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Segmentation of Rainfall Regimes by Machine Learning on a Colocalized Nexrad/Sentinel-1 Dataset

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Abstract

Precipitation measurement is an important prior for several operational and scientific applications, including weather forecasting, hazard prevention, agriculture, etc. Weather radars, such as NEXRAD, observe the air volume reflectivity and infer precipitation intensity at high resolution. However, their capabilities are limited over the ocean. C-band SAR imagery, which is sensitive to ocean surface roughness, is known to be sensitive to the effect of rain. In this study, we improve existing NEXRAD/Sentinel-1 collocations and train a U-Net deep learning model to estimate NEXRAD radar reflectivity from Sentinel-1 observations. Precipitation forecasts are returned as segmentations with thresholds at 1, 3 and 10 mm/hr. The results indicate high performance over a wide range of wind speeds and thus can provide an accurate estimate of precipitation in the absence of weather radar.
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Dates and versions

hal-03874778 , version 1 (28-11-2022)

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Aurélien Colin, Charles Peureux, Romain Husson, Ronan Fablet, Pierre Tandeo. Segmentation of Rainfall Regimes by Machine Learning on a Colocalized Nexrad/Sentinel-1 Dataset. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Jul 2022, Kuala Lumpur, Malaysia. pp.307-309, ⟨10.1109/IGARSS46834.2022.9884881⟩. ⟨hal-03874778⟩
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