Assessing the Use of Satellite-Based Estimates and High-Resolution Precipitation Datasets for the Study of Extreme Precipitation Events over the Iberian Peninsula

• Published in: Water (Basel) Vol. 10; no. 11; p. 1688
• Main Authors: Hénin, Riccardo, Liberato, Margarida, Ramos, Alexandre, Gouveia, Célia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2018
• Subjects: Climate change; Comparative analysis; Datasets; ERA5; Estimates; extreme precipitation; extreme precipitation events (EPEs); High resolution; Hydrologic cycle; Hydrologic data; Iberian Peninsula; Precipitation; Rain; Rain and rainfall; Rainfall; Real time; real-time satellite retrievals; Seasons; TRMM (TMPA) dataset; Weather forecasting; Portugal; China; Spain; Iberian Peninsula
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w10111688

An assessment of daily accumulated precipitation during extreme precipitation events (EPEs) occurring over the period 2000–2008 in the Iberian Peninsula (IP) is presented. Different sources for precipitation data, namely ERA-Interim and ERA5 reanalysis by the European Centre for Medium-Range Weather Forecast (ECMWF) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), both in near-real-time and post-real-time releases, are compared with the best ground-based high-resolution (0.2° × 0.2°) gridded precipitation dataset available for the IP (IB02). In this study, accuracy metrics are analysed for different quartiles of daily precipitation amounts, and additional insights are provided for a subset of EPEs extracted from an objective ranking of extreme precipitation during the extended winter period (October to March) over the IP. Results show that both reanalysis and multi-satellite datasets overestimate (underestimate) daily precipitation sums for the least (most) extreme events over the IP. In addition, it is shown that the TRMM TMPA precipitation estimates from the near-real-time product may be considered for EPEs assessment over these latitudes. Finally, it is found that the new ERA5 reanalysis accounts for large improvements over ERA-Interim and it also outperforms the satellite-based datasets.