Post-processing ensemble forecasts using Ensemble Model Output Statistics (EMOS)

Numerical weather prediction models have systematic errors and biases, which can be reduced through statistical correction methods. The most intuitive approach to correcting weather forecasts is to establish a statistical relationship between the forecast and the corresponding observations. Once the relationship is established, it can be used to correct future forecasts. These approaches, also known as statistical adaptation, are used on a daily basis to improve the quality of operational forecasts. Many methods have been proposed in the literature to calibrate ensemble forecasts, and these can be divided into two main groups: parametric (that has a hypothesis on the underlying distribution) and non-parametric methods. One of the most widespread and used methods of the parametric group is the so-called Ensemble Model Output Statistics (EMOS).

We show the performance of EMOS for the post-processing of probabilistic temperature forecasts at 2m. This method assumes that the underlying distribution is Gaussian (in the case of 2m T), and the parameters to be estimated are therefore the mean and the standard deviation. First results were obtained using the ECMWF operational forecast ensemble against SYNOP observations. Further studies were carried out to investigate the reliability of the estimated parameters using not only the raw ensemble data, but also the ECMWF deterministic operational forecast HRES and the control member. The results showed an improvement, especially for the shorter lead-times. The improvement was measured using weather scores for forecast verification and performance: bias, CRPS (Continuous Ranked Probability Score) and CRPSS (Continuous Ranked Probability Skill Score). These post-processed forecasts will serve as a reference for future studies.