EGU2020-22298
https://doi.org/10.5194/egusphere-egu2020-22298
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Towards operational post-processing of probabilistic temperature forecasts at MeteoSwiss

Jan Rajczak1, Regula Keller1,2, Jonas Bhend1, Christoph Spirig1, Stephan Hemri1, Lionel Moret1, and Mark Liniger1
Jan Rajczak et al.
  • 1Swiss Federal Office of Meteorology and Climatology, MeteoSwiss
  • 2Center for Climate Systems Modelling, C2SM, ETH Zurich

MeteoSwiss is currently developing a post-processing suite for the territory of Switzerland. The system aims to provide optimized multi-variable (i.e. temperature, precipitation, wind and cloud cover), spatial and probabilistic predictions. The system will combine information in a seamless manner from the in-house short range and regional (COSMO-E/1) of 1 resp. 2 km resolution and the medium range ECMWF IFS NWP systems. At the example of probabilistic temperature forecasts, this contribution discusses recent advances and experiences at developing, applying and operationalizing non-homogenous Gaussian regression, also known as ensemble model output statistics (EMOS).

Over the complex terrain of Switzerland, postprocessing leads to a substantial improvement of temperature forecasts by up to 30% in terms of CRPS with respect to elevation-corrected direct model output (DMO) even by a basic EMOS only relying on DMO of temperature. Incorporating suitable predictors, such as the atmospheric boundary layer height, leads to a further gain in forecast quality. Results also show that combining high- (COSMO-E) and coarse-resolution (IFS) NWP output can not only provide a seamless medium-range forecast, but also further increase prediction skill during the time horizon when both models are available. Finally, we discuss first attempts to produce high-resolution spatial PP fields for arbitrary locations by exploiting a global EMOS framework with multiple static (e.g. geographic characteristics) and dynamic predictors derived from NWP data.

How to cite: Rajczak, J., Keller, R., Bhend, J., Spirig, C., Hemri, S., Moret, L., and Liniger, M.: Towards operational post-processing of probabilistic temperature forecasts at MeteoSwiss, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22298, https://doi.org/10.5194/egusphere-egu2020-22298, 2020

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