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Evolving ocean monitoring With GNSS-R: promises in surface wind speed and prospects for rain detection

Asgarimehr, 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,

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To link to this item DOI: 10.1109/IGARSS.2019.8900414


After 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.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:100514

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