Improved Wind Speed Prediction Results by Artificial Neural Network Method

2018 ◽  
pp. 651-665
Author(s):  
Asilhan Sevinc Sirdas ◽  
Akatas Nilcan ◽  
Izgi Ercan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Neural Network Method ◽  
Wind Speed Prediction ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Speed Prediction ◽  
Artificial Neural

scholarly journals Intelligent Wind Retrieval from Chinese Gaofen-3 SAR Imagery in Quad Polarization

2019 ◽  
Vol 36 (11) ◽  
pp. 2121-2138 ◽  
Author(s):  
Weizeng Shao ◽  
Shuai Zhu ◽  
Xiaopeng Zhang ◽  
Shuiping Gou ◽  
Changzhe Jiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Retrieval Algorithm ◽  
Neural Network Method ◽  
Wind Retrieval ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Sar Imagery

AbstractThis study proposes the use of the artificial neural network for wind retrieval with Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) data. More than 10 000 images acquired in wave mode and quad-polarization strip map were collected over global seas throughout the 2-yr mission. The GF-3 operated in a quad-polarization channel—vertical–vertical (VV), vertical–horizontal (VH), horizontal–horizontal (HH), and horizontal–vertical (HV). These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts at a 0.125° grid. The newly released wind retrieval algorithm for copolarization (VV and HH) SAR included CMOD7 and C-SARMOD2. We developed an algorithm based on an artificial neural network method using the SAR-measured normalized radar cross section at quad-polarization channels, herein named QPWIND_GF. Simulations using the QPWIND_GF showed that the correlation coefficient of wind speed was 0.94. We then validated the retrieval wind speeds against the measurements at a 0.25° grid from the Advanced Scatterometer. A comparison showed that the root-mean-square error (RMSE) of wind speed was 0.74 m s−1, which was better than the wind speed obtained using state-of-the-art methods—including, for example, CMOD7 (RMSE 0.88 m s−1) and C-SARMOD2 (RMSE 1.98 m s−1). The finding indicated that the accuracy of wind retrieval from GF-3 SAR images was significantly improved. Our work demonstrates the advanced feasibility of an artificial neural network method for SAR marine applications.

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