Event Details

Coupling Extended Pareto distributions with random forests to post-process heavy rainfall ensemble forecasts

To satisfy a wide range of end-users, rainfall ensemble forecasts have to be skilful for both low precipitation and extreme events. We introduce local statistical post-processing methods based on Quantile Regression Forests and Gradient Forests with a semi-parametric extension for heavy-tailed distributions. These hybrid methods make use of the forest-based outputs to fit a parametric distribution that is suitable to model jointly low, medium, and heavy rainfall intensities. Our goal is to improve ensemble quality and value for all rainfall intensities. The proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France Meteo France forecast. In addition to improvements in overall performance, hybrid forest-based procedures produced the largest skill improvements for forecasting heavy rainfall events.

Joint work with Maxime Taillardat, Anne-Laure Fougeres and Olivier Mestre.

If you want to speak with Phillipe either before or after the talk please email a.gibberd@lancaster.ac.uk