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MKonthly assessment of TPW for Europe

Assessment texts on monthly mean tropospheric precipitable water, provided by ECSM - European Climate System Monitoring, WMO Regional Climate Centre (RCC) on Climate Monitoring

Monthly mean snow depth: maps

Maps of monthly mean snow depth derived from SYNOP observations on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring WMO-RA6-RCC-CM

Monthly mean snow depth: grid data

Grids of monthly mean snow depth derived from SYNOP observations on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring WMO-RA6-RCC-CM

WFS - Grundwasserstände und Quellschüttungen

Bei der Darstellung der Messstellen mit aktuellen Messwerten wird unterschieden zwischen Trend (letzter Wert im Vergleich zum Vorwochenwert) und letzter Wasserstand bzw. prozentuale Quellschüttung im Vergleich zum langjährigen Monatsmittelwert. Die Trenddarstellung erfolgt nur dann, wenn der letzte Messwert nicht älter als 8 Tage und der vorletzte Wert nicht älter als 15 Tage ist, die Darstellung im Vergleich zum langj. Monatsmittelwert, wenn mehr als 10 vollständige Abflussjahre vorliegen.

Monthly mean surface air temperature: maps

Maps of monthly mean temperature, derived from CLIMAT bulletins on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring

Monthly mean of sunshine duration: grid data

Grids of monthly mean sunshine duration derived from CLIMAT bulletins on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring

Monthly means of TPW for Europe

Maps of monthly mean values of precipitable water derived from SATEM bulletins by gridding to 5x5 degree grid and interpolation to a 1x1 degree grid.(near realtime product), provided by WMO Regional Climate Centre (RCC) on Climate Monitoring

Monthly mean of sunshine duration: maps

Maps of monthly mean sunshine duration derived from CLIMAT bulletins on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring

Langjähriges Mittel der Lufttemperatur 1981-2010 (Umweltatlas)

Langjährige Verteilung der mittleren Lufttemperaturen in 2 m Höhe in Berlin und dem näheren Umland (Gesamtjahr, Frühling, Sommer, Herbst, Winter). Die Berechnung des 30-jährigen Temperaturmittels erfolgte auf Grundlage der mittleren Monatswerte für den Zeitraum vom 01.01.1981 bis zum 31.12.2010.

Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to monthly averages. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month): ERA5_land_rh2m_avg_monthly_YYYY_MM.tif Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Monthly Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2 Original ERA5-Land dataset license: https://apps.ecmwf.int/datasets/licences/copernicus/ CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Data is also available in EU LAEA (EPSG: 3035) projection: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/ab06ed25-84af-43c9-b1c3-57e3b6ad8d29 Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

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