Deep learning for improved global precipitation in numerical weather prediction systems

TL;DR

This paper demonstrates that a deep learning UNET model can significantly improve global precipitation predictions by learning physical relationships from reanalysis data, outperforming current operational models.

Contribution

It introduces a residual learning-based UNET architecture for data-driven precipitation modeling, showing potential to enhance operational weather forecasts.

Findings

Deep learning model doubles the skill of operational models in correlation metrics.

Residual UNET effectively captures physical relationships in precipitation formation.

Results suggest potential for hybrid models in future operational forecasting.

Abstract

The formation of precipitation in state-of-the-art weather and climate models is an important process. The understanding of its relationship with other variables can lead to endless benefits, particularly for the world's monsoon regions dependent on rainfall as a support for livelihood. Various factors play a crucial role in the formation of rainfall, and those physical processes are leading to significant biases in the operational weather forecasts. We use the UNET architecture of a deep convolutional neural network with residual learning as a proof of concept to learn global data-driven models of precipitation. The models are trained on reanalysis datasets projected on the cubed-sphere projection to minimize errors due to spherical distortion. The results are compared with the operational dynamical model used by the India Meteorological Department. The theoretical deep learning-based model shows doubling of the grid point, as well as area averaged skill measured in Pearson correlation coefficients relative to operational system. This study is a proof-of-concept showing that residual learning-based UNET can unravel physical relationships to target precipitation, and those physical constraints can be used in the dynamical operational models towards improved precipitation forecasts. Our results pave the way for the development of online, hybrid models in the future.