Extended Abstract (1.5M)P4.18
NASA-Langley web-based operational real-time cloud retrieval products from geostationary satellites
Rabindra Palikonda, AS&M, Hampton, VA; and D. Phan, M. M. Khaiyer, M. L. Nordeen, J. K. Ayers, D. A. Spangenberg, D. R. Doelling, Y. Yi, P. Minnis, L. Nguyen, Q. Trepte, and S. Sun-Mack
Ground-based instruments measuring cloud and radiative parameters can provide important atmospheric monitoring, but cover very little of the Earth's surface. Satellite imager data can be used to retrieve a number of cloud and radiative parameters over large-scale regions. With validation from ground-based instrumentation, satellite-derived datasets can be a valuable asset in climatic studies and complement the surface-based measurements. NASA Langley Research Center (LaRC) historically provided Geostationary Operational Environmental Satellite (GOES) satellite-derived cloud and radiation datasets, spanning a period of several years, covering the central United States. More recently, GOES retrievals from the entire contintental United States have been initiated, as well as retrievals over Europe (Meteosat Second Generation; MSG), Asia (Feng-Yun-2; FY2C) and the Pacific Ocean and Australia (Multi-functional Transport Satellite; MTSAT-1R).
The retrieval methodology combines the Visible Infrared Solar-Infrared Split-Window Technique (VISST; Minnis et al. 1995, 1998), Solar-Infrared Infrared Split-Window Technique (SIST), and the Solar-Infrared Infrared Near-Infrared Technique (SINT). In addition to the VIS (0.65 µm) and IR (11.0 µm) channels, VISST employs as many as three additional channels; 1.6, 3.9, and 12 or 13.3 um, depending on application and availability, resulting in improved retrieval accuracy. The SIST provides a more accurate assessment of cloud cover and height than possible using only IR data. 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. These near-real time datasets should be valuable for numerical weather prediction validation and assimilation.
Poster Session 4, Operational Products
Wednesday, 1 February 2006, 2:30 PM-2:30 PM, Exhibit Hall A2
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