Wind Maps and GIS Layers
Understand how wind resources are distributed across any location worldwide - whether it is an entire country or a specific 100-acre plot along a ridgeline. By incorporating scientifically derived wind resource data directly into your own internal tools and GIS mapping software, you can improve both the efficiency of your wind prospecting and the quality of your early decision making.
Vaisala offers GIS formatted data for key wind variables including: wind speed, wind direction, power density, and Weibull k and A. This information is available at 20, 50, and 80m hub heights.
|GIS Data Layer||Annual Mean Values||Monthly Mean Values||Available Resolution|
|Weibull k and A;||X||15km|
Multiple Format Delivery Options
- Arc ASCII Grid
- CSV files
- KMZ files
We produced our dataset by implementing an innovative physics-based NWP (Numerical Weather Prediction) modeling approach. Unlike traditional models that merely interpolate observed wind speeds between widely dispersed points, our system simulates the interaction between the entire atmosphere and the earth's surface, to create a more robust and accurate wind climatology. This technique captures the myriad processes responsible for wind—from jet level dynamics to surface level processes and everything in between. Using proven, state-of-the-art methods supported and continually enhanced by the global atmospheric science and research community, Vaisala is able to create realistic wind fields throughout the world.
Global Dataset Highlights
- Mesoscale 10-year WRF (Weather Research and Forecasting) model run
- Model constrained by high-quality inputs from the NCAR/NCEP reanalysis, which incorporates real observational data
- Validated by 4000 NCEP-ADP network stations worldwide
- Validation Results: The difference between annual mean wind speed data provided by Vaisala and actual on-site measurements from NCAR/NCEP reanalysis is less than 0.5 m/s at 50% of the observational stations and less than 1 m/s at 78% of the stations. The overall bias is +0.05 m/s relative to NCEP-ADP observations, and the RMSE (Root Mean Square Error) is 0.93 m/s.
Vaisala's wind spatial mapping is based on the latest atmospheric science techniques and involves running a mesoscale NWP model for an entire project area. We primarily employ an NWP model called WRF (Weather Research and Forecasting). This model is widely supported and continually enhanced be the global atmospheric science and research community.
For spatial mapping purposes, the WRF Model is run across the specified domain, producing a single calendar year of data where each individual day of that year has been drawn from the last 10 years. This methodology produces wind speed values that are representative of the long-term mean conditions while maintaining the seasonal cycle of the wind resource. When observational data are available, Vaisala can run the WRF model for a time concurrent with the observations to create a corrected map using a Model Output Statistics (MOS) process, which removes bias and reduces Root Mean Squared Error (RMSE). WRF can be run at horizontal resolutions from 4.5km to 500m. We can also downscale the data to a horizontal resolution of 90m or finer using a microscale diagnostic model called TVM.