Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting

Abstract

Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric extension for heavy-tailed distributions. Our goal is to improve ensemble quality for all types of precipitation events, heavy-tailed included, subject to a good overall performance. Our hybrid proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the M{é}t{é}o-France ensemble prediction system called PEARP. They provide calibrated pre-dictive distributions and compete favourably with state-of-the-art methods like Analogs method or Ensemble Model Output Statistics. In particular, hybrid forest-based procedures appear to bring an added value to the forecast of heavy rainfall.

References in corpus (9)

• Neural networks for post-processing ensemble weather forecasts
• Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics
• Extended Generalised Pareto Models for Tail Estimation
• Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction
• Probabilistic wind speed forecasting on a grid based on ensemble model output statistics
• Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting
• Combining predictive distributions for statistical post-processing of ensemble forecasts
• Mixture EMOS model for calibrating ensemble forecasts of wind speed
• Combining low-dimensional ensemble postprocessing with reordering methods

Cited by in corpus (5)

• Neural networks for post-processing ensemble weather forecasts
• Isotonic Distributional Regression
• A new low-cost technique improves weather forecasts across the world
• Machine learning for total cloud cover prediction
• Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk