Applications for Near-Real Time Satellite Cloud and Radiation Products

At NASA Langley Research Center, a variety of cloud, clear-sky, and radiation products are being derived at different scales from regional to global using geostationary satellite (GEOSat) and lower Earth-orbiting (LEOSat) imager data. With growing availability, these products are becoming increasingly valuable for weather forecasting and nowcasting. These products include, but are not limited to, cloud-top and base heights, cloud water path and particle size, cloud temperature and phase, surface skin temperature and albedo, and top-of-atmosphere radiation budget. Some of these data products are currently assimilated operationally in a numerical weather prediction model. Others are used unofficially for nowcasting, while testing is underway for other applications. These applications include the use of cloud water path in an NWP model, cloud optical depth for detecting convective initiation in cirrus-filled skies, and aircraft icing condition diagnoses among others. This paper briefly describes a currently operating system that analyzes data from GEOSats around the globe (GOES, Meteosat, MTSAT, FY-2) and LEOSats (AVHRR and MODIS) and makes the products available in near-real time through a variety of media. Current potential future use of these products is discussed

Near-Real Time Cloud Retrievals from Operational and Research Meteorological Satellites

A set of cloud retrieval algorithms developed for CERES and applied to MODIS data have been adapted to analyze other satellite imager data in near-real time. The cloud products, including single-layer cloud amount, top and base height, optical depth, phase, effective particle size, and liquid and ice water paths, are being retrieved from GOES- 10/11/12, MTSAT-1R, FY-2C, and Meteosat imager data as well as from MODIS. A comprehensive system to normalize the calibrations to MODIS has been implemented to maximize consistency in the products across platforms. Estimates of surface and top-of-atmosphere broadband radiative fluxes are also provided. Multilayered cloud properties are retrieved from GOES-12, Meteosat, and MODIS data. Native pixel resolution analyses are performed over selected domains, while reduced sampling is used for full-disk retrievals. Tools have been developed for matching the pixel-level results with instrumented surface sites and active sensor satellites. The calibrations, methods, examples of the products, and comparisons with the ICESat GLAS lidar are discussed. These products are currently being used for aircraft icing diagnoses, numerical weather modeling assimilation, and atmospheric radiation research and have potential for use in many other applications

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-real time globally from both geostationary (GEO) and low-earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented