WindNinja is a computer program that computes spatially varying wind fields for wildland fire application.

See the WindNinja project website for more details.

Also, check out WindNinja-Mobile application.

Take a look at our WindNinja story map to see a quick overview.

Wind is one of the most influential environmental factors affecting wildland fire behavior. The complex terrain of fire-prone landscapes causes local changes in wind speed and direction that are not predicted well by standard weather models or expert judgment. WindNinja was developed to help fire managers predict these winds.

WindNinja is a computer program that computes spatially varying wind fields for wildland fire and other applications requiring high resolution wind prediction in complex terrain. It was developed to be used by emergency responders within their typical operational constraints of fast simulation times (seconds), low CPU requirements (single processor laptops), and low technical expertise. WindNinja can be run in three different modes depending on the application and available inputs. The first mode is a forecast, where WindNinja uses coarser resolution mesoscale weather model data from the US National Weather Service to forecast wind at future times. The second mode uses one or more surface wind measurements to build a wind field for the area. The third mode uses a user-specified average surface wind speed and direction. Other required inputs for a WindNinja simulation include elevation data for the modeling area (which WindNinja can obtain from Internet sources), date and time, and dominant vegetation type. A diurnal slope flow model and non-neutral atmospheric stability model can be turned on or off. Outputs of the model are ASCII Raster grids of wind speed and direction (for use in spatial fire behavior models such as FARSITE and FlamMap), a GIS shapefile (for plotting wind vectors in GIS programs), and a .kmz file (for viewing in Google Earth). WindNinja is typically run on domain sizes up to 50 kilometers by 50 kilometers and at resolutions of around 100 meters. WindNinja runs on 32- and 64-bit versions of Windows XP and later operating systems and can also be run on versions of Linux, however building from source code is required (see Building WindNinja with CMake). 

Note: Software developers and researchers can visit the WindNinja Project Development website.

Software Installation Notes and Instructions:

  • Forest Service users: If you are installing on a Forest Service imaged machine and run into permission issues during your installation, try installing to this location: C:\FireApps
  • A Windows Firewall warning may appear the first time the application is launched, but can be dismissed and the simulation will run normally.
  • Hardware recommendations: Minimum: 4 GB RAM, 2.0 GHz processing speed. Recommended: 8 GB RAM, 2.4 GHz processing speed
  • Older versions of WindNinja DO NOT need to be uninstalled before installing this version.
  • Once installed, WindNinja can be started from Start > Programs > WindNinja-x.x.x > WindNinja-x.x.x.
  • There are 4 tutorials that can be opened from Start > Programs > WindNinja-x.x.x > Tutorial. These are strongly recommended for new users.

For incident support:
see FAQ for frequently asked questions,
see the change log for release notes,
or contact the WindNinja Incident Support Team at

Select Publications & Products

Wagenbrenner, NS, Forthofer, JM, Lamb, BK, Shannon, KS, Butler, BW, (2016) Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja. Atmos. Chem. Phys. 16:5229-5241, doi:10.5194/acp-16-5229-2016.

Forthofer, JM, Butler, BW, Wagenbrenner, NS, (2014) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements. Int. J. Wildland Fire, 23:969-931. doi:10.1071/WF12089.

Forthofer, JM, Butler, BW, McHugh, CW, Finney, MA, Bradshaw, LS, Stratton, RD, Shannon, KS, Wagenbrenner, NS, (2014) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part II. An exploratory study of the effect of simulated winds on fire growth simulations. Int. J. Wildland Fire. 23:982-994. doi:10.1071/WF12090.

Forthofer, JM, Shannon, KS, Butler, BW (2011) Initialization of high resolution surface wind simulations using National Weather Service (NWS) gridded data. In '11th International Wildland Fire Safety Summit. Missoula, MT', April 4-8, 2011. (International Association of Wildland Fire)

Forthofer, J.; Shannon, K.; and Butler, B. 2009. Simulating diurnally driven slope winds with WindNinja. In: Proceedings of 8th Symposium on Fire and Forest Meteorological Society; 2009 October 13-15; Kalispell, MT (2,037 KB; 13 pages)

Forthofer, J. M. 2007. Modeling wind in complex terrain for use in fire spread prediction. Fort Collins, CO: Colorado State University, Thesis. (528 KB; 123 pages)

Forthofer, J.; Butler, B. 2007. Differences in simulated fire spread over Askervein Hill using two advanced wind models and a traditional uniform wind field. In: Butler, B. W.; Cook, W. comps. The fire environment--innovations, management, and policy; conference proceedings; 2007 26-30 March: Destin, FL. Proceedings RMRS-P-46CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: 123-127. (581 KB; 5 pages)