Extensive, archived data provides a historical perspective of wind conditions across the entire project site, overcoming limitations of on-site data.

WindLogics believes that our knowledge of a location improves with the amount of data available for analysis. We employ archived, long-term weather data in our methodologies to improve the value of measured wind data and better understand the wind variability at a specific location.

Weather data (including wind data) is collected by national weather services from a wide variety of sources. These services then produce three-dimensional gridded data sets using a physics-based assimilation process, blending the data using physically consistent processes. The result is a valuable source of past weather conditions across time and space – going back over 40 years and covering the globe.

WindLogics recognized the value of this information years ago and has organized and retained terabytes of three-dimensional weather data, providing an unmatched long-term history of the weather. To study a site, WindLogics uses this exclusive archived weather data, plus data compiled by the National Centers for Environmental Prediction and other public sources to analyze and derive long-term results. Our primary data sources include:

WindLogics Archives

  • Worldwide – horizontal grid at 50-100 km gridpoint spacing, from the surface up to 15,000 meters above ground surface, and every 3 hours since 2005
  • North America – horizontal grid at 20 km spacing, from the surface up to 15,000 meters above ground surface, and every hour since 2001

Reference Data Sources

  • National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis Data – global grid at 250 km spacing, every 6 hours for 60 years
  • North American Regional Reanalysis (NARR) – grid over North America at 32 km spacing, every 3 hours for 25 years
  • Integrated Global Radiosonde Archive (IGRA) – global at 1,500 balloon-sounding locations, every 12 hours for 68 years (varies by station)

Using this archived data, in combination with our sophisticated weather models, we are able to study the most remote locations – even those without on-site data.