Wind farm developers have seen an overall decline in revenue over the five years through 2025, with industry activity down following a surge in wind farm construction in 2020. This decline has come in spite of advancements in wind turbine technology, which have made wind farms more cost-effective and efficient, expanding the accessibility of wind power. Overall, industry revenue has been declining at a CAGR of 18.0% over the past five years to reach an estimated $10.1 billion in 2025, when industry revenue is set to decline 7.4% amid a lack of federal support. Over the past five years, the Production Tax Credit (PTC) has heavily influenced wind farm development. The PTC provides a credit for every kilowatt-hour of electricity produced from renewable sources. When the PTC is set to expire, demand for wind farm construction increases as companies rush to take advantage of the credit. The number of new wind farm projects slows when the PTC is extended or expires. Wind farm construction boomed in 2020, with the PTC set to expire that year. The extension of the PTC through 2021 continued to spur new construction, but growth slowed considerably in 2022. While the 2022 Inflation Reduction Act extended the PTC through 2024, global supply chain issues and high interest rates have slowed industry activity through 2025. High materials prices and growing wage costs amid stalling projects have driven down average industry profit. The wind farm construction industry is set to see mild growth as wind farm construction becomes more efficient and demand for electricity continues to grow. This growth is threatened by the Trump administration, however, with the administration having paused all leasing of federal lands and waters for new wind farms and directed federal agencies to stop issuing permits for all wind farms pending federal review. While these actions face legal challenges, they are set to severely slow down construction, particularly that of offshore wind farms. Still, revenue is forecast to rise at a CAGR of 1.0% to $10.6 billion through the end of 2030 as more states adopt ambitious renewable energy standards.

In 2023, the wind energy average plant load factor (PLF) in the French region of Island of France, where Paris is located, was about **** percent. The Upper East and Great East regions followed, with an average PLF of nearly ** percent each.

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.

The datasets contain the Wind first-level sectoral index and the Shanghai Composite Index of the Chinese stock market. The primary sectoral index covers 11 sectors including real estate, finance, public utilities, telecommunications services, information technology, health care, daily consumption, optional consumption, industry, materials, and energy. Due to its strong financial attributes, real estate was once merged into and separated from the Wind Finance sector after March 2007. To make industry data comparable and model result more accurate, this manuscript selects the daily frequency yield data of the Wind first-level industry index from March 2, 2007 to October 28, 2022. The time series includes a total of 3810 trading days.

Graph and download economic data for Producer Price Index by Industry: Turbine and Turbine Generator Set Units Manufacturing (PCU333611333611) from Jun 1982 to Jul 2025 about manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

The Hill of Towie wind farm open dataset provides over 8 years of comprehensive operational data from a commercial wind farm in Scotland suitable for various wind energy research topics. This dataset includes:

• Hill_of_Towie_turbine_metadata.csv: Metadata for the 21 Siemens SWT-2.3-VS-82 turbines including coordinates, rated power, rotor diameter and hub height.
• 2016.zip, 2017.zip, … , 2024.zip: 10-minute statistics and alarm log data from the Hill of Towie SCADA system zipped by year from January 2016 to August 2024 (inclusive). The data was downloaded from the Siemens wind farm SCADA Backup facility and converted to .csv format without modification except dropping certain columns to ensure anonymity for SCADA system users. All timestamps are in UTC and 10-minute timestamps represent the end of the 10-minute period.
• Files ending in “_description.csv” provide descriptions of SCADA Backup file types, key 10-minute columns and key alarm codes.
• Hill_of_Towie_AeroUp_install_dates.csv: provides information on AeroUp installation timing for each turbine.
• Hill_of_Towie_ShutdownDuration.zip: contains 10-minute statistics calculated from a combination of the SCADA data and RES operator logs. This data quantifies downtime in seconds for each turbine for each 10 minutes (0 means no downtime, 600 means 10 minutes of downtime).

The dataset has been released by RES on behalf of TRIG under a CC-BY-4.0 open data license and is provided as is. RES is the world’s largest independent renewable energy company and has been an industry innovator for over 40 years. RES’ retrofit upgrade products such as AeroUp and TuneUp have been developed using expertise from deep knowledge of the industry. TRIG, the owner of Hill of Towie, was one of the first investment companies investing in renewable energy infrastructure projects listed on the London Stock Exchange and is now a member of the FTSE-250 index.

The Hill of Towie open dataset provides a unique opportunity to study energy yield increase following wind farm upgrades. Upgrades contained in the dataset include:

• T13 AeroUp installation completed 29 September 2021
• AeroUp roll out to all other turbines from 14 July 2022 to 26 May 2023. Further details of install dates are provided in Hill_of_Towie_AeroUp_install_dates.csv.
• TuneUp deployed to 8 of 9 test turbines 14 March 2024 (T02, T03, T06, T08, T09, T13, T16, T20) and the 9th test turbine (T21) on 2 May 2024

The Hill of Towie open dataset is applicable to many areas of research including:

• validation of pre-construction energy yield models (including wind flow and wake models)
• refinement of post-construction energy yield analysis techniques
• wind farm performance analysis, alarm prediction and forecasting research

Example usage of the dataset is shown in this repository: https://github.com/resgroup/hill-of-towie-open-source-analysis

Acknowledgement

Thank you to TRIG for allowing us to make this dataset publicly available.

Thank you to the creators of the Kelmarsh and SMARTEOLE open datasets, which provided the inspiration for this dataset.

In the United States, the wind energy operator, NextEra Energy, owned **** gigawatts of wind power, making it the leading wind energy operator as of 2016. Regulated utilities in the United States only build a small share of wind assets but most prefer to sign power purchase agreements (PPAs) with independent generators rather than building their own projects. In recent years, the U.S. turbine market has been dominated by just a few original equipment manufacturers (OEMs) which has further supported the trend of consolidation.

Global Wind Power Industry The total wind power generated worldwide has been increasing substantially year after year since 2001. In 2017, the global cumulative installed wind power capacity amounted to ***** gigawatts. China, the United States, and Germany are the top three wind power producers worldwide. As of 2017, China had installed about ****** gigawatts of wind power cumulatively.

Wind Turbine Market in the United States The Alta Wind Energy Center in California is the largest wind power project installed in the United States as of 2018. It has the capacity to produce ***** megawatts of wind energy. The second largest U.S. wind power project is the Roscoe Wind Project, based in Texas, with a wind energy capacity of ***** megawatts. Over the last decade, wind energy has become less and less expensive. In 2009, it cost about **** million U.S. dollars to produce one megawatt of wind energy and by 2019, the price index for wind energy had dropped to ******* U.S. dollars per megawatt.

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.

Values of bird sensitivity, wind profitability (net present value in $US millions), average utility across simulations and sorted by overall rank for selected sites, corresponding to labels in the tradeoff plot (Fig 8) and average utility map (Fig 9).

“Wind Power Market, Update 2019 – Global Market Size, Average Price, Turbine Market Share, and Key Country Analysis to 2030” is the latest market analysis report from GlobalData, the industry analysis specialist that offers comprehensive information and understanding of the wind power market. The report provides a clear overview of and detailed insight into the global wind power market. It explains the key drivers and challenges affecting the market and provides data covering historic and forecast market size, installed capacity and generation globally, and in ten key wind power markets – The US, Canada, Brazil, Germany, Spain, UK, South Africa, China, India, and Australia.
The report uses data and information sourced from proprietary databases, primary and secondary research, and in-house analysis by GlobalData’s team of industry experts. Read More

“Wind Power Market, Update 2018 – Global Market Size, Average Price, Turbine Market Share, and Key Country Analysis to 2025” is the latest market analysis report from GlobalData, the industry analysis specialists that offer comprehensive information and understanding of the wind power market.
The report provides a clear overview of and detailed insight into the global wind market. It explains the key drivers and challenges affecting the market and provides data covering historic and forecast market size, average capital cost, installed capacity and generation globally, and in eleven key wind power markets – China, India, Brazil, Mexico, Canada, France, UK, US, South Africa, Germany, and Japan.
The report uses data and information sourced from proprietary databases, primary and secondary research, and in-house analysis by GlobalData’s team of industry experts. Read More

China Wind Mover Equipment: Number of Employee: Average data was reported at 10.268 Person th in Dec 2013. This records a decrease from the previous number of 11.049 Person th for Dec 2012. China Wind Mover Equipment: Number of Employee: Average data is updated monthly, averaging 10.598 Person th from Jan 2012 (Median) to Dec 2013, with 13 observations. The data reached an all-time high of 11.049 Person th in Dec 2012 and a record low of 7.252 Person th in Mar 2012. China Wind Mover Equipment: Number of Employee: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHW: Boiler and Prime Mover Equipment: Wind Mover Equipment.

Environmental impacts of wind energy facilities increasingly cause concern, a central issue being bats and birds killed by rotor blades. Two approaches have been employed to assess collision rates: carcass searches and surveys of animals prone to collisions. Carcass searches can provide an estimate for the actual number of animals being killed but they offer little information on the relation between collision rates and, for example, weather parameters due to the time of death not being precisely known. In contrast, a density index of animals exposed to collision is sufficient to analyse the parameters influencing the collision rate. However, quantification of the collision rate from animal density indices (e.g. acoustic bat activity or bird migration traffic rates) remains difficult. We combine carcass search data with animal density indices in a mixture model to investigate collision rates. In a simulation study we show that the collision rates estimated by our model were at least as precise as conventional estimates based solely on carcass search data. Furthermore, if certain conditions are met, the model can be used to predict the collision rate from density indices alone, without data from carcass searches. This can reduce the time and effort required to estimate collision rates. We applied the model to bat carcass search data obtained at 30 wind turbines in 15 wind facilities in Germany. We used acoustic bat activity and wind speed as predictors for the collision rate. The model estimates correlated well with conventional estimators. Our model can be used to predict the average collision rate. It enables an analysis of the effect of parameters such as rotor diameter or turbine type on the collision rate. The model can also be used in turbine-specific curtailment algorithms that predict the collision rate and reduce this rate with a minimal loss of energy production.

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.

Despite the past crises surrounding the coronavirus pandemic and the Ukraine conflict and the associated trade policy upheavals, the industry has seen relatively stable sales growth over the past five years. The energy transition and the associated orders for the construction of solar and wind power plants also provided growth impetus for many industry players. Overall, a significant proportion of the industry's positive sales development is attributable to the associated switch to renewable energies. In the current year, the industry will continue to be impacted by the negative economic effects of the Ukraine conflict due to high material costs. As a result, industry turnover is expected to increase by 0.2% to €18.2 billion in the current year. Between 2020 and 2025, it fell by an average of just 0.2% per year. The profit margin has fallen from a medium to a low level in recent years.Since the start of the pandemic, the sector has been suffering in particular from the decline in production volumes in the industry. When there is less production, there is also less demand for new machines and their installation. In addition, companies tend to postpone investments due to the uncertain economic situation. Even if a positive trend in production volume can be expected again in 2025, it is still likely to be significantly below the pre-crisis level of 2019. Without the German economy's strong focus on exports and the increase in global trade volumes forecast for 2025, the sector would be in a worse position. The recovery of the manufacturing industry and thus also of the sector as an associated service provider will be driven by the dynamic economic recovery abroad.For the period from 2025 to 2030, IBISWorld expects average annual growth of 0.4% and industry turnover of 18.5 billion euros in 2030. This positive trend is likely to be due to the positive development of global trade and rising production volumes. Positive impetus will also come from the energy transition and the automotive industry's switch to electromobility, as new systems will need to be installed in each case. IBISWorld expects the number of industry players and employees to increase slightly by 2030.

Observed metocean data, analyses and climate studies provide the oil and gas industry with essential information and knowledge for the design and engineering of offshore installations, such as production platforms and pipelines, and for assessing workability conditions. In addition the information is used for supporting the planning of, for example diving operations and the installation of pipelines, and the forecasting of storms and heavy weather conditions, which might require timely evacuation or other safety measures to be taken during the operation of offshore installations. To support these activities, and to complement metocean data and information, that can be retrieved from public sources, major oil and gas companies are active in monitoring and collecting metocean data themselves. This is done all over the world and over many years the oil and gas companies have acquired together a large volume of data sets. These data sets are acquired, processed and analysed both in joint industry projects (JIPs) as well as in field surveys and monitoring activities performed for individual companies. Often these data sets are acquired at substantial cost and in remote areas. These datasets are managed by the metocean departments of the oil and gas companies and stored in various formats and are only exchanged on a limited scale between companies. Despite various industry co-operative joint projects, there is not yet a common awareness of available data sets and no systematic indexing and archival of these data sets within the industry. Furthermore there is only limited reporting and access to these data sets and results of field studies for other parties, in particular the scientific community. Opening up these data sets for further use will provide favourable conditions for creating highly valuable extra knowledge of both local and regional ocean and marine systems. To stimulate and support a wider application of these industry metocean datasets a System of Industry Metocean data for the Offshore and Research Communities (SIMORC) has been established within the framework of the SIMORC project. The SIMORC project is co-funded by the European Commission for a 2 year project period starting 1st June 2005.
The SIMORC system consists of an index metadatabase and a database of actual data sets that together are accessible through the Internet. The index metadatabase is public domain, while access to data is regulated by a dedicated SIMORC Data Protocol. This contains rules for access and use of data sets by scientific users, by oil & gas companies, and by third parties. All metocean data sets in the database have undergone quality control and conversion to unified formats, resulting in consistent and high quality, harmonized data sets. SIMORC is a unique and challenging development, undertaken by major ocean data management specialists: MARIS (NL) coordinator and operator, BODC (UK) and IOC-IODE (UNESCO), and the International Association of Oil & Gas Producers (OGP), involving participation of major oil & gas companies that bring in their considerable data sets. The objective is to expand the coverage of the SIMORC database with regular submissions of major oil & gas companies, while the SIMORC service will be operated as part of OGP services.

This dataset contains the annual index of wind-driven rain (sum of all wind-driven rain spells in each year) derived from the UK Climate Projections (UKCP18) for a range of future global warming levels provided on a 5 km British National Grid (BNG). The annual index is calculated for eight wall orientations corresponding to the cardinal and ordinal points of the compass.

Wind-driven rain occurs when falling rain is blown by a horizontal wind so that it falls diagonally towards the ground. 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 units of 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.

The magnitudes of 1 in 3 year wind-driven rain spells (i.e. wet spells that would be expected to occur, on average, once every three years) are used to divide the UK into four zones in Approved Document C of the buildings regulations. The magnitudes of 1 in 3 year wind-driven rain spells were calculated for the baseline period (1981-2000) and 20-year periods corresponding to 2°C and 4°C of warming. The magnitudes of all wet spells (here, sum of hourly values of the wind-driven rain metric, I) were calculated, and the largest wet spell in each year was found (in the accompanying report, the magnitude of a wet spell is given the symbol Is' ["Is prime"] and has units of litres per metre-squared per spell). For each time period, the largest spells in all years and ensemble members were pooled together. A Gumbel distribution was fitted to the pooled data and used to estimate the magnitude of the 1 in 3 year wet spells across the UK.

Wind-driven rain is required for buildings standards. It is a major source of moisture in walls. Areas subject to very high levels of wind-driven rain may not be suitable for cavity-wall insulation. Under certain circumstances, cavity-wall insulation can enhance the transfer of moisture through walls to the inside of a building causing mould and damp problems.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company China-shipbuilding-industry-group-Haizhuang-wind-power-co.-LTD..

China Wind Mover Equipment: YoY: Number of Employee: Average data was reported at -1.612 % in Dec 2012. This records an increase from the previous number of -6.543 % for Nov 2012. China Wind Mover Equipment: YoY: Number of Employee: Average data is updated monthly, averaging -3.570 % from Jan 2012 (Median) to Dec 2012, with 12 observations. The data reached an all-time high of 7.110 % in Feb 2012 and a record low of -11.729 % in May 2012. China Wind Mover Equipment: YoY: Number of Employee: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHW: Boiler and Prime Mover Equipment: Wind Mover Equipment.

Characteristics of the two data sets analysed, including the number of turbines investigated, the total number of turbine-nights, the number of bat call recordings, the total number of carcasses found, and the average wind speed with standard deviation.