Application of Artificial Neural Networks for Sea-Surface Wind-Speed Retrieval From IRS-P4 (MSMR) Brightness Temperature

Indian Remote Sensing Satellite Multifrequency Scanning Microwave Radiometer (MSMR)-measured brightness temperatures (TB) in 6.6-, 10.65-, 18-, and 21-GHz channels with dual polarizations were utilized to retrieve sea-surface wind speed (SSWS). A concurrent and collocated database was constructed on MSMR TB- and deep-sea (DS)-buoy-recorded wind speeds for the period of June 1999-July 2001 over the north Indian Ocean. A radial-basis-function-based artificial-neural-network (ANN) algorithm was developed to estimate SSWS from MSMR TB values. Multiple ANNs were generated by the systematic variation of the architecture of input- and hidden-layer nodes. The performance of the most successful algorithm was evaluated based on statistical summary and network performance. The accuracy of the ANN-based wind-speed algorithm was compared with DS-buoy observations, and the result was then compared with the output of the regression analysis between buoy- and MSMR operational-global-retrieval-algorithm (OGRA)-derived SSWS values. On the average, 84% (92%) of ANN-estimated MSMR SSWS observations are within ±2 m/s ( ±3 m/s) when compared with DS-buoy observations. The correlation and root mean square error of 0.80 and 1.79 m/s, respectively, for ANN-predicted SSWS values are much better than that obtained from OGRA. The performance of the ANN algorithm was also evaluated during a super cyclone (October 1999) over the Bay of Bengal. The ANN algorithm could capture the high cyclonic winds, and the values match reasonably well with Special Sensor Microwave/Imager and SeaWinds Scatterometer (QuikSCAT) operational wind products.

[1]  S. Thiria,et al.  Wind ambiguity removal by the use of neural network techniques , 1991 .

[2]  Clemens Simmer,et al.  Estimating longwave net radiation at sea surface from the Special Sensor Microwave/Imager (SSM/I) , 1997 .

[3]  M. M. Ali,et al.  Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach , 2004 .

[4]  Helmut Schiller,et al.  Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements , 2003, Neural Networks.

[5]  Validation of multi-channel scanning microwave radiometer on-board Oceansat-I , 2004 .

[6]  K. Kasturirangan,et al.  Ocean research in India: perspective from space , 2000 .

[7]  Dudley B. Chelton,et al.  A satellite-derived climatology of global ocean winds , 2006 .

[8]  M. M. Ali,et al.  Estimation of mixed-layer depth from surface parameters , 2006 .

[9]  Thomas Meissner,et al.  Wind-Vector Retrievals Under Rain With Passive Satellite Microwave Radiometers , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  V. Krasnopolsky,et al.  A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager , 1995 .

[11]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[12]  Rashmi Sharma,et al.  A Comparative Assessment of Surface Wind Speed and Sea Surface Temperature over the Indian Ocean by TMI, MSMR, and ERA-40 , 2007 .

[13]  W. Liu,et al.  Bulk Parameterization of Air-Sea Exchanges of Heat and Water Vapor Including the Molecular Constraints at the Interface , 1979 .

[14]  F. Wentz.  A well‐calibrated ocean algorithm for special sensor microwave / imager , 1997 .

[15]  Kevin J. Hussey,et al.  Global satellite measurements of water vapour, wind speed and wave height , 1981, Nature.