Do Climate Models Need Microphysical and Convective Parameterizations to Generate Accurate Precipitation Fields?

TL;DR

This paper introduces a machine learning approach that predicts precipitation directly from observable ERA5 fields, bypassing traditional microphysical and convective parameterizations in climate models.

Contribution

It demonstrates that ML models can accurately reproduce precipitation fields and diurnal cycles, improving over existing parameterizations and datasets.

Findings

ML_ERA5 closely reproduces ERA5 precipitation across all intensities.

ML_IMERG matches satellite observations and captures extremes better than ERA5.

ML_IMERG reduces ERA5's overestimation of light precipitation.

Abstract

Accurately representing surface precipitation is crucial for the operational use of weather and climate models. Presently, global numerical weather prediction (NWP) models struggle to accurately generate precipitation due to their parametrization of unresolved deep convective clouds and, in regions of grid-resolved ascent, inadequate parameterizations of cloud microphysics. Here we bypass these parameterizations with a machine learning model that diagnoses precipitation from 13 ERA5 fields that are easily observed and assimilated, as opposed for example, to fields like rain or cloud liquid water. We train a pair of models; ML_ERA5 using ERA5 precipitation as the target, and ML_IMERG using a satellite based precipitation product. ML_ERA5 closely reproduces the ERA5 precipitation at all intensities. When evaluated against the satellite dataset, ML_IMERG closely matches observations, notably reproducing the diurnal cycle of the satellite product. ML_IMERG generally captures extremes better than ERA5 while also reducing ERA5's overproduction of light precipitation. When evaluated against a third ground-and-radar-based dataset, ML_IMERG inherits the strengths of the satellite dataset which is superior to ERA5 in the summer months.