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WindScan contains globally gridded, high resolution offshore wind speeds and directions on a 0.250 global grid and multiple time resolutions down to 6 hourly measurements. With 22 years of wind speed and directional data derived from multiple satellite measurements, WindScan provides weather risk analysis as well as site specific resource analysis for any offshore site in the world, whilst the 0.25° resolution allows analysis of differences in wind speed between sites, for example within individual offshore wind energy development zones.

The utilisation of multiple satellite measurements fills in the data gaps (in both space and time) of individual satellite samplings and reduces the subsampling aliases and random errors.

WindScan development was in response to the high demand for high resolution wind speed and direction data from the offshore wind energy industry. For example, developers want to make more accurate forecasts of offshore life cycle wind power output and annual variability/site climatology.

Assessment of composite global sea surface wind speeds[edit]

Global 0.250 gridded, blended products with temporal resolutions of 6 hours, 12 hours and daily have become feasible since mid-2002, mid-1995 and January 1991, respectively (with 75% time coverage and 90% spatial coverage between 650S–650N). If the coverage is relaxed, the feasible times can be extended to earlier periods.

Sea surface wind speed observations[edit]

Sea surface wind has been traditionally observed from platforms such as ships and buoys. However, even today, observations still have very limited spatial coverage over the ocean surface. Sea surface wind speed has also been operationally observed from satellite sensors, starting with a US Defense Meteorological Satellite Program (DMSP) satellite in July 1987 to the constellation of 5 US satellites since 2000.

In situ observations still play a critical role in calibrating and validating satellite observations. However, with the dense satellite sampling, in-situ observations play a minor role in reducing random and sampling errors in blended analyses using satellite observations.[1]

The short-lived wind satellites are not used (e.g., the US National Aeronautics and Space Agency Scatterometer (NSCAT), the joint US/Japan SeaWinds on the Advanced Earth Observing Satellites (ADEOS I and ADEOS II), non-US satellites (e.g. the European Remote Sensing Satellites (ERS) 1 and 2, which have narrow observing swaths and interrupted observations), and satellites from which sea surface wind speed can also be retrieved (presently with less accuracy) along with the primary product of sea level (e.g. the joint US/French altimetry satellites of Ocean Topography Experiment (TOPEX/Poseidon) and the follow-on Jason 1). Inclusion of these data would have limited positive impact for the corresponding time periods on the blended product.

Among the satellites used, the passive DMSP observations are from the microwave radiometers on the Special Sensor Microwave Imager (SSMI [2]). Later additions to these passive microwave observations are the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI [3]) and the Advanced Microwave Scanning Radiometer of NASA’s Earth Observing System (Aqua (satellite)) AMSR-E.[4] The scatterometer (e.g. the Quick Scatterometer (QuikSCAT)), which is active by nature, uses microwave radar and retrieves both wind speed and wind direction.[5]

North Europe wind speed sample.JPG

Summary of data coverage[edit]

At the temporal resolution of daily, one SSMI satellite provides data coverage over about 75% of the global 0.250 oceanic boxes between 650S–650N. With two or more satellites, the spatial coverage increases to about 100%.

At the temporal resolution of 12 hourly, one satellite provides data coverage to less than 30% of the oceanic grid boxes.

The addition of the second SSMI satellite drastically increases the spatial coverage to just below 75%.

With three or more satellites, the spatial coverage increases to above 95%.

At the temporal resolution of 6 hourly, the spatial coverage is less than 30% with two or fewer satellites.

The coverage is about 42% with the three SSMI satellites.

With the addition of the TMI, the spatial coverage increases to about 56%.

The addition of the QuikSCAT further increases the spatial coverage to about 66%. This modest increase is due to the close sampling times of the QuikSCAT and the SSMI satellites, although their ascending and descending tracks are out-of-phase.

However, the addition of the AMSR-E dramatically increases the spatial coverage to above 90%. The critical importance of the AMSR-E for high resolution products (6 hourly in this case) is due to its unique sampling times compared to the other satellites.

Following this satellite data blending process, on the global 0.250 grid, blended products with temporal resolutions of 6 hours, have become feasible.

Research findings using WindScan[edit]

North sea average wind speeds months.JPG
Offshore wind rose sample.JPG

Recent research undertaken using WindScan has shown significant differences in wind power output from different development areas in the North Sea. This has a large impact on the European wind farm industry. Different zones in the North Sea at the same distance from the coastline can give power outputs which differ by over 40%. Even within a single zone the energy yield can differ by up to 13% depending on the location chosen for development.[6] The blended satellite data has also been used to generate a global rank of the offshore wind resource at current offshore wind farms.[7]


  1. ^ Zhang et al., 2006, Assessment of composite global sampling: Sea surface wind speed, Geophysical Research Letters, Vol. 33, L17714
  2. ^ Hollinger, J., R. Lo, and G. Poe, Special Sensor Microwave/Imager User's Guide, Naval Research Laboratory, Washington, D.C., Sep. 14, 1987
  3. ^ Kummerow, C., 1998: Beamfilling errors in passive microwave rainfall retrievals. J. Appl. Meteor., 37, 356–370
  4. ^ Wentz, F. J., and Meissner, T., 1999, AMSR Ocean Algorithm, Vol. 2 (Santa Rosa, CA, USA: Remote Sensing Systems)
  5. ^ Dunbar et al., 1991a, 1991b; Liu et al., 1998, Geophysical Research Letters, Vol. 33, L17714, 2006
  6. ^ Raynor, M., 2009
  7. ^