학술논문
An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds
Document Type
Periodical
Author
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of. 8(11):5317-5323 Nov, 2015
Subject
Language
ISSN
1939-1404
2151-1535
2151-1535
Abstract
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.