TPSAS-NF1676L-13349-DND
Progress on the effort to calibrate historical AVHRR solar channels and derive a CERES-consistent cloud climatology form AVHRR data is presented.
Patrick Minnis↗
Engineering topics
Qing Trepte
Publications and source records attributed to Qing Trepte.
A 3-Channel Algorithm for Retrieving Consistent Cloud Properties Across Different Geostationary Satellites
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David Painemal↗
Development of a Consistent GEOsat Cloud Property Dataset for the CERES Climate Data Record
Cloud properties are critical for understanding the Earth’s radiation budget and cloud feedbacks. At NASA Langley Research Center, the Satellite ClOud and Radiative Property retrieval System (SatCORPS) provides real-time and historical analyses of clouds derived from Geostationary satellite (GEOsat) data for weather and climate applications. For the Clouds and the Earth’s Radiant Energy System (CERES) program, the global constellation of GEOsats has been analyzed since 2000 to help characterize and account for the diurnal cycle of clouds and their radiative impacts in the CERES climate data record. Obtaining consistent cloud properties over the GEOsat data record during the CERES era is a major objective but a significant challenge considering the diversity of imaging capabilities deployed during that time. The GEOsat data analysis approach for the current CERES Edition-4 (Ed4) data products was focused on accuracy and consistency with MODIS by employing as much spectral information as possible from each satellite. However, the inconsistent use of spectral information across GEOsats led to marked discontinuities in the spatial and temporal record of cloud properties that had to be accounted for post facto in downstream CERES processing. This paper reports progress in developing a new GEOsat analysis system for the next CERES edition (Ed5) that has potential to improve cross-platform consistency and continuity. In this approach, the spectral channel complement is limited to just 3-channels during daytime, ~0.65 µm (VIS), ~3.9 µm (NIR), and ~10.8 µm (IR), common to nearly all of the satellites in the record. At night, a 2-channel approach is taken with the NIR and IR, and ~6.7 µm bands that includes a machine learning approach for optically thick cloud properties. A tradeoff is the potential for reduced accuracy particularly using data from the more advanced satellites that have more spectral channels (e.g. SEVIRI, AHI and ABI) that are known to help improve thin cirrus detection, cloud-aerosol discrimination and estimates in other difficult conditions that challenge cloud remote sensing. The new continuity approach is applied to one month of global GEOSat data for each year of the CERES record since 2000 and compared with the Ed4 GEO and MODIS cloud property time series in order to evaluate the level of improved consistency in the GEOsat record and to assess the accuracy impacts. Cloud fraction will also be assessed with CALIPSO data. Outstanding issues and challenges will be discussed. The results are expected to guide future work needed to develop a more robust GEOsat cloud data record for CERES.
CERES CDR↗
MODIS and VIIRS Neural Network Cloud Detection Over Antarctic During Nighttime for CERES Edition 5
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Sunny Sun-Mack↗
Seasonal Surface Spectral Emissivity Derived From MODIS Data
Surface emissivity is essential for many remote-sensing applications including the retrieval of surface skin temperature from satellite-based infrared measurements, the determination of cloud detection thresholds, and the estimation of the surface longwave radiation emission, an important component of the energy budget of the surface-atmosphere interface. The CERES (Clouds and the Earth’s Radiant Energy System) Project is measuring broadband shortwave and longwave radiances and deriving cloud properties from the MODIS on Terra and Aqua and from the VIIRS on NOAA-19 and NOAA-20 orbiters to produce combined global radiation and cloud property data sets. Zhou et al. (IEEE Trans. Geosci. Remote Sens., 49, 2011) used Infrared Atmospheric Sounding Interferometer (IASI) data to create a high spectral resolution surface emissivity atlas for remote sensing and modeling applications. The IASI measures spectral radiances between 3.62 and 15.5 μm. The VIIRS I4 channel width is from 3.55 to 3.93 μm, while MODIS Band 20 is from 3.66 to 3.84 m. Comparisons of top-of-atmosphere (TOA) radiance calculations with MODIS and VIIRS observations for these bands relative to bands in the mid-infrared suggest that the IASI emissivity atlas near 3.7 µm may not be suitable for CERES cloud retrievals. In this paper, the IASI emissivities for the VIIRS and MODIS bands centered near 11m are used to derive surface skin temperature from nighttime MODIS/VIIRS data. The Goddard Earth Observing System for Instrument Teams (GEOS-IT) numerical weather analyses provide temperature and water vapor profiles fused to correct the observed radiances for atmospheric absorption and emission. Global seasonal emissivity maps are then derived for the VIIRS and MODIS 3.7-m bands that are consistent with the derived skin temperatures and the observed TOA radiances. These seasonal climatology maps are validated and will be used in CERES Edition 5 and other CERES-related cloud retrieval algorithms to provide improved clear-sky radiances and derived cloud properties.
Surface Emissivity↗
A 3-Channel Algorithm for Retrieving Spatially and Temporally Continuous Cloud Properties Across Different Geostationary Satellite Imagers
Cloud property retrieval algorithms for passive satellite imagers are generally designed to take advantage of all the useful spectral information available for a particular satellite. This strategy optimizes accuracy and reduces misidentification and retrieval biases, particularly for modern satellites with many spectral channels. However, the application of dissimilar algorithms tailored for different satellite sensor scan present a problem within the climate data record (CDR). Algorithm inconsistencies can introduce artificial trends in the CDR that are tied to instrument changes rather than physical changes, especially when older satellites with limited spectral information are included. The NASA CERES (Clouds and the Earth’s Radiant Energy System) data record provides global cloud property retrievals across 23 years and more than 25 satellites. With the goal of producing a spatially and temporally continuous record of cloud properties, the CERES cloud working group has developed algorithms that use only 3 channels that are common to most geostationary satellite imagers: 0.65, 3.9, and 10.8 μm.
Sarah Bedka↗
Seasonal Surface Spectral Emissivity Derived from VIIRS Data
Surface emissivity is essential for many remote-sensing applications including the retrieval of surface skin temperature from satellite-based infrared measurements, the determination of cloud detection thresholds, and the estimation of the surface longwave radiation emission, an important component of the energy budget of the surface-atmosphere interface. The CERES (Clouds and the Earth’s Radiant Energy System) Project is measuring broadband shortwave and longwave radiances and deriving cloud properties from the MODIS on Terra and Aqua and from the VIIRS on NOAA-19 and NOAA-20 orbiters to produce combined global radiation and cloud property data sets. Zhou et al. (IEEE Trans. Geosci. Remote Sens., 49, 2011) used Infrared Atmospheric Sounding Interferometer (IASI) data to create a high spectral resolution surface emissivity atlas for remote sensing and modeling applications. The IASI measures spectral radiances between 3.62 and 15.5 µm. The VIIRS I4 channel width is from 3.55 to 3.93 µm, while MODIS Band 20 is from 3.66 to 3.84 µm. Comparisons of top-of-atmosphere (TOA) radiance calculations with MODIS and VIIRS observations for these bands suggest that the IASI emissivity atlas near 3.7 µm may not be suitable for CERES cloud retrievals. In this paper, the IASI emissivities for the VIIRS and MODIS bands centered near 11µm are used to derive surface skin temperature from nighttime MODIS/VIIRS data. The Goddard Earth Observing System for Instrument Teams (GEOS-IT) numerical weather analyses provide temperature and water vapor profiles fused to correct the observed radiances for atmospheric absorption and emission. Global seasonal emissivity maps are then derived for the VIIRS and MODIS 3.7µm bands that are consistent with the derived skin temperatures and the observed TOA radiances. These seasonal climatology maps will be validated and used in CERES Edition 5 and other CERES-related cloud retrieval algorithms to provide improved clear-sky radiances and derived cloud properties.
Surface Emissivity↗