Wind Maps and GIS Layers


    Product Description
    Vaisala 3TIER Services can help you understand how wind resources are distributed across any area worldwide. We tailor our approach to the needs of the client and offer everything from country and continent-wide Wind GIS layers for initial prospecting to custom spatial mapping to faciliate wind project planning and optimal met tower siting​. 

    Efficient Prospecting with GIS Data Layers

    Our Wind GIS Data Layers offer access to the most advanced wind dataset ever created and allow you to incorporate scientifically derived wind resource data directly into your own GIS mapping software. This enables in-depth analysis, including site prospecting, sorting, selection, and even project planning. Our GIS layers can be used together to gain a complete picture of wind resource potential as well as with third-party layers, such as elevation, land use, soil type, topography, transportation, and transmission infrastructure, to factor in many of the variables that affect the viability of a site. This enables you to identify promising locations more quickly, gain perspective on the risks of every potential site in your portfolio, and flag problem sites early in the process. 

    Wind Power Density GIS Data Layer Sample 

    Custom Spatial Mapping Available Globally​

    Vaisala's custom spatial maps utilitize the latest mesoscale NWP (Numerical Weather Prediction) models and can be delivered even before on-site observations have been collected. The analysis includes easily interpreted annual and monthly mean wind speed maps and the accompanying data files that can be integrated with your own internal systems for further evaluation. Spatial mapping can be performed at resolutions ranging from 4.5km down to a few meters depending on the needs of the client. On-site observations can also be statistically integrated into the analysis to enhance accuracy.

    Applying the Most Advanced Techniques Available

    Our custom spatial maps and Wind GIS Data Layers use proven, state-of-the-art techniques to deliver wind resource data anywhere in the world. To expedite GIS data layer delivery, a 5km resolution global dataset has been derived for wide-scope prospecting across states, countries, and regions. Higher resolution spatial maps, as fine as 90 meters, are developed individually for each project site and can be combined with other information, such as power density and/or power capacity factor mapping or temporal analysis.​ 

    Technical Overview

    Wind GIS Data Layers

    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 LayerAnnual Mean ValuesMonthly Mean ValuesAvailable Resolution

    Wind Speed




    Wind Direction



    Power Density




    Weibull k and A



    Multiple Format Delivery Options

    • Arc ASCII Grid
    • CSV files
    • GeoTIFFs
    • 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. Find our validation papers here.

    ​​Custom Spatial Mapping 

    ​Product Features

    • ​​Maps of ​annual and monthly wind speeds
    • ​CSV files of annual and monthly wind speeds at all grid points

    ​​​​Custom Options

    • ​​Resolution options from 4.5km down to 90m (or finer)
    • ​Data provided at any custom height between 10 - 200m ​​

    ​​​​Custom Features​

    ​​​Power Density Mapping​​

    • ​​Maps of annual and monthly mean power density and/or power capacity factor
    • ​CSV files of annual and monthly mean power density and/or power capacity factor at all grid points ​​

    Site Analysis Report​​​​

    • ​​Plot of monthly mean wind speeds
    • Histogram of hourly distribution of wind speed
    • Annual wind rose
    • Graph of annual mean diurnal cycle of wind speeds
    • Graphs of monthly diurnal cycle of wind speed for each calendar month
    • 12x24 tabular formatted data of wind speed values
    • Tabular formatted data of mean wind speed and Weibull parameters for each wind direction

    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

    ​I​​f you are unfamiliar with the technical terminology on this page, please visit our Glossary for more information.