This dataset provides values for PRECIPITATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This data package contains radiation and temperature data, at hourly resolution, for Europe, aggregated by Renewables.ninja from the NASA MERRA-2 reanalysis. It covers the European countries using a population-weighted mean across all MERRA-2 grid cells within the given country.

Citation

"Open Power System Data. 2020. Data Package Weather Data. Version 2020-09-16. https://doi.org/10.25832/weather_data/2020-09-16. (Primary data from various sources, for a complete list see URL)."

Original Data

Acknowlegement

Foto von NOAA auf Unsplash

The average for 2022 based on 39 countries was 829 mm per year. The highest value was in Iceland: 1940 mm per year and the lowest value was in Moldova: 450 mm per year. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

The European climatological gauge-adjusted radar precipitation dataset, EURADCLIM, addresses the need for an accurate (sub-)daily precipitation product covering 8000000 square kilometers of Europe at high spatial resolution. It consists of 1-h and 24-h precipitation accumulations (every clock-hour) at a 2-km grid for the period 2013 through 2020. It is based on the European Meteorological Network (EUMETNET) Operational Program on the Exchange of weather RAdar Information (OPERA) gridded radar dataset of 15-min instantaneous surface rain rates. For EURADCLIM, first methods have been applied to further remove non-meteorological echoes from these images by applying two statistical methods and a satellite-based cloud type mask. Second, the radar composites are merged with the rain gauge data from the European Climate Assessment & Dataset (ECA&D) in order to substantially improve its quality. We expect to rerun EURADCLIM once a year over the entire period, using all available ECA&D rain gauge data, and extend it with one year of data. This will result in a new version of this dataset. Reason of deprecation: The next version has improved removal of non-meteorological echoes, better rain gauge coverage from 2013 to 2020, and the years 2021 and 2022 have been added Alternative dataset version can be found here: https://dataplatform.knmi.nl/dataset/rad-opera-24h-rainfall-accumulation-euradclim-2-0

The dataset presents climate impact indicators related to extreme precipitation in Europe under current climate conditions. The suite of indicators include recent historic records, recurrence intervals, and other relevant statistical measures to evaluate the magnitude and frequency of extreme precipitation events. These are provided as gridded products, with one product covering the whole of Europe, and the other higher resolution product focused on 20 European cities that were identified as vulnerable to urban pluvial flooding based on stakeholder surveys. This dataset makes use of precipitation data available in the Climate Data Store (i.e. E-OBS gridded land-only observational dataset and ERA5 reanalysis) combined with additional datasets capable of improving the spatial and temporal resolution of the precipitation data, making it suitable for pluvial flood analysis at city scales. These are derived from i) the network of meteorological stations included in the European Climate Assessment & Dataset (ECA&D) programme and ii) dynamically downscaled ERA5 reanalysis at 2 km x 2 km (ERA5-2km) using the regional climate model COSMO-CLM and accounting for urban parameterization, specifically performed for the 20 European cities identified as vulnerable to urban pluvial flooding. At the European scale, E-OBS and ERA5 precipitation data are used to compute indicators at different temporal resolutions (i.e. daily, monthly, yearly, and 30-year) according to the type of indicator. The precipitation amounts at fixed return periods are also computed for point observations from meteorological stations using the ECA&D network and are then interpolated onto the E-OBS grid. At the city scale, a dynamically downscaled ERA5-2km precipitation data are instead used to derive daily indicators, allowing city stakeholders to detect and rank local extreme precipitation events and evaluate their magnitude. This dataset was produced on behalf of the Copernicus Climate Change Service.

This dataset provides monthly total precipitation amounts (in millimeters) recorded across Europe. Each row represents the total precipitation for one month and year, facilitating analyses of hydrological cycles, droughts, flooding risks, and long-term climate variability. The data can be used for time series analysis, climate modeling, and environmental studies focused on European precipitation patterns over multiple decades.

Hourly geographically aggregated weather data for Europe. This data package contains radiation and temperature data, at hourly resolution, for Europe, aggregated by Renewables.ninja from the NASA MERRA-2 reanalysis. It covers the European countries using a population-weighted mean across all MERRA-2 grid cells within the given country.

Data from the NIMROD system data describe rain-rate observations in Northern Europe taken by NIMROD, which is a very short range forecasting system used by the Met Office. Composite European data are available from April 2002 until present, collected by a network of rain radars at northern European stations. Radar images from the 15 C-band (5.3 cm wavelength) radars around Europe at 5 km resolution, are received by the Nimrod system at 15 minute intervals. Data products are available since April 2002, whilst image products are available from February 2003. Each file has been compressed and then stored within daily tar archive files.

The precipitation rate analysis uses processed radar and satellite data, together with surface reports and Numerical Weather Prediction (NWP) fields. Europe has a network of 15 C-band rainfall radars and data form these are processed by the Met Office NIMROD system. The data files contain integer precipitation rates in unit of (mm/hr)*32. Each value is between 0 and 32767. In practice it is rare to see a value in excess of 4096 i.e. 128 mm/hr

CEDA are not able to fulfil requests for data that are missing from this archive. The data may be available at a cost by contacting the Met Office directly with required dates. It is worth contacting CEDA first to check if the reason for the gap is already identified as being due to the data not existing at all.

The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets in Europe. We used the Rainfall Erosivity Database on the European Scale(REDES) which contains 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (Less than 500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods.

This statistic presents the number of days with precipitation in leading European countries between January 1 and March 31, 2018. According to data provided by LAL, the Netherlands had ** days of rainfall during this period, the most of any country.

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 spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020. Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation. File naming: Average of daily sum: era5_land_prectot_avg_weekly_YYYY_MM_DD.tif Sum of daily sum: era5_land_prectot_sum_weekly_YYYY_MM_DD.tif The date in the file name determines the start day of the week (Saturday). Values are mm * 10. Example: Value 218 = 21.8 mm

Global sea surface temperature (SST) anomalies can affect terrestrial precipitation via ocean-atmosphere interaction known as climate teleconnection. Non-stationary and non-linear characteristics of the ocean-atmosphere system make the identification of the teleconnection signals difficult to be detected at a local scale as it could cause large uncertainties when using linear correlation analysis only. This paper explores the relationship between global SST and terrestrial precipitation with respect to long-term non-stationary teleconnection signals during 1981-2010 over three regions in North America and one in Central America. Empirical mode decomposition as well as wavelet analysis is utilized to extract the intrinsic trend and the dominant oscillation of the SST and precipitation time series in sequence. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant SST regions are extracted based on the correlation coefficient. With these characterized associations, individual contribution of these SST forcing regions linked to the related precipitation responses are further quantified through nonlinear modeling with the aid of extreme learning machine. Results indicate that the non-leading SST regions also contribute a salient portion to the terrestrial precipitation variability compared to some known leading SST regions. In some cases, these estimated contributions reveals some clues of the coupling interactions between oceanic and atmospheric processes. This dataset is associated with the following publication: Chang, N., S. Imen, K. Bai, and J. Yang. Multi-scale Quantitative Precipitation Forecasting Using Nonlinear and Nonstationary Teleconnection Signals and Artificial Neural Network Models. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, USA, 548: 305-321, (2017).

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

Rainfall accumulation (mm) HDF5 European Radar composites Hourly accumulations 2 km x 2 km grid 15 minute updates at DT+15 minutes

Quality: Odyssey will generate and archive composite products from raw single site radar data using common pre-processing and compositing algorithms. Expected performance : - The target availability of composite products produced, delivered and archived will be an average of 99.0%. - Composite products produced within 15 minutes of data time on at least 95% of occasions. - Composite products delivered(1) within 20 minutes of data time on at least 95% of occasions. - Normally, there would be no downtime during maintenance slots and planned switching of nodes - Where available, gaps in the archive hosted in Météo France will be populated with data archived in Exeter within 7 working days of notification. Performance Measure: - Performance of composite availability and timeliness will be measured at the Odyssey system. - Production timeliness will be recorded as the completion time of composite generation at Odyssey. - Availability and timeliness will be measured monthly. Fault resolution: - For system or hardware faults that affect availability, the target will be to respond and fix the fault within 2 hours of notification on 98% of occasions. Contingency: - The Odyssey service will be maintained despite IT infrastructure failures at one of the Odyssey nodes. - Contingency will be provided by the back-up Odyssey node. Switching of operational status between Odyssey nodes will occur within 30 minutes of outage. - The main Odyssey archive will be hosted at Météo France and a backup hosted at the Met Office Support Cover: - The ability to switch operational Odyssey node will be provided 24 hours a day (24/7/365) - Other support activities will take place during Normal Working Hours (of the responsible member) Service Failures: A tolerable level of service failure would be: - one ‘break of up to 15 minutes in any 7 day period - one ‘break’ of up to 60 minutes in any quarter of a year - one ‘break’ over 60 minutes in any one year, with service being restored within 4 hours. A ‘break’ denotes a reduction in service delivery, however the service will be deemed to be met if the agreed alternative output is being supplied. Service description: - Instantaneous Surface Rain rate - Domain – Whole of Europe - Projection – Lambert Equal Area - Update frequency – 15 minutes - Issue time – Approximately 15 minutes after data time - Delivery method – FTP to NM(H)S via GTS/RMDCN or Internet - Available in real-time and via archive - Format: HDF5 (structure is compliant with the Eumetnet ODIM specification) Permitted use: To be confirmed Fault reporting: Reported to the UK Met Office Weather Desk. (1) Applies only to NMS receiving composite products by direct RMDCN connections

Daily and monthly rainfall records for our station at Roads in Co. Kerry.

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present downscaled climate data for the CORDEX EUR11 domain at a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperature lapse rates. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height. The resulting data consist of a daily temperature and precipitation timeseries. The data is distributed under a: Creative Commons: Attribution 4.0 International (CC BY 4.0) license.

This dataset provides precipitation and near surface air temperature for Europe as Essential Climate Variables (ECVs) and as a set of Climate Impact Indicators (CIIs) based on the ECVs. ECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. CIIs contain condensed climate information which facilitate relatively quick and efficient subsequent analysis. Therefore, CIIs make climate information accessible to application focussed users within a sector. The ECVs and CIIs provided here were derived within the water management sectoral information service to address questions specific to the water sector. However, the products are provided in a generic form and are relevant for a range of sectors, for example agriculture and energy. The data represent the current state-of-the-art in Europe for regional climate modelling and indicator production. Data from eight model simulations included in the Coordinated Regional Climate Downscaling Experiment (CORDEX) were used to calculate a total of two ECVs and five CIIs at a spatial resolution of 0.11° x 0.11° and 5km x 5km. The ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. They are bias adjusted using the EFAS gridded observations as a reference dataset. Note these are model output data, not observation data as is the general case for ECVs. The CIIs are provided as mean values over a 30-year time period. For the reference period (1971-2000) data is provided as absolute values, for the future periods the data is provided as absolute values and as the relative or absolute change from the reference period. The future periods cover 3 fixed time periods (2011-2040, 2041-2070 and 2071-2100) and 3 "degree scenario" periods defined by when global warming exceeds a given threshold (1.5 °C, 2.0 °C or 3.0 °C). The global warming is calculated from the global climate model (GCM) used, therefore the actual time period of the degree scenarios will be different for each GCM. This dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service.

E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions. For a considerable number of countries the number of stations used is the complete national network and therefore much more dense than the station network that is routinely shared among NMHSs (which is the basis of other gridded datasets). The density of stations gradually increases through collaborations with NMHSs within European research contracts. Initially, in 2008, this gridded dataset was developed to provide validation for the suite of Europe-wide climate model simulations produced as part of the European Union ENSEMBLES project. While E-OBS remains an important dataset for model validation, it is also used more generally for monitoring the climate across Europe, particularly with regard to the assessment of the magnitude and frequency of daily extremes. The position of E-OBS is unique in Europe because of the relatively high spatial horizontal grid spacing, the daily resolution of the dataset, the provision of multiple variables and the length of the dataset. Finally, the station data on which E-OBS is based are available through the ECA&D webpages (where the owner of the data has given permission to do so). In these respects it contrasts with other datasets. The dataset is daily, meaning the observations cover 24 hours per time step. The exact 24-hour period can be different per region. The reason for this is that some data providers measure between midnight to midnight while others might measure from morning to morning. Since E-OBS is an observational dataset, no attempts have been made to adjust time series for this 24-hour offset. It is made sure, where known, that the largest part of the measured 24-hour period corresponds to the day attached to the time step in E-OBS (and ECA&D).

The average for 2022 based on 27 countries was 756 mm per year. The highest value was in Slovenia: 1162 mm per year and the lowest value was in Cyprus: 498 mm per year. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

According to a survey carried out in 2024, almost three-quarters of Spaniards stated that they had experienced worse-than-usual extreme weather events. Of the European countries considered, Italy was the second one where more than half of the respondents also agreed that they have been feeling worse-than-usual events, like droughts, floods, storms, and extreme temperatures.