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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid

Authors: Schmit, Max; Weiler, Markus;

German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid

Abstract

Raster Meta informations DWD-grid_ma_1991_2020_DGM25.tif Sources Deutscher Wetterdienst (DWD) (2021): Vieljährige Raster der potentiellen Evapotranspiration über Gras (per Kalendermonat) 1991-2020. Version v1.x: Climate Data Center (CDC). Online available onhttps://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/evapo_p Deutscher Wetterdienst (DWD) (2021): Raster der vieljährigen Mittel der Niederschlagshöhe für Deutschland 1991-2020. Version v1.0: Climate Data Center (CDC). Online available on https://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/precipitation Deutscher Wetterdienst (DWD) (2021): Raster der vieljährigen Mittel der Lufttemperatur (2 m) für Deutschland 1991-2020. Version v1.0: Climate Data Center (CDC). Online available on https://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/air_temperature_mean Bands n_wihj: the precipitation sum of the winter months (october-march) in mm, from source 2 n_sohj: the precipitation sum of the summer months (april-september) in mm, from source 2 n_yearly: the yearly precipitation sum in mm, from source 2 t_yearly: the yearly mean temperature in 1/10°C, from source 3 et_yearly: the yearly potential evapotranspiration sum in mm, from source 1: parameter VPGB (grass reference potential evapotranspiration from AMBAV Model) HYRAS_ma_1991_2020_DGM25.tif Sources Rauthe, M. et al. 2013. A Central European precipitation climatology. Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift. doi:10.1127/0941-2948/2013/0436. Online available on https://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/hyras_de/precipitation Bands n_HYRAS_wihj: the precipitation sum of the winter months (october-march) in mm n_HYRAS_sohj: the precipitation sum of the summer months (april-september) in mm n_HYRAS_year: the yearly precipitation sum in mm REGNIE_ma_1991_2020_DGM25.tif Sources Deutscher Wetterdienst (DWD). REGNIE Dataset. Online avalailable on https://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/regnie/ Bands n_regnie_wihj: the precipitation sum of the winter months (october-march) in mm n_regnie_sohj: the precipitation sum of the summer months (april-september) in mm n_regnie_year: the yearly precipitation sum in mm

Overview These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model. The base rasters from DWD are: HYRAS precipitation REGNIE precipitation DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground) To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model. Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus. The following image illustrates the generation of the refined rasters on a small example window:

Country
Germany
Related Organizations
Keywords

Meteorology, 550, Germany, Hydrology, 551

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average