchronous orbits was used as a reference in the backward merging process in which earlier satellites were adjusted and merged to the reference. Observations from earlier satellites were recalibrated to remove their calibration drifting errors relative to the reference using sequential overlapping observations. This included removal of spurious warming drifts in the MSU observations onboard NOAA‐11, NOAA‐12, and NOAA‐14 and a spurious cooling drift in the NOAA‐15 AMSU‐A observations. Temperature changes resulting from diurnal sampling drifts were corrected using an observation‐based semi‐physical model developed in this study. Other adjustments included channel frequency differences between MSU and AMSU‐A companion channels and instrument blackbody warm target effect on observed radiances. These adjustments resulted in inter‐consistent TMT records spanning MSU, AMSU‐A, and ATMS. The merged time series produced a global mean TMT trend of 0.092 ± 0.043 K/decade during 1979–2021 and a total tropospheric trend of 0.142 ± 0.045 K/decade after removal of a stratospheric cooling effect in TMT. Remarkably, the total tropospheric trends during the latest half period were nearly doubled the earlier half period over the global ocean.

Processing and Validation of the STAR COSMIC-2 Temperature and Water Vapor Profiles in the Neutral Atmosphere

The global navigation satellite system (GNSS) radio occultation (RO) is becoming an essential component of National Oceanic and Atmospheric Administration (NOAA) observation systems. The constellation observing system for meteorology, ionosphere, and climate (COSMIC) 2 mission and the Formosa satellite mission 7, a COSMIC follow-on mission, is now the NOAA’s backbone RO mission. The NOAA’s dedicated GNSS RO SAtellite processing and science Application Center (RO-SAAC) was established at the Center for Satellite Applications and Research (STAR). To better quantify how the observation uncertainty from clock error and geometry determination may propagate to bending angle and refractivity profiles, STAR has developed the GNSS RO data processing and validation system. This study describes the COSMIC-2 neutral atmospheric temperature and moisture profile inversion algorithms at STAR. We used RS41 and ERA5, and UCAR 1D-Var products (wetPrf2) to validate the accuracy and uncertainty of the STAR 1D-Var thermal profiles. The STAR-RS41 temperature differences are less than a few tenths of 1 K from 8 km to 30 km altitude with a standard deviation (std) of 1.5–2 K. The mean STAR-RS41 water vapor specific humidity difference and the standard deviation are −0.35 g/kg and 1.2 g/kg, respectively. We also used the 1D-Var-derived temperature and water vapor profiles to compute the simulated brightness temperature (BTs) for advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) channels and compared them to the collocated ATMS and CrIS measurements. The BT differences of STAR COSMIC-2-simulated BTs relative to SNPP ATMS are less than 0.1 K over all ATMS channels.

Top-of-the-atmosphere reflected shortwave radiative fluxes from GOES-R

Abstract. Under the GOES-R activity, new algorithms are being developed at the National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) to derive surface and top-of-the-atmosphere (TOA) shortwave (SW) radiative fluxes from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R. This paper describes a support effort in the development and evaluation of the ABI instrument capabilities to derive such fluxes. Specifically, scene-dependent narrow-to-broadband (NTB) transformations are developed to facilitate the use of observations from ABI at the TOA. Simulations of NTB transformations have been performed with MODTRAN 4.3 using an updated selection of atmospheric profiles and implemented with the final ABI specifications. These are combined with angular distribution models (ADMs), which are a synergy of ADMs from the Clouds and the Earth's Radiant Energy System (CERES) and from simulations. Surface conditions at the scale of the ABI products as needed to compute the TOA radiative fluxes come from the International Geosphere–Biosphere Programme (IGBP). Land classifications at 1/6∘ resolution for 18 surface types are converted to the ABI 2 km grid over the contiguous United States (CONUS) and subsequently re-grouped to 12 IGBP types to match the classification of the CERES ADMs. In the simulations, default information on aerosols and clouds is based on that used in MODTRAN. Comparison of derived fluxes at the TOA is made with those from CERES, and the level of agreement for both clear and cloudy conditions is documented. Possible reasons for differences are discussed. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).

Verification and Validation of the COSMIC-2 Excess Phase and Bending Angle Algorithms for Data Quality Assurance at STAR

In recent years, Global Navigation Satellite System (GNSS) radio occultation (RO) has become a critical observation system for global operational numerical weather prediction. Constellation Observing System for Meteorology, Ionosphere, Climate (COSMIC) 2 (COSMIC-2) has been a backbone RO mission for NOAA. NOAA also began to purchase RO data from commercial sources in 2020. To ensure the consistent quality of RO data from different sources, NOAA Center for Satellite Applications and Research (STAR) has developed capabilities to process all available RO data from different missions. This paper describes the STAR RO processing systems which convert the pseudo-range and carrier phase observations to excess phases and bending angles (BAs). We compared our COSMIC-2 data products with those processed by the University Corporation for Atmospheric Research (UCAR) COSMIC Data Analysis and Archive Center (CDAAC). We processed more than twelve thousand COSMIC-2 occultation profiles. Our results show that the excess phase difference between UCAR and STAR is within a few centimeters at high altitudes, although the difference increases towards the lower atmosphere. The BA profiles derived from the excess phase are consistent with UCAR. The mean relative BA differences at impact height from 10 to 30 km are less than 0.1% for GLObal NAvigation Satellite System (GLONASS) L2C signals and Global Positioning System (GPS) L2C and L2P signals. The standard deviations are 1.15%, 1.15%, and 1.32% for GLONASS L2C signal and for GPS L2C and L2P signals, respectively. The BA profiles agree with those derived from European Center for Medium-range Weather Forecast (ECMWF) reanalysis version 5 (ERA5). The Signal-to-Noise-Ratio (SNR) plays an essential role in the processing. The STAR BA profiles with higher L1 SNRs (L1 at 80 km) tend to yield more consistent results than those from UCAR, with a negligible difference and a smaller deviation than lower SNR profiles. Profiles with lower SNR values tend to show a more significant standard deviation towards the surface during the open-loop stage in the lower troposphere than those of higher SNR. We also found that the different COSMIC-2 clock solutions could contribute to the significant relative BA difference at high altitudes; however, it has little effect on the lower troposphere comparisons given larger BA values.

Orbit Simulator for Satellite and Near-Space Platforms Supporting Observing System Simulation Experiments

Abstract Developed at the National Oceanic and Atmospheric Administration (NOAA) and the Joint Center for Satellite Data Assimilation (JCSDA), the Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) provides a vehicle to quantitatively evaluate the impacts of emerging environmental observing systems or emerging in situ or remote sensing instruments on NOAA numerical weather prediction (NWP) forecast skill. The typical first step for the OSSE is to simulate observations from the so-called nature run. Therefore, the observation spatial, temporal, and view geometry are needed to extract the atmospheric and surface variables from the nature run, which are then input to the observation forward operator (e.g., radiative transfer models) to simulate the new observations. This is a challenge for newly proposed systems for which instruments are not yet built or platforms are not yet deployed. To address this need, this study introduces an orbit simulator to compute these parameters based on the specific hosting platform and onboard instrument characteristics, which has been recently developed by the NOAA Center for Satellite Applications and Research (STAR) and added to the GCOP framework. In addition to simulating existing polar-orbiting and geostationary orbits, it is also applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also global navigation satellite system/radio occultation (GNSS/RO) instruments. For passive atmospheric sounders, it calculates the geometric parameters of proposed instruments on different platforms, such as time varying location (latitude and longitude), scan geometry (satellite zenith and azimuth angles), and ground instantaneous field of view (GIFOV) parameters for either cross-track or conical scanning mechanisms. For RO observations, it determines the geometry of the transmitters and receivers either on satellites or stratospheric balloons and computes their slant paths. The simulator has been successfully applied for recent OSSE studies (e.g., evaluating the impacts of future geostationary hyperspectral infrared sounders and RO observations from stratospheric balloons). Significance Statement An orbit simulator for satellite and near-space platforms for supporting observing system simulation experiments (OSSE) is developed in this study. It can compute spatial, temporal, and view geometry parameters of the new observations based on the specific hosting platform and onboard instrument characteristics. These parameters are important for the OSSE to extract atmospheric profiles and surface properties from nature run data and simulate new observations. In addition to simulating existing polar-orbiting and geostationary orbits, it is applicable to emerging near-space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also radio occultation instruments.

Assessment of the Reprocessed Suomi NPP VIIRS Enterprise Cloud Mask Product

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite continually provides global observations used to retrieve over 20 VIIRS Environmental Data Record (EDR) products. Among them, the cloud mask product is essential for many other VIIRS EDR products such as aerosols, ocean color, and active fire. The reprocessed S-NPP VIIRS Sensor Data Record (SDR) data produced by NOAA/Center for Satellite Applications and Research (STAR) have shown improved stability and consistency. Recently, the VIIRS Enterprise Cloud Mask (ECM) has been reprocessed using the reprocessed VIIRS SDR data. This study assesses the reprocessed ECM product by comparing the reprocessed cloud mask types and cloud probability with those from the operational VIIRS ECM product. It found that the overall differences are small. Most of the discrepancies occur between neighboring types at the cloud edge. These findings help lay the foundation for the user community to understand the reprocessed ECM product. In addition, due to the better quality of the reprocessed VIIRS SDR data that are utilized to generate the reprocessed ECM product, it is expected that the reprocessed ECM product will have better stability and consistency compared to the operational ECM products. Therefore, the reprocessed ECM product is a useful benchmark for the user community.

Inverting COSMIC-2 Phase Data to Bending Angle and Refractivity Profiles Using the Full Spectrum Inversion Method

The radio occultation technique provides stable atmospheric measurements that can work as a benchmark for calibrating and validating satellite-sounding data. Launched on 25 June 2019, the Constellation Observing System for Meteorology, Ionosphere, and Climate 2 and Formosa Satellite Mission 7 (COSMIC-2/FORMOSAT-7) are expected to produce about 5000 high-quality RO observations daily over the tropics and subtropics. COSMIC-2 constellation consists of 6 Low Earth Orbit (LEO) satellites in 24° inclination orbits at 720 km altitude and distributed mainly between 45°N to 45°S. The COSMIC-2 observations have uniform temporal coverage between 30°N to 30°S. This paper presents an independent inversion algorithm to invert COSMIC-2 geometry and phase data to bending angle and refractivity. We also investigate the quality of Global Navigation Satellite System (GNSS) and LEO position vectors derived from the UCAR COSMIC Data Analysis and Archive Center (CDAAC). The GNSS and LEO position vectors are stable with LEO position variations < 1.4 mm/s. The signal-to-noise ratio (SNR) on the L1 band ranges from 300–2600 v/v with a mean of 1600 v/v. The inversion algorithm developed at NOAA Center for Satellite Applications and Research (STAR) uses the Full Spectrum Inversion (FSI) method to invert COSMIC-2 geometry and phase data to bending angle and refractivity profiles. The STAR COSMIC-2 bending angle and refractivity profiles are compared with in situ radiosonde, the current COSMIC-2 products derived from CDAAC, and the collocated European Center for Medium-Range Weather Forecasts (ECMWF) climate reanalysis data ERA5. The mean bias at 8–40 km altitude among the UCAR, ERA5, and STAR is <0.1% for both bending and refractivity, with a standard deviation in the range of 1.4–2.3 and 0.9–1.1% for bending angles refractivity, respectively. In the lowest 2 km, the RO bias relative to ERA-5 shows a strong latitudinal and SNR dependence.

Initial Assessment of the COSMIC-2/FORMOSAT-7 Neutral Atmosphere Data Quality in NESDIS/STAR Using In Situ and Satellite Data

A COSMIC-1/FORMOSAT-3 (Constellation Observing System for Meteorology, Ionosphere, and Climate-1 and Formosa Satellite Mission 3) follow-on mission, COSMIC-2/FORMOSAT-7, had been successfully launched into low-inclination orbits on 25 June 2019. COSMIC-2 has a significantly increased Signal-to-Noise ratio (SNR) compared to other Radio Occultation (RO) missions. This study summarized the initial assessment of COSMIC-2 data quality conducted by the NOAA (National Oceanic and Atmospheric Administration) Center for Satellite Applications and Research (STAR). We use validated data from other RO missions to quantify the stability of COSMIC-2. In addition, we use the Vaisala RS41 radiosonde observations to assess the accuracy and uncertainty of the COSMIC-2 neutral atmospheric profiles. RS41 is currently the most accurate radiosonde observation system. The COSMIC-2 SNR ranges from 200 v/v to about 2800 v/v. To see if the high SNR COSMIC-2 signals lead to better retrieval results, we separate the COSMIC-2–RS41 comparisons into different SNR groups (i.e., 0–500 v/v group, 500–1000 v/v group, 1000–1500 v/v group, 1500–2000 v/v group, and >2000 v/v group). In general, the COSMIC-2 data quality in terms of stability, precision, accuracy, and uncertainty of the accuracy is very compatible with those from COSMIC-1. Results show that the mean COSMIC-2–RS41 water vapor difference from surface to 5 km altitude for each SNR groups are equal to −1.34 g/kg (0–500 v/v), −1.17 g/kg (500–1000 v/v), −1.33 g/kg (1000–1500 v/v), −0.93 g/kg (1500–2000 v/v), and −1.52 g/kg (>2000 v/v). Except for the >2000 v/v group, the high SNR measurements from COSMIC-2 seem to improve the mean water vapor difference for the higher SNR group slightly (especially for the 1500–2000 v/v group) comparing with those from lower SNR groups.

The Reprocessed Suomi NPP Satellite Observations

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.

The Long-Term Trend of Upper-Air Temperature in China Derived from Microwave Sounding Data and Its Comparison with Radiosonde Observations

AbstractCurrently, the satellite Microwave Sounding Unit (MSU/AMSU) datasets developed from three organizations—Remote Sensing Systems (RSS), the University of Alabama at Huntsville (UAH), and the NOAA Center for Satellite Applications and Research (STAR)—are often used to monitor the global long-term trends of temperatures in the lower troposphere (TLT), midtroposphere (TMT), total troposphere (TTT), troposphere and stratosphere (TTS), and lower stratosphere (TLS). However, the trend in these temperatures over China has not been quantitatively assessed. In this study, the decadal variability and long-term trend of upper-air temperature during 1979–2018 from three MSU datasets are first evaluated over China and compared with the proxy MSU dataset simulated from homogenized surface and radiosonde profiles (EQU) at 113 stations in China. The regional mean MSU trends over China during 1979–2018 are 0.22–0.27 (TLT), 0.15–0.22 (TMT), 0.20–0.27 (TTT), 0.02–0.14 (TTS), and from −0.33 to −0.36 (TLS) K decade−1, whereas the EQU trends are 0.31 (TLT), 0.19 (TMT), 0.24 (TTT), 0.07 (TTS), and −0.26 (TLS) K decade−1. The trends from RSS generally show a better agreement with those from EQU. The trends from both MSU and EQU exhibit seasonal and regional difference with a larger warming in TLT in February and March, and stronger cooling in TLS from late winter to spring. The TLT and TMT over the Tibetan Plateau and northwestern China show larger warming trends. The variability from MSU and EQU agree well except TLT in Tibet and southern China. The major difference in regional mean temperatures over China between MSU and EQU is related primarily to the satellite instrument changes during 1979–98 and the radiosonde system changes in China in the 2000s.

Comparison of the Sentinel-1B Synthetic Aperture Radar With Airborne Microwave Sensors in an Extra-Tropical Cyclone

In Winter 2017, the University of Massachusetts Amherst's Imaging Wind and Rain Airborne Profiler (IWRAP) was flown on a National Oceanic and Atmospheric Administration (NOAA) WP-3D Hurricane Hunter aircraft under the direction of scientists from Center for Satellite Applications and Research (STAR) at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) over the North Atlantic ocean out of Shannon, Ireland. IWRAP is a dual-frequency, conically scanning, profiling Doppler radar initially developed by Microwave Remote Sensing Laboratory (MIRSL) at the University of Massachusetts Amherst that is routinely installed on the NOAA WP-3D research aircraft. The flight on February 6, 2017, targeted a region of high winds (greater than 30 m/s) that was also observed by the Sentinel-1B satellite's synthetic aperture radar. Sentinel-1B was configured to observe in extended wide swath mode in both VV- and VH-polarizations, whereas the IWRAP C-band radar was configured to measure all of VV-, VH-, and HH-polarizations. IWRAP and Sentinel-1B VV and VH normalized radar cross section (NRCS) at the same Earthincidence angle along the flight path match reasonably well during the entire flight, but some additional trends between aircraft and satellite can be observed. IWRAP VV-polarized NRCS generally match the CMOD5.h geophysical model function (GMF), suggesting errors in the Sentinel-1B processing chain.

Calibration Improvements in S-NPP VIIRS DNB Sensor Data Record Using Version 2 Reprocessing

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi-NPP is equipped with a Day/Night Band (DNB), a major advancement in nighttime imaging capability. DNB data quality has been largely improved through several calibration updates since early launch. The reprocessed VIIRS DNB sensor data record (SDR) data at the NOAA Center for Satellite Applications and Research (STAR) accommodate all updates, such as DNB modulated relative spectral responses (RSRs), monthly DNB offset and gain ratio, straylight correction, and geolocation terrain correction since launch. In addition, one of the major improvements is in the DNB offset computing using the pitch maneuver-based deep space view that is free of airglow. The reprocessed DNB data results in radiometrically consistent calibrated SDR that allows the user community to use high-quality DNB data in long-term applications. Until the end of 2016, DNB high gain stage (HGS) operational calibration involved estimation of dark offsets using the new moon-based nighttime measurements over the Pacific Ocean. However, the faint emission from airglow during the new moon nights led to an overestimation of the dark offset by an amount equivalent to that of the airglow. This directly impacts the calibrated data by underestimating the calibrated radiance values. The impact is prominent at low light radiance, which can also lead to negative radiances. This paper quantifies the reprocessing led improvements in the calibrated VIIRS DNB data, analyzes the impact of airglow on operational calibrated radiance product, and explains how the absolute accuracy for low light radiance has been largely improved for the entire reprocessed DNB archive.

The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 1: Model descriptions

Abstract. The NEMS GFS Aerosol Component Version 2.0 (NGACv2) for global multispecies aerosol forecast has been developed at the National Centers of Environment Prediction (NCEP) in collaboration with the NESDIS Center for Satellite Applications and Research (STAR), the NASA Goddard Space Flight Center (GSFC), and the University at Albany, State University of New York (SUNYA). This paper describes the continuous development of the NGAC system at NCEP after the initial global dust-only forecast implementation (NGAC version 1.0, NGACv1). With NGACv2, additional sea salt, sulfate, organic carbon, and black carbon aerosol species were included. The smoke emissions are from the NESDIS STAR's Global Biomass Burning Product (GBBEPx), blended from the global biomass burning emission product from a constellation of geostationary satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a polar-orbiting sensor (QFED2). This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been used by many applications such as for regional air quality model lateral boundary conditions, satellite sea surface temperature (SST) physical retrievals, and the global solar insolation estimation. Positive impacts have been seen in these applications.

Assessment of the NOAA S-NPP VIIRS Geolocation Reprocessing Improvements

Long-term time series analysis requires consistent data records from satellites. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar orbiting Partner (S-NPP) satellite launched in 2011 requires a major effort to produce consistently calibrated sensor data records (SDR). Accurate VIIRS geolocation products are critical to other VIIRS products and products from other instruments on the S-NPP satellite. This paper presents methods for assessing major improvements to the VIIRS geolocation products in the ongoing National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) reprocessing that incorporates all corrections in calibration parameters and SDR algorithms since launch to present. In this study, we analyzed the history of VIIRS geometric calibration parameter updates to identify optimal parameters to account for geolocation errors in the early days of the mission. A sample area located in North Western Africa was selected for validation purposes after analyzing global VIIRS and Landsat control point matching results. Geolocation products over the study region were reprocessed and I-bands/M-bands geolocation improvements were characterized by comparing geolocation errors before and after the reprocessing. Our results indicate that all short-term geolocation anomalies before the latest operational geometric calibration parameter update on 22 August 2013 were effectively minimized after reprocessing, with geolocation errors reduced from −47.1 ± 83.8 m to −23.3 ± 51.1 m (along scan) and from −15.6 ± 43.6 m to −5.9 ± 37.7 m (along track). Terrain correction for the VIIRS Day-Night-Band (DNB) was not implemented in the NOAA operational processing until 22 May 2015. In the reprocessing, it will be implemented to the entire DNB geolocation data record. DNB reprocessing improvement due to this implementation was evaluated using nighttime observations over point sources at sea level and over high altitude. Our results show that the implementation of terrain correction will reduce DNB geolocation errors at off-nadir high elevation locations from up to 9 km to ~0.5 pixel (0.375 km), comparable to those at sea level site. The reprocessed geolocation dataset will be distributed online for end-users to access.

UTILIZATION OF NEAR REAL-TIME NOAA-AVHRR SATELLITE OUTPUT FOR EL NIÑO INDUCED DROUGHT ANALYSIS IN INDONESIA (CASE STUDY: EL NIÑO 2015 INDUCED DROUGHT IN SOUTH SULAWESI)

Drought is becoming one of the most important issues for government and policy makers. National food security highly concerned, especially when drought occurred in food production center areas. Climate variability, especially in South Sulawesi as one of the primary national rice production centers is influenced by global climate phenomena such as El Niño Southern Oscillation or ENSO. This phenomenon can lead to drought occurrences. Monitoring of drought potential occurrences in near real-time manner becomes a primary key element to anticipate the drought impact. This study was conducted to determine potential occurrences and the evolution of drought that occurred as a result of the 2015 El Niño event using the Vegetation Health Index (VHI) from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite products. Composites analysis was performed using weekly Smoothed and Normalized Difference Vegetation Index (or smoothed NDVI) (SMN), Smoothed Brightness Temperature Index (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). This data were obtained from The Center for Satellite Applications and Research (STAR) - Global Vegetation Health Products (NOAA) website during 35-year period (1981-2015). Lowest potential drought occurrences (highest VHI and VCI value) caused by 2015 El Niño is showed by composite analysis result. Strong El Niño induced drought over the study area indicated by decreasing VHI value started at week 21st. Spatial characteristic differences in drought occurrences observed, especially on the west coast and east coast of South Sulawesi during strong El Niño. Weekly evolution of potential drought due to the El Niño impact in 2015 indicated by lower VHI values (VHI < 40) concentrated on the east coast of South Sulawesi, and then spread to another region along with the El Nino stage.

Impact Assessment of Himawari-8 AHI Data Assimilation in NCEP GDAS/GFS with GSI

AbstractAs the first of the next-generation geostationary meteorological satellites, Himawari-8 was successfully launched in October 2014 by the Japan Meteorological Agency (JMA) and placed over the western Pacific Ocean domain at 140.7°E. It carries the Advanced Himawari Imager (AHI), which provides full-disk images of Earth at 16 bands in the visible and infrared domains every 10 min. Efforts are currently ongoing at the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite, Data, and Information Service (NESDIS)/Center for Satellite Applications and Research (STAR) to assimilate Himawari-8 AHI radiance measurements into the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation analysis system (GSI). All software development within the GSI to allow for assimilation of Himawari-8 AHI radiance has been completed.This study reports on the assessment of AHI preassimilation data quality by comparing observed clear-sky ocean-only radiances to those simulated using collocated ECMWF analysis, as well as describing procedures implemented for quality control. The impact of the AHI data assimilation on the resulting analyses and forecasts is then assessed using the NCEP Global Forecast System (GFS). A preliminary assessment of the assimilation of AHI data from infrared water vapor channels and atmospheric motion vectors (AMVs) on top of the current global observing system shows neutral to marginal positive impact on analysis and forecast skill relative to an assimilation without AHI data. The main positive impact occurs for short- to medium-range forecasts of global upper-tropospheric water vapor. The results demonstrate the feasibility of direct assimilation of AHI radiances and highlight how humidity information can be extracted within the assimilation system.

Progress and Developments of Downburst Prediction Applications of GOES

Abstract The National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Research (STAR) has developed and evaluated a suite of products that assess convective storm–generated downburst potential derived from Geostationary Operational Environmental Satellite-13–15 (GOES-13–15). The existing suite of downburst prediction algorithms employs the GOES sounder to calculate risk based on conceptual models of favorable environmental thermodynamic profiles for downburst occurrence. A diagnostic nowcasting product, the Microburst Windspeed Potential Index (MWPI), is designed to identify attributes of a favorable downburst environment: 1) the presence of large CAPE and 2) the presence of a surface-based or elevated mixed layer with a large temperature lapse rate. This paper provides an updated assessment of the MWPI algorithm, presents case studies demonstrating effective operational use of the MWPI product, and presents validation results for the Great Plains and mid-Atlantic coastal region of the United States. MWPI data were collected for downburst events that occurred during the convective seasons of 2007–13 and were validated against surface observations of convective wind gusts as recorded by wind sensors in high quality mesonetworks over the southern Great Plains and the Chesapeake Bay region. Favorable validation results include a correlation greater than 0.6 and low mean error [<0.1 knot (kt; where 1 kt = 0.51 m s−1)] between MWPI values and measured confirmed downburst wind speeds over contrasting climate regions of the continental United States. Case studies over the mid-Atlantic region and northern Florida highlight the adaptability of the MWPI algorithm to severe convective storm forecasting and warning operations.

Ocean color products from the Korean Geostationary Ocean Color Imager (GOCI)

The first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI), which is onboard South Korean Communication, Ocean, and Meteorological Satellite (COMS), was successfully launched in June of 2010. GOCI has a local area coverage of the western Pacific region centered at around 36°N and 130°E and covers ~2500 × 2500 km(2). GOCI has eight spectral bands from 412 to 865 nm with an hourly measurement during daytime from 9:00 to 16:00 local time, i.e., eight images per day. In a collaboration between NOAA Center for Satellite Applications and Research (STAR) and Korea Institute of Ocean Science and Technology (KIOST), we have been working on deriving and improving GOCI ocean color products, e.g., normalized water-leaving radiance spectra (nLw(λ)), chlorophyll-a concentration, diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), etc. The GOCI-covered ocean region includes one of the world's most turbid and optically complex waters. To improve the GOCI-derived nLw(λ) spectra, a new atmospheric correction algorithm was developed and implemented in the GOCI ocean color data processing. The new algorithm was developed specifically for GOCI-like ocean color data processing for this highly turbid western Pacific region. In this paper, we show GOCI ocean color results from our collaboration effort. From in situ validation analyses, ocean color products derived from the new GOCI ocean color data processing have been significantly improved. Generally, the new GOCI ocean color products have a comparable data quality as those from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. We show that GOCI-derived ocean color data can provide an effective tool to monitor ocean phenomenon in the region such as tide-induced re-suspension of sediments, diurnal variation of ocean optical and biogeochemical properties, and horizontal advection of river discharge. In particular, we show some examples of ocean diurnal variations in the region, which can be provided effectively from satellite geostationary measurements.

The NOAA Products Validation System (NPROVS)

Abstract The following report summarizes the NOAA Products Validation System (NPROVS), operated at the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Research (STAR). NPROVS provides centralized collocation and intercomparison of multiple suites of satellite-derived atmospheric sounding products, global operational radiosonde and dropsonde observations, and numerical weather prediction (NWP) data. The report addresses the screening and processing of radiosonde and dropsonde data, the method of collocating to the respective satellite product data, and perceived characteristic differences among the respective satellite products. The analysis of characteristic tendencies among the satellite products underscores the need for absolute consistency when compiling validation datasets of respective satellite, ground target, and NWP observations in order to minimize the varying degrees of inherent differences among these systems. The access and collocation of sonde and satellite observations occur on a daily basis with the routine archiving of all collocated data at STAR. The minimum requirement for retaining a given sonde is that the collective temperature and moisture profiles extend vertically at least 5 km without a gap. Retained reports are further processed including analysis to identify suspicious observations, temperature and moisture profile features, and impacts when applying sampling constraints. The collocation approach is optimized for each satellite system to select a single “closest” sounding from each satellite that lies within 6 h and 250 km of a given sonde. The NPROVS analytical and graphical interface provides a dual troubleshooting function to assure the integrity of collocations and validation function for intercomparing the satellite products. Results indicate notable differences in the perceived characteristic of the products systems with seasonal tendencies.