This map displays the wind forecast over the next 72 hours across the contiguous United States, in 3 hour increments, including wind direction, wind gust, and sustained wind speed.Zoom in on the Map to refine the detail for a desired area. The Wind Gust is the maximum 3-second wind speed (in mph) forecast to occur within a 2-minute interval within a 3 hour period at a height of 10 meters Above Ground Level (AGL). The Wind Speed is the expected sustained wind speed (in mph) for the indicated 3 hour period at a height of 10 meters AGL. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces gridded forecasts of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Wind Speed Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wspd.binWind Gust Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wgust.binWind Direction Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.wdir.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.Alternate SymbologyFeature Layer item that uses Vector Marker Symbols to render point arrows, easily altered by user. The color palette uses the Beaufort Scale for Wind Speed. https://www.arcgis.com/home/item.html?id=45cd2d4f5b9a4f299182c518ffa15977 This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Wind resource data for North America was produced using the Weather Research and Forecasting Model (WRF). The WRF model was initialized with the European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-Interm) data set with an initial grid spacing of 54 km. Three internal nested domains were used to refine the spatial resolution to 18, 6, and finally 2 km. The WRF model was run for years 2007 to 2014. While outputs were extracted from WRF at 5 minute time-steps, due to storage limitations instantaneous hourly time-step are provided for all variables while full 5 min resolution data is provided for wind speed and wind direction only.

The following variables were extracted from the WRF model data: - Wind Speed at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Wind Direction at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Temperature at 2, 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Pressure at 0, 100, 200 m - Surface Precipitation Rate - Surface Relative Humidity - Inverse Monin Obukhov Length

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.

Data on daily prevailing wind direction (Please visit the reference link for other climate information). The multiple file formats are available for datasets download in API.

These data represent the average monthly wind speed and direction at the surface of the ocean. Source data includes values from January 1, 1979, to December 31, 2010, at hourly temporal resolution, with a spatial resolution of 0.313 degrees latitude x 0.312 degrees longitude. Values for wind speed are in meters per second and wind direction in degrees from True North.

National Digital Forecast Database (NDFD) MetadataThe National Digital Forecast Database (NDFD) Web Services provide a set of gridded weather forecasts for various sensible weather elements in near real-time. These forecasts are generated by a collaboration between the National Weather Service (NWS) field offices and the National Centers for Environmental Prediction (NCEP). The NDFD Web Services offer a seamless, digital mosaic of weather forecasts that can be accessed by users to obtain up-to-date information on a variety of weather conditions.The NDFD's forecasts are gridded, meaning they cover large geographic areas with weather data at specific intervals, providing high-resolution, geographically distributed forecasts. These forecasts can include temperature, precipitation, wind speed and direction, cloud cover, and other meteorological parameters.These web services are hosted by the Office of Dissemination’s CloudGIS team, which ensures the forecasts are readily accessible and deliverable over the internet. Users, including meteorologists, developers, and anyone interested in weather data, can query these web services for up-to-date forecasts in a digital format, enabling integration into applications, websites, and other platforms.NDFD’s Web Services Descriptions:12-Hour Probability of Precipitation Web Service's data layer is the likelihood, expressed as a percent, of a measurable precipitation event (1/100th of an inch or more) at a grid point during the 12-hour valid period. The 12-hour valid periods begin and end at 0000 and 1200 Coordinated Universal Time (UTC).Apparent Temperature Web Service: contains data that is the perceived temperature derived from either a combination of temperature and wind (Wind Chill) or temperature and humidity (Heat Index) for the indicated hour. When the temperature at a particular grid point falls to 50 F or less, wind chill will be used for that point for the Apparent Temperature. When the temperature at a grid point rises above 80 F, the heat index will be used for Apparent Temperature. Between 51 and 80 F, the Apparent Temperature will be the ambient air temperature.Dew Point Temperature Web Service's data is the expected dew point temperature for the indicated hour. Dew point temperature is a measure of atmospheric moisture. It is the temperature to which air must be cooled in order to reach saturation (assuming air pressure and moisture content are constant).Maximum Temperature Web Service's data is the daytime maximum temperature observed from 7 AM to 7PM LST.Minimum Temperature Web Service's data is predicted minimum temperature for a specific location at a given time, allowing users to visualize the lowest expected temperatures across a geographical area.Precipitation Amount Web Service's data is the expected quantity of liquid precipitation accumulated over a six-hourly period. A quantitative precipitation forecast (QPF) will be specified when a measurable (1/100th of an inch or more) precipitation type is forecast for any hour during a QPF valid period. NDFD valid periods for QPF are 6 hours long beginning and ending at 0000, 0600, 1200 and 1800 UTC. QPF includes the liquid equivalent amount for snow and ice.Relative Humidity Web Service's data is a ratio, expressed as a percent, of the amount of atmospheric moisture present relative to the amount that would be present if the air were saturated. Since the latter amount is dependent on temperature, relative humidity is a function of both moisture content and temperature.Sky Cover Web Service’s data is the predicted percentage of the sky that will be covered by opaque clouds at a given time, provided by the National Digital Forecast Database (NDFD). It is a forecast of how much of the sky will be obscured by clouds, expressed as a percentage value.Snow Amount Web Service's data is the expected total accumulation of new snow during a 6-hour period. A snow accumulation grid will be specified whenever a measurable snowfall is forecast for any hour during a valid period. Valid periods for the NDFD begin and end at 0600, 1200, 1800, and 0000 UTC.Temperature Web Service: contains data that is the expected temperature in degrees Fahrenheit valid for the indicated hour.Wave Height Web Service's data is the average height (from trough to crest) of the one-third highest waves valid for the top of the designated hour. Wave Height is a combination of wind waves and swell.Wind Direction Web Service's data is the expected sustained 10-meter wind direction for the indicated hour, using 36 points of a compass.Wind Gust Web Service's data is the maximum 3-second wind speed forecast to occur within a 2-minute interval at a height of 10 meters. Wind gust forecasts are valid at the top of the indicated hour.Wind Speed Web Service's data is the expected sustained 10-meter sustained wind speed for the indicated hour.Wind Speed and Direction Web Service's data is the expected sustained 10-meter wind direction for the indicated hour, using 36 points of a compass. Wind Speed is the expected sustained 10-meter sustained wind speed for the indicated hour. Wind barbs (shown below) are used to denote wind speed and direction.Update Frequency: The data in these service updates hourly. (Click here to see specific Valid Times for update Frequency)Link to graphical web page: https://digital.weather.govLink to data download (grib2): https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/Link to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:These web services are time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.These particular services can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.Valid Time Table:ServiceValid Time12-Hour Probability of Precipitation Web ServiceThe 12-hour valid periods begin and end at 0000 and 1200 Coordinated Universal Time (UTC).Apparent Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Dew Point Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Maximum Temperature Web ServiceDisplay 0z every dayMinimum Temperature Web ServiceDisplay at 12z every dayPrecipitation Amount Web ServiceCONUS/OCONUS (forecast is valid at 0z,6z,12z and 18z)Relative Humidity Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Sky Cover Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Snow Amount Web ServiceCONUS/OCONUS (forecast is valid at 0z,6z,12z and 18z)Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wave Height Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Direction Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Gust Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind SpeedCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Speed and Direction Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)

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

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 2021.

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 2021.

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.

Continuous dataset collection since 2014 through Urban Observatory (UO) sensors. The data covers the geographical area of the North East of England centred on Newcastle upon Tyne (for geographical extent and location of sensors see http://uoweb1.ncl.ac.uk). Data is collected from a variety of sensor platforms with different performance metrics, sampling regimes and sensitivity levels. Information on individual sensors should be consulted before use at http://uoweb1.ncl.ac.uk. Data can be downloaded or accessed via a REST API at http://uoweb1.ncl.ac.uk. Weather metrics include: Rain Int, Solar Radiation, Max Wind Speed, Rain Acc, Humidity, Pressure, Temperature, Wind Direction, Daily Accumulation Rainfall, Visibility, Wind Speed, Wind Gust, Rainfall

Overview In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon. Data Details A PNNL computer is used as the base station to download the meteorological data acquired by the data logger at each site via a cellular modem. The data collected will be made available to the National Oceanic and Atmospheric Administration each hour and used to support the short-term forecasting project by providing an independent evaluation of the added value of new data to meteorological forecasts. Each meteorological station consists of a solar-powered data acquisition system and wind speed, wind direction, temperature, humidity, barometric pressure, and solar radiation sensors on a 3-m tower. Specifically, the stations are comprised of the following instruments and equipment: Campbell Scientific CM6 Tripod Campbell Scientific CR10X Measurement and Control System R.M. Young 05106 Wind Monitor Vaisala HMP45C Temperature and Humidity Probe Vaisala PTB101B Barometric Pressure Sensor Li-Cor LI200X Pyranometer RavenXT Cellular Modem The data logger is used to sample, at 1-second intervals, the horizontal wind speed and direction at 3 meters above ground level (AGL); the air temperature, relative humidity, barometric pressure, and solar radiation at 2 meters AGL; and the logger temperature and power supply. The logger outputs the 1-minute averages of these measurements to final storage and power on the cellular modem, so the data can be retrieved and downloaded to a base station computer. The data are archived as 1-hour comma-delimited ASCII files (see "Table 2. Format of the WFIP2 Comma-delimited ASCII Data Files" in wfip2-met-data.pdf). All dates and times in the file names and data records are in UTC and denote the end of the 1-minute average. Data Quality Data for each primary measurement at every site are automatically plotted daily and reviewed about every three days. Instrument outages or events are reported with the Instrument and Model Data Problem Log at: .

National Digital Forecast Database (NDFD) MetadataThe National Digital Forecast Database (NDFD) Web Services provide a set of gridded weather forecasts for various sensible weather elements in near real-time. These forecasts are generated by a collaboration between the National Weather Service (NWS) field offices and the National Centers for Environmental Prediction (NCEP). The NDFD Web Services offer a seamless, digital mosaic of weather forecasts that can be accessed by users to obtain up-to-date information on a variety of weather conditions.The NDFD's forecasts are gridded, meaning they cover large geographic areas with weather data at specific intervals, providing high-resolution, geographically distributed forecasts. These forecasts can include temperature, precipitation, wind speed and direction, cloud cover, and other meteorological parameters.These web services are hosted by the Office of Dissemination’s CloudGIS team, which ensures the forecasts are readily accessible and deliverable over the internet. Users, including meteorologists, developers, and anyone interested in weather data, can query these web services for up-to-date forecasts in a digital format, enabling integration into applications, websites, and other platforms.NDFD’s Web Services Descriptions:12-Hour Probability of Precipitation Web Service's data layer is the likelihood, expressed as a percent, of a measurable precipitation event (1/100th of an inch or more) at a grid point during the 12-hour valid period. The 12-hour valid periods begin and end at 0000 and 1200 Coordinated Universal Time (UTC).Apparent Temperature Web Service: contains data that is the perceived temperature derived from either a combination of temperature and wind (Wind Chill) or temperature and humidity (Heat Index) for the indicated hour. When the temperature at a particular grid point falls to 50 F or less, wind chill will be used for that point for the Apparent Temperature. When the temperature at a grid point rises above 80 F, the heat index will be used for Apparent Temperature. Between 51 and 80 F, the Apparent Temperature will be the ambient air temperature.Dew Point Temperature Web Service's data is the expected dew point temperature for the indicated hour. Dew point temperature is a measure of atmospheric moisture. It is the temperature to which air must be cooled in order to reach saturation (assuming air pressure and moisture content are constant).Maximum Temperature Web Service's data is the daytime maximum temperature observed from 7 AM to 7PM LST.Minimum Temperature Web Service's data is predicted minimum temperature for a specific location at a given time, allowing users to visualize the lowest expected temperatures across a geographical area.Precipitation Amount Web Service's data is the expected quantity of liquid precipitation accumulated over a six-hourly period. A quantitative precipitation forecast (QPF) will be specified when a measurable (1/100th of an inch or more) precipitation type is forecast for any hour during a QPF valid period. NDFD valid periods for QPF are 6 hours long beginning and ending at 0000, 0600, 1200 and 1800 UTC. QPF includes the liquid equivalent amount for snow and ice.Relative Humidity Web Service's data is a ratio, expressed as a percent, of the amount of atmospheric moisture present relative to the amount that would be present if the air were saturated. Since the latter amount is dependent on temperature, relative humidity is a function of both moisture content and temperature.Sky Cover Web Service’s data is the predicted percentage of the sky that will be covered by opaque clouds at a given time, provided by the National Digital Forecast Database (NDFD). It is a forecast of how much of the sky will be obscured by clouds, expressed as a percentage value.Snow Amount Web Service's data is the expected total accumulation of new snow during a 6-hour period. A snow accumulation grid will be specified whenever a measurable snowfall is forecast for any hour during a valid period. Valid periods for the NDFD begin and end at 0600, 1200, 1800, and 0000 UTC.Temperature Web Service: contains data that is the expected temperature in degrees Fahrenheit valid for the indicated hour.Wave Height Web Service's data is the average height (from trough to crest) of the one-third highest waves valid for the top of the designated hour. Wave Height is a combination of wind waves and swell.Wind Direction Web Service's data is the expected sustained 10-meter wind direction for the indicated hour, using 36 points of a compass.Wind Gust Web Service's data is the maximum 3-second wind speed forecast to occur within a 2-minute interval at a height of 10 meters. Wind gust forecasts are valid at the top of the indicated hour.Wind Speed Web Service's data is the expected sustained 10-meter sustained wind speed for the indicated hour.Wind Speed and Direction Web Service's data is the expected sustained 10-meter wind direction for the indicated hour, using 36 points of a compass. Wind Speed is the expected sustained 10-meter sustained wind speed for the indicated hour. Wind barbs (shown below) are used to denote wind speed and direction.Update Frequency: The data in these service updates hourly. (Click here to see specific Valid Times for update Frequency)Link to graphical web page: https://digital.weather.govLink to data download (grib2): https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/Link to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:These web services are time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.These particular services can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.Valid Time Table:ServiceValid Time12-Hour Probability of Precipitation Web ServiceThe 12-hour valid periods begin and end at 0000 and 1200 Coordinated Universal Time (UTC).Apparent Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Dew Point Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Maximum Temperature Web ServiceDisplay 0z every dayMinimum Temperature Web ServiceDisplay at 12z every dayPrecipitation Amount Web ServiceCONUS/OCONUS (forecast is valid at 0z,6z,12z and 18z)Relative Humidity Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Sky Cover Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Snow Amount Web ServiceCONUS/OCONUS (forecast is valid at 0z,6z,12z and 18z)Temperature Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wave Height Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Direction Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Gust Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind SpeedCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)Wind Speed and Direction Web ServiceCONUS displays every hourOCONUS displays every 3 hours (3z,6z,9z,12z etc.)

Weather Data collected by CIMIS automatic weather stations. The data is available in CSV format. Station data include measured parameters such as solar radiation, air temperature, soil temperature, relative humidity, precipitation, wind speed and wind direction as well as derived parameters such as vapor pressure, dew point temperature, and grass reference evapotranspiration (ETo).

Climate data have been collected at Scott Base continuously since 1957 and more recently from Arrival Heights and is one of the longest continuous climate records in Antarctica. Climate parameters measured include: wind speed and direction, air temperature, relative humidity, barometric pressure, and global, diffuse and direct solar radition. Climate data are collected on a daily basis from both sites. At Scott Base, this takes two forms: a standard daily observation at 0900 NZDT, and continuous data collection at 10 minute and hourly intervals using a CR10X data logger. The initial record of the 0900 daily observations began on 1 March 1957 with air temperature, air pressure, wind speed and direction, and global solar radiation being measured with standard instrumentation (wind measurements since 1972). This record constitutes the reference record. In January 1997 an electronic weather station (EWS) was added to collect and archive 10 minute and hourly data. The daily manual observations continued so as to provide a continuous reference and daily record. Historically, Arrival Heights only had a wind recorder (since January 1984). A data logger was installed in January 1999 and measured air temperature, relative humidity and global solar radition using a secondary network sensor, as well as wind speed and direction. A barometric pressure sensor was installed in 2001. A standard 10m mast was installed and all sensors were moved to the new Arrival Heights laboratory in 2007. 10-minute and hourly data are recorded. Data are retrieved and archived from both automatic stations daily, as well as manual observations from Scott Base and available on New Zealand's national climate database.

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. This dataset provides future weather data under two emissions scenarios - RCP4.5 and RCP8.5 - across two 10-year periods (2045-2054 and 2085-2094). It also includes simulated historical weather data for 1995-2004 to serve as the baseline for climate impact assessments. We strongly recommend using this built-in baseline rather than external sources (e.g., TMY3) for two key reasons: (1) it shares the same model grid as the future projections, thereby minimizing geographic-averaging bias, and (2) both historical and future datasets were generated by the same RCM, so their differences yield anomalies largely free of residual model bias. This dataset offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The authors observed an anomalous warming signal over the Great Plains in the end-of-century (2085 - 2094) RCP4.5 time slice. This anomaly is absent in the mid-century slice (2045 - 2054) under RCP4.5 and in both the mid- (2045 - 2054) and end-of-century (2085 - 2094) slices under RCP8.5. Consequently, we recommend that users exercise particular caution when using the RCP4.5 2085-2094 data, especially for analyses involving the Great Plains region.

PaperThis dataset is associated with the paper published in Scientific Data, titled "SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array." You can access the paper: https://www.nature.com/articles/s41597-024-03427-5If you find this dataset useful, please consider citing our paper: Scientific Data Paper@article{zhou2024sdwpf, title={SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array}, author={Zhou, Jingbo and Lu, Xinjiang and Xiao, Yixiong and Tang, Jian and Su, Jiantao and Li, Yu, and Liu, Ji and Lyu, Junfu and Ma, Yanjun and Dou, Dejing},journal={Scientific Data},volume={11},number={1},pages={649},year={2024},url = {https://doi.org/10.1038/s41597-024-03427-5},publisher={Nature Publishing Group}}Baidu KDD Cup Paper@article{zhou2022sdwpf,title={SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022}, author={Zhou, Jingbo and Lu, Xinjiang and Xiao, Yixiong and Su, Jiantao and Lyu, Junfu and Ma, Yanjun and Dou, Dejing}, journal={arXiv preprint arXiv:2208.04360},url = {https://arxiv.org/abs/2208.04360}, year={2022}}BackgroundThe SDWPF dataset, collected over two years from a wind farm with 134 turbines, details the spatial layout of the turbines and dynamic context factors for each. This dataset was utilized to launch the ACM KDD Cup 2022, attracting registrations from over 2,400 teams worldwide. To facilitate its use, we have released the dataset in two parts: sdwpf_kddcup and sdwpf_full. The sdwpf_kddcup is the original dataset used for the Baidu KDD Cup 2022, comprising both training and test datasets. The sdwpf_full offers a more comprehensive collection, including additional data not available during the KDD Cup, such as weather conditions, dates, and elevation.sdwpf_kddcupThe sdwpf_kddcup dataset is the original dataset used for Baidu KDD Cup 2022 Challenge. The folder structure of sdwpf_kddcup is:sdwpf_kddcup --- sdwpf_245days_v1.csv --- sdwpf_baidukddcup2022_turb_location.csv --- final_phase_test --- infile --- 0001in.csv --- 0002in.csv --- ... --- outfile --- 0001out.csv --- 0002out.csv --- ...The descriptions of each sub-folder in the sdwpf_kddcup dataset are as follows:sdwpf_245days_v1.csv: This dataset, released for the KDD Cup 2022 challenge, includes data spanning 245 days.sdwpf_baidukddcup2022_turb_location.csv: This file provides the relative positions of all wind turbines within the dataset.final_phase_test: This dataset serves as the test data for the final phase of the Baidu KDD Cup. It allows for a comparison of methodologies against those of the award-winning teams from KDD Cup 2022. It includes an 'infile' folder containing input data for the model, and an 'outfile' folder which holds the ground truth for the corresponding output. In other words, for a model function y = f(x), x represents the files in the 'infile' folder, and the ground truth of y corresponds to files in the 'outfile' folder, such as {001out} = f({001in}).More information about the sdwpf_kddcup used for Baidu KDD Cup 2022 can be found in Baidu KDD Cup Paper: SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022sdwpf_fullThe sdwpf_full dataset offers more information than what was released for the KDD Cup 2022. It includes not only SCADA data but also weather data such as relative humidity, wind speed, and wind direction, sourced from the Fifth Generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate (ERA5). The dataset encompasses data collected over two years from a wind farm with 134 wind turbines, covering the period from January 2020 to December 2021. The folder structure of sdwpf_full is:sdwpf_full--- sdwpf_turb_location_elevation.csv--- sdwpf_2001_2112_full.csv--- sdwpf_2001_2112_full.parquetThe descriptions of each sub-folder in the sdwpf_full dataset are as follows:sdwpf_turb_location_elevation.csv: This file details the relative positions and elevations of all wind turbines within the dataset.sdwpf_2001_2112_full.csv: This dataset includes data collected two years from a wind farm containing 134 wind turbines, spanning from Jan. 2020 to Dec. 2021. It offers comprehensive enhancements over the sdwpf_kddcup/sdwpf_245days_v1.csv, including:Extended time span: It spans two years, from January 2020 to December 2021, whereas sdwpf_245days_v1.csv covers only 245 days.Enriched weather information: This includes additional data such as relative humidity, wind speed, and wind direction, sourced from the Fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate (ERA5).Expanded temporal details: Unlike during the KDD Cup Challenge where timestamp information was withheld to prevent data linkage, this version includes specific timestamps for each data point.sdwpf_2001_2112_full.parquet: This dataset is identical to sdwpf_2001_2112_full.csv, but in a different data format.

Coastal managers and ocean engineers rely heavily on projected average and extreme wave conditions for planning and design purposes, but when working on a local or regional scale, are faced with much uncertainty as changes in the global climate impart spatially varying trends. Future storm conditions are likely to evolve in a fashion that is unlike past conditions and is ultimately dependent on the complicated interaction between the Earth’s atmosphere and ocean systems. Despite a lack of available data and tools to address future impacts, consideration of climate change is increasingly becoming a requirement for organizations considering future nearshore and coastal vulnerabilities. To address this need, the USGS used winds from four different atmosphere-ocean coupled general circulation models (AOGCMs) or Global Climate Models (GCMs) and the WaveWatchIII numerical wave model to compute historical and future wave conditions under the influence of two climate scenarios. The GCMs respond to specified, time-varying concentrations of various atmospheric constituents (such as greenhouse gases) and include an interactive representation of the atmosphere, ocean, land, and sea ice. The two climate scenarios are derived from the Coupled Model Inter-Comparison Project, Phase 5 (CMIP5; World Climate Research Programme, 2013) and represent one medium-emission mitigation scenario (RCP4.5; Representative Concentration Pathways) and one high-emissions scenario (RCP8.5). The historical time-period spans the years 1976 through 2005, whereas the two future time-periods encompass the mid (years 2026 through 2045) and end of the 21st century (years 2081 through 2099/2100). Continuous time-series of dynamically downscaled hourly wave parameters (significant wave heights, peak wave periods, and wave directions) and three-hourly winds (wind speed and wind direction) are available for download at discrete deep-water locations along four U.S. coastal regions: • Pacific Islands • West Coast • East Coast • Alaska Coasts The Alaskan region includes a total of 25 model output points. Six output points surround the Arctic coast, eight surround the Aleutian Islands, four are within the shallow region of the Bering Sea, and the remaining seven are within the Gulf of Alaska. The U.S. West Coast region stretches from the U.S.- Mexico border to the U.S.- Canada border and includes open coast areas of California, Oregon, and Washington. The West Coast region includes fifteen model output points. Eight model output points are co-located with observation buoys and are identified by National Oceanic and Atmospheric Administration National Data Buoy Center (NDBC, http://www.ndbc.noaa.gov/) station numbers (N46229, N46213, N46214, N46042, N46028, N46069, N46219, N46047). The U.S. East and Gulf Coasts encompass fifteen coastal states stretching from the Gulf Coast States and Florida in the south to the U.S.-Canada border north of Maine. The region includes seventeen model output points; seven are co-located with NDBC observation buoys (N44011, N44014, N41001, N41002, N41010, N42001, N42055). Data summaries for the U.S. East and Gulf Coast regions are provided from the 1.25° x 1.00° global (NWW3) wave model grid (described in Data and Methods section below). Data summaries for the U.S. West Coast region are available from the NWW3 grid and from the finer resolution 0.25° x 0.25° Eastern North Pacific (ENP) grid nested within the NWW3 grid. Data summaries for the southern coast of Alaska are also available from the ENP grid. In cases where model data exist for both the NWW3 and ENP grids, both sets of data are available for download (http://dx.doi.org/10.5066/F7D798GR). The data and cursory overviews of changing conditions along the coasts are summarized in Storlazzi and others (2015) and Erikson and others (2016). References Cited: Erikson, L.H., Hegermiller, C.A., Barnard, P.L., and Storlazzi, C.D., 2016, Wave projections for United States mainland coasts: U.S. Geological Survey pamphlet to accompany data release, https://res1doid-o-torg.vcapture.xyz/10.5066/F7D798GR. Erikson, L.H., Hegermiller, C.A., Barnard, P.L., Ruggiero, P., and van Ormondt, M., 2015b, Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios: Journal of Ocean Modelling, v. 96, p. 171–185, https://res1doid-o-torg.vcapture.xyz/10.1016/j.ocemod.2015.07.004. Erikson, L.H., Hemer, M.A., Lionello, P., Mendez, F.J., Mori, N., Semedo, A., Wang, X.L., and Wolf, J., 2015a, Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling: Proceedings Coastal Sediments 2015, 13 p., https://res1doid-o-torg.vcapture.xyz/10.1142/9789814689977_0243. Meinshausen, M., Smith, S.J., Calvin, K., Daniel, J.S., Kainuma, M.L.T., Lamarque, J-F., Matsumoto, K., Montzka, S.A., Raper, S.C.B., Riahi, K., Thomson, A., Velders, G.J.M., and van Vuuren, D.P.P., 2011, The RCP greenhouse gas concentrations and their extensions from 1765 to 2300: Climate Change, v. 109, p. 213–241, https://res1doid-o-torg.vcapture.xyz/10.1007/s10584-011-0156-z. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., and Wilbanks, T.J., 2010, The next generation of scenarios for climate change research and assessment: Nature, v. 463, p. 747–756, https://res1doid-o-torg.vcapture.xyz/10.1038/nature08823. Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafai, P., 2011, RCP 8.5: Exploring the consequence of high emission trajectories: Climatic Change, v. 109, p. 33–57, https://res1doid-o-torg.vcapture.xyz/10.1007/s10584-011-0149-y. Storlazzi, C.D., Shope, J.B., Erikson, L.H., Hegermiller, C.A., and Barnard, P.L., 2015, Future wave and wind projections for United States and United States-affiliated Pacific Islands: U.S. Geological Survey Open-File Report 2015–1001, 426 p., https://res1doid-o-torg.vcapture.xyz/10.3133/ofr20151001. Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012, An overview of CMIP5 and the experiment design: Bulletin of the American Meteorological Society, v. 93, p. 485–498, https://res1doid-o-torg.vcapture.xyz/10.1175/BAMS-D-11-00094.1. Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M.A., Clarke, L.E., Edmonds, J.A., 2011, RCP4.5: A pathway for stabilization of radiative forcing by 2100: Climatic Change, v. 109, p. 77–94, https://res1doid-o-torg.vcapture.xyz/10.1007/s10584-011-0151-4. van Vuuren, D.P., Edmonds, J.A., Kainuma, M., Riahi, K., Thomson, A.M., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J., and Rose, S., 2011, The representative concentration pathways: an overview: Climatic Change, v. 109, p. 5–31, https://res1doid-o-torg.vcapture.xyz/10.1007/s10584-011-0148-z.

The dataset contain meteorological time series encompassing wind dynamics, and air temperature and relative humidity that were collected in the proximity of the streambed of selected riverine sites within the Hampshire Avon catchment (UK). Six rivers within sub-catchments of contrasting geology (clay, greensand, chalk) and associated river morphology were investigated. Data were obtained from field-based measurements in seasonal campaigns conducted between spring 2013 and winter 2014.

30-minute summary data at NPP T-WEST met station. Average air temperature, relative humidity, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP T-WEST site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-jrn&identifier=210437030 Webpage with information and links to data files for download

Five million international ship observations of the Pacific trade winds are presented in monthly summaries. For each month in the period from 1947 to 1973, a 10-degree latitude by 2-degree ... longitude grid includes various wind and wind stress information. Coverage within the grid is not 100% as most observations occurred along shipping routes. Most of the raw wind observations were Beaufort estimates that were converted to wind speed and direction.

This dataset is a unique compilation of field-based meteorological observations and wind power generation data, collected directly from one of our company's operational sites. The dataset represents a detailed hourly record, starting from January 2, 2017. This rich dataset provides real-world insights into the interplay between various weather conditions and wind energy production.

Context and Inspiration: The dataset was conceived out of the necessity to understand the dynamic relationship between meteorological variables and their impact on wind power generation. By collecting data directly from the field and the wind turbine installations, we aim to provide a comprehensive and authentic dataset that can be instrumental for industry-specific research, operational optimization, and academic purposes.

Data Collection: Data was meticulously gathered using state-of-the-art equipment installed at the site. The meteorological instruments measured temperature, humidity, dew point, and wind characteristics at different heights, while power generation data was recorded from the wind turbines' output. This dataset is a unique compilation of field-based meteorological observations and wind power generation data, collected directly from one of our company's operational sites. The dataset represents a detailed hourly record, starting from January 2, 2017. This rich dataset provides real-world insights into the interplay between various weather conditions and wind energy production.

Potential Uses: This dataset is ideal for industry experts, researchers, and data scientists exploring renewable energy, especially wind power. It can aid in developing predictive models for power generation, studying environmental impacts on renewable energy sources, and enhancing operational efficiency in wind farms.