Mean Wind Speed (2020–2024)

Overview

This dataset represents the global mean near-surface wind speed derived from ERA5 reanalysis data for the period 2020–2024.

Wind speed is an important environmental variable influencing:

• Atmospheric chloride transport
• Marine aerosol dispersion
• Coastal exposure severity
• Atmospheric corrosion potential
• Evaporation and drying rates
• Environmental exposure assessment

The raster represents long-term mean wind conditions at 10 meters above ground level and provides a climatological characterization of global wind behavior.

Units:

• Meters per second (m/s)

Background

Wind speed is a key environmental parameter within atmospheric corrosion and environmental exposure modeling because it influences the transport and persistence of airborne contaminants, marine aerosols, and atmospheric moisture.

Wind speed directly affects:

• Chloride deposition rates
• Marine aerosol transport distance
• Coastal exposure severity
• Surface drying rates
• Atmospheric mixing
• Environmental forcing conditions

Higher wind speed environments often exhibit increased transport of marine aerosols and airborne contaminants, particularly in coastal and offshore environments.

This dataset was developed to provide global wind speed estimates suitable for atmospheric corrosion assessment, environmental modeling, and GIS-based exposure analysis.

Modeling Methodology

The wind speed framework utilizes ERA5 hourly reanalysis data to characterize long-term wind conditions at 10 meters above ground level.

Primary inputs include:

• ERA5 10-meter zonal wind component (u10)
• ERA5 10-meter meridional wind component (v10)

The modeling framework incorporates:

Wind Vector Calculation

Wind speed was calculated using the vector magnitude of the horizontal wind components:

Wind Speed = √(u² + v²)

where:

• u = zonal wind component
• v = meridional wind component

Temporal Aggregation

Hourly wind speed values were aggregated to produce:

• Annual wind speed surfaces
• Multi-year climatological averages
• Mean wind speed conditions for 2020–2024

Spatial Processing

• ERA5 data acquisition
• Global temporal aggregation
• Spatial harmonization
• Resampling to approximately 1 km resolution

Environmental Integration

The resulting dataset supports atmospheric transport analysis, chloride deposition modeling, coastal exposure assessment, and environmental severity mapping.

Interpretation Guidelines

Higher wind speed environments generally increase atmospheric transport potential and environmental exposure severity.

Spatial Resolution

Data Sources

Primary environmental inputs include:

• ERA5 Reanalysis Dataset
• European Centre for Medium-Range Weather Forecasts (ECMWF)
• Copernicus Climate Change Service (C3S)

Derived environmental layers supported by this dataset include:

• Wind Direction
• WindRIX Terrain–Wind Exposure Index
• Chloride Deposition
• Atmospheric Corrosion Layers
• Environmental Exposure Layers

Intended Applications

This dataset may be used for:

• Atmospheric corrosion assessment
• Chloride deposition modeling
• Coastal exposure analysis
• Marine aerosol transport studies
• Environmental severity assessment
• Wind exposure analysis
• GIS visualization
• Environmental modeling
• Enterprise API workflows

Related Datasets

Corrosion Layers

• ISO 9223 Steel Corrosion Rate
• ISO 9223 Zinc Corrosion Rate
• ISO 9223 Aluminum Corrosion Rate
• ISO 9223 Copper Corrosion Rate

Supporting Atmospheric Layers

• Mean Chloride Deposition
• Mean Sulfate Deposition
• Mean Annual Temperature
• Mean Relative Humidity
• Time of Wetness (TOW)

Supporting Coastal & Terrain Layers

• Distance to Coast
• Bathymetry 2024 – Terrain Elevation
• WindRIX Terrain–Wind Exposure Index
• Wind Resultant Direction (0–360°)
• Wind Speed

Related SSI™ Dataset

This dataset is a supporting climatic input to the Solar Suitability Index (SSI™), a global multi-parameter model developed by AtmosphericIQ LLC.

For the full solar suitability model and derived classification datasets, see:
Global Solar Suitability Index (SSI™) – Continuous Score (1–100)

Attribution

Joseph Mazzella
AtmosphericIQ LLC
Engineering Director, Inc.

Dataset Citation

Mazzella, J. (2026). Mean Wind Speed Raster (2020–2024) Derived from ERA5 Reanalysis Data. AtmosphericIQ LLC / Engineering Director, Inc.

Supporting Dataset Citations

ERA5 Reanalysis

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049.
https://doi.org/10.1002/qj.3803

Copernicus Climate Data Store

Copernicus Climate Change Service (C3S). ERA5 Hourly Data on Single Levels.
https://cds.climate.copernicus.eu/

ECMWF

European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 Reanalysis Dataset.
https://www.ecmwf.int/

Version Information