Daily precipitation events

For historical data, the model performance of ERA5 and the meteoblue Learning Multimodel (mLM) is significantly better than the satellite observation CHIRPS2. Satellite observations typically have a larger model skill than numerical weather forecast models for heavy precipitation and close to the equator. 

Probability of detection (POD), false alarm rates (FAR) and Heidke skill score (HSS) for three different daily precipitation events (1 mm; 10 mm; 50 mm) for the historical reanalysis model ERA5, the numerical weather forecast model GFS, the satellite observation CHIRPS2 and the meteoblue multi-model.
  Daily precipitation > 1 mm Daily precipitation > 10 mm Daily precipitation > 50 mm
ERA5 0.69 0.51 0.45 0.43 0.64 0.35 0.11 0.76 0.14
GFS 0.69 0.54 0.42 0.40 0.69 0.30 0.09 0.83 0.12
CHIRPS2 0.41 0.55 0.30 0.42 0.69 0.31 0.18 0.79 0.19
NEMS 0.60 0.50 0.42 0.39 0.65 0.30 0.09 0.80 0.13
meteoblue Learning Multimodel (mLM) 0.70 0.49 0.47 0.48 0.64 0.36 0.09 0.73 0.14

Heidke Skill Score (HSS) for precipitation events of > 1mm/day for the reanalysis model ERA5 (not available as forecast) used for long term historical analysis. Verification is based on all daily data of the year 2017.

Annual precipitation sums

The meteoblue multimodel mix is significantly better than the historical reanalysis model ERA5 for annual precipitation sums. We recommend the use of the meteoblue Learning Multimodel (mLM) for the operational forecast, because daily precipitation events as well as annual precipitation sums are satisfactorily reproduced. Note that the measured precipitation sums in Romania are inaccurate, resulting in non-reliable values of the mean percentage error (MPE) in this region. 

MPE [%] for the historical reanalysis model ERA5. Verification is based on all daily data of the year 2017. 

MPE [%] for the meteoblue Learning Multimodel (mLM) used in operational forecasting. Verification is based on all daily data of the year 2017.