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.
At NASA Langley Research Center (LaRC), radiances from multiple satellites are analyzed in near real-time to produce
cloud products over many regions on the globe. These data are valuable for many applications such as diagnosing
aircraft icing conditions and model validation and assimilation. This paper presents an overview of the multiple products
available, summarizes the content of the online database, and details web-based satellite browsers and tools to access
satellite imagery and products.
Imagers on many of the current and future operational meteorological satellites in geostationary Earth orbit (GEO) and lower Earth orbit (LEO) have enough spectral channels to derive cloud microphysical properties useful for a variety of applications. The products include cloud amount, phase, optical depth, temperature, height and pressure, thickness, effective particle size, and ice or liquid water path, shortwave albedo, and outgoing longwave radiation for each imager pixel. Because aircraft icing depends on cloud temperature, droplet size, and liquid water content as well as aircraft variables, it is possible to estimate the potential icing conditions from the cloud phase, temperature, effective droplet size, and liquid water path. A prototype icing index is currently being derived over the contiguous USA in near-real time from Geostationary Operational Environmental Satellite (GOES-10 and 12) data on a half-hourly basis and from NOAA-16 Advanced Very High Resolution (AVHRR) data when available. Because the threshold-based algorithm is sensitive to small errors and differences in satellite imager and icing is complex process, a new probability based icing diagnosis technique is developed from a limited set of pilot reports. The algorithm produces reasonable patterns of icing probability and intensities when compared with independent model and pilot report data. Methods are discussed for improving the technique for incorporation into operational icing products.