Abstract
A new approach for modeling daily precipitation (RR) at very high spatial resolution (25 m × 25 m) was introduced. It was used to develop the Precipitation Atlas for Germany (GePrA). GePrA is based on 2357 RR time series measured in the period 1981–2018. It provides monthly percentiles (p) of the large-scale RR patterns which were mapped by a thin plate spline interpolation (TPS). A least-squares boosting (LSBoost) approach and orographic predictor variables (PV) were applied to integrate the small-scale precipitation variability in GePrA. Then, a Weibull distribution (Wei) was fitted to RRp. It was found that the mean monthly sum of RR ( R R ¯ s u m ) is highest in July (84 mm) and lowest in April (49 mm). A great dependency of RR on the elevation (ε) was found and quantified. Model validation at 425 stations showed a mean coefficient of determination (R2) of 0.80 and a mean absolute error (MAE) of less than 10 mm in all months. The high spatial resolution, including the effects of the local orography, make GePrA a valuable tool for various applications. Since GePrA does not only describe R R ¯ s u m , but also the entire monthly precipitation distributions, the results of this study enable the seasonal differentiation between dry and wet period at small scales.
Highlights
Precipitation impacts the environment in a number of ways
Model validation at 425 stations showed a mean coefficient of determination (R2 ) of 0.80 and a mean absolute error (MAE) of less than 10 mm in all months
It was demonstrated that the annual precipitation cycle depends on regions and orographic properties such as elevation, relative elevation, and topographic sheltering
Summary
Precipitation impacts the environment in a number of ways. It drives the water cycle by determining the availability of water [1] and by affecting the exchange of water between the atmosphere and the land surface [2]. Many sectors, including forestry, agriculture, water resource management, food security, hydropower, and disaster management, depend on the spatiotemporal dynamics of precipitation regimes [2,4]. They are in need of accurate, local information on the statistical properties of precipitation [5]. Precipitation patterns are driven by different temporal cycles of the large-scale atmospheric circulation [13] and diurnal warming [14]
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