| Title: |
Segmentation of Rainfall Regimes by Machine Learning on a Colocalized Nexrad/Sentinel-1 Dataset |
| Authors: |
Colin, Aurélien; Peureux, Charles; Husson, Romain; Fablet, Ronan; Tandeo, Pierre |
| Contributors: |
Equipe Observations Signal & Environnement (Lab-STICC_OSE); Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC); École Nationale d'Ingénieurs de Brest (ENIB); Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Bretagne Sud (UBS)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB); Institut Mines-Télécom Paris (IMT); Département Mathematical and Electrical Engineering (IMT Atlantique - MEE); IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); Collecte Localisation Satellites (CLS); ANR-19-CHIA-0016,OceaniX,Physics-Informed AI for Observation-driven Ocean AnalytiX(2019) |
| Source: |
IGARSS 2022: IEEE International Geoscience and Remote Sensing Symposium ; IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium ; https://imt-atlantique.hal.science/hal-03874778 ; IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Jul 2022, Kuala Lumpur, Malaysia. pp.307-309, ⟨10.1109/IGARSS46834.2022.9884881⟩ |
| Publisher Information: |
CCSD; IEEE |
| Publication Year: |
2022 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
SAR; Sentinel-1; NEXRAD; ocean; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [SDE]Environmental Sciences |
| Subject Geographic: |
Kuala Lumpur; Malaysia |
| Description: |
International audience ; 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. |
| Document Type: |
conference object |
| Language: |
English |
| DOI: |
10.1109/IGARSS46834.2022.9884881 |
| Availability: |
https://imt-atlantique.hal.science/hal-03874778; https://imt-atlantique.hal.science/hal-03874778v1/document; https://imt-atlantique.hal.science/hal-03874778v1/file/IGARSS2022_acolin.pdf; https://doi.org/10.1109/IGARSS46834.2022.9884881 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.3BC362BB |
| Database: |
BASE |