Location
Utah State University
Start Date
5-10-2010 11:00 AM
Description
The QuikSCAT scatterometer has proved to be a valuable tool in measuring the near-surface wind vector over the ocean. In raining conditions the instrument effectiveness is diminished by rain contamination of the radar return. To compensate for rain effects, two alternative estimation techniques have been proposed, simultaneous wind-rain retrieval and rainonly retrieval, which are appropriate under certain conditions. This paper proposes and outlines a Bayes estimator selection technique whereby a best estimate is selected from the simultaneous wind-rain, the rain-only and the conventional wind-only estimates. In this paper the Bayes estimator selection technique is introduced with a quick overview of the application to QuikSCAT wind and rain estimation. Results are demonstrated at both conventional and high resolutions for a case study which indicate that wind and rain estimates after Bayes estimator selection are more consistent with measured rain and have reduced noise levels over those produced by any of the individual estimators.
Improved Wind and Rain Estimation Over the Ocean Using QuikSCAT
Utah State University
The QuikSCAT scatterometer has proved to be a valuable tool in measuring the near-surface wind vector over the ocean. In raining conditions the instrument effectiveness is diminished by rain contamination of the radar return. To compensate for rain effects, two alternative estimation techniques have been proposed, simultaneous wind-rain retrieval and rainonly retrieval, which are appropriate under certain conditions. This paper proposes and outlines a Bayes estimator selection technique whereby a best estimate is selected from the simultaneous wind-rain, the rain-only and the conventional wind-only estimates. In this paper the Bayes estimator selection technique is introduced with a quick overview of the application to QuikSCAT wind and rain estimation. Results are demonstrated at both conventional and high resolutions for a case study which indicate that wind and rain estimates after Bayes estimator selection are more consistent with measured rain and have reduced noise levels over those produced by any of the individual estimators.