Evolving ocean monitoring with GNSS-R: promises in surface wind speed and prospects for rain detectionAsgarimehr, M., Zavorotny, V., Zhelavskaya, I., Foti, G., Wickert, J. and Reich, S. (2019) Evolving ocean monitoring with GNSS-R: promises in surface wind speed and prospects for rain detection. In: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 28 JUL- 2 AUG 2019, Yokohoma, Japan, pp. 8692-8695, https://doi.org/10.1109/IGARSS.2019.8900414. (Proceedings of IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium) Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/IGARSS.2019.8900414 Abstract/SummaryAfter developing a wind speed retrieval algorithm, derived winds from measurements of UK TechDemoSat-1 (TDS-1), from May 2015 to July 2017, are compared to wind products of Advanced Scatterometer showing a reliable performance, especially during rain events. However, a rain signature in GNSS-R observations, a decrease in the value of the bistatic radar cross section at low winds, is demonstrated, which can potentially enable the technique to detect precipitation over oceans induced by low-to-moderate winds. This phenomenon is investigated and finally characterized as the rain splash effect altering the ocean surface roughness. To improve the quality of derived winds, a machine learning technique is implemented for the wind speed inversion as a geophysical model function. The trained feedforward neural network shows a significant improvement of 17% in the wind speed RMSE compared to the LS approach. In the end, one can conclude that space-borne ocean monitoring is evolving existing products with a potential for novel geophysical applications.
Altmetric Deposit Details University Staff: Request a correction | Centaur Editors: Update this record |
Lists
Lists
