Abstract

Microwave scatterometers, which use radar backscatter measurements to infer the near-surface wind vector, are unique in their ability to monitor global wind vectors at high resolutions. However, scatterometer observations which are contaminated by land proximity or rain events produce wind estimates which have increased bias and variability, making them unreliable for many applications. Fortunately, the effects of these sources of contamination can be mitigated. Land contamination of backscatter measurements occurs when land partially fills the antenna illumination area. This reduces and masks the wind-induced backscatter signal. Land contamination is mitigated by quantifying the amount of contamination in a single observation using a metric referred to as the land contribution ratio (LCR). LCR levels which give rise to inadmissible levels of error in the wind estimates are determined and used to discard land-contaminated observations. Using this method results in contamination-free wind estimates which can be made as close to the coast as 5 km, an improvement of 25 km compared to previous methods. Rain contamination of scatterometer observations results from rain-induced scattering effects which modify the wind-induced backscatter. Rain backscatter effects are modeled phenomenologically to assess the impact of rain on the observed backscatter. Given the backscatter effects of wind and rain, there are three estimators: wind-only (WO), simultaneous wind and rain (SWR) and rain-only (RO), which have optimal performance in different wind and rain conditions. Rain contamination of wind estimates is mitigated using a new Bayes estimator selection (BES) technique which optimally selects WO, SWR, or RO estimates as they are most appropriate. BES is a novel adaptation of Bayes decision theory to operate on parameter estimates which may have different dimensions. The BES concept is extended to include prior selection and noise reduction techniques which generalizes BES to a wider variety of wind fields and further increase wind estimate accuracy. Overall, BES has wind estimation performance which surpasses that of either the WO or SWR wind estimates individually, and also provides a viable rain-impact flag.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering

Rights

http://lib.byu.edu/about/copyright/