The raster dataset of urban heat island modelling shows the fine-scale (100m pixel size) temperature differences (in degrees Celsius °C) across 100 European cities, depending on the land use, soil sealing, anthropogenic heat flux, vegetation index and climatic variables such as wind speed and incoming solar radiation. In the framework of the Copernicus European Health contract for the Copernicus Climate Change Service (C3S), VITO provided 100m resolution hourly temperature data (2008-2017) for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). As the cities vary in size, so do the model domains. They have been defined with the intention to have a more or less constant ratio of urban vs. non-urban pixels (as defined in the CORINE land use map), with a maximum of 400 by 400 pixels (due to computational restraints). From this data set, the average urban heat island intensity is mapped for the summer season (JJA), which is the standard way of working in the scientific literature (e.g. Dosio, 2016). The UHI is calculated by subtracting the rural (non-water) spatial P10 temperature value from the average temperature map. The 100 European cities for the urban simulations were selected based on user requirements within the health community.
This vector dataset shows the Urban Heat Island (UHI) intensity (in degrees Celsius °C) for 100 European cities, based on their elevation above sea level, land use, soil sealing, vegetation index and anthropogenic heat flux. The Urban Heat Island intensity exacerbates high temperatures in cities and thus may pose additional risks to human thermal comfort and health. The UHI intensity is represented by spatial P90 (90th percentile) urban heat island intensity of a given city ("P90" field in the dataset). This indicator is calculated by subtracting the rural (non-water) spatial P10 (10th percentile) temperature value from the average, height-corrected (to exclude terrain effects), air temperature map. This indicator represents the specific exposure of single cities and due to the height correction will be comparable across Europe. The dataset has been created by VITO within the Copernicus Health contract for C3S and is based on UrbClim model (De Ridder et al. 2015). The 100 European cities for the urban simulations were selected based on user requirements within the health community.
This vector dataset provides the climate suitability index values (0-100%) for tiger mosquito (Aedes albopictus) for 100 European cities for the years 2008-2009 (P90 - 90th percentile). Aedes Albopictus has become a common occurrence in Southern Europe and transmits diseases such as Zika, dengue and chikungunya. The climatic suitability for tiger mosquito depends on factors such as sufficient amounts of rainfall, high summer temperatures and mild winters. Climate change is anticipated to further facilitate the spread of tiger mosquitoes across Europe by changing temperature and precipitation patterns, thereby increasing the suitable habitat. In the framework of the Copernicus Climate Change Service (C3S) SIS European Health, VITO (https://vito.be/en) has provided to the Climate Data Store 100m resolution hourly temperature data for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). From this dataset, this climate suitability dataset has been generated based on annual precipitation and the average temperature in January and during the summer period (months June, July and August) for the years 2008-2009, following the methodology by European Centre for Disease Prevention and Control (ECDC, 2009). This approach considers empirical suitability functions, which link a number of (aggregated) climate variables to the suitability of a habitat for a given vector species, e.g. for a species to be active a minimum threshold of temperature is required below which the species is not active. Similarly some species cannot overwinter if the winter is too cold (e.g. January temperature lower than a given value). The P90 indicator represents the specific exposure of single cities and is independent of the model domain or size of a city. The 100 European cities for the urban simulations were selected based on user requirements within the health community.
This metadata refer to the dataset presenting the annual change in the percentage of coastal area per European country that is suitable for infections from vibrio species between 2003 and 2021. Vibrio bacteria can lead to severe gastrointestinal infections, skin and ear infections, and more severe health outcomes, including necrotising fasciitis, amputation, sepsis, and death. In Europe, cases have steadily increased over the years in countries with national surveillance; however, vibriosis is not a notifiable disease in the EU. Increasing sea temperatures have led to higher percentages of coastal areas with brackish waters in Europe showing suitable conditions for the transmission for non-cholerae Vibrio bacteria.
This metadata refer to the dataset presenting the annual change in heatwave exposure of people over 65, expressed as the deviation in annual person-days of heatwave exposure relative to the 1986-2005 baseline. Heat exposure poses acute health risks, particularly to older people (ie, people older than 65 years), people with underlying, chronic respiratory, kidney, or heart disease, people living in urban areas, and people with little means to access cooling mechanisms. These heat-related health risks are of particular relevance to Europe, as the continent is experiencing ageing populations, urbanisation, and a high prevalence of chronic diseases.
This raster dataset provides the modelling of the climate suitability index values (0-100%) for tiger mosquito (Aedes albopictus) for 100 European cities for the years 2008-2009, with a resolution of 100 m. Aedes Albopictus has become a common occurrence in Southern Europe and transmits diseases such as Zika, dengue and chikungunya. The climatic suitability for tiger mosquito depends on factors such as sufficient amounts of rainfall, high summer temperatures and mild winters. Climate change is anticipated to further facilitate the spread of tiger mosquitoes across Europe by changing temperature and precipitation patterns, thereby increasing the suitable habitat. In the framework of the Copernicus Climate Change Service (C3S) SIS European Health, VITO has provided to the Climate Data Store 100m resolution hourly temperature data for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). From this dataset, this climate suitability dataset has been generated based on annual precipitation and the average temperature in January and during the summer period (months June, July and August) for the years 2008-2009, following the methodology by European Centre for Disease Prevention and Control (ECDC, 2009). The 100 European cities for the urban simulations were selected based on user requirements within the health community.
The original EMEP grids have been reprojected into the European standard projection in order to facilitate re-use of EMEP information on air emissions, deposition and critical loads in EEA map products. Organisations responsible for delivering national data to EMEP should always use the grid in polar stereographic projection as provided by EMEP (http://www.emep.int/grid/index.html). EEA has reprojected the grid used by EMEP for analyses on air emissions (150*150 km2 and 50*50 km2 grids covering Europe)
The WVQB31 TTAAii Data Designators decode as: T1 (W): Warnings T1T2 (WV): Volcanic ash clouds (SIGMET) A1A2 (QB): Bosnia and Herzegovina (Remarks from Volume-C: NilReason)
The SPQB31 TTAAii Data Designators decode as: T1 (S): Surface data T1T2 (SP): Special aviation weather reports A1A2 (QB): Bosnia and Herzegovina (Remarks from Volume-C: NilReason)
DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]