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 monthly mean temperature in the United Kingdom is typically highest in July and August. During this period, the monthly mean temperature peaked in July 2018, at *****degrees Celsius. In April 2025, the UK recorded a mean temperature of *** degrees Celsius, slightly higher than the temperature recorded the same month a year prior.

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 April 2025, the mean air temperature was 10.3 degrees Celsius, slightly higher than 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.

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.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.09°C.]What does the data show? This dataset shows the change in summer average temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, summer is defined as June-July-August. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions.The dataset uses projections of daily average air temperature from UKCP18 which are averaged over the summer period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare summer average temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.PeriodDescription1981-2000 baselineAverage temperature (°C) for the period2001-2020 (recent past)Average temperature (°C) for the period2001-2020 (recent past) changeTemperature change (°C) relative to 1981-20001.5°C global warming level changeTemperature change (°C) relative to 1981-20002°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-20003°C global warming level changeTemperature change (°C) relative to 1981-20004°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Summer Average Temperature Change is calculated from the UKCP18 regional climate projections using the 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), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Summer Average Temperature Change, an average is taken across the 21 year period.We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with 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 as it 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?These data contain a field for each warming level and the 1981-2000 baseline. They are named 'tas summer change' (change in air 'temperature at surface'), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'tas summer change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tas summer change 2.0 median' is named 'tas_summer_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas summer change 2.0°C median’ values.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, the Summer Average Temperature Change was 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 ‘higher’ 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 higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period 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. Useful linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

The average temperature across the United Kingdom presented a trend of continuous growth since 1961. During the first period, from 1961 to 1990, the country recorded an average temperature of *** degrees Celsius. In the next period, from 1991 to 2020, the UK's average temperature increased by *** degrees Celsius and increased further by *** degrees Celsius between 2014 and 2023. In the latter year, figures remained at ** degrees Celsius, *** degrees warmer than the average recorded between 1961 and 1990, illustrating the effects of climate change. Nevertheless, 2022 was the warmest year in the United Kingdom.

The wettest months in the United Kingdom tend to be at the start and end of the year. In the period of consideration, the greatest measurement of rainfall was nearly 217 millimeters, recorded in December 2015. The lowest level of rainfall was recorded in April 2021, at 20.6 millimeters. Rainy days The British Isles are known for their wet weather, and in 2024 there were approximately 164 rain days in the United Kingdom. A rainday is when more than one millimeter of rain falls within a day. Over the past 30 years, the greatest number of rain days was recorded in the year 2000. In that year, the average annual rainfall in the UK amounted to 1,242.1 millimeters. Climate change According to the Met Office, climate change in the United Kingdom has resulted in the weather getting warmer and wetter. In 2022, the annual average temperature in the country reached a new record high, surpassing 10 degrees Celsius for the first time. This represented an increase of nearly two degrees Celsius when compared to the annual average temperature recorded in 1910. In a recent survey conducted amongst UK residents, almost 80 percent of respondents had concerns about climate change.

UKCP09 Regional values Annual averages - Winter (Nov-Apr) heat wave duration 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.

UKCP09: 5 km gridded data - Annual averages for the winter coldwave duration. The data set contains 12 files (one for each month for the 1961-1990 average period). The individual grids are named according to the following convention: variablename_mmm_Average_Actual.txt where mmm is the month name (e.g. Jan).

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/.

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.

This dataset contains hourly water temperature data and hourly air temperature data of an experimental mesocosm facility from 21st April to 7th November 2023. The sixteen mesocosms (1 m deep, 2 m diameter) were filled with water from Windermere. The water temperature was measured every five minutes and an hourly average was calculated. Air temperature was measured by a weather station within the mesocosm compound. The experiment aimed to investigate different N:P nutrient ratios and water temperature was measured as this is an important factor needed to understand the results. Full details about this dataset can be found at https://doi.org/10.5285/406aee8f-3b85-4be6-ae7d-c92c8168a06f

The size of the Weather Forecasting Services market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 8.35% during the forecast period.The weather forecasting services market encompasses a host of technologies and services which predict future atmospheric conditions ranging from traditional meteorological observations and numerical models to advanced satellite imagery and artificial intelligence powered analytics.Weather forecasts form an essential part of almost all sectors, providing individuals with the basis upon which they may make intelligent decisions and thus mitigate risks. In agriculture, for instance, forecasts guide farmers in adjusting planting, irrigation, and harvesting schedules. The aviation industry uses weather information to plan flight routes for safety and efficiency in the process. Energy companies utilize the forecasts to predict the energy demand and change their power generation according to their needs. More importantly, weather forecasts have been pivotal in disaster preparedness and response: communities have been able to prepare for and mitigate extreme weather events. Recent developments include: April 2023: AccuWeather announced the introduction of the WeatherShow Enhancer, a recently developed software and hardware display system built on AccuWeather's popular StoryTeller+ Touchscreen System. The Enhancer immediately gives users a distinct and superior look through significantly improved special effects. It will multiply the capabilities of users' current weather system, thereby improving storytelling with less effort. Furthermore, the Enhancer will reduce weather show production costs., March 2023: Aquila has expanded into providing technical services for weather forecasting. The UK Ministry of Defence (MOD) has awarded Aquila a contract to provide and support a new weather forecasting radar serving Royal Air Force (RAF) Akrotiri on Cyprus. Aquila is upgrading and supporting air traffic control systems at more than sixty Ministry of Defence (MOD) air stations across the UK and overseas. The new contract represents the first partnership for Aquila in the weather, climate forecasting, and environmental monitoring space.. Key drivers for this market are: Changing Weather Patterns Resulting in Unpredictability of Rainfall, Increasing Penetration of Advanced Technologies in Weather Forecasting Services. Potential restraints include: Complexity in the Manufacturing Process with Countable Number of Folds, Huge Price Tags with Products from Prominent Vendors. Notable trends are: Agriculture Segment is Expected to Hold a Significant Share of the Market.

The global Weather Forecasting Services market is experiencing robust growth, projected to reach $2.93 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 8.35% from 2025 to 2033. This expansion is driven by several key factors. Increasing reliance on accurate weather data across various sectors, including agriculture, aviation, energy, and transportation, fuels demand for sophisticated forecasting services. Advancements in technology, such as the integration of artificial intelligence (AI), machine learning (ML), and high-resolution satellite imagery, are enhancing forecast accuracy and precision, thereby attracting more customers. Furthermore, the growing awareness of the economic impact of extreme weather events is prompting governments and businesses to invest heavily in improved weather forecasting infrastructure and services. The market's competitive landscape is characterized by a mix of established players like IBM and AccuWeather, alongside specialized meteorological firms and regional providers. This competition fosters innovation and drives prices down, making these services accessible to a wider range of users. The market segmentation is likely diverse, encompassing various service types (e.g., short-term, long-term forecasts; hyperlocal forecasts; specialized forecasts for specific industries), delivery methods (e.g., online platforms, mobile apps, APIs), and customer segments (e.g., businesses, governments, individuals). While precise segment data is unavailable, it's reasonable to expect that the business sector, particularly in high-risk industries like agriculture and energy, constitutes a substantial portion of the market. Geographic distribution is likely concentrated in regions with advanced technological infrastructure and a high need for precise weather information, such as North America and Europe, with growth potential in developing economies as their infrastructure and awareness improve. Despite the strong growth trajectory, challenges such as data limitations in certain regions, the complexity of weather patterns, and the need for continuous technological upgrades remain. Recent developments include: April 2023: AccuWeather announced the introduction of the WeatherShow Enhancer, a recently developed software and hardware display system built on AccuWeather's popular StoryTeller+ Touchscreen System. The Enhancer immediately gives users a distinct and superior look through significantly improved special effects. It will multiply the capabilities of users' current weather system, thereby improving storytelling with less effort. Furthermore, the Enhancer will reduce weather show production costs., March 2023: Aquila has expanded into providing technical services for weather forecasting. The UK Ministry of Defence (MOD) has awarded Aquila a contract to provide and support a new weather forecasting radar serving Royal Air Force (RAF) Akrotiri on Cyprus. Aquila is upgrading and supporting air traffic control systems at more than sixty Ministry of Defence (MOD) air stations across the UK and overseas. The new contract represents the first partnership for Aquila in the weather, climate forecasting, and environmental monitoring space.. Key drivers for this market are: Changing Weather Patterns Resulting in Unpredictability of Rainfall, Increasing Penetration of Advanced Technologies in Weather Forecasting Services. Potential restraints include: Changing Weather Patterns Resulting in Unpredictability of Rainfall, Increasing Penetration of Advanced Technologies in Weather Forecasting Services. Notable trends are: Agriculture Segment is Expected to Hold a Significant Share of the Market.

Projected regional average change in seasonal and annual temperature and precipitation extremes for the IPCC SREX regions for CMIP5. The data were produced in 2013 by the Intergovernmental Panel on Climate Change (IPCC) Working Group II (WGII) Chapter 14 supplementary material (SM) author team for the IPCC Fifth Assessment Report (AR5).

Regional average seasonal and annual temperature and precipitation extremes for the periods 2016-2035, 2046-2065 and 2081-2100 for CMIP5 General Circulation Model (GCM) projections are compared to a baseline of 1986-2005 from each model's historical simulation. The temperature and precipitation data are based on the difference between the projected periods and the historical baseline for which the 25th, 50th and 75th percentiles, and the lowest and highest responses among the 32 models which are expressed for temperature as degrees Celsius change and for precipitation as a per cent change. The temperature responses are averaged over the boreal winter and summer seasons; December, January, February (DJF) and June, July and August (JJA) respectively. The precipitation responses are averaged over half year periods, boreal winter (BW); October, November, December, January, February and March (ONDJFM) and boreal summer (BS); April, May, June, July, August and September (AMJJAS).

Regional averages are based on the SREX regions defined by the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (IPCC, 2012: also known as "SREX"). Added to the SREX regions are additional regions containing the two polar regions, the Caribbean, Indian Ocean and Pacific Island States. The data are further categorised by the land and sea mask for each SREX region.

This repository provides a continuous hydrometeorological record of the Met Office Observation-based research Boundary Layer Facility at the semi-rural field site (18 Ha) of Cardington (52° 06′ N, 00° 25′ W, 29 m ± 1 m amsl) in central-southern England between 2004 and 2024. The dataset contains recorded surface meteorology, radiation and subsoil from sensor measurements at 30 minute averaging period and measured by instruments mounted on 2 m, 10 m, 25 m and 50 m masts.

Instruments mounted on 2 m, 10 m, 25 m and 50 m masts include: • Vector Instruments T302 PRT temperature sensors were located at all heights. • Screened and aspirated HMP155s were located at all heights. • Gill HS50 3-D horizontally symmetric ultrasonic anemometers were located at all heights. • Licor Li-7500 open-path hygrometer was located at 10m. • Setra Model 270 transducer measured barometric pressure at 1.5 m. • Michell chilled mirror hygrometer measured dew and frost point temperature at 1.2 m • Covariances over 30 minute intervals were used to calculate the turbulent heat fluxes. The sonic data have cross-wind speed correction, coordinate rotation, detrending and despiking applied. • For latent heat flux calculations over 30 minute intervals, the 10-m covariance using the Licor hygrometer should be used as standard.

Surface instrumentation includes: • Rainfall is measured with a Met Office Mk5 tipping-bucket gauge with a 0.2 mm accuracy. • Screened and aspirated Rotronics Hydroclip2 measured grass canopy air temperature and RH located at 0.4 m, 0.15 m and 0.08 m.

Radiation instrumentation includes: • Clear-domed Kipp and Zonen CM21 pyranometers located at 2 m measured global downwelling, diffuse downwelling, and upwelling components (of wavelength between 0.3-3 μm). • Kipp and Zonen CG4 pyrgeometers located at 2 m measured the downwelling and upwelling longwave radiation (4.5–40 μm). • Grass canopy, or skin temperature was measured radiometrically with the Heitronics KT15 pyrometer.

Aerosol and visibility instrumentation includes: • A Belfort 6230A instrument located at 2 m measured visual range through air (visibility) (2004-April 2011). • A Biral HSS VPF-730 instrument located at 2 m measured visual range through air (visibility), and for the determination of present weather (April 2011-2024). • Visible total scattering coefficients were measured with MRI integrating nephelometer (2004-2011) and Optec integrating nephelometer (2011-2024) located at 3 m. Subsoil instrumentation includes: • Delta-T ML2/ML3 theta probes measured volumetric soil moisture at depths of 10, 22, 57 and 160 cm. • Delta-T PRT measured soil temperature at 1, 4, 7, 10, 17, 35, 65 and 100 cm (2004-March 2012). • Delta-T ST2-396 thermistor probes measured soil temperature at 1, 4, 7, 10, 17, 35, 65 and 100 cm (March 2012-2024). • Hukseflux HFP01SC flux plate measured ground heat flux. • Druck 1830 pressure transducer measured water table depth.

A full list of NetCDF variables can be found in “A continuous hydrometeorological record (2004–2024) at the Met Office surface site of Cardington, UK.” Osborne et al. (2025). This paper should be referenced in any research/publications pertaining to this dataset.

To ensure optimal traceability and transparency of data, comprehensive metadata is included.

Species - TemperatureDatasheet showing the species-specific abundance, total abundance, species richness, and Sh diversity index of bumblebees recorded on Beewalk surveys across 50 UK transect sites. These are listed against the average maximum temperatures for the social bumblebee colony period (April-July) for the year preceding the survey, the average miniimum temperature for the solitary bumblebee colony stage (August-March) preceding the survey, and the number of heatwave days in the year preceding surveys.

Deployed in April 2014, the weather station is located in the South Arm of Holyrood Bay, at the Marine Institute's Holyrood Marine Base in Conception Bay. The station consists of a AIRMAR WX200 WeatherStation and a Belfort 6500 Visibility Sensor.The WX200 WeatherStation sensors measure apparent wind speed and direction, barometric pressure, air temperature, relative humidity, and dew point. Wind chill values are provided from calculations using temperature and wind speed. The Belfort 6500 Visibility Sensor monitors visibility conditions over a range of 6 meters to 80 kilometers. Data from the station is available in near-real time and is utilized by mariners in this region in support of safer and more efficient marine operations. cdm_data_type=TimeSeries cdm_timeseries_variables=station_name,longitude,latitude Conventions=COARDS, CF-1.6, ACDD-1.3, NCCSV-1.2 Easternmost_Easting=-53.13495 featureType=TimeSeries geospatial_lat_max=47.38866 geospatial_lat_min=47.38866 geospatial_lat_units=degrees_north geospatial_lon_max=-53.13495 geospatial_lon_min=-53.13495 geospatial_lon_units=degrees_east infoUrl=https://www.smartatlantic.ca/station_alt.html?id=conceptionbay institution=MI instrument_1_description=Anemometer instrument_1_id=Anemometer-462 instrument_1_manufacturer=nan instrument_1_type=Sensor instrument_1_version=nan instrument_2_description=Telemetry instrument_2_id=Telemetry-461 instrument_2_manufacturer=B&B Electronics instrument_2_type=Equipment instrument_2_version=nan instrument_3_description=Tide Station instrument_3_id=Tide Station-460 instrument_3_manufacturer=RBR instrument_3_type=Equipment instrument_3_version=nan instrument_4_description=Weather Station instrument_4_id=2671619 instrument_4_manufacturer=AirMar WX-200 instrument_4_type=Sensor instrument_4_version=44-835-1-01 keywords_fra=other keywords_vocabulary=GCMD Science Keywords Northernmost_Northing=47.38866 platform=coastal structure platform_description=Holyrood WeatherTide Station platform_id=Weather Station-84 platform_vocabulary=https://vocab.nerc.ac.uk/collection/L06/current/ project=SmartAtlantic sourceUrl=(local files) Southernmost_Northing=47.38866 standard_name_vocabulary=CF Standard Name Table v79 summary_fra=Déployée en avril 2014, la station météorologique est située dans le bras sud de Holyrood Bay, à la base marine Holyrood de l'Institut maritime à la baie Conception. La station est composée d'une station météorologique AIRMAR WX200 et d'un capteur de visibilité Belfort 6500. Les capteurs de la station météorologique WX200 mesurent la vitesse et la direction du vent apparent, la pression barométrique, la température de l'air, l'humidité relative et le point de rosée. Les valeurs du refroidissement éolien sont fournies à partir de calculs effectués à l'aide de la température et de la vitesse du vent. Le capteur de visibilité Belfort 6500 surveille les conditions de visibilité sur une portée de 6 mètres à 80 kilomètres. Les données de la station sont disponibles en temps quasi réel et sont utilisées par les navigateurs de cette région pour appuyer des opérations maritimes plus sécuritaires et plus efficaces. time_coverage_end=2023-02-03T12:48:38Z time_coverage_start=2014-05-01T17:45:20Z title_fra=station météorologique Holyrood Wharf uuid=bdb7c3fd-70c2-4bc2-bbcf-cf1efc8a568b Westernmost_Easting=-53.13495

Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC): Ensemble member output from Unified Model runs as described in Flack et al. (2018): Convective-Scale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405

The dataset contains ensemble run output from 36 hour long runs under different model set ups (see details below) for 6 case studies (see Flack et al. 2018 for greater detail). The case studies (and model output available in the dataset) chosen related to a spectrum of 'convective adjustment time scales', defined as the ratio between the convective available potential energy (CAPE) and its rate of release at the convective scale. 'control' run files contain large scale rainfall rates and amounts whilst the 'control_multilevel' files contain various parameters on various levels, including mean sea level pressure, zonal, meridional and vertical wind components, specific humidity and temperature.

• Case A: 20th April 2012, part of the Dynamical and Microphysical Evolution of Convective Storms (DYMECS) field experiment (Stein et al. 2015), showing typical conditions for scattered showers in the United Kingdom.
• Case B: 12 August 2013, for a case where a surface low was situated over Scandinavia and the Azores high was beginning to build, leading to persistent northwesterly flow.
• Case C: 23rd July 2013, relating to the fifth intensive observation period (IOP 5) of the Convective Precipitation Experiment (COPE; Leon et al. 2016). A low pressure system was centered to the west of the United Kingdom with several fronts ahead of the main center, which later decayed.
• Case D: 2nd August 2013, covering IOP 10 of the COPE field campaign, with convection initiating at 1100 UTC. The synoptic situation shows a low pressure system centered to the west of Scotland, which led to southwesterly winds and a convergence line being set up along the North Cornish coastline (in southwest England).
• Case E: 27th July 2013, covers the period of IOP 7 of the COPE field campaign where two mesoscale convective systems (MCS) influenced the U.K.’s weather throughout the forecast period.
• Case F: 5th August 2013, was chosen for the complex situation for considering convective-scale perturbation grown and a second case driven by the boundary conditions as seen during IOP 12 of the COPE campaign

A brief description of the model run IDs and model setup is given below.

The model used to create these ensembles is the Met Office Unified Model (MetUM). The United Kingdom Variable resolution (UKV) configuration is used, and so the data has a grid spacing of approximately 1.5 km. This was run at version 8.2 and run with the MetUM Graphical User Interface (GUI).

run ID: xkyib

This is the control experiment and everything is kept identical to the operational running of this configuration of the MetUM.

run ID: xldef

Here the Gaussian potential temperature perturbations are added into the model. Full details of the perturbation method are described in Flack et al. (2018) Convective-Scale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405, however a brief overview is given below:

A Gaussian distribution (defined using random numbers between +/- 1 at each grid point, with the seed determined by the time the model is ran) is created at every grid point in the domain. A superposition is created and rescaled to 0.1 K so as to be an appropriate amplitude for boundary layer noise. Each of the Gaussian distributions have a standard deviation of 9km so as to be added onto an appropriate scale for the model. The perturbations are added in at a model hybrid height of 261.6 m (approximately the 8th model level).

An iRobot Seaglider (Serial Number 534) carrying a Seabird CT Sail, Paine pressure sensor and Wetlabs ECO-puck was deployed in the Celtic Sea, Northwest European Shelf for 21 days between the 4th and 25th April 2015. It maintained a position within 10 km of 49° 24.3’ N, 8° 32.9’W and completed 1547 profiles between the sea surface and 120 m water depth. Its mission was to observe the evolution of the water column structure and the accumulation of phytoplankton biomass during spring phytoplankton bloom. Following the extraction of raw data and application of manufacturer calibrations, thermal lag corrections were applied to the temperature following the methods of Garau et al. (2011) and drawing upon a flight model similar to that described by Frajka-Williams et al (2011). Unrealistically high and low values of salinity, derived after thermal inertia corrections, were removed. Further, salinity values within 40 m of the surface (where the vertical speed of the glider was typically unstable) that were greater than 3 standard deviations from the mean salinity within top 40 m were removed. Each salinity profile was smoothed with an 8 m running mean window. Four calibrated CTD casts taken within 1.6 km of the glider were used to calibrate the gliders temperature and salinity. Based on the mean temperature and salinity of water between 80 m and 105 m the glider CT sensors were found to be reading 0.0277°C and 0.0024 psu too low. These constant offsets were corrected for. Chlorophyll-a fluorescence was derived based on the manufacturers calibrations and checked against a fluorometer on the CTD. There is evidence of quenching within the surface 30-40 m during the day which has not been removed or corrected for here. Temperature, salinity and chlorophyll-a fluorescence were gridded onto regular 1 m depth intervals and the profile average position and time calculated. The glider was funded by the NERC Sensors on Gliders Programme and deployed during a UK Natural Environment Research Council (NERC) Shelf Sea Biogeochemistry Programme cruise (DY029). The processed data are held at BODC in Matlab format.