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

Abstract

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.

Evaluation of Daily Precipitation Extremes in Reanalysis and Gridded Observation‐Based Data Sets Over Germany

“…These models are able to model data which are highly skewed and have heavy‐tailed distributions and, thus, are highly non‐Gaussian. There are also some other methods that were used in previous studies, including using the fractions skill score to assess the skill of a data set to represent the occurrence of extreme events in a reference data set (Lockhoff et al., 2019), and using the Taylor diagram to characterize the spatial structure of extreme events (Kharin et al., 2005).…”

“…Temperature and precipitation are two meteorological variables that are highly relevant to our lives. Previous studies have shown that precipitation extremes in gridded data products are often less reliable, compared to temperature extremes (Donat et al., 2014; Kharin et al., 2005; Lockhoff et al., 2019; Mannshardt‐Shamseldin et al., 2010; Zolina et al., 2004). Therefore, in this paper, we evaluate four gridded data products in terms of their performance on representing daily precipitation extremes over Germany.…”

Changes in Terrestrial Evaporation across Poland over the Past Four Decades Dominated by Increases in Summer Months

“…From the daily gridded values, the country scale, annual air temperature, and precipitation estimates were calculated. Due to E-OBS uncertainties caused by gauge measurement errors, lack of wind corrections, and possible station relocations [38], the areal precipitation averages are considered to be underestimated [39,40]. To overcome the underestimation of the precipitation data available, a correction factor was applied to the annual precipitation totals derived from the E-OBS data.…”

Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

“…Also, they showed that outputs of this model has not any advantage in compared with satellite or reanalysis data in Africa. Lockhoff et al (2019) evaluated some PCP outputs (data of a synoptic station, three satellite-based models, and two reanalysis-based models) in western and central Europe. Their results show that the quality of the datasets largely depends on the region, season, and PCP characteristic.…”

“…Numerous studies have so far been carried out around the world so far on global PCP datasets. They include studies performed in Central and Western Europe (Lockhoff et al 2019), China (An et al 2020;Bai et al 2020;Li et al 2018), Central Asia (Lu et al 2021), Australia and Africa (Awange et al 2019), India (Bhattacharyya et al 2022;Kolluru et al 2020), Thailand (Gunathilake et al 2021), Iraq and Eastern Africa (Salman et al 2019), Kenya, Uganda, and Tanzania (Garibay et al 2021). In addition, various research works in Iran have addressed global precipitation datasets for arid and semiarid regions (Darand and Khandu 2020;Eini et al 2019;Fallah et al 2020;Ghajarnia et al 2015;Hosseini-Moghari et al 2018;Izadi et al 2021;Keikhosravi-Kiany et al 2021;Nasseri et al 2021;Saemian et al 2021;Shayeghi et al 2020;Taghizadeh et al 2021).…”