Evolution of global rain intensities over the TRMM and GPM era using a deep-learning algorithm to assess the impact of climate change on Earth water cycle

Authors

Laurent Barthès (ORCID: https://orcid.org/0000-0001-5736-5041)
Vibolroth Sambath (ORCID: https://orcid.org/0000-0001-9781-9319)
Audrey Martini
Nicolas Viltard (ORCID: https://orcid.org/0000-0001-5120-1249)
Cécile Mallet (ORCID: https://orcid.org/0000-0002-0943-3510)

Institutions

Atmosphere Systems and Services (FR)
Institut d'Électronique et des Technologies du numéRique (FR)
Laboratoire atmosphères, milieux, observations spatiales (FR)

Publication Details

Journal: SPIRE - Sciences Po Institutional REpository
Published: 2025-04-28
Primary Topic: Climate variability and models
Type: article
Field-Weighted Citation Impact: 0.00

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article

Evolution of global rain intensities over the TRMM and GPM era using a deep-learning algorithm to assess the impact of climate change on Earth water cycle

Laurent Barthès, Vibolroth Sambath, Audrey Martini, Nicolas Viltard et al.

Apr 28, 2025

SPIRE - Sciences Po Institutional REpository

Climate variability and models

article

Evolution of global rain intensities over the TRMM and GPM era using a deep-learning algorithm to assess the impact of climate change on Earth water cycle

Laurent Barthès, Vibolroth Sambath, Audrey Martini, Nicolas Viltard, Cécile Mallet

April 28, 2025article en

Abstract

No abstract available for this paper.

SPIRE - Sciences Po Institutional REpository

Atmosphere Systems and Services (FR), Institut d'Électronique et des Technologies du numéRique (FR), Laboratoire atmosphères, milieux, observations spatiales (FR)

Openalex Percentile: Top 62%

Climate variability and models

Open Access PDF OpenAlex

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