Neural networks approach to high vertical resolution atmospheric temperature profile retrieval from spaceborne high spectral resolution infrared sounder measurements

AIRS (Atmospheric Infra-Red Sounder) as NASA's first high spectral resolution sounding instrument provides both new and improved measurements of clouds, atmosphere, and land and oceans, with higher accuracy and higher resolution required by future weather and climate models. It will largely improve the deficiencies of the inability of current sounders (e.g. HIRS-3) to obtain high vertical resolution of retrieved atmosphere profiles. In this paper, temperature profiles with 1 km vertical resolution at 100 pressure layers, from surface up to 0.005 hPa, were retrieved on different spectral bands and on different types of terrain in the middle latitude area by using a three-layered feed-forward neural networks with back-propagation algorithm. Results show that temperature profiles with accuracy of less than 1K in 1 km thick tropospheric layers can be achieved by using AIRS data and neural networks method. And the Qinghai-Tibet Plateau has a measurably impact on the retrieval accuracy which is corresponding to the spectral bands used in performing retrievals. A promising approach to the elimination of this effect is to apply additional predictors which are non-satellite observed (e.g. surface altitude).