https://data.mundialis.de:/geonetwork/srv/api/records
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mundialis GeoNetwork catalogue
type: Catalog
label: mundialis GeoNetwork catalogue (mundialis)
open search description: eng/portal.opensearch
uri lookup endpoint: eng/rdf.search?any=
publisher: mundialis
themes: GEMET - INSPIRE themes, version 1.0
themes: Continents, countries, sea regions of the world.
themes: INSPIRE priority data set
themes: Classification of spatial data services
themes: Service Type
language: eng
dataset: Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data
dataset: Copernicus Digital Elevation Model (DEM) for Europe at 100 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
dataset: ERA5-Land weekly: Total precipitation, weekly time series for Europe at 1 km resolution (2016 - 2020)
dataset: ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: mundialis
dataset: GEMET - INSPIRE themes, version 1.0
dataset: Continents, countries, sea regions of the world.
dataset: INSPIRE priority data set
dataset: Classification of spatial data services
dataset: Service Type
dataset: Atmospheric conditions
dataset: Meteorological geographical features
dataset: Land cover
dataset: Land use
dataset: Europe
dataset: Elevation
dataset: Human health and safety
dataset: zenodo.6147830
dataset: https://zenodo.org/record/7427021
dataset: LICENSE.html
dataset: https://zenodo.org/record/7426990
dataset: https://zenodo.org/record/7434376
dataset: https://zenodo.org/record/7432432
dataset: https://zenodo.org/record/7434447
dataset: https://zenodo.org/record/7434396
dataset: zenodo.14987385
dataset: zenodo.6211990
dataset: https://zenodo.org/record/6559048/files/ERA5_prectot_weekly_avg_v01.zip
dataset: https://zenodo.org/record/6559048/files/ERA5_prectot_weekly_sum_v01.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2019.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2020.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2021.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2022.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2023.zip
dataset: https://zenodo.org/record/7427010
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_PROBAV_V1.0.1_4326_monthly_2019.zip
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_PROBAV_V1.0.1_4326_monthly_2020.zip
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2021.zip
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2022.zip
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2023.zip
dataset: mundialis GmbH & Co. KG

mundialis
type: Organization

GEMET - INSPIRE themes, version 1.0
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Continents, countries, sea regions of the world.
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

INSPIRE priority data set
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Classification of spatial data services
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Service Type
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

https://data.mundialis.de/geonetwork/srv/resources/records/782cdbf5-bfda-4813-a5d7-336ebbc241bb
type: CatalogRecord
primary topic: dataset
issued: 2024-07-02
modified: 2024-07-02
references: https://data.mundialis.de/geonetwork/srv/resources/records/782cdbf5-bfda-4813-a5d7-336ebbc241bb/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/782cdbf5-bfda-4813-a5d7-336ebbc241bb/formatters/xml
format: application/xml
  type: IMT
  label: XML

Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data
type: Dataset
abstract: 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 - 07/2021. 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 decadal averages. …
keyword: relative humidity
keyword: geospatial analysis
keyword: environment
keyword: meteorology
keyword: ERA5-Land
keyword: CHELSA
keyword: Europe
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Atmospheric conditions
theme: Meteorological geographical features
theme: geoscientificInformation
theme: climatologyMeteorologyAtmosphere
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-36.00 18.00, -36.00 82.00, 70.00 82.00, 70.00 18.00, -36.00 18.00))
temporal: 2000-01-01 / 2021-07-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: Creative Commons Namensnennung – 4.0 International (CC BY 4.0)
license: { "id": "cc-by/4.0", "name": "Creative Commons Namensnennung – 4.0 Internationa", "url": "http://creativecommons.org/licenses/by/4.0/", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: zenodo.6147830
data quality: derived from ERA5-Land reanalysis data, spatially enhanced by image fusion with CHELSA data
title: Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2022) derived from ERA5-Land data
abstract: 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. …
temporal: 2000-01-01 / 2023-12-31
distribution: https://zenodo.org/record/7427021
title: Landcover classification map of Germany 2021 based on Sentinel-2 data
abstract: This landcover map was produced with a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. Even though the project is concluded, the annual land cover classification product is continuously generated. This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2020), processed by mundialis Processing was performed for blocks of federal stat…
keyword: Sentinel-2
keyword: Classification
keyword: Land Cover
keyword: mFUND
keyword: MAJA
keyword: Infrastuktur
keyword: Umwelt
keyword: Regionen und Städte
keyword: mfund-projekt:incora
keyword: mfund-fkz:19F2079C
keyword: incora
keyword: Germany
theme: Land cover
theme: Land use
temporal: 2021-01-01 / 2021-12-31
accrual periodicity: irregular
license: Data licence Germany - attribution - version 2.0 or later (DL-DE->BY-2.0) | Datenlizenz Deutschland - Namensnennung - Version 2.0 oder neuer
license: { "id": "dl-by-de/2.0", "name": "Datenlizenz Deutschland Namensnennung 2.0", "url": "https://www.govdata.de/dl-de/by-2-0", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: LICENSE.html
data quality: derived from Sentinel-2 MSI - Level 3A-WASP
title: Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2021) derived from ERA5-Land data
abstract: 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 - 07/2021. 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 Data provided is the daily averages of relative humidity. Resultant value…
license: { "id": "cc-by/4.0", "name": "Creative Commons Namensnennung – 4.0 International", "url": "http://creativecommons.org/licenses/by/4.0/", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: https://zenodo.org/record/7426990
distribution: https://zenodo.org/record/7434376
distribution: https://zenodo.org/record/7432432
distribution: https://zenodo.org/record/7434447
distribution: https://zenodo.org/record/7434396
title: ERA5-Land daily: Total precipitation (2000 - 2020)
abstract: 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. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to: - aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) 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. Th…
keyword: precipitation
keyword: Leptospirosis
theme: Europe
theme: environment
theme: health
temporal: 2000-01-01 / 2020-12-31
license: intellectualPropertyRights
license: Licence to Use Copernicus Products (Version 1.2, Nov. 2019): "All users of Copernicus Products must provide clear and visible attribution to the Copernicus programme. The Licensee will communicate to the public the source of the Copernicus Products by crediting the Copernicus Climate Change and Atmosphere Monitoring Services" (full licence available here: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

https://data.mundialis.de/geonetwork/srv/resources/records/ab06ed25-84af-43c9-b1c3-57e3b6ad8d29
type: CatalogRecord
primary topic: dataset
issued: 2024-07-11
modified: 2024-07-11
references: https://data.mundialis.de/geonetwork/srv/resources/records/ab06ed25-84af-43c9-b1c3-57e3b6ad8d29/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/ab06ed25-84af-43c9-b1c3-57e3b6ad8d29/formatters/xml
format: application/xml
  type: IMT
  label: XML

https://data.mundialis.de/geonetwork/srv/resources/records/d401d629-94d7-4b2c-927f-eec54948698f
type: CatalogRecord
primary topic: dataset
issued: 2023-02-28
modified: 2023-02-28
references: https://data.mundialis.de/geonetwork/srv/resources/records/d401d629-94d7-4b2c-927f-eec54948698f/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/d401d629-94d7-4b2c-927f-eec54948698f/formatters/xml
format: application/xml
  type: IMT
  label: XML

https://data.mundialis.de/geonetwork/srv/resources/records/c3bfc5ce-03dd-4cb7-9600-4a5587733c80
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/c3bfc5ce-03dd-4cb7-9600-4a5587733c80/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/c3bfc5ce-03dd-4cb7-9600-4a5587733c80/formatters/xml
format: application/xml
  type: IMT
  label: XML

https://data.mundialis.de/geonetwork/srv/resources/records/d5334e64-bae5-4894-ad02-fc4286a9e500
type: CatalogRecord
primary topic: dataset
issued: 2025-03-07
modified: 2025-03-07
references: https://data.mundialis.de/geonetwork/srv/resources/records/d5334e64-bae5-4894-ad02-fc4286a9e500/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/d5334e64-bae5-4894-ad02-fc4286a9e500/formatters/xml
format: application/xml
  type: IMT
  label: XML

https://data.mundialis.de/geonetwork/srv/resources/records/74d0e58f-9f51-444e-a5a7-eff4c20f05b1
type: CatalogRecord
primary topic: zenodo.6211990
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/74d0e58f-9f51-444e-a5a7-eff4c20f05b1/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/74d0e58f-9f51-444e-a5a7-eff4c20f05b1/formatters/xml
format: application/xml
  type: IMT
  label: XML

Copernicus Digital Elevation Model (DEM) for Europe at 100 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
type: Dataset
abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 100 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 100 m, bilinear resampling was performed in GRASS GIS (using r.proj) and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.
keyword: digital terrain model
keyword: elevation
keyword: Copernicus
keyword: DEM
keyword: DSM
keyword: geomorphometry
keyword: environment
keyword: Europe
keyword: remote sensing
keyword: radar
keyword: space
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Elevation
theme: elevation
theme: geoscientificInformation
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-32.00 18.00, -32.00 82.00, 70.00 82.00, 70.00 18.00, -32.00 18.00))
temporal: 2010-01-01 / 2015-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: no limitations to public access
license: Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International (CC-BY-SA 4.0)
license: { "id": "cc-by-sa/4.0", "name": "Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International", "url": "http://creativecommons.org/licenses/by-sa/4.0/", "quelle": "Source: mundialis" }
license: EO data provided under COPERNICUS by the European Union and ESA. For more information see: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/55df6882-8593-4123-b738-3558c20ad98c
distribution: zenodo.6211990
data quality: derived from Copernicus DEM GLO-30

https://data.mundialis.de/geonetwork/srv/resources/records/1d2911b1-38f3-4bf2-81ec-2afd0e4a4325
type: CatalogRecord
primary topic: zenodo.6559048
issued: 2024-07-02
modified: 2024-07-02
references: https://data.mundialis.de/geonetwork/srv/resources/records/1d2911b1-38f3-4bf2-81ec-2afd0e4a4325/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/1d2911b1-38f3-4bf2-81ec-2afd0e4a4325/formatters/xml
format: application/xml
  type: IMT
  label: XML

ERA5-Land weekly: Total precipitation, weekly time series for Europe at 1 km resolution (2016 - 2020)
type: Dataset
abstract: 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. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Processing steps: The original hourly ERA5-Land 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 fin…
keyword: precipitation
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Leptospirosis
keyword: Mosquito borne Flaviviruses
theme: Europe
theme: Meteorological geographical features
theme: environment
theme: geoscientificInformation
theme: health
theme: climatologyMeteorologyAtmosphere
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-32.00 18.00, -32.00 82.00, 61.00 82.00, 61.00 18.00, -32.00 18.00))
temporal: 2016-01-01 / 2020-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: intellectualPropertyRights
license: Licence to Use Copernicus Products (Version 1.2, Nov. 2019): "All users of Copernicus Products must provide clear and visible attribution to the Copernicus programme. The Licensee will communicate to the public the source of the Copernicus Products by crediting the Copernicus Climate Change and Atmosphere Monitoring Services" (full licence available here: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf
license: { "id": "other-open", "name": "Licence to Use Copernicus Products", "url": "https://ads.atmosphere.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf", "quelle": "Source: Copernicus Programme" }
distribution: https://zenodo.org/record/6559048/files/ERA5_prectot_weekly_avg_v01.zip
distribution: https://zenodo.org/record/6559048/files/ERA5_prectot_weekly_sum_v01.zip
data quality: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

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type: CatalogRecord
primary topic: zenodo.12189668
issued: 2024-07-02
modified: 2024-07-02
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ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: 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. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Processing steps: The original hourly ERA5-Land 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…
keyword: MOOD-H2020
keyword: Precipitation
keyword: Rift Valley fever
keyword: RVF
keyword: Mauritania
theme: Human health and safety
theme: geoscientificInformation
theme: health
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temporal: 2019-01-01 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: intellectualPropertyRights
license: Licence to Use Copernicus Products (Version 1.2, Nov. 2019): "All users of Copernicus Products must provide clear and visible attribution to the Copernicus programme. The Licensee will communicate to the public the source of the Copernicus Products by crediting the Copernicus Climate Change and Atmosphere Monitoring Services" (full licence available here: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf
license: { "id": "other-open", "name": "Licence to Use Copernicus Products", "url": "https://ads.atmosphere.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf", "quelle": "Source: Copernicus Programme" }
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2019.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2020.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2021.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2022.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2023.zip
data quality: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

https://data.mundialis.de/geonetwork/srv/resources/records/ddbbc194-7000-4ff3-9d41-8d99ad90bc6c
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
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https://data.mundialis.de/geonetwork/srv/resources/records/cbc4aad4-bde6-4ef7-8079-6df637bc729a
type: CatalogRecord
primary topic: zenodo.12189682
issued: 2024-07-02
modified: 2024-07-02
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CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: - CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m - CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management. The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices. Processing steps: To cov…
keyword: MOOD-H2020
keyword: Water
keyword: Water bodies
keyword: RVF
keyword: Rift Valley fever
keyword: Mauritania
theme: Human health and safety
theme: geoscientificInformation
theme: health
spatial: Polygon
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temporal: 2019-01-01 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: intellectualPropertyRights
license: Generated using European Union's Copernicus Land Monitoring Service information
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_PROBAV_V1.0.1_4326_monthly_2019.zip
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_PROBAV_V1.0.1_4326_monthly_2020.zip
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2021.zip
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2022.zip
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2023.zip
data quality: Dataset has been processed from: - CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m - CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m

Atmospheric conditions
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Meteorological geographical features
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Land cover
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Land use
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Europe
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in scheme: Continents, countries, sea regions of the world.

Elevation
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Human health and safety
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

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