Evaluation of Daily Temperature Extremes in the ECMWF ERA5 Reanalysis and Operational Weather Forecasts

Abstract

The daily maximum and minimum temperatures are among the most relevant meteorological variables in weather forecasts and climate monitoring. Their spatial and temporal evolution from synoptical to decadal scales are driven by numerous physical processes and climate feedbacks. Despite the significant improvements in weather forecasting over the last decades, forecasts of daily temperature extremes are still hampered by systematic errors. In this work we perform an integrated evaluation of the daily temperature extremes of the (i) ECMWF ERA5 reanalyses and (ii) ECMWF operational weather forecasts. The observations for the evaluation are taken from the Global Historical Climatology Network (GHCN) addressing: (i) the long-term assessment of the analysis produced by the ERA5 reanalysis, comprising a 40-year period (from 1980 to 2019); and (ii) the assessment of the ECMWF operational forecasts for a 5- year period (from 2017 to 2021). The evaluation carried out is global, however considering the GHCN station distribution and temporal availability, particular focus was given to four regions: Europe, Australia, East and West United States. The results identify a general underestimation of the daily maximum and overestimation of the daily minimum temperatures in both ERA5 analysis and operational forecasts, highlighting a known limitation of the ECMWF model in underestimating the diurnal temperature range. Our results also indicate a reduction of the errors in ERA5 when comparing the latest decade with the 1980’s, which is likely to be associated with an enhanced quality of the analysis due to a higher constrain emerging from the satellite data. The ERA5 analysis outperforms 1 day-ahead weather forecasts, which show some degree of improvement in the considered 5-year period, being associated with model upgrades. This work was developed in the framework of the CoCO2 project. CoCO2 project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 958927.