Neural network microwave precipitation retrievals and modeling results
Author(s)Leslie, R. Vincent; Blackwell, William J.; Bickmeier, Laura J.; Jairam, Laura G.
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We describe a simulation methodology used to develop and validate precipitation retrieval algorithms for current and future passive microwave sounders with emphasis on the NPOESS (National Polar-orbiting Operational Environmental Satellite System) sensors. Precipitation algorithms are currently being developed for ATMS, MIS, and NAST-M. ATMS, like AMSU, will have channels near the oxygen bands throughout 50-60 GHz, the water vapor resonance band at 183.31 GHz, as well as several window channels. ATMS will offer improvements in radiometric and spatial resolution over the AMSU-A/B and MHS sensors currently flying on NASA (Aqua), NOAA (POES) and EUMETSAT (MetOp) satellites. The similarity of ATMS to AMSU-A/B will allow the AMSU-A/B precipitation algorithm developed by Chen and Staelin to be adapted for ATMS, and the improvements of ATMS over AMSU-A/B suggest that a superior precipitation retrieval algorithm can be developed for ATMS. Like the Chen and Staelin algorithm for AMSU-A/B, the algorithm for ATMS to be presented will also be based a statisticsbased approach involving extensive signal processing and neural network estimation in contrast to traditional physics-based approaches. One potential advantage of a neural-network-based algorithm is computational speed. The main difference in applying the Chen-Staelin method to ATMS will consist of using the output of the most up-to-date simulation methodology instead of the ground-based weather radar and earlier versions of the simulation methodology. We also present recent progress on the millimeter-wave radiance simulation methodology that is used to derive simulated global ground-truth data sets for the development of precipitation retrieval algorithms suitable for use on a global scale by spaceborne millimeter-wave spectrometers. The methodology utilizes the MM5 Cloud Resolving Model (CRM), at 1-km resolution, to generate atmospheric thermodynamic quantities (for example, humidity and hydrometeor profiles). These data are then input into a Radiative Transfer Algorithm (RTA) to simulate at-sensor millimeter-wave radiances at a variety of viewing geometries. The simulated radiances are filtered and resampled to match the sensor resolution and orientation.
Proceedings of SPIE--the International Society for Optical Engineering
Society of Photo-Optical Instrumentation Engineers
Leslie, R. Vincent et al. “Neural network microwave precipitation retrievals and modeling results.” Microwave Remote Sensing of the Atmosphere and Environment VI. Ed. Azita Valinia, Peter H. Hildebrand, & Seiho Uratsuka. Noumea, New Caledonia: SPIE, 2008. 715406-8. ©2008 SPIE
Final published version
SPIE CID: 715406-8