An artificial neural network based fast radiative transfer model for simulating infrared sounder radiances

The first step in developing any algorithm to retrieve the atmospheric temperature and humidity parameters at various pressure levels is the simulation of the top of the atmosphere radiances that can be measured by the satellite. This study reports the results of radiative transfer simulations for the multi-channel infrared sounder of the proposed Indian satellite INSAT-3D due to be launched shortly. Here, the widely used community software k Compressed Atmospheric Radiative Transfer Algorithm (kCARTA) is employed for performing the radiative transfer simulations. Though well established and benchmarked, kCARTA is a line-by-line solver and hence takes enormous computational time and effort for simulating the multispectral radiances for a given atmospheric scene. This necessitates the development of a much faster and at the same time, equally accurate RT model that can drive a real-time retrieval algorithm. In the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of INSAT-3D. Realistic atmospheric temperature and humidity profiles have been used for training the network. Spectral response functions of GOES-13, a satellite similar in construction, purpose and design and already in use are used. The fast RT model is able to simulate the radiances for 1200 profiles in 18 ms for a 15-channel GOES profile, with a correlation coefficient of over 99%. Finally, the robustness of the model is tested using additional synthetic profiles generated using empirical orthogonal functions (EOF).