Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach

Domaine:

climategeospatial

Type de record:

paper
Créateur:
MónFabRicNick van de Giesen
Éditeur:
MDP
Hôte:
West African food systems and rural socio-economics are based on rainfed agriculture, which makes society highly vulnerable to rainfall uncertainty and frequent floods and droughts. Reliable rainfall information is currently missing. There is a sparse and uneven rain gauge distribution and, despite continuous efforts, rainfall satellite products continue to show weak correlations with ground measurements. This paper aims to investigate whether water vapor (WV) observations together with temporal information can complement thermal infrared (TIR) data for satellite rainfall retrieval in a Deep Learning (DL) framework. This is motivated by the fact that water vapor plays a key role in the highly seasonal West African rainfall dynamics. We present a DL model for satellite rainfall detection based on WV and TIR channels of Meteosat Second Generation and temporal information. Results show that the WV inhibition of low-level features enables the depiction of strong convective motions usually related to heavy rainfall. This is especially relevant in areas where convective rainfall is dominant, such as the tropics. Additionally, WV data allow us to detect dry air masses over our study area, that are advected from the Sahara Desert and create discontinuities in precipitation events. The developed DL model shows strong performance in rainfall binary classification, with less false alarms and lower rainfall overdetection (FBias <2.0) than the state-of-the-art Integrated MultisatellitE Retrievals for GPM (IMERG) Final Run.

Visit

doi.org

Tasks

computer visionimage classification

Licenses

https://creativecommons.org/licenses/by/4.0/

Similaires

Detection of Sargassum from Sentinel Satellite Sensors Using Deep Learning ApproachAdvancing Ionospheric Predictions in North Africa: A Deep Learning Approach Integrating Ground and Satellite GNSS observationsThe Potential of Deep Learning for Satellite Rainfall Detection over Data-Scarce Regions, the West African SavannaRainRunner: A Deep Learning satellite rainfall retrieval  model for West AfricaEnhancing Rainfall Forecasting in Tunisia: Application of a Hybrid Deep Learning ApproachAutomated rhinoceros detection in satellite imagery using deep learning

Detection of Sargassum from Sentinel Satellite Sensors Using Deep Learning Approach

International audience Since 2011, the proliferation of brown macro-algae of the genu

Advancing Ionospheric Predictions in North Africa: A Deep Learning Approach Integrating Ground and Satellite GNSS observations

The North Africa region faces significant challenges due to the need for ionospheric ground observat

The Potential of Deep Learning for Satellite Rainfall Detection over Data-Scarce Regions, the West African Savanna

Food and economic security in West Africa rely heavily on rainfed agriculture and are threatened by

RainRunner: A Deep Learning satellite rainfall retrieval  model for West Africa

Food and economic safety in West Africa rely heavily on rainfed agriculture and are threatened by cl

Enhancing Rainfall Forecasting in Tunisia: Application of a Hybrid Deep Learning Approach

Accurate rainfall data are essential for hydrological forecasting and climate modeling. However, man

Automated rhinoceros detection in satellite imagery using deep learning

Rhinoceroses face severe threats from poaching, habitat fragmentation, and ongoing habitat degradati