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    • B G Vasudevan

      Articles written in Journal of Earth System Science

    • Volume 113 Issue 1 March 2004 pp 89-101

      Artificial neural network approach for estimation of surface specific humidity and air temperature using multifrequency scanning microwave radiometer

      Randhir Singh B G Vasudevan P K Pal P C Joshi

      More Details Abstract Fulltext PDF

      Microwave sensor MSMR (Multifrequency Scanning Microwave Radiometer) data onboard Oceansat-1 was used for retrieval of monthly averages of near surface specific humidity (Qa) and air temperature (Ta) 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 (Qa andTa) parameters. Input data consist of nearly 28 months (June 1999 – September 2001) of monthly averages of MSMR observed brightness temperature and surface marine observations ofQa andTa 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‡C and 1.1 g kg−1 for air temperature and surface specific humidity respectively.

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