WindNinja Mobile

Name: WindNinja Mobile
Author: United States Department of Agriculture

United States Department of Agriculture

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Validated with strong research evidence

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App Summary

WindNinja Mobile is a professional tool for wildland fire managers and researchers that provides a mobile interface for running high-resolution simulations of terrain-influenced wind fields. An evaluation of the model's computational fluid dynamics (CFD) solver found that its predictions are significantly better than its previous solver on the windward and lee sides of terrain obstacles. The associated research concludes that by downscaling weather predictions to account for local terrain, the tool improves near-surface wind forecasts for operational applications.

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Detailed Description

Functionality & Mechanism Developed by the USDA Forest Service, WindNinja Mobile functions as a mobile interface for the WindNinja wind modeling system. The application initiates and visualizes high-resolution, spatially varying wind field simulations for a specified geographic area. It leverages multiple computational solvers, including conservation of mass (COM) and computational fluid dynamics (CFD), to model terrain-influenced wind. The interface facilitates remote execution of these complex simulations to support operational planning in wildland fire management and other environmental fields.

Evidence & Research Context

An evaluation study comparing the model's computational fluid dynamics (CFD) and conservation of mass (COM) solvers determined that the CFD solver yields superior predictions at windward and lee side locations.
The system improves near-surface wind forecasts when downscaling numerical weather predictions in complex terrain, particularly for wind direction under high-wind, near-neutral atmospheric stability conditions.
A diurnal slope flow component integrates sensible surface heat flux and terrain characteristics to simulate thermally driven winds, which demonstrates a qualitative impact on subsequent fire spread simulations.

Intended Use & Scope This system is intended for wildland fire managers, incident meteorologists, and researchers requiring high-resolution wind field simulations in complex terrain. Its primary utility is as an operational decision-support tool. The model provides diagnostic wind adjustments and does not replace comprehensive meteorological forecasts; accuracy may vary with atmospheric stability and local conditions.

Studies & Publications

3 publications

Peer-reviewed research associated with this app.

Validation Study

Development and evaluation of a Reynolds-averaged Navier-Stokes solver in WindNinja for operational wildland fire applications

Wagenbrenner et al. (2019) · Atmosphere

Accurately predicted wind patterns in complex terrain, outperforming the standard model.

An open source computational fluid dynamics (CFD) solver has been incorporated into the WindNinja modeling framework. WindNinja is widely used by wildland fire managers, as well as researchers and practitioners in other fields, such as wind energy, wind erosion, and search and rescue. Here, we describe the CFD solver and evaluate its performance against the WindNinja conservation of mass (COM) solver, and previously published large-eddy simulations (LES), for three field campaigns with varying terrain complexity: Askervein Hill, Bolund Hill, and Big Southern Butte. We also compare the effects of two model settings in the CFD solver, namely the discretization scheme used for the advection term of the momentum equation and the turbulence model, and provide guidance on model sensitivity to these settings. Additionally, we investigate the computational mesh and difficulties regarding terrain representation. Two important findings from this work are: (1) CFD solver predictions are significantly better than COM solver predictions at windward and lee side observation locations, but no difference was found in predicted speed-up at ridgetop locations between the two solvers, and (2) the choice of discretization scheme for advection has a significantly larger effect on the simulated winds than the choice of turbulence model.

Validation Study

Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

Wagenbrenner et al. (2016) · Atmospheric Chemistry and Physics

Improved wind forecasts during high-wind conditions but showed inconsistent results in variable atmospheric conditions.

Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

In the Media

AI is ready to help fight wildfires, and the 'WindNinja' is next

The National Oceanic and Atmospheric Administration (NOAA) launched the NOAA Fire Weather Testbed in June 2023 at NOAA's Global Systems Laboratory in Boulder to address mounting concerns about wildfire ferocity and the need for better wildfire technology research. The WindNinja tool represents next-generation AI-ready technology for fighting wildfires. The testbed focuses on advancing research and tools for wildfire prediction and management.

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Fighting Fire with WindNinja Mobile

More than 6.5 million acres in the United States burned during 2021, with much of the destruction fueled by the dangerous combination of wildfire and wind that changes direction, intensity, and speed throughout the day. Wind variability contributes to wildfire spread and has cost firefighters' lives. WindNinja Mobile provides critical wind prediction tools to help firefighters anticipate and respond to changing wind conditions during wildfire events.

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