Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile

TL;DR

This study evaluates statistical post-processing methods, specifically EMOS and Bayesian model averaging, to improve the calibration and accuracy of ensemble temperature forecasts in Santiago de Chile, accounting for altitude differences.

Contribution

It introduces an altitude-adapted parameter estimation approach for ensemble post-processing, demonstrating improved forecast skill over raw ensembles.

Findings

All post-processing methods improved forecast calibration and accuracy.

EMOS with altitude-based parameter estimation achieved the best results.

Post-processing significantly reduces underdispersion in ensemble forecasts.

Abstract

Currently all major meteorological centres generate ensemble forecasts using their operational ensemble prediction systems; however, it is a general problem that the spread of the ensemble is too small, resulting in underdispersive forecasts, leading to a lack of calibration. In order to correct this problem, different statistical calibration techniques have been developed in the last two decades. In the present work different post-processing techniques are tested for calibrating 9 member ensemble forecast of temperature for Santiago de Chile, obtained by the Weather Research and Forecasting (WRF) model using different planetary boundary layer and land surface model parametrization. In particular, the ensemble model output statistics (EMOS) and Bayesian model averaging techniques are implemented and since the observations are characterized by large altitude differences, the estimation of model parameters is adapted to the actual conditions at hand. Compared to the raw ensemble all tested post-processing approaches significantly improve the calibration of probabilistic and the accuracy of point forecasts. The EMOS method using parameter estimation based on expert clustering of stations (according to their altitudes) shows the best forecast skill.