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

arXiv:2404.10024v1 Announce Type: cross
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 …

abstract act arxiv box climate cs.ai cs.et cs.lg data data-driven deep learning forecasting however network numerical paradigm physics physics.ao-ph physics-informed prediction quantification simulation simulations the simulation transformers type uncertainty weather weather forecasting weather prediction