Over the past two decades, the average wind speed in the United Kingdom has remained relatively stable. In 2024, the average wind speed in the UK was 8.4 knots. Speeds peaked during this period in 2015 at 9.4 knots, before falling to 8.4 knots the following year. One knot is equivalent to one nautical mile per hour. Overall, wind speeds have mostly remained between eight and nine knots, dropping to a low of 7.8 in 2010. The first and fourth quarters were the windiest Since 2010, the first and fourth quarters of each year generally recorded the highest wind speeds. The highest quarterly wind speed averages occurred in the first quarter of 2020, with speeds of approximately 11.5 knots. Between 2015 and 2023, the most noticeable deviation from the 10-year mean was recorded in February 2020. In this month wind speeds were 4.2 knots higher than normal. Optimal wind conditions for wind energy The United Kingdom has some of the best wind conditions in Europe for wind power, so it is no surprise that it plays an important role in the country's energy mix. As of 2023, there were 39 offshore wind farms operating in the UK, by far the most in Europe. Furthermore, in the same year, offshore wind power additions in the UK reached 1.14 gigawatts.

Wind speed averages in the United Kingdom are generally highest in the first and fourth quarters of each calendar year – the winter months. Since 2010, the UK’s highest wind speed average was recorded in the first quarter of 2020, at 11.5 knots. During this period, 2010 was the only year that had the greatest wind speeds outside the winter months, with an average of 8.4 knots in the third quarter. In 2024, wind speeds ranged between a low of 7.9 knots in the third quarter and 9.4 knots in the first quarter. With few exceptions, UK wind speeds generally average at least eight knots annually. 2015 marked the year with the highest average wind speed in the UK (since the beginning of the reporting period in 2001), reaching an average of 9.4 knots. Wind power The UK has some of the best wind conditions in Europe for wind power. By 2023, there were 39 offshore wind farms operating across the UK, by far the most in Europe. Meanwhile, offshore wind power additions in the UK reached 1.14 gigawatts that same year. Quarterly rainfall Another weather phenomenon, UK rainfall also tends to be heaviest in the winter months. The average rainfall in the second quarter of 2024 was 254.5 millimeters, with figures in 2011 spiking to 738.6 millimeters. That year, precipitation levels in some parts of Scotland were the highest in one hundred years, while southern parts of England kept remarkably dry.

What does the data show?

The dataset is derived from projections of seasonal mean wind speeds from UKCP18 which are averaged to produce values for the 1981-2000 baseline and two warming levels: 2.0°C and 4.0°C above the pre-industrial (1850-1900) period. All wind speeds have units of metres per second (m / s). These data enable users to compare future seasonal mean wind speeds to those of the baseline period.

What is a warming level and why are they used?

The wind speeds were calculated from the UKCP18 local climate projections which used a high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g., decades) for this scenario, the dataset is calculated at two levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), so this dataset allows for the exploration of greater levels of warming.

The global warming levels available in this dataset are 2°C and 4°C in line with recommendations in the third UK Climate Risk Assessment. The data at each warming level were calculated using 20 year periods over which the average warming was equal to 2°C and 4°C. The exact time period will be different for different model ensemble members. To calculate the seasonal mean wind speeds, an average is taken across the 20 year period. Therefore, the seasonal wind speeds represent those for a given level of warming.

We cannot provide a precise likelihood for particular emission scenarios being followed in the real world in the future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected under current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate; the warming level reached will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.

What are the naming conventions and how do I explore the data?

The columns (fields) correspond to each global warming level and two baselines. They are named 'windspeed' (Wind Speed), the season, warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. For example, ‘windspeed winter 2.0 median’ is the median winter wind speed for the 2°C projection. Decimal points are included in field aliases but not field names; e.g., ‘windspeed winter 2.0 median’ is ‘ws_winter_20_median’.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, seasonal mean wind speeds were calculated for each ensemble member and then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.

Data source

The seasonal mean wind speeds were calculated from daily values of wind speeds generated from the UKCP Local climate projections; they are one of the standard UKCP18 products. These projections were created with a 2.2km convection-permitting climate model. To aid comparison with other models and UK-based datasets, the UKCP Local model data were aggregated to a 5km grid on the British National grid; the 5km data were processed to generate the seasonal mean wind speeds.

Useful links

Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal.

In February 2025, the average wind speed was 0.7 knots below the long-term mean (from 2002 to 2021). The largest deviation occurred in February 2020, when winds increased by 4.2 knots compared to the average speed.

The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2022.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.

For further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.

The UK mean wind data describes the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data is collected by observation stations across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to present.

The Department for Business, Enterprise & Regulatory Reform's wind speed database is available from this website. It contains estimates of the annual mean wind speed throughout the UK. The data is the result of an air flow model that estimates the effect of topography on wind speed. There is no allowance for the effect of local thermally driven winds such as sea breezes or mountain/valley breezes. The model was applied with 1km square resolution and takes no account of topography on a small scale or local surface roughness (such as tall crops, stone walls or trees), both of which may have a considerable effect on the wind speed. The data can only be used as a guide and should be followed by on-site measurements for a proper assessment. Each value stored in the database is the estimated average for a 1km square at either 10m, 25m or 45m above ground level (agl). The database uses the Ordnance Survey grid system for Great Britain and the grid system of the Ordnance Survey of Northern Ireland.

1999-2017 - London SWT Weather data

1. Site: SWT
2. Country\City\Location: UK\London\ Southwark WoodsfordTower SWT
3. Latitude: 51.487760; Longitude: -0.091069
4. Anemometer height: 60 m
5. Weather station owner: Bill Legassick, Southwark Council. Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705
6. Email: Bill.Legassick@southwark.gov.uk

1. Sensor:
2. Sensor type:---- Model -------------: Date Installed
3. Anemometer : CDL Windset (EC8) ......... : 1999
4. Rain gauge : Campbell Scientific ARG-100 : 1999
5. Temperature: T107_C .....................: 1999
6. Humidity : HMP45A .....................: 1999

1. Files: Are Zipped
2. Filenames: Weather_Data_2008.CSV
3. Filetype: comma delimited

4. Header Row:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly AverageDirection,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,MaximumHourly Rainfall Rate,Time of Rainfall

5. Data: hourly averages

The Department for Business, Enterprise & Regulatory Reform's wind speed database is available from this website. It contains estimates of the annual mean wind speed throughout the UK. The data is the result of an air flow model that estimates the effect of topography on wind speed. There is no allowance for the effect of local thermally driven winds such as sea breezes or mountain/valley breezes. The model was applied with 1km square resolution and takes no account of topography on a small scale or local surface roughness (such as tall crops, stone walls or trees), both of which may have a considerable effect on the wind speed. The data can only be used as a guide and should be followed by on-site measurements for a proper assessment. Each value stored in the database is the estimated average for a 1km square at either 10m, 25m or 45m above ground level (agl). The database uses the Ordnance Survey grid system for Great Britain and the grid system of the Ordnance Survey of Northern Ireland.

Site specific (293 individual stations) monthly average (1981 - 2010)

        The data consists of:
        Max Temp (degrees C) 
        Min Temp (degrees C) 
        Sunshine (hours) 
        Rainfall (mm) 
        Raindays >=1.0mm (days) 
        Days of Air Frost (days) 
        Monthly mean wind speeds at 10m (knots)

        District and Region monthly average (1961-1990, 1971-2000, 1981-2010)

        The data consists of:
        Max Temp (degrees C) 
        Min Temp (degrees C) 
        Sunshine (hours) 
        Rainfall (mm) 
        Raindays >=1.0mm (days) 
        Days of Air Frost (days)

        UK monthly average (1961-1990, 1971-2000, 1981-2010)

        The data consists of:
        Max Temp (degrees C) 
        Min Temp (degrees C) 
        Sunshine (hours) 
        Rainfall (mm) 
        Raindays >=1.0mm (days) 
        Days of Air Frost (days)

Materials and Methods

Fieldwork and sample processing

Field collection and sample processing has been described previously by Cuff, Tercel, et al., (2022), with the exception of weather variables. Extraction, amplification and sequencing of DNA, and bioinformatic analysis is described by Cuff, Tercel, et al. (2022) and Drake et al. (2022). The resultant sequencing read counts were converted to presence-absence data of each detected prey taxon in each individual spider.

Weather data

Weather data were taken from publicly available reports from the Cardiff Airport weather station (6.6 km from the study site) via “Wunderground” (Wunderground, 2020) from 1/1/2018 to 17/9/2018 (the last field collection). Weather data were also separately extracted for the week preceding each of the two 2017 collection dates (3/8/2017 to 9/8/2017 and 29/8/2017 to 4/9/2017). Specifically, daily average temperatures (°C), daily average dew point (°C), maximum daily wind speed (mph), daily sea level pressure (Hg) and day length (min; sunrise to sunset) were recorded. Precipitation data were downloaded via the UK Met Office Hadley Centre Observation Data (UK Met Office, 2020) as regional precipitation (mm) for South West England & Wales. Weather data were converted to mean values for seven days preceding the collection of spider samples to correspond with the longevity of DNA in the guts of spiders (Greenstone et al., 2014).

Statistical Analysis

All analyses were conducted in R v4.0.3 (R Core Team, 2020). To assess how weather affects spider trophic interactions, we analysed dietary changes across weather gradients using multivariate models. To identify whether this was likely to be driven by changes in prey abundance, we assessed the corresponding changes in the prey communities and then used null models to ascertain whether spiders were responding to prey abundance changes through density-independent prey choice. Given the dependence of spiders on webs for foraging, we also compared web height and area over weather gradients to assess whether this may be a component of adaptive foraging. To assess the inter-annual consistency of prey choices in response to weather conditions, we also assessed whether prey preference data could be used to improve the predictive power of null models. For this, we generated null models for 2017 data with prey abundance weighted by prey preferences estimated with the 2018 data. This allowed us to assess the consistency of prey choice under similar conditions, but also provides insight as to whether this framework can be used to predict predator responses to diverse prey communities under dynamic conditions. We detail the specific stages of this analytical framework below.

Sampling completeness and diversity assessment

To assess the diversity represented by the dietary analysis and the invertebrate community sampling, and the completeness of those datasets, coverage-based rarefaction and extrapolation were carried out, and Hill diversity calculated (Chao et al., 2014; Roswell, Dushoff, & Winfree, 2021). This was performed using the ‘iNEXT’ package with species represented by frequency-of-occurrence across samples (Chao et al., 2014; Hsieh et al., 2016; Figures S4 & S6).

Relationships between weather, spider trophic interactions and prey community composition

Prey species that occurred in only one spider individual were removed before further analyses to prevent outliers skewing the results. Spider trophic interactions were related to temporal and weather variables in multivariate generalized linear models (MGLMs) with a binomial error family (Wang, Naumann, Wright, & Warton, 2012). Trophic interactions were related to temporal variables and their pairwise interactions (including spider genus to account for any confounding effect), weather variables and their pairwise interactions, and weather variables and their interactions with spider genus and time (to account for any confounding effects) in three separate MGLMs. These variables were separated into different models (Temporal model, Weather Interaction model and Confounding effects model) to improve model fit and reduce singularity. Invertebrate communities from suction sampling were related to temporal and weather variables in identically structured MGLMs (excluding the spider genus variable) with a Poisson error family.

All MGLMs were fitted using the ‘manyglm’ function in the ‘mvabund’ package (Wang et al., 2012). ‘Temporal model’ independent variables were Julian day (day), mean day length in minutes for the preceding week (day length), spider genus (for dietary models only, to ascertain any effect of spider taxonomic differences on dietary differences over time and day lengths) and all two-way interactions between these variables. ‘Weather interaction model’ independent variables were mean temperature, precipitation, dewpoint, wind speed and pressure for the preceding week, and pairwise interactions between weather variables. ‘Confounding effects’ model independent variables were day (to investigate the interaction between time and weather), spider genus (for dietary models only, to ascertain any effect of spider taxonomic differences on dietary differences over time and day lengths), mean temperature, precipitation, dewpoint, wind speed and pressure for the preceding week, and two-way interactions of each weather variable with day and genus.

Trophic interaction and community differences were visualised by non-metric multidimensional scaling (NMDS) using the ‘metaMDS’ function in the ‘vegan’ package (Oksanen et al., 2016) in two dimensions and 999 simulations, with Jaccard distance for spider diets and Bray-Curtis distance for invertebrate communities. For the dietary NMDS, outliers (n = 21; samples containing rare taxa) obscured variation on one axis and were thus removed to facilitate separation of samples and achieve minimum stress. For visualization of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the ‘ordisurf’ function in the ‘ggplot’ package (Wickham, 2016).

Relationships between web characteristics and weather variables

Web area and height were compared against weather and temporal variables using a multivariate linear model (MLM) with the ‘manylm’ command in ‘mvabund’ (Wang et al., 2012). Log-transformed web area and height comprised the multivariate dependent variable, and day, spider genus, temperature, precipitation, dewpoint, wind, pressure and two-way interactions between each of these and day and genus comprised the independent variables.

Variation in spider prey choice across weather conditions

To separately represent spiders from different weather conditions in prey choice analyses, sample dates for every spider were clustered based on the mean weather conditions (temperature, precipitation, dewpoint, wind and pressure) of the week before collection (7 days, to align approximately with spider gut DNA half-life; Greenstone et al., 2014). Alongside data from 2018 (n = 24 collection dates), two sampling periods from 2017 were included in the clustering to ascertain similarity of weather conditions for additional inter-annual prey choice analyses described below. The clustering process is described in the Supplementary Information of the manuscript. Five clusters were generated: High Pressure (HPR), Hot (HOT), Wet Low Dewpoint (WLD), Dry Windy (DWI), Wet Moderate Dewpoint (WMD), and 2017 (2017 sampling periods).

Prey preferences of spiders in each of the weather clusters was analysed using network-based null models in the ‘econullnetr’ package (Vaughan et al., 2018) with the ‘generate_null_net’ command. Consumer nodes in this case represented spiders belonging to each of the weather clusters. Econullnetr generates null models based on prey abundance, represented here by suction sample data, to predict how consumers will forage if based on the abundance of resources alone. These null models are then compared against the observed interactions of consumers (i.e., interactions of spiders within each weather cluster with their prey) to ascertain the extent to which resource choice deviated from random (i.e., density dependence). The trophic network was visualised with the associated prey choice effect sizes using ‘igraph’ (Csardi & Nepusz, 2006) with a circular layout, and as a bipartite network using ‘ggnetwork’ (Briatte, 2021; Wickham, 2016). The normalised degree of each weather cluster node was generated using the ‘bipartite’ package (Dormann, Gruber, & Fruend, 2008) and compared against the normalised degree of the same node in the null network to determine whether spiders were more or less generalist than expected by random. Prior to the prey choice analysis, an hemipteran prey identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty.

Validating and predicting relationships between years

To test how generalisable the results are and the extent to which weather drives prey preferences, we used a measure of prey preference (observed/expected values; observed interaction frequencies divided by interaction frequencies expected by null models) from the above prey choice analysis to assess whether we could more accurately predict observed trophic interactions under similar weather conditions for data from a linked study at the same location in 2017. These additional data represent a subset of the spider taxa analysed above (Tenuiphantes tenuis and Erigone spp.) collected using the same methods by the same researchers and in the same locality (Cuff, Drake, et al., 2021).

The similarity in weather conditions between the 2017 study period and each of the five 2018 weather clusters was determined via NMDS of the weather data in two dimensions with

2011\ London\ SWT Weather data
Data Type: Weather station
Site information:
Latitude: 51.487760
Longitude: -0.091069
Anemometer height: 60 m
Owner: Bill Legassick, Southwark Council. Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705
Email: Bill.Legassick@southwark.gov.uk
Sensor information
Sensor type Model Date installed
Anemometer CDL Windset (EC8) 1999
Rain gauge Campbell Scientific ARG-100 1999
Temperature probe T107_C 1999
Humidity probe HMP45A 1999
Files: Are Zipped
Filenames: Weather_Data_2008.CSV
Filetype: comma delimited
Header Row:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly Average Direction,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,Maximum Hourly Rainfall Rate,Time of Rainfall
Data: hourly averages2011\ London\ SWT Weather data

What does the data show?

Wind-driven rain refers to falling rain blown by a horizontal wind so that it falls diagonally towards the ground and can strike a wall. The annual index of wind-driven rain is the sum of all wind-driven rain spells for a given wall orientation and time period. It’s measured as the volume of rain blown from a given direction in the absence of any obstructions, with the unit litres per square metre per year.

Wind-driven rain is calculated from hourly weather and climate data using an industry-standard formula from ISO 15927–3:2009, which is based on the product of wind speed and rainfall totals. Wind-driven rain is only calculated if the wind would strike a given wall orientation. A wind-driven rain spell is defined as a wet period separated by at least 96 hours with little or no rain (below a threshold of 0.001 litres per m2 per hour).

The annual index of wind-driven rain is calculated for a baseline (historical) period of 1981-2000 (corresponding to 0.61°C warming) and for global warming levels of 2.0°C and 4.0°C above the pre-industrial period (defined as 1850-1900). The warming between the pre-industrial period and baseline is the average value from six datasets of global mean temperatures available on the Met Office Climate Dashboard: https://climate.metoffice.cloud/dashboard.html. Users can compare the magnitudes of future wind-driven rain with the baseline values.

What is a warming level and why are they used?

The annual index of wind-driven rain is calculated from the UKCP18 local climate projections which used a high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g., decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), so this dataset allows for the exploration of greater levels of warming.

The global warming levels available in this dataset are 2°C and 4°C in line with recommendations in the third UK Climate Risk Assessment. The data at each warming level were calculated using 20 year periods over which the average warming was equal to 2°C and 4°C. The exact time period will be different for different model ensemble members. To calculate the value for the annual wind-driven rain index, an average is taken across the 20 year period. Therefore, the annual wind-driven rain index provides an estimate of the total wind-driven rain that could occur in each year, for a given level of warming.

We cannot provide a precise likelihood for particular emission scenarios being followed in the real world in the future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected under current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate; the warming level reached will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.

What are the naming conventions and how do I explore the data?

Each row in the data corresponds to one of eight wall orientations – 0, 45, 90, 135, 180, 225, 270, 315 compass degrees. This can be viewed and filtered by the field ‘Wall orientation’.

The columns (fields) correspond to each global warming level and two baselines. They are named 'WDR' (Wind-Driven Rain), the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. For example, ‘WDR 2.0 median’ is the median value for the 2°C projection. Decimal points are included in field aliases but not field names; e.g., ‘WDR 2.0 median’ is ‘WDR_20_median’.

Please note that this data MUST be filtered with the ‘Wall orientation’ field before styling it by warming level. Otherwise it will not show the data you expect to see on the map. This is because there are several overlapping polygons at each location, for each different wall orientation.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, annual wind-driven rain indices were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.

Data source

The annual wind-driven rain index was calculated from hourly values of rainfall, wind speed and wind direction generated from the UKCP Local climate projections. These projections were created with a 2.2km convection-permitting climate model. To aid comparison with other models and UK-based datasets, the UKCP Local model data were aggregated to a 5km grid on the British National Grid; the 5 km data were processed to generate the wind-driven rain data.

Useful links

Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal.

CustomWeather delivers weather-informed operational intelligence, accurate forecasts to help with downtime and risk assessment, warning thresholds for forecast parameters, hind casts, and long-range forecasts to aid in site selection, installation, operations, maintenance, and decommissioning.

Hour-by-Hour Forecasts: Available as 12 hour, 48 hour and 168 hour blocks. Each hourly forecast includes weather descriptions, wind conditions, temperature, dew point, humidity, visibility, rainfall totals, snowfall totals, and precipitation probability. Available for any global location.

Custom Alerts - Custom alerts can be generated for any specific weather criteria, either in the past based on climate data or in the future based on weather forecasts. Weather alerts can also be generated that incorporate both past and future weather data.

Hourly Historical Climate Information - A comprehensive land and sea database of hourly climate information going back to 1980 for any global, coordinate location. Normalized values can be delivered for various time periods. Fields include: daylight status, sky descriptor, precipitation descriptor, temperature, wind speed, wind direction, wind gusts, wave heights, wave direction, humidity, dew point, barometric tendency, sea level pressure, sky conditions, and precipitation totals for various time periods.

Long-Range Forecasts - Features a month-by-month forecast for the next 9 months of anomalies in temperature and precipitation from normal. The variables are PRATE (precipitation rate over the entire month), TMP which is the average 2m daily temperature, TMAX and TMIN maximum and minimum daily temperatures -- all as anomaly values, or departure from normal for the month. The product is updated daily.

Detailed Marine Forecast - Features an open ocean marine forecast for morning, afternoon, evening, and overnight for the next 5 days. Forecast includes wave description, surf description, wind speed and direction, significant wave height, mean wave direction and period, wind wave direction and period, peak wave direction and period, and sea surface temperature. The marine reports are available for any global, coordinate location. The information is updated four times per day

Wind power is a rapidly growing force in UK electricity generation, with the number and size of UK wind farms surging in recent years. As the UK strives for net-zero emissions, abundant natural resources make wind energy the highest yielding option. Government support for renewables has boosted investment in wind-generating assets, with most of the capacity expansion coming from offshore wind. According to government data, the share of electricity generated from wind power increased from 23.2% in 2019-20 to 31.5% in 2023-24. Revenue is forecast to climb at a compound annual rate of 7.2% to reach £5.4 billion over the five years through 2024-25. Most of the UK's wind farms are subject to regulated prices, meaning that revenue has largely followed trends in wind generation output. Wind generation volumes have gathered speed over the past decade. However, low wind speeds and a slowdown in capacity expansion spurred a dip in wind generation volumes over the two years through 2021-22. Regulated prices have limited the effects of soaring wholesale prices on revenue. Some wind farms not subject to fixed prices have recorded a surge in revenue since the second half of 2021, while inflation-linked increases to regulated prices have also boosted growth. Revenue is set to jump by 9.3% in 2024-25. Revenue is slated to rise at a compound annual rate of 14.1% to £10.4 billion over the five years through 2029-30. Alongside developers, energy giants have a strong pipeline of large-capacity wind farms due to commence operations in the coming years. Ramped-up government support for renewables should ensure continued investment in wind energy, particularly offshore wind farms. Significant reductions in the strike price secured for wind power will weigh on growth in the short-term, though the extent of capacity expansion prevents this from being a major cause of concern. Increases in strike prices secured in the most recent Contracts for Difference (CfD) allocation round will also boost growth in the longer-term. Rising battery storage capacity will help reduce barriers to growth associated with intermittent flows of wind power, while developments in floating offshore wind should unlock further potential generating capacity.

This dataset contains data on mean wind speed and mean wind power density (calculated per square metre of rotor swept area) in UK waters, at 100m above sea surface, at annual, seasonal and monthly time scales and 10km resolution. The latest version of the dataset was produced in 2008.

UKCP09 Regional values Monthly Averages - Sunshine duration (hours per day) Long-term averages for the 1961-1990 climate baseline are also available for 14 administrative regions and 23 river basins. They have been produced for all the monthly and annual variables, apart from mean wind speed, days of sleet/snow falling, and days of snow lying, for which data start after 1961. Each regional value is an average of the 5 x 5 km grid cell values that fall within it. The datasets are provided as space-delimited text files.

The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/.

The data files are obtained by clicking on the links in the table below. Each text file contains values of the 1961-1990 baseline average for each administrative region and for each river basin. Monthly variables have 12 values for each region (one for each month) whereas annual variables have just one value (the annual average).

To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/gds_form.html.