Abstract
AbstractA comparison of moderate to extreme daily precipitation from the ERA‐5 reanalysis by the European Centre for Medium‐Range Weather Forecasts against two observational gridded data sets, EOBS and CMORPH, is presented. We assess the co‐occurrence of precipitation days and compare the full precipitation distributions. The co‐occurrence is quantified by the hit rate. An extended generalized Pareto distribution (EGPD) is fitted to the positive precipitation distribution at every grid point and confidence intervals of quantiles compared. The Kullback–Leibler divergence is used to quantify the distance between the entire EGPDs obtained from ERA‐5 and the observations. For days exceeding the local 90th percentile, the mean hit rate is 65% between ERA‐5 and EOBS (over Europe) and 60% between ERA‐5 and CMORPH (globally). Generally, we find a decrease of the co‐occurrence with increasing precipitation intensity. The agreement between ERA‐5 and EOBS is weaker over the southern Mediterranean region and Iceland compared to the rest of Europe. Differences between ERA‐5 and CMORPH are smallest over the oceans. Differences are largest over NW America, Central Asia, and land areas between 15°S and 15°N. The confidence intervals on quantiles are overlapping between ERA‐5 and the observational data sets for more than 80% of the grid points on average. The intensity comparisons indicate an excellent agreement between ERA‐5 and EOBS over Germany, Ireland, Sweden, and Finland, and a disagreement over areas where EOBS uses sparse input stations. ERA‐5 and CMORPH precipitation intensity agree well over the midlatitudes and disagree over the tropics.
Highlights
Natural hazards related to extreme precipitation cause casualties, damages to infrastructures and buildings and have direct and indirect economic impacts (MunichRE, 2018)
We compare daily precipitation from the ERA-5 reanalysis data set with daily precipitation from two observation-based data sets, EOBS and CMORPH
The comparison addresses three aspects i) the temporal co-occurrence of moderate to high extreme events in two data sets, ii) the agreement of return values for moderate to extreme nonexceedance probabilities derived from the extended generalized Pareto distribution (EGPD), and iii) a comparison of the full precipitation distribution captured by the EGPD using the Kullback-Leibler divergence
Summary
Natural hazards related to extreme precipitation (river floods, flash floods, landslides, debris flows and avalanches) cause casualties, damages to infrastructures and buildings and have direct and indirect economic impacts (MunichRE, 2018). For infrastructure planning and prevention measures, information about rare events, e.g., events that occur on average only once in a hundred years, is important. Such information can be obtained from precipitation data with statistical tools. Different types of global precipitation data sets are available (Sun et al, 2018): global precipitation data sets are based on ground observations, satellite observations, combinations of ground observations and satellite observations and on short term weather model forecasts in reanalyses data sets. Reanalyses ensure consistency of the precipitation data with the atmospheric conditions, which is important for weather and climate process studies. ERA-5 precipitation is computed in short-term forecast started from reanalysis initial conditions (Hennermann, 2020). Comparison with observational data makes sense, keeping in mind that observation data have (partly substantial) uncertainties as well (Sun et al, 2018; Kulie et al, 2010; Prein & Gobiet, 2017)
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