Title :

RainBench: Towards Global Precipitation Forecasting from Satellite Imagery

Author(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]

Conference title :

Association for the Advancement of Artificial Intelligence

City :

Virtual

Country :

Royaume-Uni

Start date of the conference :

2021-02-01

HAL domain(s) :

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

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Comment :

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