The approach for the fast calibration of rotating antenna radar backscattered signal. The example of CFOSAT mission.

The Chinese-French Ocean Satellite (CFOSAT) is an innovative space mission dedicated to the global observation and monitoring of the ocean's sea state and sea surface vector winds. CFOSAT operates two Ku-band rotating radars: the near-nadir Ku-band wave scatterometer (SWIM) and the dual-polarization, moderate-incidence-angle, Ku-band wind scatterometer (SCAT). This dual-incidence-angle instrumental configuration provides regular collocated measurements of radar backscatter to retrieve sea surface state parameters, including significant wave height, directional wave spectrum, and wind vector. Observations taken at different incidence angles have different sensitivities to sea surface parameters, such as short and long waves, surface currents, and surface temperature. Furthermore, synchronized backscatter from two different sensors can be mutually analyzed to improve the quality of sea surface wind retrievals. The joint use of two or multiple collocated data sources for geophysical retrieval requires a very high-quality of all input data, calibrated in the common reference framework. In addition to the well-studied signal distortion effects of fixed-oriented antenna design, the backscatter obtained with rotating antenna radars could potentially be influenced by additional azimuth-dependent factors, such as internal temperature variation and along-track noise amplification. Furthermore, new experimental antenna and hardware configurations can be difficult to adequately calibrate and validate quickly, which negatively impacts the speed of scientific and applied use of the acquired data. 
In this work, we propose a fast calibration approach which allows for rapid (~1 day) sigma0 calibrations. This approach is based on Numerical Weather Prediction (NWP) most probable wind histogram matching. It is applied to each instrument, satellite pass (ascending or descending), antenna azimuth, incidence angle, and polarization. All signals are then adjusted to the same level, followed by deriving a new instrument-specific Geophysical Model Function (GMF) which maps backscattered sigma0 as a function of wind speed and direction, incidence angle, naturally taking into account all factors related to the instrument (e.g. internal noise, antenna swath distortion, etc.). 
The validation of the proposed approach in application to SCAT data was done using the standard KNMI CWDP processor, where the wind vector retrieval was done for original and corrected data. A significant improvement of the retrieved wind vector quality was achieved for the left and right parts of the radar swath. The validation algorithm was applied to historical CFOSAT SWIM and SCAT data sets in terms of IFREMER Wind and Wave Operation Center (IWWOC) and incorporated into CFOSAT SCAT L2S and SWMSCAT L2S products.   
The proposed two-step strategy allows us to empirically recalibrate historical datasets of radar backscatter in cases where traditional sigma0 calibration/validation approaches are under development or the instrument faces unexpected signal level fluctuations. We anticipate that the proposed algorithm could be easily extended to most existing and future space radar configurations in order to accelerate the practical usage of satellite measurements when necessary.