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Estimating extreme precipitation events over complex terrain is challenging but crucial for evaluating the performance of climate models for the present climate and expected changes of the climate in the future. New satellites operating in the microwave wavelengths have started to open new opportunities for performing such estimation at adequate temporal and spatial scales and within sensible error limits. This paper illustrates the feasibility and limits of estimating precipitation extremes from satellite data for climatological applications. Using a high-resolution gauge database as ground truth, it was found that global precipitation measurement (GPM) constellation data can provide valuable estimates of extreme precipitation over the southern slopes of the Pyrenees, a region comprising several climates and a very diverse terrain (a challenge for satellite precipitation algorithms). Validation using an object-based quality measure showed reasonable performance, suggesting that GPM estimates can be advantageous reference data for climate model evaluation. [ABSTRACT FROM AUTHOR]

This paper evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG-F) over Europe for the period 2014–2018 in order to evaluate application of the retrievals to hydrology. IMERG-F is compared with a large pan-European precipitation dataset built on rain gauge stations, i.e., the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R2 = 0.8), important discrepancies are revealed in mountainous regions, specifically the Alps, Pyrenees, west coast of the British Isles, Scandinavia, the Iberian and Italian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG-F has no gauges available for adjustment. If rain gauges are available, IMERG-F yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG-F systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG-F overestimates precipitation in the British Isles, Italian Peninsula, Adriatic coastline, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data. [ABSTRACT FROM AUTHOR]

A gridded precipitation dataset derived from the high-density rain gauge network of the Ebro River Basin Authority is used to evaluate the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG) level-3 estimates. Although aggregated values compare well, several differences are found between climate regions. The research investigates the role of orography and gauge density on IMERG performance. There are important discrepancies over un-instrumented areas in the Pyrenees (R2 = 0.31) but the correlation dramatically increases (R2 > 0.71) when at least one rain gauge is available for calibration, even in complex, high-altitude terrain (>1500 m). IMERG overestimates precipitation at both lower altitudes (<500 m), especially in summer and autumn because of convective activity, and mid-altitudes (600–1200 m) in the northwestern study area, where weather is dominated by the advection of wet maritime air masses. The main conclusion is that IMERG performance strongly depends on altitude and the precipitation regime. IMERG is nonetheless a suitable alternative to gridded gauge-derived only products for hydrologic operations, especially in areas with a sparse rain gauge network. • The performance of IMERG precipitation product in complex terrain areas is evaluated. • Seasonal variability and altitude play a key role in product performance. • IMERG compares well at aggregated level but underestimates precipitation maxima. • Best scores are at mid-elevations and the worst are at low and high elevations. • IMERG is useful in areas where rain gauges are sparse, even in complex regions. [ABSTRACT FROM AUTHOR]