Seamless multi-model postprocessing for air temperature forecasts in complex topography

TL;DR

This paper presents a multi-model statistical postprocessing method that combines high-resolution and global ensemble forecasts to improve local air temperature predictions in complex terrain, achieving significant accuracy gains.

Contribution

It introduces a seamless multi-model EMOS approach that enhances temperature forecast calibration and accuracy over single-model methods in complex topography.

Findings

Mixed EMOS improves forecasts by 30% over high-resolution NWP.

Outperforms single-model EMOS by 8-12%.

Valley locations benefit most from model combination.

Abstract

Statistical postprocessing is routinely applied to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only processing the high-resolution NWP. We calibrate and combine 2-m air temperature predictions for a large set of Swiss weather stations at the hourly time-scale. The multi-model EMOS approach ('Mixed EMOS') is able to improve forecasts by 30\% with respect to direct model output from the high-resolution NWP. A detailed evaluation of Mixed EMOS reveals that it outperforms either single-model EMOS version by 8-12\%. Valley location profit particularly from the model combination. All forecast variants perform worst in winter (DJF), however calibration and model combination improves forecast quality substantially.