Global post-processing of ERA5 precipitation product via graph-based neural networks

Total precipitation is a key variable of the weather state, accumulated over a given period. Beyond their direct relevance, high-quality precipitation data are of importance for driving downstream applications in hydrology, e.g. river streamflow and runoff forecasting. However, common measurements of precipitation are either precise but sparse (as for in-situ recordings) or global but uncertain (as for spaceborne observations). Though reanalysis products such as ECMWF’s ERA5 provide a best estimate of the state of the atmosphere, the quality of their total precipitation reconstruction is imperfect. Following reports that ERA5 is prone to overestimating the occurrence of drizzle at the cost of underestimating extreme precipitation, prior work explored data-driven models for local post-processing to address the latter. However, the local models employed in preceding work do not easily extend to a global post-processing setup and an exclusive emphasis on outliers limits the ability to represent the full distribution of precipitation intensity, which limits their relevance.

 

In this work, we propose a novel approach for precipitation post-processing which models the entire globe in a single forward pass and models dryness, light rain and heavy rain alike. The post-processer is based on a graph neural network architecture, trained on decades of gauge-calibrated multi-source weighted estimates of precipitation. We demonstrate that our model learns to bias-correct ERA5 total precipitation information and consistently improves upon the baseline while maintaining its global applicability. Further experiments will detail the nature of its improvements and may explore its benefits for downstream applications.