This dataset provides information about the number of properties, residents, and average property values for Northwind Road cross streets in Las Cruces, NM.

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The 2023 National Offshore Wind data set (NOW-23) is the latest wind resource data set for offshore regions in the United States, which supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published about a decade ago and is currently one of the primary resources for stakeholders conducting wind resource assessments in the continental United States. The NOW-23 data set was produced using the Weather Research and Forecasting Model (WRF) version 4.2.1. A regional approach was used: for each offshore region, the WRF setup was selected based on validation against available observations. The WRF model was initialized with the European Centre for Medium Range Weather Forecasts 5 Reanalysis (ERA-5) data set, using a 6-hour refresh rate. The model is configured with an initial horizontal grid spacing of 6 km and an internal nested domain that refined the spatial resolution to 2 km. The model is run with 61 vertical levels, with 12 levels in the lower 300m of the atmosphere, stretching from 5 m to 45 m in height. The MYNN planetary boundary layer and surface layer schemes were used the North Atlantic, Mid Atlantic, Great Lakes, Hawaii, and North Pacific regions. On the other hand, using the YSU planetary boundary layer and MM5 surface layer schemes resulted in a better skill in the South Atlantic, Gulf of Mexico, and South Pacific regions. A more detailed description of the WRF model setup can be found in the WRF namelist files linked at the bottom of this page. For all regions, the NOW-23 data set coverage starts on January 1, 2000. For Hawaii and the North Pacific regions, NOW-23 goes until December 31, 2019. For the South Pacific region, the model goes until 31 December, 2022. For all other regions, the model covers until December 31, 2020. Outputs are available at 5 minute resolution, and for all regions we have also included output files at hourly resolution. The NOW-23 data are provided here as HDF5 files. Examples of how to use the HSDS Service to Access the NOW-23 files are linked below. A list of the variables included in the NOW-23 files is also linked below. No filters have been applied to the raw WRF output.

This data release contains eight datasets that represent the entirety of the data collected for a study that examined breeding-bird densities in native mixed-grass prairie from 2003 to 2012 at and adjacent to wind facilities in North Dakota and South Dakota, USA. Data were collected to determine breeding-bird density per 100 hectares (ha) by distance bands from turbines and by excluding habitat that may not be considered suitable as breeding habitat for particular bird species. A subset of the data that included only one year prior to turbine construction and several years post-construction and that lent itself to a before-after-control-impact (BACI) assessment was published as its own data release and paper in 2016 in Conservation Biology by authors J. Shaffer and D. Buhl. The all-inclusive data release described hereafter is of the same basic format but includes all years and all study sites (also referred to as study plots), even those that did not lend themselves to a BACI ass ...

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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

Electrical Energy Matrix: Number Individually Simulated: North: Wind: 2028 data was reported at 426.000 MW in May 2025. This stayed constant from the previous number of 426.000 MW for Apr 2025. Electrical Energy Matrix: Number Individually Simulated: North: Wind: 2028 data is updated monthly, averaging 426.000 MW from Jan 2024 (Median) to May 2025, with 17 observations. The data reached an all-time high of 426.000 MW in May 2025 and a record low of 426.000 MW in May 2025. Electrical Energy Matrix: Number Individually Simulated: North: Wind: 2028 data remains active status in CEIC and is reported by National Electric System Operator. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBA011: Electrical Energy: Matrix.

The data covers the calculated yields of wind farms in German territory and the German exclusive economic zone (EEZ) and the relevant wind conditions under the assumption of an expansion scenario (S16) of offshore wind energy for ten consecutive meteorological years. The calculations were carried out with the numerical weather model WRF using a parameterization of wind farms according to Fitch. The data are available in 10-minute temporal and 2 km x 2 km spatial resolution for the North Sea for the meteorological years 2013-2022. The variables of the data set are wind speed (WS) and wind direction (WD) at 9 height levels between 50 m and 350 m, the power (POWER) of the wind turbines from each grid cell and the air density. A detailed description of the variables can be found in the files.

The data covers the calculated yields of wind farms in German territory and the German exclusive economic zone (EEZ) and the relevant wind conditions, assuming an expansion of offshore wind energy in continuous expansion years. The calculations were carried out with the numerical weather model WRF using a parameterization of wind farms according to Fitch. The data are available in 10-minute temporal and 2 km x 2 km spatial resolution for the North Sea for the meteorological year 2006. The years in the file names refer to the respective year of expansion. The variables of the data set are wind speed (WS) and wind direction (WD) at 9 height levels between 50 m and 350 m, the power (POWER) of the wind turbines from each grid cell and the air density. A detailed description of the variables can be found in the files.

The data covers the wind conditions in the North Sea in 2006. The calculations were carried out with the numerical weather model WRF. The data are available in 10-minute temporal and 2 km x 2 km spatial resolution for the North Sea for the meteorological year 2006. The variables of the data set are wind speed (WS) and wind direction (WD) at 9 height levels between 50 m and 350 m and the air density. A more detailed description of the variables can be found in the files.

The database created encompasses a comprehensive collection of operational and environmental parameters from over seventy offshore wind farms situated in the Baltic, North, and Irish Seas. This dataset was compiled to analyze the impact of wind farm density on their efficiency and capacity factors. The objective was to support the development of a robust analytical framework capable of assessing the limitations and optimization potentials of offshore wind energy under varying geographic and climatic conditions.

Database Structure

The database consists of several key components structured to facilitate both broad and detailed analyses:

1. Wind Farm Characteristics:

• Name and location.
• Technical Specifications: Turbine power, hub height, rotor diameter, installed capacity (in MW), and total area (in km²).

1. Operational Data:

• Annual Production Data: Historical yearly energy output recorded, including sources.
• Capacity Factor Data: Real-world capacity factor based on the actual output compared to the theoretical maximum output.

1. Environmental and Technical Parameters:

• Wind Data: Average wind speed at given height, Weibull distribution parameters.

1. Model Inputs and Outputs:

• Finite wind-farm effect scaling parameter
• Predicted Capacity Factors: Results from the closed-form analytical model applied to each wind farm, estimating potential capacity factors under ideal, isolated, and infinite array conditions.

Data Collection Methodology

Data were primarily sourced from publicly available databases, technical reports and other sources. These are listed in a report.

The data covers the calculated yields of wind farms in German territory and the German exclusive economic zone (EEZ) and the relevant wind conditions under the assumption of an expansion of offshore wind energy defined in different scenarios. The calculations were carried out with the numerical weather model WRF using a parameterization of wind farms according to Fitch. The data are available in 10-minute temporal and 2 km x 2 km spatial resolution for the North Sea for the meteorological year 2006. The variables of the data set are wind speed (WS) and wind direction (WD) at 9 height levels between 50 m and 350 m, the power (POWER) of the wind turbines from each grid cell and the air density. A detailed description of the variables can be found in the files.

Salinity, temperature, depth (STD) and other data was collected using the United States Coast Guard (USCG) ships BURTON ISLAND, GLACIER and NORTH WIND by the US Navy. Data was collected in the Arctic Ocean from July 30, 1971 to August 5, 1977 and submitted by the Naval Postgraduate School, Monterey, CA. Station Data was submitted on tape which has been processed. Data has been processed by NODC to the NODC standard High-Resolution CTD/STD (F022) format. The F022 format contains high-resolution data collected using CTD (conductivity-temperature-depth) and STD (salinity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity, and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t), and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. A text record is available for comments.

The KNW (KNMI North Sea Wind) atlas is based on the ERA-Interim reanalyses dataset which captures more than 40 years (January 1979 - August 2019) of meteorological measurements and generates 3D wind (temperature, etc) fields consistent with these measurements and the laws of physics. This dataset is downscaled using the state-of-the-art weather forecasting model, HARMONIE with a horizontal grid of 2.5 km. The vertical profile of wind speed was calibrated against the 200 m tall Cabauw measurement mast to obtain a single wind shear correction coefficient which was applied throughout the whole dataset. The result is a high resolution dataset of more than 40 years: the KNW dataset.

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The global market for wind resource data loggers is experiencing robust growth, driven by the expanding renewable energy sector and the increasing need for accurate wind resource assessment to optimize wind farm development and operations. The market, currently valued at approximately $250 million in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of around 8% from 2025 to 2033, reaching an estimated $450 million by 2033. This growth is fueled by several key factors. Firstly, the global push towards decarbonization and the significant investment in wind energy projects worldwide are creating a substantial demand for reliable wind data acquisition systems. Secondly, advancements in data logger technology, including improved accuracy, enhanced data transfer capabilities (both active and passive), and integration with sophisticated data analytics platforms, are further driving market expansion. The increasing adoption of sophisticated wind resource monitoring techniques, particularly in offshore wind farm development where accurate data is crucial, is another major contributor to market growth. Segmentation by application reveals strong demand across wind resource monitoring and assessment, with wind resource monitoring currently dominating the market share. Active data transfer systems currently hold a larger market share compared to passive systems due to their real-time capabilities. Key players like Vaisala, NRG Systems, and Campbell Scientific are leveraging their technological expertise and established market presence to capitalize on these trends. Geographical analysis reveals that North America and Europe currently hold significant market shares due to their mature wind energy industries and stringent environmental regulations. However, the Asia-Pacific region is poised for substantial growth in the coming years, driven by rapid economic development and large-scale investments in wind energy infrastructure in countries like China and India. While the market faces certain restraints such as the high initial investment costs associated with deploying data logger systems and the potential for data inaccuracies due to environmental factors, the overall growth trajectory remains positive, driven by the long-term strategic importance of accurate wind resource data in the global transition to renewable energy. The competitive landscape is characterized by a mix of established players and emerging companies, leading to innovation and price competition.

Gridded data, in ascii text format, of wind turbines at the spatial extent of the North Sea (2015-2016). Data are produced at three different spatial resolutions: 0.1, 0.25 and 0.5 decimal degree, respectively. Values represent estimated proportion of grid cell occupied by structures.

Data were derived as model inputs to support two Cefas projects (COSM and** EcoConnect**) under the** INSITE** (INfluence of man-made Structures In The Ecosystem) programme.

This data layer contains waterbird density blocks extracted from the Minerals Management Service's Mid-Atlantic Waterbird Data Beta Version 1.0. This application is based on their Marine Mammal and Seabird Computer Database Analysis System (MMS-CDAS). The origin of the underlying data is a U.S. Fish & Wildlife Service aerial survey conducted between December, 2001 and March, 2003. The source data were collected on-transect (120m width, 60 m each side) for transects conducted in the mouth of Chesapeake Bay, in Delaware Bay, and in offshore waters from the beach outward. Aerial surveys of waterbirds were flown to at least 12 nautical miles (22.2 km) offshore from northern New Jersey to the Virginia / North Carolina border. All waterbird species in the USFWS database from winter surveys only were exported to density blocks. Information in the MMS application suggests the winter surveys only are valid for calculating densities because they were flown on specific transects. The spatial extent of the database (and thus of this data set) is from the Virginia/North Carolina border to the Hudson Canyon (including Delaware Bay and the lowest part of Chesapeake Bay).This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/UtilityTelecom/MD_OffshoreWindEnergyPlanning/FeatureServer/3

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Overview Winds. A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and are combined to derive the horizontal wind field over the radar. These data typically are sampled and averaged hourly and usually have 6-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz and 8 km for the 449 MHz systems. Temperature. To measure atmospheric temperature, a radio acoustic sounding system (RASS) is used in conjunction with the wind profile. These data typically are sampled and averaged for five minutes each hour and have a 60-m vertical resolution up to 1.5 km for the 915 MHz and 60 m up to 3.5 km for the 449 MHz. Moments and Spectra. The raw spectra and moments data are available for all dwells along each beam and are stored in daily files. For each day, there are files labeled "header" and "data." These files are generated by the radar data acquisition system (LAP-XM) and are encoded in a proprietary binary format. Values of spectral density at each Doppler velocity (FFT point), as well as the radial velocity, signal-to-noise ratio, and spectra width for the selected signal peak are included in these files. Attached zip files, 449mhz-spectra-data-extraction.zip and 449mhz-moment-data-extraction.zip, include executables to unpack the spectra, (GetSpectra32.exe) and moments (GetMomSp32.exe), respectively. Documentation on usage and output file formats also are included in the zip files. Data Details Note, the b0 data is identical to 00 data but a netcdf extraction of the b0 data was also created for the duration of the WFIP2 campaign. Data Quality Various quality control (QC) algorithms developed over the years process data in real time on the radar software layer. These algorithms, which run in real time, act on time-series, spectra, moment, and consensus data layers that are persisted in different forms.