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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Online Access: | https://doi.org/10.1109/JSTARS.2023.3344371 https://doaj.org/article/d51b9957d18a465490545663443b0465 |
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ftdoajarticles:oai:doaj.org/article:d51b9957d18a465490545663443b0465 2024-02-11T10:06:32+01:00 Development and Application of a GNSS-R Error Model for Hurricane Winds Rajeswari Balasubramaniam Christopher S. Ruf 2024-01-01T00:00:00Z https://doi.org/10.1109/JSTARS.2023.3344371 https://doaj.org/article/d51b9957d18a465490545663443b0465 EN eng IEEE https://ieeexplore.ieee.org/document/10365151/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2023.3344371 https://doaj.org/article/d51b9957d18a465490545663443b0465 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2336-2346 (2024) CYGNSS GNSS-R hurricanes science antenna wind sensitivity Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 article 2024 ftdoajarticles https://doi.org/10.1109/JSTARS.2023.3344371 2024-01-14T01:50:55Z 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. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17 2336 2346 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
CYGNSS GNSS-R hurricanes science antenna wind sensitivity Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
CYGNSS GNSS-R hurricanes science antenna wind sensitivity Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Rajeswari Balasubramaniam Christopher S. Ruf Development and Application of a GNSS-R Error Model for Hurricane Winds |
topic_facet |
CYGNSS GNSS-R hurricanes science antenna wind sensitivity Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Rajeswari Balasubramaniam Christopher S. Ruf |
author_facet |
Rajeswari Balasubramaniam Christopher S. Ruf |
author_sort |
Rajeswari Balasubramaniam |
title |
Development and Application of a GNSS-R Error Model for Hurricane Winds |
title_short |
Development and Application of a GNSS-R Error Model for Hurricane Winds |
title_full |
Development and Application of a GNSS-R Error Model for Hurricane Winds |
title_fullStr |
Development and Application of a GNSS-R Error Model for Hurricane Winds |
title_full_unstemmed |
Development and Application of a GNSS-R Error Model for Hurricane Winds |
title_sort |
development and application of a gnss-r error model for hurricane winds |
publisher |
IEEE |
publishDate |
2024 |
url |
https://doi.org/10.1109/JSTARS.2023.3344371 https://doaj.org/article/d51b9957d18a465490545663443b0465 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2336-2346 (2024) |
op_relation |
https://ieeexplore.ieee.org/document/10365151/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2023.3344371 https://doaj.org/article/d51b9957d18a465490545663443b0465 |
op_doi |
https://doi.org/10.1109/JSTARS.2023.3344371 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
17 |
container_start_page |
2336 |
op_container_end_page |
2346 |
_version_ |
1790604310072000512 |