ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models

Understand

Numerical simulations of Earth's weather and climate require substantial amounts of computation.

• This has led to a growing interest in replacing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time.
• Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive.
• This has made them a popular target for neural network-based emulators.