An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds

High-quality winds over the ocean surface, at an enhanced spatio-temporal resolution are required for a better understanding of the dynamics of the ocean and atmosphere. Altimetry helps in increasing the frequency of satellite observations. Traditional algorithms for wind speed retrievals from altimeter consider only the backscatter (sigma-0) and possibly the significant wave height (SWH). In this study, we propose an artificial neural network (ANN) model function for AltiKa on board Satellite for ARgos and ALtiKa (SARAL) to relate wind speed to sigma-0, SWH, the width of the waveform leading edge, the two brightness temperatures (TBK and TBKa), and the amplitude of the 1-Hz echo. These parameters influence either the backscatter from the ocean or the propagation of the altimeter radar signal. The wind estimates have significantly improved by incorporating these parameters.

[1]  S. Durden,et al.  A physical radar cross-section model for a wind-driven sea with swell , 1985, IEEE Journal of Oceanic Engineering.

[2]  Saleh Abdalla.  SARAL/AltiKa Wind and Wave Products: Monitoring, Validation and Assimilation , 2015 .

[3]  Mark A. Bourassa,et al.  A Flux Parameterization Including the Effects of Capillary Waves and Sea State , 1999 .

[4]  Saleh Abdalla,et al.  Ku-Band Radar Altimeter Surface Wind Speed Algorithm , 2012 .

[5]  Bertrand Chapron,et al.  A two-parameter wind speed algorithm for Ku-band altimeters , 2002 .

[6]  Yang Yu,et al.  An improved wind speed algorithm for “Jason-1” altimeter under tropical cyclone conditions , 2014, Acta Oceanologica Sinica.

[7]  J. Lefèvre,et al.  A significant wave height dependent function for TOPEX/POSEIDON wind speed retrieval , 1994 .

[8]  Remko Scharroo,et al.  One- and Two-Dimensional Wind Speed Models for Ka-Band Altimetry , 2014 .

[9]  Michael H. Freilich,et al.  A new approach for determining fully empirical altimeter wind speed model functions , 1994 .

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

[11]  M. M. Ali,et al.  Estimation of sonic layer depth from surface parameters , 2007 .

[12]  M. M. Ali,et al.  A Neural Network Approach to Estimate Tropical Cyclone Heat Potential in the Indian Ocean , 2012, IEEE Geoscience and Remote Sensing Letters.

[13]  Dongliang Zhao,et al.  A Spectral Approach for Determining Altimeter Wind Speed Model Functions , 2003 .

[14]  Paul Snoeij,et al.  A summary of the VIERS-1 scatterometer model , 1998 .

[15]  M. Donelan,et al.  Radar scattering and equilibrium ranges in wind‐generated waves with application to scatterometry , 1987 .

[16]  P. Janssen,et al.  VIERS-1 scatterometer model , 1998 .

[17]  Saleh Abdalla,et al.  Calibration of SARAL/AltiKa Wind Speed , 2014, IEEE Geoscience and Remote Sensing Letters.

[18]  Dudley B. Chelton,et al.  A geosat altimeter wind speed algorithm and a method for altimeter wind speed algorithm development , 1991 .

[19]  Y. Kuroda,et al.  Comparison of wind data from QuikSCAT and buoys in the Indian Ocean , 2007 .

[20]  M. M. Ali,et al.  A soft‐computing cyclone intensity prediction scheme for the Western North Pacific Ocean , 2013 .

[21]  Bertrand Chapron,et al.  Ocean Wave Slope Observations Using Radar Backscatter and Laser Altimeters , 2004 .