In:
Journal of Atmospheric and Oceanic Technology, American Meteorological Society, Vol. 36, No. 11 ( 2019-11), p. 2121-2138
Abstract:
This 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.
Type of Medium:
Online Resource
ISSN:
0739-0572
,
1520-0426
DOI:
10.1175/JTECH-D-19-0048.1
Language:
Unknown
Publisher:
American Meteorological Society
Publication Date:
2019
detail.hit.zdb_id:
2021720-1
detail.hit.zdb_id:
48441-6
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