P5.36
Deriving atmospheric temperature and humidity profiles from AMSU-A and AMSU-B measurements using neural network techniques
PAPER WITHDRAWN
Lei Shi, SeaSpace Corp., Poway, CA
Retrieval schemes are developed based on neural network techniques to derive atmospheric temperature and humidity profiles from the NOAA-15 and NOAA-16 Advanced Microwave Sounding Unit-A (AMSU-A) and Unit-B (AMSU-B) measurements. The neural network training sets are built based on the pole-to-pole global AMSU-A and AMSU-B measurements and the corresponding analysis data generated at the National Center for Environmental Prediction. For the temperature retrieval, all 15 channels of AMSU-A data are used to maximize the vertical resolution of the measurement. The preliminary study based on five days of global AMSU-A data from a winter season has yielded good retrieval results for the temperature profiles from the near-surface levels to 10 hPa pressure level. Further study is underway that includes the global AMSU-A data from other seasons. For the humidity profile retrieval, the scheme is based on the 15-channel AMSU-A measurement and the 5-channel AMSU-B measurement. These channels either contribute directly to water vapor measuring, or set constraints to the retrieval pattern. The retrieval of specific humidity profile shows the root mean square deviations of 1.1 g/kg at the surface, between 0.8 and 1 g/kg in the lower atmosphere (700 - 1000 hPa), and much smaller values toward the middle and upper atmosphere.
Poster Session 5, New Technology and Methods (Continued)
Thursday, 18 October 2001, 9:15 AM-11:00 AM
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