Random Forests’ Postprocessing Capability of Enhancing Predictive Skill on Subseasonal Timescales - a Flow-Dependent View on Central European Winter Weather

Weather predictions two to four weeks in advance, called the subseasonal timescale, are highly relevant for socio-economic decision makers. Unfortunately, the skill of numerical weather prediction models at this timescale is generally low. Here, we use probabilistic Random Forest- (RF) based machine learning models to postprocess the Sub-seasonal to Seasonal (S2S) reforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF). We show, that these models are able to improve the forecasts slightly in a 20-winter mean at lead times of 14 , 21 and 28 days for wintertime Central European mean 2-meter temperatures compared to the lead-time-dependent mean bias corrected ECMWF’s S2S reforecasts and RF-based models using only reanalysis data as input. Predictions of the occurrence of cold wave days are improved at lead times of 21 and 28 days. Thereby, forecasts of continuous temperatures show a better skill than forecasts of binary occurrences of cold wave days. Furthermore, we analyze if the skill depends on the large-scale flow configuration of the atmosphere at initialization, as represented by Weather Regimes (WR). We find that the WR at the start of the forecast influences the skill and its evolution across lead times. These results can be used to assess the conditional improvement of forecasts initialized during one WR in comparison to forecasts initialized during another WR.