Lessons learnt from implementing a postprocessing suite for probabilistic seamless weather forecasts

MeteoSwiss is currently implementing a new NWP postprocessing suite for providing automated local weather forecasts to the general public. As these forecasts are nowadays mainly accessed via smartphone app, we aimed at global postprocessing approaches, that is, optimizing forecasts not only at observation sites but at any location in Switzerland. The system takes advantage of both regional area and global NWP ensemble models with different forecast horizons for providing seamless probabilistic predictions over two weeks leadtime. Finally, the postprocessing suite also considers operational aspects such as robustness towards missing or delayed input data or the ability to cope with limited reforecasts records for training.

Both ensemble model output statistics (EMOS) and machine learning (ML) methods are applied for postprocessing the target parameters temperature, precipitation, wind and cloud cover. Forecast skill in terms of CRPS improves by up to 30% compared to direct model output, with largest benefits for temperature and wind in areas of complex orography and only marginal gains for precipitation during seasons with a high fraction of convective situations. The postprocessing of multiple NWP sources not only allows seamless forecasts, but also proved more skillful than single-model postprocessing. EMOS postprocessing performed well even in case only short reforecast records were available, but was outperformed by ML approaches given sufficient training data. While this general-purpose postprocessing suite improves forecasts overall, it showed weaknesses in some warning-relevant weather situations. Future developments will aim at extending its applicability to these less frequent situations, target further parameters, and extending the use of ML methods.