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A neural network (NN) based algorithm for retrieval of precipitable water vapor (PWV) from the Atmospheric Infrared Sounder (AIRS) observations is proposed. An exact radial basis function (RBF) network is selected, in which the at-sensor brightness temperatures are the input variables, and PWV is the output variable. The training data sets for the RBF network are mainly simulated from the fast radiative transfer model (Community Radiative Transfer Model, CRTM) and the latest global assimilation data. The algorithm is validated by retrieving the PWV over west area in China using AIRS data. Compared with the AIRS PWV products, the RMSE of the PWV retrieved by our algorithm is 0.67 g/cm2, and a comparison between the retrieved PWV and radiosonde data is carried out. The result suggests that the RBF neural network based algorithm is applicable and feasible in actual conditions. Furthermore, spatial resolution of water vapor derived by RBF neural network is superior as compared to that of AIRS-L 2 standard product. Finally a PCA scheme is used for the preliminary investigation of the compression of AIRS high dimension observations.

Based on the thermal radiative transfer equation (RTE), a new atmospheric correction method named Single Band Water Vapor Dependent (SBWVD) method is developed for land surface temperature (LST) retrieval for the FY-3A Medium Resolution Spectral Imager (MERSI) with only one thermal infrared (TIR) channel. Assuming that the surface emissivity is known, water vapor content (WVC) is the only one parameter for input to the SBWVD algorithm to retrieve LST from MERSI TIR observations. FY-3A MERSI Level 2 water vapor product is employed to evaluate the performance of the proposed method, and a 2-D data interpolation procedure is applied in order to match the MERSI L1B data in spatial resolution. Some tests, including numerical simulation for MERSI sensor and the synchronous measurements of MERSI and the radiosondes for the radiative calibration of the FY-3A tests in Qinghai Lake, have been carried out for the proposed algorithm, respectively. The results show that the difference between the retrieved LST and the in-situ measurements is less than 0.6 K for most situations. The comparison with the MODIS LST products (V5) shows that the root mean square error (RMSE) is under 0.72 K. Thus, our proposed new algorithm is applicable for the atmospheric correction and LST retrieval using MERSI TIR channel observations.

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