Details

Autor(en) / Beteiligte

• Colin, Aurelien
• Peureux, Charles
• Husson, Romain
• Fablet, Ronan
• Tandeo, Pierre

Titel

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

Ist Teil von

• IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, p.307-309

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• 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 fore-casts 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.