A generalized simulation capability for rotating- beam scatterometers

Zhen Li, Ad Stoffelen, Anton Verhoef
2019 Atmospheric Measurement Techniques  
<p><strong>Abstract.</strong> Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT (China France Oceanography SATellite), WindRad (Chinese Wind Radar) on FY-3E, and SeaWinds on QuikSCAT. Besides the comparison of the so-called first rank solution retrieval skill of the input wind field, other
more » ... ures of merit (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the wind vector cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir but also to investigate resolution and accuracy through incorporating and analyzing the spatial response functions of the simulated Level-1B data for each WVC.</p>
doi:10.5194/amt-12-3573-2019 fatcat:jcn6buez7bfq3dxeu7xd4d3x2a