In 2023/2024, the average winter temperature in Germany was *** degrees Celsius. That winter was part of a growing list of warmer winters in the country. Figures had increased noticeably compared to the 1960s. Warmer in the winter Everyone has a different perception of what actually makes a cold or warm winter, but the fact is that winter temperatures are, indeed, changing in Germany, and its 16 federal states are feeling it. Also in 2022/2023, Bremen and Hamburg in the north recorded the highest average figures at around 4 degrees each. The least warm states that year, so to speak, were Thuringia, Saxony, and Bavaria. The German National Meteorological Service (Deutscher Wetterdienst or DWD), a federal office, monitors the weather in Germany. Global warming Rising temperatures are a global concern, with climate change making itself known. While these developments may be influenced by natural events, human industrial activity has been another significant contributor for centuries now. Greenhouse gas emissions play a leading part in global warming. This leads to warmer seasons year-round and summer heat waves, as greenhouse gas emissions cause solar heat to remain in the Earth’s atmosphere. In fact, as of 2022, Germany recorded **** days with a temperature of at least 30 degrees Celcius, which was more than three times the increase compared to 2021.

Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

Temperature in Germany increased to 11.19 celsius in 2024 from 10.89 celsius in 2023. This dataset includes a chart with historical data for Germany Average Temperature.

This statistic shows the average temperature in Germany in winter 2023/24, with a comparison to the previous year, by federal state. That winter, the average temperature in Berlin was *** degrees Celsius.

In 2024, the average summer temperature in Germany was **** degrees Celsius. This was basically unchanged compared to the year before. While figures fluctuated during the given timeline, there were regular peaks, and in general, temperatures had grown noticeably since the 1960s. Not beating the heat German summers are getting hotter, and as desired as warm weather may be after months of winter (which, incidentally, also warms up year after year), this is another confirmation of global warming. Higher summer temperatures have various negative effects on both nature and humans. Recent years in Germany have seen a growing number of hot days with a temperature of at least 30 degrees, with **** recorded in 2023. However, this was a decrease compared to the year before. The number of deaths due to heat and sunlight had peaked in 2015. Rain or shine All the German states saw less sunshine hours in 2023 compared to the previous year. The sunniest states were Baden-Württemberg, Bavaria and Saarland. Meanwhile, summer precipitation in Germany varied greatly during the same timeline as presented in this graph, but 2022 was one of the dryest years yet.

This dataset contains outputs from two runs of a coupled atmosphere-ocean model at DKRZ in Hamburg. The runs were made in 1990 and they include a control run and an IPCC Scenario A run. We received 100 years of monthly 10-year climatologies of 2m temperature, precipitation, net surface solar radiation, and reflected surface solar radiation in GRIB0 format. We also received outputs from 100-year transient runs (control, IPCC Scenario A, and IPCC Scenario D). These included monthly means of 59 parameters at the surface and 15 isobaric levels. We were notified in May 1993 that there was a problem with the vertical interpolation in those runs, so the data are no longer in our public distribution, but they remain in our archive.

In winter 2024/25, the average temperature in Bremen was 3.6 degrees Celsius. This made it the warmest federal state during this timeline, followed by Schleswig-Holstein. The coldest at the same time was Bavaria, with an average temperature of 0.9 degrees Celsius.

Average temperature since 1881 in Germany, time series for areal means for federal states and combinations of federal states.

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:

Abstract: Climate time series for Germany derived from observations of the German Meteorological Service (Deutscher Wetterdienst / DWD) provided in daily resolution at a grid width of 250 meters for the period from 1961 to 2020 (current status February 2023). The following variables were processed: Daily total global radiation, separately for a horizontal and an inclined plane; daily total precipitation; daily mean, minimum and maximum 2m-air temperature; daily mean water vapor saturation deficit; daily mean wind speed. The temperature data sets are available in two different versions: V5 including a residual correction and V6 without.

TableOfContents: Daily total global radiation at horizontal plane (grhds); daily total global radiation at inclined plane (grids); daily total precipitation (rrds); daily mean water vapor saturation deficit (sddm); daily mean 2m-air temperature (tadm); daily minimum 2m-air temperature (tadn); daily maximum 2m-air temperature; daily mean wind speed (wsdm)

TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate: 1961-01-01 00:00:00; temporalExtent_endDate: 2020-12-31 23:59:59; temporalDuration: 60; temporalDurationUnit: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none

Methods: Spatialization of gridded climate fields is performed, merging Model Output Statistics (MOS) downscaling with surface parameterization techniques (Böhner and Antonic, 2009; Böhner and Bechtel, 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg and Böhner (2023).

A description of the methods used can be found in:

Dietrich, H.; Wolf, T.; Kawohl, T.; Wehberg, J.; Kändler, G.; Mette, T.; & Röder, A. & Böhner, J. (2019). Temporal and Spatial High-Resolution Climate Data from 1961 to 2100 for the German National Forest Inventory (NFI). Annals of Forest Science 76, 6. https://doi.org/10.1007/s13595-018-0788-5

Kawohl, T.; Dietrich, H.; Wehberg, J.; Böhner, J.; Wolf, T. & Röder, A. (2017). Das Klima in 80 Jahren – Wein- statt Waldbau? – AFZ-Der Wald 15: 32-35.

For GIS-based Terrain-parameterization methods and their application in statistical-dynamical downscaling see, e.g.:

Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015.

Böhner, J. & Bechtel, B. (2018): GIS in Climatology and Meteorology. – In: Huang, B. [Ed.]: Comprehensive Geographic Information Systems. – Vol. 2, pp. 196–235. Oxford: Elsevier. http://dx.doi.org/10.1016/B978-0-12-409548-9.09633-0.

Quality: --

Units: MJ/m2; MJ/m2; mm; hPa; degC; degC; degC; m/s

GeoLocation: westBoundCoordinate: 278750; westBoundCoordinateUnit: m; eastBoundCoordinate: 923000; eastBoundCoordinateUnit: m; southBoundCoordinate: 5234000; southBoundCoordinateUnit: m; northBoundCoordinate: 6102750; northBoundCoordinateUnit: m; ProjectCoordinateSystem: Transverse_Mercator; ProjectionCoordinateSystemParameters: [+proj=utm +datum=WGS84 +zone=32 +no_defs]. geoLocationPlace:Germany; UTMZone: 32

Size: Files are first packed into zip-archives and then further grouped together into one tar-archive per variable and 10-year period. The original file size is between about 4 and 7.5 GB per year and variable. The file size of the tar archives ranges between 3 GB and 70 GB.

Format: SAGA-Grid (.sgrd), https://saga-gis.sourceforge.io/en/index.html

DataSources: DWD Climate Data Center (CDC): Historical daily station observations (temperature, pressure, precipitation,sunshine duration, etc.) for Germany, version v21.3, 2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-kl-historical and DWD Climate Data Center (CDC): Historical daily precipitation observations for Germany, version v21.3,2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-more_precip-historical. http://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/

Contact: Prof. Dr. Jürgen Böhner, Universität Hamburg, Center for Earth System Research and Sustainability, Institute of Geography, Bundesstraße 55, 20146 Hamburg, juergen.boehner (at) uni-hamburg.de; https://www.geo.uni-hamburg.de/en/geographie/mitarbeiterverzeichnis/boehner.html

Webpage: https://www.waldklimafonds.de/ and https://www.lwf.bayern.de/boden-klima/wasserhaushalt/223446/index.php

This statistic shows the average precipitation amount in winter in Germany from 1960 to 2024. Thus far in 2023/ 2024, there were 270 liters per square meter recorded during winter.

Data sets of current German weather stations updated hourly or every twelve hours. Data sets, in German, include: * Daily mean values ??of temperature, updated hourly. Daily archive since 29.1.2008 * Daily maximum and minimum temperature, updated every 12 hours. Daily archive since 21.7.2008 * Monthly mean values ??of temperature and deviation, updated daily . * Rainfall in the last 12 hours and monthly total, updated every 12 hours . * Monthly totals of precipitation and relative to langj. means in%, updated every 12 hours. Monthly Archive since Feb. 2008 * Air pressure and pressure tendency, updated hourly.

Abstract: Climate time series for Germany derived from observations of the German Meteorological Service (Deutscher Wetterdienst / DWD) provided in daily resolution at a grid width of 250 meters for the period from 1961 to 2020 (current status February 2023). The following variables were processed: Daily total global radiation, separately for a horizontal and an inclined plane; daily total precipitation; daily mean, minimum and maximum 2m-air temperature; daily mean water vapor saturation deficit; daily mean wind speed. The temperature data sets are available in two different versions: V5 including a residual correction and V6 without. TableOfContents: Daily total global radiation at horizontal plane (grhds); daily total global radiation at inclined plane (grids); daily total precipitation (rrds); daily mean water vapor saturation deficit (sddm); daily mean 2m-air temperature (tadm); daily minimum 2m-air temperature (tadn); daily maximum 2m-air temperature; daily mean wind speed (wsdm) TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate: 1961-01-01 00:00:00; temporalExtent_endDate: 2020-12-31 23:59:59; temporalDuration: 60; temporalDurationUnit: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none Methods: Spatialization of gridded climate fields is performed, merging Model Output Statistics (MOS) downscaling with surface parameterization techniques (Böhner and Antonic, 2009; Böhner and Bechtel, 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg and Böhner (2023). A description of the methods used can be found in: Dietrich, H.; Wolf, T.; Kawohl, T.; Wehberg, J.; Kändler, G.; Mette, T.; & Röder, A. & Böhner, J. (2019). Temporal and Spatial High-Resolution Climate Data from 1961 to 2100 for the German National Forest Inventory (NFI). Annals of Forest Science 76, 6. https://doi.org/10.1007/s13595-018-0788-5 Kawohl, T.; Dietrich, H.; Wehberg, J.; Böhner, J.; Wolf, T. & Röder, A. (2017). Das Klima in 80 Jahren – Wein- statt Waldbau? – AFZ-Der Wald 15: 32-35. For GIS-based Terrain-parameterization methods and their application in statistical-dynamical downscaling see, e.g.: Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015. Böhner, J. & Bechtel, B. (2018): GIS in Climatology and Meteorology. – In: Huang, B. [Ed.]: Comprehensive Geographic Information Systems. – Vol. 2, pp. 196–235. Oxford: Elsevier. http://dx.doi.org/10.1016/B978-0-12-409548-9.09633-0. Quality: -- Units: MJ/m2; MJ/m2; mm; hPa; degC; degC; degC; m/s GeoLocation: westBoundCoordinate: 278750; westBoundCoordinateUnit: m; eastBoundCoordinate: 923000; eastBoundCoordinateUnit: m; southBoundCoordinate: 5234000; southBoundCoordinateUnit: m; northBoundCoordinate: 6102750; northBoundCoordinateUnit: m; ProjectCoordinateSystem: Transverse_Mercator; ProjectionCoordinateSystemParameters: [+proj=utm +datum=WGS84 +zone=32 +no_defs]. geoLocationPlace:Germany; UTMZone: 32 Size: Files are first packed into zip-archives and then further grouped together into one tar-archive per variable and 10-year period. The original file size is between about 4 and 7.5 GB per year and variable. The file size of the tar archives ranges between 3 GB and 70 GB. Format: SAGA-Grid (.sgrd), https://saga-gis.sourceforge.io/en/index.html DataSources: DWD Climate Data Center (CDC): Historical daily station observations (temperature, pressure, precipitation,sunshine duration, etc.) for Germany, version v21.3, 2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-kl-historical and DWD Climate Data Center (CDC): Historical daily precipitation observations for Germany, version v21.3,2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-more_precip-historical. http://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/ Contact: Prof. Dr. Jürgen Böhner, Universität Hamburg, Center for Earth System Research and Sustainability, Institute of Geography, Bundesstraße 55, 20146 Hamburg, juergen.boehner (at) uni-hamburg.de; https://www.geo.uni-hamburg.de/en/geographie/mitarbeiterverzeichnis/boehner.html Webpage: https://www.waldklimafonds.de/ and https://www.lwf.bayern.de/boden-klima/wasserhaushalt/223446/index.php Created within project "Veränderte Produktivität und Kohlenstoffspeicherung der Wälder Deutschlands angesichts des Klimawandels" (WP-KS-KW, https://www.waldklimafonds.de/index.php?id=13913&fkz=22WC400312) funded by the Federal Ministry of Food and Agriculture (BMEL). The data update until 2020 was performed in the project "Wasserhaushalt im Klimawandel" (WHH-KW, https://www.fnr.de/index.php?id=11150&fkz=22WK414104) funded by the Federal Ministry of Food and Agriculture (BMEL) and the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) within the framework of the Forest Climate Fund.

Context

This dataset contains weather details of five most important countries including Germany and Italy which was affected greatly with Covid_19 spread.

Content

It is believed that climate conditions might be one of the major reasons for the spread of covid_19. This Dataset contains climate changes occured from 19th February to 17th April 2020. This contains the climate changes recorded for every 10 mins on the aforementioned countries.

File Description

The file contains below columns:

Temperature - Actual Temperature Recorded in degree celsius Wind_speed - Wind Speed Description - Description of the current weather Weather - Categorical value depicts the types of weather name - Depicts the country name temp_min - Minimum temperature recorded temp_max - Maximum temperature recorded

Other variables are pretty much self explanatory.

Acknowledgements

As part of my thesis project, this dataset was being prepared with a help of web scraper which will trigger an open source REST API end point for every 10 minutes. It was hosted in an EC2 instance which will update a CSV file periodically. Thought that this could contribute for the analysis of Covid_19 spread, hence shared the same.

Hope this could be useful!

Inspiration

As mentioned earlier, Climate could be one of the significant factors which spreads covid_19. Need to analyse further on the same. Italy could be considered for the research as we have the climate data for that country. Alongside, this country was affected largely.

Influences of climate change on urban climate are often investigated in the context of increased values for high temperatures or precipitation extremes. As a consequence, many studies concentrate on the climate change impacts that happen during summer. When looking at projected temperature and precipitation changes, mean changes are larger in winter than in summer, at least for northern Germany. At the same time, the distribution of temperature is broadened, which implies that winters with temperatures below freezing point or snowfall will still happen in the future.

The aim of this project is to quantify climate change related impacts on the winter climate of Hamburg in detail. For this purpose, an existing canopy resolving model (MITRAS) will be expanded to allow a detailed analysis of precipitation including snow and of frost distribution at the local scale. With this tool the impact on the local winter climate, as well as of adaption measures developed for a changed summer climate are investigated. To ensure model results occur in time a code optimisation and parallelisation is part of this project.

This project was financed within the framework of the Helmholtz Institute for Climate Service Science (HICSS), a cooperation between Climate Service Center Germany (GERICS) and University Hamburg, Germany (https://www.hicss-hamburg.de/projects/urban_winter_hamburg/).

Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10, 79098 Freiburg im Breisgau, Germany.

• Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2024.
• Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.
• Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).
• Detailed information on gap-filled data is provided.
• Note: All times are provided in UTC, not local time.

For more details read `FRWRTM_2024_AirTemperature_MetaData.txt`.

The diurnal cycle of both air temperature and wind speed is reflected by considerable differences if open site conditions are compared to forests. This new two-hourly, open dataset covering a high spatial and temporal variability, enables multiple purposes and capabilities due to its diversity and sample size. The dataset provides station pairs, each consisting of one station in the open field and one related station in the forest, located in central Europe, more precisely in southern Germany in the Black Forest (Kinzig; Breg; Brugga) and the Bavarian Alps (Dreisäularbach; Nationalpark Berchtesgaden) as well as the Austrian Alps (Brixenbachtal). Associated meta data specify parameters to characterize the environment and the reference between the paired stations.

The air temperature measurements consist of 128 station pairs from 6 winter seasons and 6 different study sites with a total amount of 173 682 (time steps with availability of open and forest values). The wind speed measurements consist of 64 station pairs from 3 winter seasons and 4 different study sites with a total amount of 115 211. The dataset was initially collected to study the spatio-temporal variability of micrometeorological variables describing the energy balance of the snowpack, but is provided for multiple purposes as examining forest effects on micrometeorological data, validating climate or snow models as well as developing new transfer functions.

Boundary conditions are given below and a comprehensive description of the dataset including analyses and applications follows in the open access article:

Klein, M.; Garvelmann, J.; Förster, K. Revisiting Forest Effects on Winter Air Temperature and Wind Speed—New Open Data and Transfer Functions. Atmosphere 2021, 12, 710. https://doi.org/10.3390/atmos12060710

Meta data The meta data consists of 12 descriptive characteristics. Pair_ID gives an identification name which includes the year of sampling and the acronym of the study site as well as both stations. The Location parameter is a local description of the study site. Elevation, Exposure and Slope have values for the open and forest stations, while Effective_LAI, Canopy_Openness and Distance_Forest_Edge stands for the forest station. With Distance_Open_Station the distance between both stations is designated. The Exposure parameter is defined counterclockwise as follows: 0° and 360° is north, 90° is west and consequently 180° is south and 270° east. Only a few parameters of Distance_Forest_Edge and Distance_Open_Station are not available. These values are marked with NA.

Pair_ID: Identification of the station pair [-]

Location: Local description [-]

Elevation_Open: Elevation in the open field [m a.s.l.]

Elevation_Forest: Elevation in the forest [m a.s.l.]

Exposure_Open: Exposure in the open field counterclockwise (0°/360° = north; 90° = west, etc.)

Exposure_Forest: Exposure in the forest counterclockwise (0°/360° = north; 90° = west, etc.)

Slope_Open: Slope in the open field [°]

Slope_Forest: Slope in the forest [°]

Effective_LAI: Effective leaf area per ground area [-]

Canopy_Openness: Openness of the forest canopy [%]

Distance_Forest_Edge: Distance of the forest station to the closed forest edge [m]

Distance_Forest_Station: Distance between the paired stations [m]

Time series data The time series data consists of air temperature datasets and wind speed datasets, which are named after the Pair_ID described above. According to the two-hour intervals, there are 12 measurements per day. The datasets are structured in the same way as follows: The time stamp (Heading: Date), the measurement in the open (Heading: Air_Temp_Open; Wind_Open) and the measurement in the forest (Heading: Air_Temp_Forest; Wind_Forest). Missing values are marked with NA. Remaining information in terms of number of stations, distribution of observations concerning the study sites and winter seasons, the absolute number of available measurements of both stations as well as additional information are listed following.

Air temperature

128 station pairs (73 open; 59 forest)

Kinzig – KIN (9 station pairs/2012; 10 station pairs/2013)

Breg – BRE (7 station pairs/2012; 9 station pairs/2013; 8 station pairs/2014)

Brugga – BRU (5 station pairs/2013; 14 station pairs/2014; 5 station pairs/2015)

Brixenbachtal – BRX (3 station pairs/2015)

Dreisäulerbach – DSB (7 station pairs/2016; 3 station pairs/2017)

Nationalpark Berchtesgaden – NPB (8 station pairs/2015; 26 station pairs/2016; 14 station pairs/2017)

173 682 total measurements with both values available

Variables: Date [yyyy-MM-dd hh:mm:ss]; Air_Temp_Open [°C]; Air_Temp_Forest [°C]

2 h time interval between measurements

Indication for missing value: NA

Additional information: Air temperature values measured at open stations corrected for radiative heating. Near surface wind speed is measured at 2 m above surface.

Wind speed

64 station pairs (27 open; 34 forest)

Brugga – BRU (5 station pairs/2015)

Brixenbachtal – BRX (3 station pairs/2015)

Dreisäulerbach – DSB (7 station pairs/2016; 3 station pairs/2017)

Nationalpark Berchtesgaden – NPB (7 station pairs/2015; 25 station pairs/2016; 14 station pairs/2017)

115 211 total measurements with both values available

Variables: Date [yyyy-MM-dd hh:mm:ss]; Wind_Open [ms-1]; Wind_Forest [ms-1]

2 h time interval between measurements

Indication for missing value: NA

Additional information: Near surface wind speed is measured at 2 m above surface.

Author Contributions JG led and supervised the field work to collect the data and compiled the dataset. Editing and preparation referring to the publication by JG, MK and KF.

Acknowledgements The presented data was collected during the following research projects:

“Field Observations and Modelling of Spatial and Temporal Variability of Processes Controlling Basin Runoff during Rain on Snow Events” funded by the German Research Foundation (DFG) and carried out at the Chair of Hydrology (PI Stefan Pohl), University of Freiburg, Germany;

“Alpine water resources research: Observing and modeling the spatio-temporal variability of snow dynamics and water- and energy fluxes” funded by Helmholtz Water Alliance and carried out at the Institute of Meteorology and Climate Research (IMK-IFU, PI Jakob Garvelmann, research group Harald Kunstmann), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany. Technical infrastructure from TERENO;

“Storylines of Socio-Economic and Climatic drivers for Land use and their hydrological impacts in Alpine Catchments (STELLA)” funded by the Austrian climate and energy fond and carried out at the Institute of Geography (PI Ulrich Strasser), University of Innsbruck, Austria.

Many thanks to Daniel Günther, Franziska Zieger, Michael Warscher and others for assistance in field work and Emil Blattmann and the staff from KIT-Campus Alpin for technical support. At the University of Innsbruck Elisabeth Mair led the field work within the STELLA-project. Furthermore, we would like to thank Nationalpark of Berchtesgaden for supporting the micrometeorological and snow hydrological measurement campaign.

In June 2025, the average temperature in Berlin was **** degrees Celsius. This was an increase compared to the June a year ago.

DE: Annual Surface Temperature: Change Since 1951 1980 data was reported at 1.304 Number in 2021. This records a decrease from the previous number of 2.499 Number for 2020. DE: Annual Surface Temperature: Change Since 1951 1980 data is updated yearly, averaging 1.302 Number from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 2.506 Number in 2014 and a record low of -0.706 Number in 1996. DE: Annual Surface Temperature: Change Since 1951 1980 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.GGI: Environmental: Climate Risk: OECD Member: Annual.

Data used for the scatter-plots in the ems-2018 extended abstract