Objectives When the global navigation satellite system reflectometry (GNSS-R) technique is applied for sea surface wind speed retrieval, continuous geophysical model functions are often used to fit the observable extracted from the delay/Doppler map (DDM) to wind speed empirically. A large systematic deviation exists in the above method due to the few samples at 0-5 m/s and 12-20 m/s. To solve the problem, an adaptively cumulative distribution function (CDF) matching method for bias correction is proposed.
Methods In this method, CDF matching is performed between the retrieved and reference wind speed sequences, and the least square method is used to adaptively find the optimal polynomial to fit the wind bias sequence and correct it. Public data products are used for validation.
Results Test results show that the root mean square errors (RMSEs) after correction are reduced by 6% and 15%, and the biases are reduced by 45% and 25% at 0-5 m/s and 12-20 m/s respectively. And overall bias is improved by 25%.
Conclusions Wind retrieval accuracy is obviously improved for wind ranges with few samples, and the probability distribution of wind speed retrievals is more consistent with that in nature. However, the bad performance at high wind speeds shows that the tendencies from DDM observable to wind speed at low and high wind speeds are extremely different because of the low sensitivity of DDM observables at high winds. The piecewise function is a good choice, but it is difficult to determine the piecewise point and keep it smooth.
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