Atmospheric stability classification for offshore wind shear and turbulence assessment
Marine atmospheric boundary layer stability controls how wind speed and turbulence intensity change with height. Combining satellite SST, ERA5 reanalysis, and SAR surface winds lets developers stratify candidate offshore zones by Monin-Obukhov regime before committing to met-mast positions.
Sensors
- Sentinel-1 SAR (C-band, ESA): Provides surface wind speed fields at roughly 1 km spatial resolution (after NRCS-to-wind inversion via CMOD5.n or CMOD7 geophysical model functions) with a 6-day repeat at mid-latitudes per satellite, 3-day with both units operational. Captures spatial structure of the marine boundary layer wind field that point measurements and reanalysis both smear out.
- Meteosat SEVIRI (EUMETSAT): Spinning Enhanced Visible and InfraRed Imager aboard Meteosat Second Generation delivers sea-surface temperature retrievals at approximately 3 km nadir resolution with a 15-minute repeat cycle (5-minute in rapid-scan mode). The IR 10.8 µm and 12.0 µm split-window channels drive the SST product used to estimate air-sea temperature difference and surface heat flux sign.
- GOES-R ABI (NOAA): Advanced Baseline Imager on GOES-16 and GOES-18 provides SST at roughly 2 km resolution with full-disc imagery every 10 minutes and mesoscale sectors every 1 minute. Covers the western Atlantic and eastern Pacific offshore wind development zones with temporal density that SEVIRI cannot match for those longitudes.
- ERA5 reanalysis (ECMWF): Hourly atmospheric reanalysis at 0.25-degree (~31 km) horizontal resolution. Supplies 10 m wind, 2 m air temperature, sea-level pressure, boundary layer height, and surface sensible heat flux fields needed to compute Monin-Obukhov length and Obukhov stability parameter. Archive runs from 1940 to near-real-time with roughly 5-day latency for the most recent data.
Why stability class changes the number that matters to a developer
The power law exponent used to extrapolate a 10 m wind measurement to hub height is not a constant. Under strongly unstable conditions (large upward surface heat flux, typically daytime or warm-air-over-cold-sea situations), the boundary layer mixes vigorously and the shear exponent across the 10-to-150 m layer can fall below 0.06. Under stable stratification (cold-air-over-warm-sea, common in winter in the North Sea and along the US East Coast), the exponent regularly exceeds 0.14 and turbulence intensity drops sharply while wind veer increases. The difference in estimated annual energy production between those two assumptions, applied to a 150 m hub-height machine, can exceed 5 percent. That is not a rounding error; it is the margin between a project that clears a bank's hurdle rate and one that does not.
The Monin-Obukhov (MO) similarity framework provides the physical basis for stability-dependent vertical extrapolation. The MO length L encodes the ratio of mechanical to buoyant turbulence production. Computing L requires surface friction velocity and surface heat flux, neither of which a satellite measures directly. What satellites do measure, with high spatial and temporal resolution, are the two quantities that drive heat flux sign and magnitude over the ocean: sea-surface temperature and near-surface wind speed. ERA5 supplies the air temperature and humidity needed to close the bulk flux calculation.
Classifying regimes: from SST and SAR to Monin-Obukhov length
The workflow begins with co-located SEVIRI or ABI SST retrievals and ERA5 2 m air temperature fields to compute the air-sea temperature difference (delta-T) at each grid point across the candidate zone. A positive delta-T (sea warmer than air) indicates unstable or near-neutral conditions; negative delta-T indicates stable stratification. This is not a precise flux estimate, but it is a reliable indicator of stability regime sign and, when binned into five MO stability classes following the Pasquill-Gifford or Zilitinkevich scheme, it reproduces the dominant regime at a given location and season with reasonable fidelity when validated against published met-mast records from campaigns such as the FINO1 platform in the German Bight.
Sentinel-1 SAR wind retrievals then enter as the surface wind speed input to a bulk aerodynamic flux algorithm (COARE 3.0 or similar). The SAR wind field resolves mesoscale and sub-mesoscale features, including coastal jets, wind shadows from islands, and SST-forced convergence lines, that ERA5 at 31 km cannot capture. Each SAR scene is tagged with its ERA5-derived stability class at acquisition time. Over a multi-year archive, typically five or more years of Sentinel-1 data, the result is a stability-stratified wind speed climatology: not just the mean wind at 10 m, but the conditional distributions of wind speed under each of the five stability classes. That conditional structure is what feeds the stability-corrected power law or log-law extrapolation to hub height.
What the method delivers and what it cannot
The output is a set of gridded maps at roughly 1 km spatial resolution covering the candidate zone, showing the frequency of occurrence of each stability class by season and wind direction sector, the stability-weighted mean shear exponent, and a first-order turbulence intensity estimate derived from the MO framework. These products are most useful for ranking sub-zones within a large lease area, identifying corridors where stable stratification is persistently dominant (and where turbine fatigue loads may therefore be underestimated by neutral-assumption extrapolations), and guiding the placement of met masts to sample the most consequential regimes.
The limits are real and should be stated plainly. SAR wind retrievals become unreliable at wind speeds below about 3 m/s (insufficient Bragg scattering) and above roughly 25 m/s (wave breaking contaminates the NRCS signal). SST retrievals from SEVIRI and ABI are blocked by cloud, which is precisely the condition that often accompanies the most dynamically interesting boundary layer states. ERA5 at 31 km resolution misrepresents coastal gradients and orographic channelling; the reanalysis is a background field, not a substitute for local measurement. None of this analysis constitutes a bankable wind resource assessment. The IEC 61400-1 and IEC 61400-3 standards require in-situ measurements at or near hub height for project financing. The satellite-derived stability climatology narrows the spatial uncertainty before mast deployment; it does not eliminate the need for one.
Seasonal and geographic patterns worth knowing before you site
Offshore wind development zones show characteristic stability seasonality. In the North Sea and Baltic, winter brings cold continental air masses over relatively warm water, producing persistently unstable or near-neutral conditions and high turbulence intensity. Summer reverses this: warm air advects over cooler upwelled water, generating stable stratification, suppressed mixing, and elevated low-level wind shear. The US East Coast shows a similar but phase-shifted pattern, with stable stratification most frequent in spring and early summer when the Gulf Stream's warm flank meets cold Labrador Current air. Offshore Taiwan and Japan, the East Asian monsoon imposes a strong bimodal stability regime that a neutral-assumption extrapolation would misrepresent badly.
These patterns are recoverable from ERA5 alone, but ERA5 cannot resolve the spatial gradients that matter for micrositing. A SAR-derived stability climatology at 1 km resolution routinely reveals sub-zones where SST fronts or coastal upwelling cells impose a local stability regime that differs from the regional mean by one full Pasquill class. Placing a met mast in the wrong sub-zone, then extrapolating its measurements across the full lease area assuming spatial homogeneity, is a known source of pre-construction energy yield error.
Putting the analysis to work: from maps to mast placement
A practical workflow for a 500 km² candidate lease area runs as follows. First, ERA5 and SST data are used to generate a 10-year monthly stability-class frequency map at 0.25-degree resolution. This identifies the dominant seasonal regimes and any obvious spatial gradients at coarse scale. Second, all available Sentinel-1 scenes over the area are processed through CMOD5.n or CMOD7, co-registered to the ERA5 stability classification at scene acquisition time, and assembled into a stability-stratified wind atlas. Third, the conditional wind speed distributions under each stability class are fed into a stability-corrected vertical extrapolation to produce hub-height wind speed estimates with explicit uncertainty bounds that vary by stability class rather than assuming a single neutral profile.
The resulting maps identify two or three priority sub-zones where the stability regime is most distinct from the lease-area mean. Those are the locations where a met mast or floating LiDAR deployment will return the highest information value per capital pound spent. Satellize has applied analogous multi-source satellite climatology workflows in other resource assessment contexts, including the Tonga crop-estimation programme, and the same data-fusion logic transfers directly to offshore wind stability characterisation. A typical project delivery covers the full archive processing, the gridded stability climatology, and a written interpretation of the results with mast-placement recommendations.
Typical figures
| SAR wind field spatial resolution | ~1 km (after NRCS inversion; native GRD pixel 10 m, typically multi-looked to 100-500 m before wind retrieval) |
| Sentinel-1 revisit at mid-latitudes | 3-6 days (both satellites operational); single-satellite 6-day repeat |
| SEVIRI SST spatial resolution | ~3 km at nadir; degrades toward limb |
| SEVIRI SST temporal resolution | 15 minutes (standard); 5 minutes (rapid-scan service) |
| GOES-R ABI SST spatial resolution | ~2 km at nadir |
| GOES-R ABI temporal resolution | 10 minutes full disc; 1 minute mesoscale sector |
| ERA5 horizontal resolution | 0.25 degrees (~31 km); hourly output |
| ERA5 archive depth | 1940 to near-real-time (~5-day latency for recent data) |
| Sentinel-1 archive depth | 2014 to present (Sentinel-1A); 2016-2021 (Sentinel-1B); 2023 to present (Sentinel-1C) |
| SAR wind speed retrieval range | Reliable approximately 3-25 m/s; degraded below 3 m/s (low Bragg scatter) and above ~25 m/s (wave breaking) |
Analytics Satellize can run
| Stability-class frequency climatology | Bulk aerodynamic flux calculation (COARE 3.0 or equivalent) applied to ERA5 air temperature and SEVIRI/ABI SST to derive Monin-Obukhov length; binned into five Pasquill-Gifford stability classes | Gridded GeoTIFF maps (monthly and seasonal) of stability-class frequency of occurrence across the candidate lease area |
| Stability-stratified SAR wind atlas | Sentinel-1 NRCS inverted via CMOD5.n or CMOD7; each scene tagged with ERA5-derived stability class at acquisition time; conditional wind speed distributions computed per class and per directional sector | NetCDF wind atlas with per-stability-class conditional wind speed statistics at 1 km resolution; compatible with WAsP and OpenWind import formats |
| Stability-corrected hub-height wind speed estimate | MO similarity theory applied to stability-stratified surface wind distributions; stability-dependent log-law or power-law extrapolation from 10 m to specified hub height (80-200 m range) | Gridded hub-height mean wind speed and P90 estimate with explicit stability-driven uncertainty bounds; PDF report with methodology annex |
| Turbulence intensity proxy map | MO-framework turbulence intensity parameterisation (following IEC 61400-1 Annex C approach) applied to stability-class frequencies and mean wind speed | GIS layer of estimated turbulence intensity by stability class and wind speed bin; flagged zones where TI exceeds IEC turbulence category thresholds |
| Met-mast placement recommendation | Spatial clustering of stability-class frequency maps combined with stability-stratified wind speed gradient analysis to identify sub-zones with highest information value | Written report identifying two to four priority mast locations with justification; includes seasonal stability regime summary for each candidate position |
| SST-front and upwelling detection | Multi-temporal SEVIRI or ABI SST compositing to identify persistent SST gradients within the lease area; spatial correlation with SAR wind speed anomalies to confirm atmospheric response | Annotated SST composite maps with identified front positions and associated wind speed perturbation magnitude; quarterly update GIS layer |
Who does the work
We can get this done for you. Satellize runs its own analyst desk and a strong science team. You do not buy a data feed and work out what it means; our people source the imagery, run the analysis described on this page, and hand you the answer with its confidence limits stated. Discuss this requirement.