Development and Application of a GNSS-R Error Model for Hurricane Winds
A parametric error model is developed to represent the uncertainty in retrieval of hurricane force wind speed by a spaceborne GNSS-R instrument. The functional form of the model is constructed based on a bottom–up consideration of the primary contributing sources of uncertainty. Scaling p...
| Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
IEEE
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.1109/JSTARS.2023.3344371 https://doaj.org/article/d51b9957d18a465490545663443b0465 |
| Summary: | A parametric error model is developed to represent the uncertainty in retrieval of hurricane force wind speed by a spaceborne GNSS-R instrument. The functional form of the model is constructed based on a bottom–up consideration of the primary contributing sources of uncertainty. Scaling parameters in the model are tuned in a top–down manner using a large population of wind speed retrievals by the CYGNSS satellite, which are colocated in space and time with HWRF reanalysis hurricane winds in the North Atlantic during 2018–2022. The root-mean-squared difference between CYGNSS and HWRF winds is found to depend on a number of variables, two of which are wind speed and receive antenna gain. The parametrized error model represents these dependencies. The model can be used as a design tool to predict expected performance as a function of instrument design. In particular, the model predicts the antenna gain required to achieve a particular level of wind speed uncertainty at a particular wind speed. A case study is considered in which a receive antenna gain of at least 20 dBi is found to be required to reliably distinguish between a Category 4 and Category 5 hurricane. This has implications for the optimal design of a future GNSS-R instrument intended for hurricane observations. |
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