which corresponding to an hours

Context

High precision and reliable wind speed forecasting is a challenge for meteorologists. Severe wind due to convective storms, causes considerable damages (large scale forest damage, outage, buildings/houses damage, etc.). Convective events such as thunderstorms, tornadoes as well as large hail, strong winds, are natural hazards that have the potential to disrupt daily life, especially over complex terrain favoring the initiation of convection. Even ordinary convective events produce severe winds which causes fatal and costly damages. Therefore, wind speed prediction is an important task to get advanced severe weather warning. This dataset contains the responses of a weather sensor that collected different weather variables such as temperatures and precipitation.

Content

The dataset contains 6574 instances of daily averaged responses from an array of 5 weather variables sensors embedded in a meteorological station. The device was located on the field in a significantly empty area, at 21M. Data were recorded from January 1961 to December 1978 (17 years). Ground Truth daily averaged precipitations, maximum and minimum temperatures, and grass minimum temperature were provided.

Attribute Information

1. DATE (YYYY-MM-DD)
2. WIND: Average wind speed [knots]
3. IND: First indicator value
4. RAIN: Precipitation Amount (mm)
5. IND.1: Second indicator value
6. T.MAX: Maximum Temperature (°C)
7. IND.2: Third indicator value
8. T.MIN: Minimum Temperature (°C)
9. T.MIN.G: 09utc Grass Minimum Temperature (°C)

Citation Request

If you want to cite this data:

fedesoriano. (April 2022). Wind Speed Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/datasets/fedesoriano/wind-speed-prediction-dataset

This statistic shows the average Wi-Fi network connection speeds worldwide until 2020, with projections looking towards 2023 (in Mbps). The average speed in 2020 was 50.8 Mbps, almost a 40 percent increase from 2019. In 2021, it is expected to increase by another 16 percent from 2020 with 58.9 Mbps.

GeoTIFF raster data with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). This link provides access to the following layers: (1) Wind speed (WS): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file (2) Power Density (PD): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file. (3) Elevation (ELEV): at ground level (4) Air Density (RHO): at ground level (5) Ruggedness Index (RIX): at ground level All layers have 250m resolution.

Four groups of wind speed series

The 2019 Statistics on Public Expenditures for Economic Development (SPEED) database contains public expenditure data for 164 countries from 1980 to 2017 for ten sectors: agriculture, communication, education, defense, health, mining, social protection, fuel and energy, transport, and transport and communication combined as one sector. Indicators reported include percentage of sector expenditure in total expenditure, percentage of total expenditure to total gross domestic product, and per capita sector and total expenditure in constant prices. The data were compiled from multiple sources, including the International Monetary Fund, the World Bank, and national governments, and conducted extensive data checks and adjustments to ensure consistent spending measurements over time that are free of exchange-rate fluctuations and currency denomination changes. SPEED is a user-friendly tool that could help governments to better allocate their resources to be consistent with their policy objectives, and citizens’ needs and priorities. Because of the wide coverage of time periods, countries, and sectors, it could help policymakers and researchers to better understand the linkages between different types of public expenditure and development. It could also help examine historical trends and compare those to other countries. SPEED is funded by the CGIAR Research Program on Policies, Institutions, and Markets (PIM).

This meteorological data is provided by the Inner Mongolia Meteorological Bureau and includes data from three stations.

There is no description for this dataset.

The text file "Wind speed.txt" contains hourly wind speed data in miles per hour and associated data source flags from January 1, 1948, to September 30, 2019. The primary data for water year 2018 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2019) and processed following the guidelines documented in Over and others (2010). The processed data were appended to ARGN18.WDM (Bera, 2019) and renamed as ARGN19.WDM. Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup”. The hourly wind speed data from the Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2019) station at St. Charles, Illinois, and the National Weather Service station at O'Hare International Airport were used as "backup". Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2019) provided the data collected by National Weather Service from the station at O'Hare International Airport. Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2019, Meteorological data, accessed on November 6, 2019, at http://gonzalo.er.anl.gov/ANLMET/. Bera, M., 2019, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2018: U.S. Geological Survey data release, ​https://doi.org/10.5066/P9H8P0F7. Midwestern Regional Climate Center, 2019, Meteorological data, accessed on November 6, 2019, at https://mrcc.illinois.edu/CLIMATE/. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2019. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on November 6, 2019, at http://dx.doi.org/10.13012/J8MW2F2Q.

Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. Unit: m s-1. The Wind Speed variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-01-30

Data revision: 2021-10-05

Contact points:

Metadata Contact: ECMWF - European Centre for Medium-Range Weather Forecasts

Resource Contact: ECMWF Support Portal

Data lineage:

Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

Resource constraints:

License Permission

This License is free of charge, worldwide, non-exclusive, royalty free and perpetual. Access to Copernicus Products is given for any purpose in so far as it is lawful, whereas use may include, but is not limited to: reproduction; distribution; communication to the public; adaptation, modification and combination with other data and information; or any combination of the foregoing.

Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice:

• Generated using Copernicus Climate Change Service information [Year]

and/or

• Generated using Copernicus Atmosphere Monitoring Service information [Year]

More information on Copernicus License in PDF version at: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

Online resources:

Data download from original source

High Speed Data Transfer System Market Outlook

The global market size for High Speed Data Transfer Systems was valued at USD 15 billion in 2023 and is forecasted to reach USD 45 billion by 2032, growing at a CAGR of approximately 13% during the forecast period. This remarkable growth can be attributed to the increasing demand for higher bandwidth, the proliferation of connected devices, and the advent of technologies like 5G and IoT that necessitate rapid and reliable data transfer.

One of the primary growth factors for the high-speed data transfer system market is the explosion of data generated worldwide. With the rise of big data analytics, cloud computing, and the Internet of Things (IoT), there is an unprecedented need for efficient and fast data transfer solutions. Enterprises are increasingly investing in robust data transfer systems to manage and process vast amounts of data effectively, driving the market growth. Additionally, the emergence of 5G technology is revolutionizing data transfer speeds, providing new opportunities for market expansion.

Another significant driver is the increasing adoption of high-speed data transfer systems in various sectors such as healthcare, BFSI (Banking, Financial Services, and Insurance), and media and entertainment. These industries require rapid and secure data transfer to enhance their operational efficiencies and provide better services to customers. The healthcare sector, in particular, is seeing substantial investments in data transfer systems to facilitate telemedicine, electronic health records, and real-time patient monitoring, further propelling market growth.

The rise of data centers and the need for efficient data management are also contributing to the market's expansion. Data centers serve as the backbone of the modern digital economy, housing critical data and applications for businesses and consumers alike. The demand for high-speed data transfer systems in data centers is growing as enterprises seek to improve data accessibility, reduce latency, and ensure seamless data flow across networks. This trend is expected to continue, leading to significant market growth over the forecast period.

From a regional perspective, North America is anticipated to hold the largest market share due to the early adoption of advanced technologies and the presence of key market players. The Asia Pacific region is expected to witness the highest growth rate, driven by increasing investments in infrastructure development, rapid urbanization, and the growing number of internet users. Europe, Latin America, and the Middle East & Africa are also projected to experience substantial growth, supported by technological advancements and increasing demand for high-speed data transfer solutions.

Component Analysis

The high-speed data transfer system market is segmented by component into hardware, software, and services. The hardware segment includes devices such as routers, switches, and cables that facilitate data transfer. This segment is expected to dominate the market due to the continuous advancements in networking technology and the increasing need for robust and reliable hardware solutions. The demand for high-performance hardware components is rising, driven by the need for faster data transfer speeds and improved network efficiency.

The software segment encompasses various applications and platforms that enable efficient data transfer and management. This includes data transfer protocols, network management software, and data compression tools. The software segment is expected to witness significant growth, driven by the increasing adoption of cloud-based solutions and the need for advanced data management capabilities. Software solutions play a crucial role in optimizing data transfer processes, reducing latency, and ensuring data security, thereby driving market growth.

The services segment includes consulting, integration, and maintenance services that support the deployment and management of high-speed data transfer systems. This segment is also poised for substantial growth as enterprises seek expert guidance to implement and maintain these complex systems. The demand for professional services is increasing as businesses aim to optimize their data transfer infrastructures, improve operational efficiencies, and ensure seamless data flow across networks.

Overall, the component analysis highlights the critical role that hardware, software, and services play in the high-speed data transfer system market. Each component segment is expe

The Global Wind Atlas version 3 data-sets contain microscale wind information at approximately 250m grid point spacing.The data is created by first dynamically down-scaling ERA5 reanalysis data from 2008-2017 to 3km resolution using the WRF mesoscale model.The WRF results are then generalized using DTU's generalization methodology, and then down-scaled using the WAsP model to the final 250m resolution.The data in this directory consist of the entire global tiff at the full 0.0025 degree resolution on the WGS84 map projection. These data also include four sets of overview pyramids to improve the viewing of the data at low resolution.Most of the data are named as follows: gwa_{variable}_{height}.tif, where variable is one of* wind-speed - The mean wind speed at the location for the 10 year period* power-density - The mean power density of the wind, which is related to the cube of the wind speed, and can provide additional information about the strength of the wind not found in the mean wind speed alone.* combined-Weibull-A and combined-Weibull-k - These are the all sector combined Weibull distribution parameters for the wind speed. They can be used to get an estimate of the wind speed and power density at a site. However, caution should be applied when using these in areas with wind speeds that come from multiple directions as the shapes of those individual distributions may be quite different than this combined distribution.* air-density - The air density is found by interpolating the air density from the CFSR reanalysis to the elevation used in the global wind atlas following the approach described in WAsP 12.* RIX - The RIX (Ruggedness IndeX) is a measure of how complex the terrain is. It provides the percent of the area within 10 km of the position that have slopes over 30-degrees. A RIX value greater than 5 suggests that you should use caution when interpreting the results.The files which do not follow the naming convention above are the capacity-factor layers. The capacity factor layers were calculated for 3 distinct wind turbines, with 100m hub height and rotor diameters of 112, 126, and 136m, which fall into three IEC Classes (IEC1, IEC2, and IEC3). Capacity factors can be used to calculate a preliminary estimate of the energy yield of a wind turbine (in the MW range), when placed at a location. This can be done by multiplying the rated power of the wind turbine by the capacity factor for the location (and the number of hours in a year): AEP = Prated*CF*8760 hr/year, where AEP is annual energy production, Prated is rated power, and CF is capacity factor.

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.

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 2023.

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 2023.

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 Homogenized Surface Wind Speed data consist of monthly, seasonal and annual means of hourly wind speed (kilometres per hour) at standard 10 metre level for 156 locations in Canada. Homogenized climate data incorporate adjustments (derived from statistical procedures) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from 1953 at some stations to the most recent update in 2014. Data availability over most of the Canadian Arctic is restricted to 1953 to present. The data will continue to be updated every few years (as time permits).

As of June 2024, Qatar had the fastest average mobile internet connection worldwide, nearly 335 Mbps. The United Arab Emirates (UAE) followed, registering average median speed above 323 Mbps. Fixed-connection speeds around the world When it comes to fixed broadband connections, Singapore tops the list of countries by average connection speed. Internet users in Singapore achieve an average fixed broadband connection speed of 242.01 Mbps, slightly faster than the 222.49 Mbps achieved in Chile, the second-placed country on the speed rankings. 5G and 6G – the future of mobile broadband In countries where it is in use, 5G is already bringing faster mobile internet connection speeds than ever before. In Saudi Arabia for example, the average 4G connection speed sits at 28.9 Mbps, and this speed jumps to 414.2 Mbps on a 5G connection. Now that 5G is commercially available, researchers have already turned their attention to 6G. Operating at a higher spectrum band, 6G will allow connections several times faster than 5G. User experienced data rates of 5G sit at 100 Mbps, and this speed is expected to climb to 1,000 Mbps on 6G connections. 6G is expected to not only provide faster speeds, but also enable more devices to connect to a network without causing congestion as it has a connection density ten times greater than that of 5G.

mostly on major arterials and highways. DOT uses this information for emergency response and management.The metadata defines the fields available in this data feed and explains more about the data.

The maximum wind speed during the forecast period km/hr (mdws). Week 1 and week 2 forecasted index is available daily from September 1 to August 31. Week 3 and week 4 forecasted index is available weekly (Thursday) from September 1 to August 31. Winds can significantly influence crop growth and yield mainly due to mechanical damage of plant vegetative and reproductive organs, an imbalance of plant-soil-atmosphere water relationships, and pest and disease distributions in agricultural fields. The maximum wind speed and the number of strong wind days over the forecast period represent short term and extended strong wind events respectively. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.