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.

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

Past and future weather extremes across Europe

This repository contains the annual exceedance index data for past and future weather extremes across Europe on NUTS1 scale. The code and an accompanying paper analyzing the impact of this weather extremes on the European agricultural sector on subnational scale will be published during 2023. We use a percentile-based approach to assess the annual exceedance index of the four weather extremes heat waves, cold waves, fire-risk and droughts for the past (1981–2020) and future (2006–2100) [Zhang et al., 2005]. For the past, we used daily weather records on a grid level (around 11 km at the equator) from the ERA5-Land reanalysis dataset, and for future projections, we use modelled daily weather records from EURO-CORDEX [Christensen et al., 2020, Muñoz, 2019]. For past and future fire-risk we use precalculated fire weathernindex data from ERA5 and EURO-CORDEX, respectively [Giannakopoulos et al., 2020]. We used the model average of the following driving GCMs and RCMs for future projections: ICHECs Earth System Model (EC-Earth), MPI-Ms Earth System Model (MPI-ESM-LR), SMHIs Regional Climate Model (RCA4). The baseline period for the historical scenario is 1981–2010, and for future projections 1981–2005. Daily thresholds for heat waves, cold waves, and flash droughts are estimated from the 90th percentile of the daily minimum and maximum temperature, 10th percentile of the daily minimum and maximum temperature, and 30th percentile of the soil volumetric water content (0–28cm), respectively [**Sutanto** et al., 2020]. We use a five days centre data window for all three extreme events to estimate the thresholds from the previously listed baseline periods. The annual exceedance index for heat waves is calculated as the sum of days, at least for three consecutive days; the daily temperature values exceed the thresholds for June, July, and August. For cold waves, the annual exceedance index is the sum of days, at least for three consecutive days; the daily temperature values are below the thresholds for January, February, October, November, and December. In-base, exceedance is calculated using bootstrapping (1000x repetitions) for both extreme events. Heat and cold wave exceedance indices are rescaled to NUTS1 regions using a maximum resampling. We use sequent peak analysis to detect annual flash droughts, remove minor droughts, and pool interdependent droughts for the season from June to October [**Biggs** et al., 2004]. The annual exceedance index of droughts is rescaled to NUTS1 regions by using a mean resampling. Parameters for fire-risk are listed in the table below while.

Parameters of the analysis of the percentile-based extreme.

Type	Variable	Percentile	Window	Min duration	Rescaling	Months	Bootstrapping
Heat wave	tmin and tmax	90	5	3	max	6, 7, 8	yes
Cold wave	tmin and tmax	10	5	3	max	1, 2, 10, 11, 12	yes
Flash drought	swvl 0-28cm	30	5	5	mean	6, 7, 8, 9, 10	no
Fire risk	FWI	90	5	1	mean	3, 4, 5, 6, 7, 8, 9	yes

Xuebin Zhang, Gabriele Hegerl, Francis W. Zwiers, and Jesse Kenyon. Avoiding inhomogeneity in percentile-based indices of temperature extremes. Journal of Climate, 18 (11):1641–1651, 2005. ISSN 08948755. doi: 10.1175/JCLI3366.1.

Samuel Jonson Sutanto, Claudia Vitolo, Claudia Di Napoli, Mirko D’Andrea, and Henny A.J. Van Lanen. Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale. Environment International, 134 (March 2019):105276, jan 2020. ISSN 01604120. doi: 10.1016/j.envint.2019.105276.

J. Sabater Muñoz. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2019.

O. B. Christensen, W. J. Gutowski, G. Nikulin, and S. Legutke. CORDEX Archive Design, 2020. URL https://is-enes-data.github.io/cordex_archive_specifications.pdf

Barry J. F. Biggs, Bente Clausen, Siegfried Demuth, Miriam Fendeková, Lars Gottschalk, Alan Gustard, Hege Hisdal, Matthew G. R. Holmes, Ian G. Jowett, Ladislav Kašpárek, Artur Kasprzyk, Elzbieta Kupczyk, Henny A.J. Van Lanen, Henrik Madsen, Terry J. Marsh, Bjarne Moeslund, Oldřich Novický, Elisabeth Peters, Wojciech Pokojski, Erik P. Querner, Gwyn Rees, Lars Roald, Kerstin Stahl, Lena M. Tallaksen, and Andrew R. Young. Hydrological Drought: Processes and Estimation Methods for Stream- flow and Groundwater. Elsevier, 1 edition, 2004. ISBN 0444517677.

Giannakopoulos, C., Karali, A., Cauchy, A. (2020): Fire danger indicators for Europe from 1970 to 2098 derived from climate projections, version 1.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.ca755de7

Funding
Tobias Seydewitz acknowledges funding from the German Federal Ministry of Education and Research for the [BIOCLIMAPATHS](https://www.pik-potsdam.de/en/output/projects/all/647) project (grant agreement No 01LS1906A) under the Axis-ERANET call. The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.

DESCRIPTION The data set contains historical model data from the Harmonie-Arome forecast model. Only data from the first time step, +0 hours, has been selected for each model run. The applicable time for the dataset is the same time as the start time of the forecast run.

Harmonie-Arome is a numerical forecasting model that calculates the future development of the weather in three dimensions. Harmonie-Arome is developed in an international collaboration and the version used by SMHI is based on a collaboration between Nordic and Baltic countries.

A numerical weather forecast is based on an analysis of the initial situation, a best description of the atmosphere in three dimensions based on observations and previous model driving. The analysis is done to best interpolate observations and previous modeling to a three-dimensional grid on which the model can run its physical calculations. The grid that the model uses today has 2.5 x 2.5 km horizontal resolution and vertically there are 65 model levels of the same horizontal grid with the lowest level near the ground surface. On this grid, physical parameters are calculated, of which some forecast time steps are printed as meteorological forecasts or analyses on data files. The meterolological model runs in its current configuration every hour with slightly different settings and forecast lengths up to just under three days printed on file.

In this flow, a smaller selection of parameters, mainly at or just above ground level, is available with new data every 6 hours. Examples of parameters are cloudiness, temperature, precipitation, ground pressure and wind speed and direction. All of this has a bearing on the development of the weather.

The AROME analysis has been made over an area covering Scandinavia, the northern part of the continent and the Baltic States. As Finland joins MetCoop, the area will be expanded eastwards and westwards. In addition, a number of new parameters are added to meet FMI's needs. This change is effective as of 6 September 2017.

Coordinates for the new area are given by the corners: Southwest 51.85N 0.24E Northwest 71.91N 15.32W Southeast 51.47N 32.18E Northeast 71.16N 49.61E

The grid squares are 2.5 x 2.5 km

Coordinates before 6 September: Southwest 52.04N 1.64E Northwest 72.30N 12.72W Southeast 52.04N 28.36E Northeast 72.30N 42.72E

USE At SMHI, AROME is used as a basis for forecasts in radio and television. It is also used for forecasting to commercial customers such as various newspapers and municipalities. Data from AROME can also be used as input to other models. This applies to meteorological as well as hydrological and oceanographic.

FORMAT Data is delivered in GRIB format. Information to facilitate its use can be found at: https://www.smhi.se/data/oppna-data/grib-format-1.30761

The service provides free access to MET’s archive of historical weather data and climate data, as well as information about the measurement stations.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on pressure levels from 1940 to present".

Instrumental observations of the weather have been regularly performed in the Netherlands since the end of the 17th century. An inventory was made of this period before 1854 at KNMI. The observations of the series available here appeared to have been carried out sufficiently regularly and long enough in succession.

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.

This dataset contains key characteristics about the data described in the Data Descriptor ClimateEU, Scale-free climate normals, historical time series, and future projections for Europe. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Monthly Historical information for 37 UK Meteorological Stations. Most go back to the early 1900s, but some go back as far as 1853.

Data includes:

• Mean maximum temperature (tmax)
• Mean minimum temperature (tmin)
• Days of air frost (af)
• Total rainfall (rain)
• Total sunshine duration (sun)

Station data files are updated on a rolling monthly basis, around 10 days after the end of the month. Data are indicated as provisional until the full network quality control has been carried out. After this, data are final.

No allowances have been made for small site changes and developments in instrumentation.

Data and statistics for other stations, and associated charges, can be obtained by contacting our Customer Centre.

The EURO-CLIMHIST (E-CH) database is a comprehensive tool for managing, analysing and displaying climatic (high-resolution) proxy evidence from natural and documentary archives.

At present more than 1.2 million records are already included. A selection of nearly half a million records is available on the internet, including data of the Late Maunder Minimum and Switzerland. The Late Maunder Minimum (LMM; 1675-1715) is an extraordinary interval in the historical climate record, with significant societal implications at least in Europe. It has been documented by historical sources as well as various proxy data. Beside it, almost the full dataset of Switzerland is available (1500 - 1863).

Access to the EuroHist climate data is obtained through the EuroHistClim administrator.

This dataset contains all the meteorological data acquired by the Hydrographic Offices (until 1974) and by the Autonomous Province of Trento (from 1975) at the stations of the Autonomous Province of Trento. There are around 130 stations, including both those that are still operational today and those that existed in the past, even for short periods of time.
The data already published in the hydrological annals are therefore also available. They are no longer printed because they have been replaced by the web publication (the last paper annal of Trentino is for 1988).

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of Switzerland, Western Europe. The time period coverage is from 425 to -39 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The Weather API market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, valued at approximately $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated market size of $6 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of precision agriculture necessitates accurate and timely weather data, boosting the demand for weather APIs in this sector. Secondly, the aviation, energy, and transportation and logistics industries heavily rely on precise weather forecasts for efficient operations and risk mitigation, further driving market growth. Furthermore, the tourism and travel sector leverages weather APIs to enhance user experiences through personalized travel recommendations and real-time weather updates. The increasing availability of sophisticated weather models and the growing adoption of cloud computing technologies are also contributing factors. Different API types, including current weather, forecast, and historical data APIs, cater to a wide range of applications. Competition within the market is intense, with numerous established players and emerging startups offering diverse solutions and pricing models. Geographic expansion also plays a significant role in market growth. North America and Europe currently hold the largest market shares, but rapid technological advancements and increasing internet penetration in Asia Pacific and other regions are expected to unlock substantial growth opportunities in these areas over the forecast period. However, challenges remain, including data accuracy concerns, the need for seamless data integration across different platforms, and the potential for data security breaches. Overcoming these challenges and ensuring data reliability will be crucial for sustained market growth. The evolution towards more granular and hyperlocal weather data, along with advancements in artificial intelligence and machine learning for improved forecasting accuracy, will further shape the future landscape of this dynamic market.

Observation data from international surface observation messages (SYNOP) circulating on the World Meteorological Organization (WMO) Global Telecommunication System (GTS). Atmospheric parameters measured (temperature, humidity, wind direction and strength, atmospheric pressure, precipitation height) or observed (sensitive time, cloud description, visibility) from the Earth's surface. Depending on instrumentation and local specificities, other parameters may be available (snow height, soil condition, etc.)

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

Europe's average temperature has increased significantly when compared with the pre-industrial period, with the average temperature in 2014 2.22 degrees Celsius higher than average pre-industrial temperatures, the most of any year between 1850 and 2019.

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.

For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.

The SECURES-Met dataset provides variables visible in the table.

Variable Short name Unit Aggregation methods Temporal resolution

Temperature (2m) T2M

°C

°C

spatial mean

population weighted mean (recommended)

hourly

Radiation

GLO (mean global radiation)

BNI (direct normal irradiation)

Wm-2

Wm-2

spatial mean

population weighted mean (recommended)

hourly

Potential Wind Power WP 1 normalized with potentially available area hourly

Hydro Power Potential

HYD-RES (reservoir)

HYD-ROR (run-of-river)

MW

1

summed power production

summed power production normalized with average daily production

daily

SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the spatial aggregation level

NUTS0 (country-wide),

NUTS2 (province-wide),

NUTS3 (Austria only),

and EEZ (Exclusive Economic Zones, offshore only).

The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As population weighted temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.

The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.

Met Éireanns; INSPIRE datasets comprise information on rainfall, sunshine, wind, pressure, temperature and humidity datasets from our observation network. The datasets are obtained from existing and historical recording sites. The datasets have minute data from Automatic Weather Stations, hourly data from Synoptic Weather Stations and daily and monthly data from Stations. The temporal resolution and extent is station dependent. These are national datasets. Data can be accessed via the Met Éireann website at: https://www.met.ie/climate/available-data.