Correcting Precipitation Forecast Displacement Errors Using Machine Learning

Music City Center 

In meteorological forecasting, convection-allowing grid-spacing models have significantly improved the simulation of heavy rainfall associated with warm-season convection. However, substantial errors in precipitation location persist, posing challenges for critical applications such as flood prediction. In this study, we develop machine learning (ML) tools to correct displacement errors in High-Resolution Ensemble Forecast (HREF) members using detailed mesoscale weather data from the Storm Prediction Center. The Method for Object-based Diagnostic Evaluation (MODE) was employed to identify key precipitation object characteristics, which served as inputs for ML models designed to refine centroid location errors in mesoscale convective systems across eight HREF ensemble members. Trained on data from 2018 to 2023, the models were tested in real-time during the 2024 Flash Flood and Intense Rainfall experiments. The best-performing ML model achieved an average reduction of 35–51% in storm centroid location error over the original HREF forecasts, demonstrating its potential for enhancing flood prediction accuracy.

Mesoscale convective systems

Quantitative precipitation forecast

Mesoscale weather data

Great-circle distance

Machine learning postprocessor

Probability matched mean 

Section on Statistics and the Environment