The Advanced Scatterometer (ASCAT) is a C-band scatterometer designed to be less sensitive to rain contamination than other higher frequency scatterometers. However, the radar backscatter is still affected by rain which increases error during wind estimation. The error can be reduced in rainy conditions by combining a rain backscatter model with the existing wind only (WO) backscatter model to perform simultaneous wind and rain (SWR) estimation. I derive and test several 2.5 km resolution rain backscatter models for ASCAT data which are used with the WO model to estimate the near surface winds. Various rain models optimal for different purposes are discussed. The best rain model for estimating wind speed lowers the root mean square error (RMSE) in the presence of rain by 13.6% when compared to using the WO model alone. The rain model which best predicts rain rates has a RMSE of 7.9 mm/h. A neural network (NN) is designed to discriminate the presence of rain using ASCAT's backscatter measurements. Such a NN enables the SWR algorithm to be used only on rainy samples and thus improves estimation. By removing all samples identified by the NN as rain, the WO algorithm's speed estimate improved by 2.83%.
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-10771 |
| Date | 01 December 2022 |
| Creators | Kjar, Joshua Benjamin |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | https://lib.byu.edu/about/copyright/ |
Page generated in 0.0018 seconds