Yearly mean temperatures (min, mean, max) in 1/10°C and standard deviations of Raster Quadrants (Messtischblatt-Quadranten, MTB_Q) in Germany. The data is computed from a model freely available from the DWD (German Weather Service) from 1 x 1 km grid from 1901 to 2017. The file contains the data of the 1 x 1 km grid aggregated to MTB_Q. The presnted table below is only an exemplary datasheet from a whole list of objects for R. The list is added to this entry as a freeformat file (.Rdata) for download and processing in R. A script how the data were aggregated is also given in the freeformat section of this entry.

This describes the freely available data of the DWD Climate Data Centre. Grids are derived from DWD stations and legally and qualitatively equivalent partner stations in Germany run for climatological and climate related application. Uncertainties are caused by the interpolation method, and erroneous or missing observations. When comparing grid fields for different years, it should be considered that the measurement network has changed over time. The given resolution of 1 km x 1 km is the resolution of the employed digital height model. The gridded data miss processes relevant for local climate (like urban heat island or cold air pools) which are not covered by observations of the station network or cannot be reproduced by the gridding method explained above. The actual information density depends on the station network. 1881 the monthly means of 150 stations were used in the gridding routines. The number of used stations grew steadily to 200 stations at the begin of the 20th century, and 400 before World War II. After the war, the number of stations decreased a short time, growing again to above 500 since 1951. Changes in station height caused by station relocations are accounted for with the interpolation to the reference height.

Whole Germany

1901 - 2017

None

Computing the MTBQ from the 1 x 1 grid took very long. Avoid recalculating the values if possible due to the lengthy procedure. For some MTBQ's no value could be computed due to missing information these may be imputed from geospatial models in the future.

Data was aggregated to MTBQ for each year. As a measure of uncertainity, standard deviations are added.