The highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

The annual mean temperature in the United Kingdom has fluctuated greatly since 1990. Temperatures during this period were at their highest in 2022, surpassing ** degrees Celsius. In 2010, the mean annual temperature stood at **** degrees, the lowest recorded during this time. Daily temperatures Average daily temperatures have remained stable since the turn of the century, rarely dropping below ** degrees Celsius. In 2010, they dropped to a low of **** degrees Celsius. The peak average daily temperature was recorded in 2022 when it reached **** degrees. This was an increase of *** degree Celsius compared to the long-term mean, and the most positive deviation during the period of consideration. Highs and lows The maximum average temperature recorded across the UK since 2015 was in July 2018. This month saw a maximum temperature of **** degrees Celsius. In comparison, the lowest monthly minimum temperature was in February of the same year, at just minus *** degrees. This was an especially cold February, as the previous year the minimum temperature for this month was *** degrees.

England's highest monthly mean air temperatures are typically recorded in July and August of each year. Since 2015, the warmest mean temperature was measured in July 2018 at 18.8 degrees Celsius. On the other hand, February of that same year registered the coolest temperature, at 2.6 degrees Celsius. In September 2025, the mean air temperature was 13.8 degrees Celsius, matching the figure recorded the same month the previous year. The English weather England is the warmest region in the United Kingdom and the driest. In 2024, the average annual temperature in England amounted to 10.73 degrees Celsius – around 1.1 degrees above the national mean. That same year, precipitation in England stood at about 1,020 millimeters. By contrast, Scotland – the wettest region in the UK – recorded over 1,500 millimeters of rainfall in 2024. Temperatures on the rise Throughout the last decades, the average temperature in the United Kingdom has seen an upward trend, reaching a record high in 2022. Global temperatures have experienced a similar pattern over the same period. This gradual increase in the Earth's average temperature is primarily due to various human activities, such as burning fossil fuels and deforestation, which lead to the emission of greenhouse gases. This phenomenon has severe consequences, including more frequent and intense weather events, rising sea levels, and adverse effects on human health and the environment.

Temperature in the United Kingdom decreased to 9.88 celsius in 2024 from 10.14 celsius in 2023. This dataset includes a chart with historical data for the United Kingdom Average Temperature.

This dataset contains meteorological observations taken from 72 locations around Great Britain, Ireland and Europe published in the 1900-1910 Met Office Daily Weather Reports (DWRs). These records were produced as part of the Operation Weather Rescue project.

Twice daily observations of mean sea level pressure and dry bulb temperature, along with daily wet bulb, maximum and minimum temperatures and total rainfall, were sent to the Met Office via telegraph for publication the DWRs. Some of the locations cover the entire 11 year period whereas others stopped reporting and may have been replaced by another location, and some locations were included in the DWRs from a later date. Additional observations of mean sea level pressure, dry bulb temperature and wet bulb temperature at 2pm are included for 1900 but these observations were no longer included in the DWRs after 1900. From November 1908 the German stations replaced wet bulb temperature with relative humidity.

The data is stored in two formats: in daily csv files with observations for each station and in Station Exchange Format (SEF) files for each station in separate variables. SEF is a human-readable text format saved as .tsv (tab separated values). In the csv files units are inches of mercury (inHg) for mean sea level pressure, degrees Fahrenheit (F) for all temperature variables, inches for rainfall and percent (%) for relative humidity. In the SEF files the units are hectopascals (hPa) for mean sea level pressure, degrees Celsius (C) for all temperature variables, millimetres (mm) for rainfall and percent for relative humidity.

This dataset contains gridded meteorological data associated with challenging periods of weather for highly-renewable UK and European electricity systems of the future collected during the Adverse Weather Scenarios for Future Electricity Systems project. This project is a collaboration between the Met Office, the National Infrastructure Commission and the Climate Change Committee. More details about the project can be found in the associated documentation.

Two categories of challenging weather conditions; long duration adverse events and short duration wind ramping events, are provided.

Long duration events

The long duration event component of the dataset provides daily time series at 60 x 60 km spatial resolution, covering a European domain, for surface temperature, 100 m wind speed and net surface solar radiation data, representative of a selection of adverse weather scenarios. Each adverse weather scenario is contained within a time slice of data. For summer-time events, one calendar year (January - December) of data is provided, with the summer-time event occurring in the summer of that year. For winter-time events, two calendar years of data are provided, with the winter-time event occurring in the winter (October-March) intersecting the two calendar years. In all cases, the start date, duration and severity of the adverse weather event, contained within the time slice of data, are given in the NetCDF global ttributes.

Three types of long-duration adverse weather scenarios are represented: winter-time wind-drought-peak-demand events, summer-time wind-drought-peak-demand events, and summer-time surplus generation events. These are provided at various extreme levels (1 in 2, 5, 10, 20 ,50 and 100-year events); and for a range of current and nominal future climate change warming levels (1.2 [current day, early 2020s], 1.5, 2, 3, and 4 degrees Celsius above pre-industrial level), representative of events impacting either just the UK, or Europe as a whole.

The data provided are derived from the Met Office decadal prediction system hindcast (https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/climate-models/depresys), according to the climate change impacts identified from UKCP18 (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index).

Short duration events The short duration event component of the dataset provides hourly time series at 4 x 4 km spatial resolution, covering a UK and surrounding offshore area domain, for 100 m wind speed, representative of a selection of wind generation ramping events. Each adverse weather scenario is contained within a time slice of data with up to one week before and one week after the day on which the event occurs (up to 15 days in total) provided. For the majority of events provided, the full 15 days are available, however for a small number of events which occur less than one week from the beginning or end of the underlying data used to derive this dataset, this is not possibly to supply, and these events are listed below. The start date and time along with the direction and magnitude of the ramp (change in wind capacity factor) contained within the time slice of data, are given in the NetCDF global attributes.

The short duration wind generation ramping events are representative of events impacting five separate regions of Great Britain and surrounding offshore areas, as defined in the accompanying documentation. These regions are Scotland, the East England, West England and Wales offshore North and offshore South. The events are defined by changes in wind capacity factors occurring over different length time windows (1-hour, 3-hour, 6-hour, 12-hour and 24-hour windows). These are provided at various extreme levels (1 in 2, 5, 10, 20 ,50 and 100-year events) for the 1.2 degrees Celsius above pre-industrial level (I.e. representative of early 2020s climate) and through the analysis outlined in the accompanying documentation are though to also be representative of the 2, 3, and 4 degrees Celsius above pre-industrial level nominal future climate change warming levels.

The data provided are derived from the UKCP18 local projections (https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/index).

The methods developed for characterising and representing these adverse weather scenarios, and the approach used to compile the final dataset are presented in the accompanying documentation.

Use of this data is subject to the terms of the Open Government Licence (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/). The following acknowledgment must be given when using the data: © Crown Copyright 2021, Met Office, funded by the National Infrastructure Commission.

A network for snow depth measurements was started with 4 Automatic Weather Stations in 1987 around Terra Nova Bay (North Victoria Land). It provides continuous meteo-climatological data and information, according to standard and reliable procedures, making use of robust methodologies and testing new procedures. At present, we operate a network of 13 Automatic Weather Stations working year-round plus 3 working during summer only. Data are achieved, validated, archived and diffused, and instruments are maintained.

UK ECN Meteorology Measurements

Long-term Automatic Weather Station Data

By data.world's Admin [source]

About this dataset

The UK Environmental Change Network (ECN) is a pioneering long-term environmental monitoring programme backed by 14 government departments and agencies. It has been collecting meteorology data from its terrestrial sites since 1991, using Automatic Weather Stations (AWSs) that measure essential variables such as albedo, solar radiation, air temperature and humidity, rainfall, surface wetness and soil moisture, as well as wind direction and velocity.

This dataset contains continuous hourly records of the monitoring findings measured at ECN archaeological sites – presented in terms of site codes (SITECODE), AWS numbers (AWSNO), fieldnames (FIELDNAME), date ranges for collection period start/end dates(FROM_DATE/TO_DATE), datatype (DATETYPE)and verbal descriptions of the recorded data items(DESCRIPTION). Spanning 24 years and over 50 locations across the UK’s most fragile ecosystems - this invaluable data set is profoundly useful for future environmental studies & research projects. Whether looking to evaluate past climate fluctuations or ascertain current day improvements to the environment – no other resource more concisely captures both these essential elements than this one!

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How to use the dataset

This dataset provides useful information on meteorology data from the UK Environmental Change Network's (ECN) terrestrial sites. The data collected here spans 1991 to 2015 and contains information on variables such as albedo (ground and sky), temperature, relative humidity, radiation, rainfall, surface wetness, soil moisture, wind direction and speed. This dataset can be used for various applications related to research and analysis of the data being collected in order to have a better insight into climate change at these sites over time.

• SITECODE – Unique identifier for the site
• AWSNO – Unique identifier for the Automatic Weather Station
• FIELDNAME – Name of the meteorological variable being measured
• FROM_DATE – Date when the measurement began
• TO_DATE - Date when the measurement ended
• DATETYPE - Type of data being measured (hourly, daily etc.)

• DESCRIPTION - Description of the meteorological variable being measured

  • CONTINUING - Whether or not a measurement is ongoing

By exploring each column we can get a better understanding of what type datasets it holds within it. From this we can determine what parameters are important when analyzing this dataset and which methods are best suited for this task depending on its structure. With further exploration one could learn how to read CSV files in python or use libraries such as pandas and numpy manipulate this dataset or other tools like Tableau to visualize our results in interactive charts/graphs with ease so that we can gain valuable insights from our findings

Research Ideas

• Comparing long-term trends in temperature and other meteorological variables across different sites to detect signs of climate change.
• Analyzing seasonal changes in wind direction and speed to identify areas of the UK most vulnerable to extreme weather events over time.
• Using the data on surface wetness, rainfall, and soil moisture levels to map areas with increased soil fertility for sustainable agriculture practices

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ECN_MA_qtext.csv | Column name | Description | |:----------------|:--------------------------------------------------------------------| | SITECODE | Unique identifier for the site. (String) | | AWSNO | Automatic weather station number. (Integer) | | FIELDNAME | Name of meteorological variable being measured. (String) | | FROM_DATE | Date when measurement began. (Date) | | TO_DATE | Date when measurement ended. (Date) | | DATETYPE | Type of data being measured. (String) | | CONTINUING | Whether measurement is ongoing or not. (Boolean) | | DESCRIPTION | Description of the meteorological variable being meas...

A network for incoming solar radiation measurements was started with 4 Automatic Weather Stations in 1987 around Terra Nova Bay (North Victoria Land). It provides continuous meteo-climatological data and information, according to standard and reliable procedures, making use of robust methodologies and testing new procedures. At present, we operate a network of 13 Automatic Weather Stations working year-round plus 3 working during summer only. Data are achieved, validated, archived and diffused, and instruments are maintained.

Monthly temperature deviations from the long-term mean in the United Kingdom have varied greatly in recent years. In August 2025, average temperatures were 1.1 degrees Celsius warmer than the long-term mean. In comparison, temperatures in August 2024 were 0.3 degrees Celsius warmer than the long-term mean. The most notable deviation during this period was in December 2015, when temperatures were 4.3 degrees warmer than normal.

The Japanese 25-year Reanalysis (JRA-25) represents the first long-term global atmospheric reanalysis undertaken in Asia. Covering the period 1979-2004, it was completed using the Japan Meteorological Agency (JMA) numerical assimilation and forecast system and specially collected and prepared observational and satellite data from many sources including the European Center for Medium-Range Weather Forecasts (ECMWF), the National Climatic Data Center (NCDC), and the Meteorological Research Institute (MRI) of JMA. A primary goal of JRA-25 is to provide a consistent and high-quality reanalysis dataset for climate research, monitoring, and operational forecasts, especially by improving the coverage and quality of analysis in the Asian region. In JRA-25, three-dimensional variational (3D-Var) data assimilation and a global spectral model were employed to produce 6-hourly atmospheric analysis and forecast cycles. The global spectral model was based on a 320 by 160 (~1.125 degree) Gaussian grid with T106 truncation. Vertical discretization employed a hybrid sigma-pressure coordinate utilizing 40 levels where 0.4 hPa represents the model top level. A predictive mass-flux Arakawa-Schubert scheme was utilized for cumulus parameterization, and Simple Biosphere (SiB) parameterizations for land-surface processes. Assimilated variables include temperature, relative humidity, and surface pressure from conventional observations, and also winds retrieved from geostationary satellites, radiative brightness temperature from TIROS Operational Vertical Sounder (TOVS), and precipitable water from Special Sensor Microwave/Imager (SSM/I). Variables not directly assimilated include daily sea surface temperature (SST) and sea ice based on Centennial in-situ Observation-Based Estimates (COBE), and ozone profiles based on chemical transport models constrained by observations from Total Ozone Mapping Spectrometer (TOMS).

The JRA-25 shows marked improvement over previous reanalyses in several notable areas, especially predicted (both 6-hourly and long term) precipitation, with more realistic variability and fewer spurious trends due to contamination of satellite data by volcanic eruptions. JRA-25 is also the first reanalysis to assimilate wind profiles around tropical cyclones deduced from best-track data, resulting in improved tropical cyclone analysis in a global context. In addition, low-level (stratus) cloud decks along the western subtropical coasts of continents are also better simulated, improving radiation budgets in these regions. In 2006, JMA started real-time operation of the JMA Climate Data Assimilation System (JCDAS). JCDAS employs the same system as JRA-25 and the data assimilation cycle is extended to the present time. JRA-25 and JCDAS products will enable users to conduct climate diagnostics with a long-term, and current, homogeneous reanalysis dataset. The JMA has also engaged in ongoing cooperation with ECMWF (European Center for Medium-Range Weather Forecasts) on reanalysis, including the ECMWF CDAS (ECDAS), more commonly known as ERA-Interim.

'''DEFINITION''' The subsurface temperature anomaly has been derived from the regional reanalysis of the CMEMS NWS MFC group for the North-West European Shelf Seas (product reference NWSHELF_MULTIYEAR_PHY_004_009). Horizontal averaging has been conducted over just the shelf, defined as the contiguous region surrounding the UK where sea depth is no more than 200m. The profiles of the annual mean temperature anomaly have been calculated relative to the reference period of 1993-2019. The time series shows monthly anomalies calculated from monthly means. CMEMS Ocean State Report (Mulet et al., 2018) gives the broader context for these anomalies.

'''CONTEXT''' The North-West European Shelf Seas are the waters on the continental shelf adjoining the North-East Atlantic. Geographically, they can be divided into 5 regions: the North Sea, the English Channel, the Celtic Sea, the Irish Sea, and the North-Western Approaches. The temperatures in these seas is moderated by inflow from the North Atlantic Drift (a continuation of the Gulf Stream), into the Irish Sea and English Channel from the south-west and into the North Sea from the north and north-west. In Winter, this Atlantic water maintains surface temperatures no colder than 10°C in western regions. Cooler temperatures, below 3°C, can occur in eastern parts near continental coasts. In Summer, those eastern coastal regions can warm to as much as 18°C, especially in the shallow waters of the southern North Sea. Stratification occurs in the Summer months, starting around May in the northern part of the domain, and then extending southwards (Paramor et al., 2009). Currents are dominated by the strong semi-diurnal tides which act to mix the water and reduce stratification. This is especially noticeable in the shallow southern North Sea.

'''CMEMS KEY FINDINGS''' Interannual variations of the subsurface temperature anomaly averaged across the domain range from -1°C to +1°C within the upper 100-m deep layer over the period 1993-2019. There is some long-term variability, with warm anomalies for most of the first decade of the 21st century. This is a decade in which 6 of the 10 warmest years on record occurred for air temperature over the UK (Kendon et al., 2019). The 4 other warmest years for UK climate were 2011, 2014, 2017 and 2018. 2011 and 2017 correspond to warm anomalies through the depth of the Shelf. 2018 saw an unusually hot Summer heatwave for the atmosphere, which is reflected in a short-lived and shallow warm anomaly (to 25m depth) in the reanalysis. 2014 was for UK climate the hottest year on record. This corresponds to a stronger and longer-lived anomaly in the reanalysis, but only down to around 75m depth. Temperatures in deeper layers may be moderated by inflow to the region from the North Atlantic drift. 2019 was overall slightly warmer than average in near-surface layers, and slightly cooler below 100m depth.

Context

These files provide detailed road safety data about the circumstances of personal injury road accidents in GB from 2005 to 2014,

Content

Accident file: main data set contains information about accident severity, weather, location, date, hour, day of week, road type...
Vehicle file: contains information about vehicle type, vehicle model, engine size, driver sex, driver age, car age...
Casualty file: contains information about casualty severity, age, sex social class, casualty type, pedestrian or car passenger...
Lookup file: contains the text description of all variable code in the three files

License

OGL:Open Government License http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Inspiration

This dataset contains a large amount of rows and features to explore.

Data Wrangling:

• Merge different files
• Map column code with their text strings fro a lookup table.
• How do you map lat/long to a country a city?

Data Explanatory:

• How the weather impact the number or severity of an accident?
• Does driver age has an effect on the number of accident?
• What is the relation between hour, day, week, month with number of fatal accident?
• Are certain car models safer than others?
• Is the social class of a casualty dependant of the accident severity?

Forecasting:

• Can you forecast the future daily/weekly/monthly accidents?
• What about fatal accidents can you predict them?
• Can you predict if an accident was fatal ?(similar to Titanic prediction but with more row and more variables)

Feel free to suggest other external data to this dataset.

Chemistry of the Antarctic Boundary Layer and the Interface with Snow (CHABLIS) is a Natural Environment Research Council (NERC) and Antarctic Funding Initiative (AFI) funded project, aimed at studying the chemistry of the Antarctic Boundary Layer in greater detail, and for a longer duration, than has previously been attempted. Field measurements were carried out at the British Antarctic Survey station, Halley, at the Clean Air Sector Laboratory (CASLab). Year-round measurements began in February 2004, and a summer campaign focussing on oxidants ran during January/February 2005, after which CHABLIS fieldwork ended. This dataset contains weather observations taken at Halley for the period February 2004 to February 2005. Access to this dataset is now public.

This data has been sourced from the Met Office's historical station data available here: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data

I have included the python script used to generate the dataset.

The data is held at a monthly level and contains: - max_temp: Mean daily maximum temperature - min_temp: Mean daily minimum temperature - air_frost_days: Days of air frost - rain_mm: Total rainfall - sun: Total sunshine duration (hours) - station: the station of the observation - lat: latitude of the station - long: longitude of the station - month_year: month date of the observation

The data set contains the timeseries of long-period magnetotelluric data collected at 44 sites in Great Britain during the SWIMMR-SAGE project 2021-2023. At each site, five channels were recorded with a 1s sampling rate: The magnetic field components (north-south Bx, east-west By, and the vertical component Bz) were measured using a fluxgate magnetometer manufactured from Lemi, Lviv, Ukraine. The horizontal electric field (north-south Ex, east-west Ey) was recorded using non-polarizable electrodes. The installations were performed by BGS staff following the manufacturers guidelines and best practices. For each site, a folder containing the timeseries data and a pdf containing all relevant metadata are provided. Format: YYYY-MM-DD HH:MM:SS BX BY BZ EX EY Magnetic values in units of nT (10^-9 T). Electric field values are in mV/km. A BGS open report has been published on the NERC Open Research Archive (NORA) describing the motivation, site selection and data collection. It is referenced in the metadata and can be found here: https://nora.nerc.ac.uk/id/eprint/537960/

The long-term increasing trend of annual mean temperature is only one aspect of recent climate change. Other changes in climate, seen in within-year weather patterns relevant to crop production, have also occurred since the late-19th Century. Multivariate analysis combining Principal Components Analysis and K-means clustering applied to temporal meteorological datasets (monthly summaries of rainfall, temperature and sunlight duration at Rothamsted Research, UK, between 1892 and 2016) identified ten distinct clusters of years, each with different annual weather patterns. The frequency of occurrence of the years within each cluster altered considerably during this period, with the late 20th and early 21st Century distinctly different to earlier in the 20th Century, providing clear evidence of climate change with regard to the whole weather profile rather than just warming alone. The most-frequently represented cluster of the 21st Century to date had warmer temperatures with more intense rainfall but a dry June, compared to all other clusters. Half of the clusters identified were not represented in the most-recent 25-year period. Analysis of the total biomass yield of winter wheat (Triticum aestivum L.), spring barley (Hordeum vulgare L.), and grassland amongst the different weather clusters showed that years in clusters typical of the 20th Century climate provided greater off-take than those from the early-21st Century, but this impact was less for the pasture than for the two cereal crops implying herbage production was the more resilient to the changing climate at this site.

The NSF-supported research icebreaker Laurence M. Gould operates year-round in support of the U.S. Antarctic Program, carrying out global change studies in biological, chemical, physical, and oceanographic disciplines.

This data set consists of underway data from leg LMG1304 on the R/V Laurence M. Gould. This leg started at Punta Arenas, Chile and ended at Punta Arenas, Chile.

The United Kingdom experienced an average of ******* millimeters of rainfall in 2024, a decrease of *** percent in comparison to the previous year. While 2024 saw substantial rainfall, it did not surpass the thus-far peak of the century, with ***** millimeters of rain recorded in 2000. Regional variations and seasonal patterns Rainfall distribution across the UK is far from uniform, with Scotland and Wales consistently receiving the highest annual precipitation. In 2024, they recorded an average of ******* millimeters and ******* millimeters, respectively, significantly above the UK’s average. This disparity is largely due to both countries’ mountainous terrain, which is more susceptible to Atlantic weather systems. Seasonally, the wettest months in the UK typically occur in the winter, with the highest precipitation levels seen between November and February. Climate change impact on UK weather Climate change is influencing UK weather patterns, leading to warmer and wetter conditions overall. While annual rainfall fluctuates, there is a trend towards more extreme weather events. For example, 2020 and 2022 saw rain deviations from the long-term mean in the UK of more than 100 millimeters in February. As weather patterns continue to evolve, monitoring rainfall trends remains crucial for understanding and adapting to a changing climate.

The NSF-supported research icebreaker Nathaniel B. Palmer operates year-round in support of the U.S. Antarctic Program, carrying out global change studies in biological, chemical, physical, and oceanographic disciplines.

    This data set consists of underway data from leg NBP1508 on the R/V Nathaniel B. Palmer. This leg started at and ended at .