Evaluation of ERA5 and ERA5-Land reanalysis precipitation datasets over Spain (1951–2020)

Abstract

Reanalysis precipitation estimates are widely used in the fields of meteorology and hydrology because they can provide physical, spatial, and temporal coherent long time series at a global scale. Nevertheless, as a pre-requisite for many applications their performance needs to be assessed. The objective of this study was to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) latest fifth-generation reanalysis precipitation products, i.e., ERA5 and ERA5-Land, at country scale in Spain. For doing so, we compared it against a high-resolution precipitation product of the Spanish Meteorological Agency which spans approximately 70 years (1951–2020). A comprehensive assessment (continuous, categorical, probability distribution function (pdf), spatial pattern, and temporal trend) was performed in order to ascertain the quality of the reanalysis products. Results of the analysis revealed a general agreement between observations and ERA5-Land/ERA5 estimates: spearman correlation values between 0.5 and 0.9, Root Mean Square Error (RMSE) mostly between 2 and 8 mm/d and Kling Gupta Efficiency (KGE) values >0.4. Categorical assessment additionally indicated a good performance (Heiken Skill score (HSS) score, also known as kappa, between 0.4 and 0.8). Nevertheless, performance was found to be dependent on the climatic region, precipitation intensity and orography. Correlation revealed a north-west (higher values) south-east (lower values) spatial gradient while relative bias (RBIAS) and RMSE spatial patterns were positively correlated with slope (ρ = 0.41/0.35, 0.69/0.70, respectively). In addition, as indicated by the categorical analysis, along the Mediterranean coast a wet bias (i.e., overestimation of days with precipitation) was found. Reanalysis detection capacity (kappa) shown a negative correlation with the slope (ρ = −0.29/−0.34). Worst model performance is obtained during summer months, with a generalized overestimation. The pdf assessment revealed that the ERA5-Land/ERA5 tended to overestimate light (≥1 and < 5 mm/day), and moderate (≥5 and < 20 mm/day) precipitation categories while underestimating the heavy (≥20 and < 40 mm/day) and violent (≥40 mm/day) categories. Moderate precipitation provided the best detection capacity, as indicated by the precipitation-intensity analysis. ERA5-Land/ERA5 showed a good capacity to reproduce the spatial patterns and temporal trends of the observations. ERA5-Land and ERA5, with a different spatial resolution, performed very similar in all the analysis considered. Mediterranean and northern coast were highlighted as the most critical for reanalysis modelling purposes because of its performance.