Representation of Precipitation Characteristics and Extremes in Regional Reanalyses and Satellite-and Gauge-Based Estimates over Western and Central Europe

• Published in: Journal of hydrometeorology 2019-06, Vol.20 (6), p.1123-1145
• Main Authors: Lockhoff, M., Zolina, O., Simmer, C., Schulz, J.
• Format: Article
• Language: English
• Subjects: Algorithms; Atmospheric precipitations; Climate; Daily precipitation; Data assimilation; Datasets; Detection; Extreme weather; Frequency dependence; Gauges; Precipitation; Precipitation estimation; Products; Rain gauges; Satellites; Seasons; Statistical analysis; Statistical methods; Topography
• ISSN: 1525-755X; 1525-7541
• DOI: 10.1175/JHM-D-18-0200.1

This paper evaluates several daily precipitation products over western and central Europe, identifies and documents their respective strengths and shortcomings, and relates these to uncertainties associated with each of the products. We analyze one gauge-based, three satellite-based, and two reanalysis-based products using high-density rain gauge observations as reference. First, we assess spatial patterns and frequency distributions using aggregated statistics. Then, we determine the skill of precipitation event detection from these products with a focus on extremes, using temporally and spatially matched pairs of precipitation estimates. The results show that the quality of the datasets largely depends on the region, season, and precipitation characteristic addressed. The satellite and the reanalysis precipitation products are found to have difficulties in accurately representing precipitation frequency with local overestimations of more than 40%, which occur mostly in dry regions (all products) as well as along coastlines and over cold/frozen surfaces (satellite-based products). The frequency distributions of wet-day intensities are generally well reproduced by all products. Concerning the frequency distributions of wet-spell durations, the satellite-based products are found to have clear deficiencies for maritime-influenced precipitation regimes. Moreover, the analysis of the detection of extreme precipitation events reveals that none of the non-station-based datasets shows skill at the shortest temporal and spatial scales (1 day, 0.25°), but at and above the 3-day and 1.25° scale the products start to exhibit skill over large parts of the domain. Added value compared to coarser-resolution global benchmark products is found both for reanalysis and satellite-based products.