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

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.

arXiv comment19 pages, 7 figures, 4 tables

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Cited by in corpus (1)

• Machine learning-based probabilistic forecasting of solar irradiance in Chile