ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsProfessional

4 Citations (Scopus)

Abstract

Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenged the simulation paradigm with complex network forecasts. However, they often act as data-driven black-box models that neglect the underlying physics and lack uncertainty quantification. We address these limitations with ClimODE, a spatiotemporal continuous-time process that implements a key principle of advection from statistical mechanics, namely, weather changes due to a spatial movement of quantities over time. ClimODE models precise weather evolution with value-conserving dynamics, learning global weather transport as a neural flow, which also enables estimating the uncertainty in predictions. Our approach outperforms existing data-driven methods in global and regional forecasting with an order of magnitude smaller parameterization, establishing a new state of the art.

Original languageEnglish
Title of host publicationThe Twelfth International Conference on Learning Representations
PublisherInternational Conference on Learning Representations (ICLR)
Pages1-23
Number of pages23
Publication statusPublished - 2024
MoE publication typeD3 Professional conference proceedings
EventInternational Conference on Learning Representations - Messe Wien Exhibition and Congress Center, Vienna, Austria
Duration: 7 May 202411 May 2024
Conference number: 12
https://iclr.cc/

Conference

ConferenceInternational Conference on Learning Representations
Abbreviated titleICLR
Country/TerritoryAustria
CityVienna
Period07/05/202411/05/2024
Internet address

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