Title
Sea Surface Wind Speed Retrieval Based on Empirical Orthogonal Function Analysis Using 2019-2020 CYGNSS Data
Abstract
This article proposes a new sea surface wind speed (SSWS) retrieval modeling algorithm based on the empirical orthogonal function (EOF) analysis for observations acquired by the global navigation satellite system reflectometry (GNSS-R). As a nonparametric modeling algorithm, it is simpler compared with the nonlinear methods. The influence of wind speed and incident angle on the modeling error is analyzed for the first time using a spectrum analysis. Three types of data from 80% CYGNSS 2019-2020 observations [delay Doppler map average (DDMA) and leading edge slope (LES)], signal incident angle, and the European Centre for Medium-Range Weather Forecasts Reanalysis V5 (ERA5) reference wind speed are used in the EOF analysis to establish two retrieval models. The remaining 20% of the data are used for accuracy evaluation after getting the final wind speed by the minimum variance (MV) estimator. As a result, when using three 0-20-m/s wind speeds of ERA5, Advanced Scatterometer (ASCAT), and the Modern-Era Retrospective Analysis for Research and Applications V2 (MERRA2) as contrasts, the root mean squared errors (RMSEs) are 1.51, 1.45, and 1.43 m/s, respectively. Compared with CYGNSS wind product, the performance of this algorithm is closer to the L2 Climate Data Record (CDR) V1.1 product than V1.0. The results demonstrate that the EOF algorithm has a good performance in retrieving SSWS and can better retain the influence of the incident angle on the observations.
Year
DOI
Venue
2022
10.1109/TGRS.2022.3169832
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Licenses, Delay Doppler map average (DDMA), empirical orthogonal function (EOF), global navigation satellite system reflectometry (GNSS-R), leading edge slope (LES), sea surface wind speed (SSWS) retrieval
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Jianming Wu100.34
Yanling Chen200.34
Peng Guo300.34
Xiaoya Wang400.34
Xiaogong Hu535.61
Mengjie Wu600.34
Fenghui Li701.01
Naifeng Fu801.01
Yanzhen Hao900.34