Abstract
Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explored the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalyzed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets were statistically analyzed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums were examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. However, our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation.
Highlights
IntroductionDue to the high spatiotemporal variability of precipitation, a spatially distributed quantitative estimation of rainfall rates is a challenging task
Precipitation is one of the main drivers of hydrologic and energy cycles and induces a variety of environmental processes such as runoff, erosion or floods and has been acknowledged as an EssentialClimate Variable
The original RADOLAN data suffer from many high outliers causing a positive in theinmean annual precipitation sum
Summary
Due to the high spatiotemporal variability of precipitation, a spatially distributed quantitative estimation of rainfall rates is a challenging task. The “unbiased estimation of high temporal resolution precipitation amount, especially over the oceans, and over areas of complex orography” [1] has been identified as an outstanding scientific and technological challenge. Direct rainfall measurements with rain gauges on the ground can only provide local point scale information. As rain gauges are scarce in many regions, this approach is not sufficient to capture spatial rainfall distribution, especially for smaller-scale convective storm events [2,3,4]. Ground-based weather radar and space-borne satellite observations have emerged as alternative
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