Evaluation Of Drain, A Deep-Learning Approach To Rain Retrieval From Gpm Passive Microwave Radiometer. - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Geoscience and Remote Sensing Année : 2023

Evaluation Of Drain, A Deep-Learning Approach To Rain Retrieval From Gpm Passive Microwave Radiometer.

Nicolas Viltard
Vibolroth Sambath
Pierre Lepetit
  • Fonction : Auteur
  • PersonId : 1039833
Audrey Martini
  • Fonction : Auteur
  • PersonId : 946557
Laurent Barthès
  • Fonction : Auteur
  • PersonId : 968696
Cécile Mallet

Résumé

Retrieval of rain from Passive Microwave radiometers data has been a challenge ever since the launch of the first Defense Meteorological Satellite Program in the late 80s. Enormous progress has been made since the launch of the Tropical Rainfall Measuring Mission (TRMM) in 1997 but until recently the data were processed pixel-by-pixel or taking a few neighboring pixels into account. Deep learning has obtained remarkable improvement in the computer vision field, and offers a whole new way to tackle the rain retrieval problem. The Global Precipitation Measurement (GPM) Core satellite carries similarly to TRMM, a passive microwave radiometer and a radar that share part of their swath. The brightness temperatures measured in the 37 and 89 GHz channels are used like the RGB components of a regular image while rain rate from Dual Frequency radar provides the surface rain. A U-net is then trained on these data to develop a retrieval algorithm: Deep-learning RAIN (DRAIN). With only four brightness temperatures as an input and no other a priori information, DRAIN is offering similar or slightly better performances than GPROF, the GPM official algorithm, in most situations. These performances are assumed to be due to the fact that DRAIN works on an image basis instead of the classical pixel-by-pixel basis.
Fichier principal
Vignette du fichier
DRAIN_Paper_Revision.pdf (8.38 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04011552 , version 1 (02-03-2023)
hal-04011552 , version 2 (01-06-2023)

Licence

Paternité

Identifiants

Citer

Nicolas Viltard, Vibolroth Sambath, Pierre Lepetit, Audrey Martini, Laurent Barthès, et al.. Evaluation Of Drain, A Deep-Learning Approach To Rain Retrieval From Gpm Passive Microwave Radiometer.. IEEE Transactions on Geoscience and Remote Sensing, In press, ⟨10.1109/TGRS.2023.3293932⟩. ⟨hal-04011552v2⟩
86 Consultations
24 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More