Artificial neural network approach for estimation of surface specific humidity and air temperature using Multifrequency Scanning Microwave Radiometer

摘要

Microwave sensor MSMR (Multifrequeney Scanning Microwave Radiometer) data onboard Oceansat-1 was used for retrieval of monthly averages of near surface specific humidity (Q_a) and air temperature (T_a) by means of Artificial Neural Network (ANN). The MSMR measures the microwave radiances in 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz for both vertical and horizontal polarizations. The artificial neural networks (ANN) technique is employed to find the transfer function relating the input MSMR observed brightness temperatures and output (Q_a and T_a) parameters. Input data consist of nearly 28 months (June 1999 - September 2001) of monthly averages of MSMR observed brightness temperature and surface marine observations of Q_a and T_a from Comprehensive Ocean-Atmosphere Data Set (COADS). The performance of the algorithm is assessed with independent surface marine observations. The results indicate that the combination of MSMR observed brightness temperatures as input parameters provides reasonable estimates of monthly averaged surface parameters. The global root mean square (rms) differences are 1.0℃ and 1.1g kg~(-1) lor air temperature and surface specific humidity respectively.