Researchers at the US National Science Foundation National Center for Atmospheric Research (NSF NCAR) are developing a new model to predict low-altitude winds with improved accuracy and granularity.
The capability would provide decision-makers and emergency responders with neighborhood-level insights, enabling them to better prepare for and respond to complications caused by wind events, such as wildfires and power disruptions.
Current weather models lack the resolution to capture key features of the local topography that can influence near-surface winds. For predictions to be accurate at the neighborhood scale, man-made structures and natural elements such as buildings and trees must be accurately modeled.
Over the next five years, NSF NCAR researchers will collaborate with researchers at the University of Kentucky. To model near-surface winds with great accuracy, the team is primarily leveraging two tools: a model called FastEddy that is being developed at NSF NCAR and observations obtained with a fleet of uncrewed aircraft systems (UAS) owned by the University of Kentucky.
James Pinto, NSF NCAR scientist and principal investigator of the project, said, “One of our goals is to enable support for real-time wildland firefighting as well as pinpoint what parts of the energy distribution network are most likely to be impacted by a high-wind event to inform more strategic power shutoffs.”
He added that the research enables the team to “simulate the influence of a collection of buildings or a canopy of trees on low-altitude winds and turbulence in real time.”
The project is titled LEAP-HI: Advancing Precision Neighborhood Scale Weather Forecasting with Autonomous Aircraft Systems and Adaptive Microscale Models. It is funded through NSF’s Leading Engineering for America’s Prosperity, Health, and Infrastructure (LEAP-HI) program.
FastEddy is a model that predicts temperature, humidity and winds at resolutions so fine that it can capture swirling pockets of wind. It can use either idealized wind conditions or real-world forecast outputs to drive the simulations, which are run on the Derecho supercomputer at the NSF NCAR-Wyoming Supercomputer Center.
Small errors in the mode and forecast data can have a significant impact when predicting low-altitude winds in real-world simulations. It is hoped that utilizing the model in conjunction with UAS observations will remedy this.
The UAS measurements will be combined with the micronetwork of turbulence sensors distributed across the University of Kentucky’s flight facility to better predict wind and turbulence.
The FastEddy model simulations will be evaluated against the observations. If differences between the predictions and observations still exist, the new observations will be used to tweak the inputs and parameters that drive FastEddy and further improve its predictions.
The research team hopes that the research will lead to the accurate prediction of low-altitude wind to a resolution within two meters. Once the researchers have established the technique to improve the accuracy of the simulations, they will perform additional testing at other locations with more complex terrain and land surface characteristics.
This type of prediction tool is also highly sought after in the emerging urban air mobility (UAM) industry. As government agencies and private companies look at developing UAM operations that could move people and cargo within urban environments via drones and air taxis, the FastEddy model could help guide infrastructure planning, operational efficiency and safety.
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