Wind Speed

EO Capability Benefits

Wind speed data is essential for identifying optimal locations for wind turbines by analysing historical and real-time wind patterns. It helps determine the energy production potential of a site, including cut-in and cut-out wind speeds for turbine operation. Wind speed datasets are also critical for forecasting storms and severe weather events, aiding in disaster preparedness. In addition, engineers use detailed wind datasets to design buildings, bridges, and other structures that can withstand specific wind loads. Finally, long-term wind speed records contribute to our understanding climate patterns and changes over time.

EO Capability Description

Wind speed data is often stored as gridded datasets where each grid cell contains a value representing the wind speed at that location, usually in meters per second. Temporal resolutions differ depending on the application. Hourly or sub-hourly datasets are used for real-time monitoring and forecasting, whereas daily or monthly averages are useful for climate studies and assessing long-term trends. Seasonal or annual aggregates find applications in renewable energy assessments as well as climatology.

EO-based wind speed data combines remote sensing technologies with atmospheric models. Active sensors such as Synthetic Aperture Radar (SAR) and microwave scatterometers measure surface roughness of oceans to estimate wind speed and direction over water. Over land, satellite data provides inputs to atmospheric models with which researchers can estimate wind speeds at different altitudes. Satellite-derived wind speeds are typically snapshots taken at specific times during the satellite’s orbit. For continuous monitoring, geostationary satellites or multiple polar-orbiting satellites are used.

Relevant EO Technologies

SAR

RADAR SCATTEROMETERS

IMAGING MW RADIOMETERS

Synthetic Aperture Radar (SAR) sends out microwave pulses and coherently combines their echoes along the satellite’s flight path to “synthetically” create a very long “virtual antenna”, generating sharp images–independent of daylight and largely independent of clouds–even though microwave radar uses much longer wavelengths than visible light. Changes in backscatter amplitude (i.e. the strength of the returned signal) reflect variations in surface roughness, geometry, moisture and dielectric properties (and, over land, vegetation structure). When complex images from two (or more) acquisitions are interferometrically compared (e.g. with InSAR), differences in phase—after accounting for topography and atmospheric effects—reveal surface deformation or ground displacement along the radar line of sight.

EXAMPLES

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