H169-0004
ERA-5 Daily Precipitation Compared with Observational Datasets
Abstract:
We apply the Extended Generalized Pareto Distribution (EGPD) used by Tencaliec & al. 2019. Thanks to a transition function, this method overcomes the problem of finding a threshold between upper and lower tails. The transition cumulative distribution function of the EGPD is constrained on the upper tail and lower tail to enable a GPD behavior for both small and large extremes.
EGPD is used here to characterize ERA-5 precipitation. ERA-5 is a new ECMWF climate re-analysis dataset that provides a numerical description of the recent climate by combining a numerical weather model with observations. The data set is global with a spatial resolution of 0.25° and currently covers the period from 1979 to present. ERA-5 precipitation is computed from model forecasts and therefore needs validation against observational datasets.
ERA-5 daily precipitation is compared to EOBS precipitation, a gridded dataset spatially interpolated from observations over Europe, and to CMORPH precipitation, a global satellite-based dataset. Simultaneous occurrence of extreme events is assessed with a hit rate. An intensity comparison is conducted with return levels confidence intervals and a Kullback Leibler divergence test, both derived from the EGPD.
Overall, good agreements but also strong mismatches between ERA-5 and the observational datasets can be found, depending on the feature of interest in precipitation data. This work highlights both. For example, extreme event occurrences between ERA5 and the observational datasets appear to agree. The overlap between 95% confidence intervals on return levels depends on the season and the probability of occurrence. Over Europe, the best agreement results are generally reached in region with high station density in EOBS. The global intensity comparison between ERA5 and CMORPH shows a good agreement for moderate return levels, except for some mountainous regions, but presents a large signal of disagreement in the tropics for large return levels.