Artificial neural network approach for estimation of surface specific humidity and air temperature using multifrequency scanning microwave radiometer |
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| Authors: | Randhir Singh B G Vasudevan P K Pal P C Joshi |
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| Institution: | (1) Meteorology and Oceanography Group, Space Applications Centre (ISRO), 380 015 Ahmedabad, India |
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| Abstract: | Microwave sensor MSMR (Multifrequency 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 andT
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 ofQ
a
andT
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‡C and 1.1 g kg−1 for air temperature and surface specific humidity respectively. |
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| Keywords: | ANN COADS MSMR SSM/I NCEP |
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