Neural Network Based Retrieval of Atmospheric Temperature Profile Using AMSU-A Observations

Abstract

The present study describes artificial neural network (ANN) based approach for the retrieval of atmospheric temperature profiles from AMSU-A microwave temperature sounder. The nonlinear relationship between the temperature profiles and satellite brightness temperatures dictates the use of ANN, which is inherently nonlinear in nature. Since latitudinal variation of temperature is dominant one in the Earth’s atmosphere, separate network configurations have been established for different latitudinal belts, namely, tropics, mid-latitudes, and polar regions. Moreover, as surface emissivity in the microwave region of electromagnetic spectrum significantly influences the radiance (or equivalently the brightness temperature) at the satellite altitude, separate algorithms have been developed for land and ocean for training the networks. Temperature profiles from National Center for Environmental Prediction (NCEP) analysis and brightness temperature observations of AMSU-A onboard NOAA-19 for the year 2010 have been used for training of the networks. Further, the algorithm has been tested on the independent dataset comprising several months of 2012 AMSU-A observations. Finally, an error analysis has been performed by comparing retrieved profiles with collocated temperature profiles from NCEP. Errors in the tropical region are found to be less than those in the mid-latitude and polar regions. Also, in each region the errors over ocean are less than the corresponding ones over land.