Radiative transfer simulations for the MADRAS imager of Megha-Tropiques

Journal of Earth system science

C Balaji1¹  K Srinivasa Ramanujam1¹ 

[1] Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India.$$

关键词: Radiative transfer;    artificial neural network;    Megha-Tropiques;    TRMM mission;    inverse problem.;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

This paper reports the radiative transfer simulations for the passive microwave radiometer onboard the proposed Indian climate research satellite Megha-Tropiques due to be launched in 2011. These simulations have been performed by employing an in-house polarized radiative transfer code for raining systems ranging from depression and tropical cyclones to the Indian monsoon. For the sake of validation and completeness, simulations have also been done for the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI) of the highly successful TRMM mission of NASA and JAXA. The paper is essentially divided into two parts: (a) Radiometer response with specific focus on high frequency channels in both the radiometers is discussed in detail with a parametric study of the effect of four hydrometeors (cloud liquid water, cloud ice, precipitating water and precipitating ice) on the brightness temperatures. The results are compared with TMI measurements wherever possible. (b) Development of a neural network-based fast radiative transfer model is elucidated here. The goal is to speed up the computational time involved in the simulation of brightness temperatures, necessitated by the need for quick and online retrieval strategies. The neural network model uses hydrometeor profiles as inputs and simulates spectral microwave brightness temperature at multiple frequencies as output. A huge database is generated by executing the in-house radiative transfer code for seven different cyclones occurred in North Indian Ocean region during the period 2001–2006. A part of the dataset is used to train the network while the remainder is used for testing purposes. For the purpose of testing, a typical scene from the Southwest monsoon rain is also considered. The results obtained are very encouraging and show that the neural network is able to mimic the underlying physics of the radiative transfer simulations with a correlation coefficient of over 99%.

【 授权许可】

Unknown