Titre :

RainBench: Towards Global Precipitation Forecasting from Satellite Imagery

Auteur(s) :

de Witt, Christian Schroeder [Auteur]
Tong, Catherine [Auteur]
Zantedeschi, Valentina [Auteur]
Department of Computer science [University College of London] [UCL-CS]
MOdel for Data Analysis and Learning [MODAL]
The Inria London Programme [Inria-London]
de Martini, Daniele [Auteur]
Kalaitzis, Freddie [Auteur]
Chantry, Matthew [Auteur]
Watson-Parris, Duncan [Auteur]
Bilinski, Piotr [Auteur]

Titre de la manifestation scientifique :

Association for the Advancement of Artificial Intelligence

Ville :

Virtual

Pays :

Royaume-Uni

Date de début de la manifestation scientifique :

2021-02-01

Discipline(s) HAL :

Informatique [cs]/Apprentissage [cs.LG]
Planète et Univers [physics]/Océan, Atmosphère

Résumé en anglais : [en]

Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning ...
Lire la suite >Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce \textbf{RainBench}, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release \textbf{PyRain}, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Commentaire :

Work completed during the 2020 Frontier Development Lab research accelerator, a private-public partnership with NASA in the US, and ESA in Europe

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL