A neural network based algorithm for the retrieval of precipitable water vapor from MODIS data

Abstract

A neural network (NN) based algorithm for retrieval of precipitable water vapor (PWV) from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiance is proposed. A multilayer feedforward neural network (MFNN) is selected, in which the at-sensor brightness temperature, the surface emissivity of MODIS chs. 31 and 32, and the land surface temperature (LST) are input variables, and PWV is the output variable. The input parameters for the MFNN are mainly based on the radiative transfer simulation with MOD-TRAN 4.0 code and the latest global assimilation data. The algorithm is applied to retrieval of the PWV over northeast area in china using MODIS data. Compared with the MODIS PWV products, the RMSE of the PWV retrieved by our algorithm is 0.45g/cm 2. Furthermore, a comparison of our retrieval PWVs with radiosonde data is carried out. The results show that the MFNN-based retrieval algorithm for PWV is robust and efficient. © 2010 Springer-Verlag Berlin Heidelberg.