Industries & Applications
Advancing fire weather research: testing ultrasonic wind sensors in the field
Limitations of mechanical wind sensors in mobile fire research
Background
According to data analysed by NASA scientists, wildfires are becoming more frequent, more intense, and larger in scale. San José State University’s Fire Weather Research Laboratory and Wildfire Interdisciplinary Research Center support a wide variety of research initiatives and projects. The Lab’s main mission is to conduct field studies of fire-atmospheric interactions so that extreme fire behaviour and fire weather can be better understood. WIRC uses this information to develop new prediction and observational tools to support wildland fire operations.
Understanding wind is fundamental to understanding fire behaviour. Most of the time, fires follow the wind. But in the case of plume-dominated fires, those that generate massive vertical plumes, the fire itself begins to influence the surrounding atmosphere. These intense fires can become powerful enough to create their own weather systems, including thunderstorm clouds and, in some cases, even real tornadoes. These are the types of extreme fire behaviours that the team is focused on understanding.
Challenge
The SJSU Fire Weather Research Laboratory is equipped with an extensive range of research instruments, facilities, and vehicles to support its fire weather and fire behaviour studies. Among these are a mobile radar truck and a mobile LIDAR truck, both of which are regularly deployed to active wildfires.
The LIDAR sensors allow researchers to peer into smoke plumes and capture their 3D structure. This is crucial for understanding the internal kinematics of major wildfire plumes, how they behave, how they influence surrounding winds, and how they draw in air to sustain and propel themselves.
In addition to the LIDAR system, the truck also featured a mobile weather station that included a traditional prop-and-vane wind sensor capable of measuring wind speed, temperature, humidity, and pressure.
However, this mechanical wind sensor presented challenges in mobile applications. It tended to perform poorly in turbulent airflow conditions, where wind came from multiple directions. In such cases, the vane aligned with the dominant wind direction, rather than accurately averaging inputs from various angles. For instance, if the truck was moving forward through a mix of crosswinds and headwinds, the sensor would primarily register the head-on wind, failing to reflect the true wind environment around the vehicle.
To address this, the team added GPS data to log the vehicle’s motion, enabling them to subtract that movement from the sensor readings. The goal was to isolate and identify the actual ambient wind conditions by removing the vehicle’s own influence.
Yet, even with this correction, the traditional wind sensor remained limited. Because of the design of the vane – always aligning with the dominant wind direction – it still skewed the data when the vehicle was moving faster than the surrounding wind, leading to inaccuracies in determining true wind vectors in complex wildfire environments.
Solution
To improve wind measurements during wildfire operations, the team installed a Gill ultrasonic anemometer on the LIDAR truck to compare its performance alongside the traditional mechanical wind sensor.
Nick Perlaky, Graduate Researcher, explained:
“The WindSonic measures whatever airflow comes into it, enabling us to better capture a combination of vehicle motion and outside air. When we’re out at a fire, we like having this sensor running to get a real-time sense of ambient surface wind.”
He described a typical deployment scenario:
“Typically we like to set up within about a mile of a fire. This allows us to measure how the plume might be pulling in surface winds. As the fire grows, does wind speed pick up? Does it change direction? This sensor gives us another valuable data point when we’re collecting field measurements.”
A clear example of the benefits of the Gill ultrasonic anemometer came during a brief test drive. As the truck entered the interstate and encountered a new airflow pattern from the northwest, different from what it had experienced on city streets, changes in wind direction became more pronounced (see spikes highlighted in graph).
This is where the advantages of the WindSonic sensor became evident.
Nick noted:
“Going around curves is one of the biggest challenges for the traditional anemometer. As the vehicle turns and the wind direction shifts, the mechanical sensor struggles to adapt. It flops between the old and new wind directions, causing it to bounce erratically. That results in wildly inaccurate corrected wind speeds, sometimes 50 meters per second, which is clearly incorrect.”
While some level of data cleaning is possible to remove outliers, there’s a limit to how much erroneous data can be filtered without compromising real measurements. In contrast, the ultrasonic sensor provided much more stable and realistic results, showing how the ultrasonic system handles direction changes better, especially during turns or while getting on and off highways.
The data variance supported this too: while the mechanical sensor showed direction variability around 45 meters per second, the Gill ultrasonic sensor’s variance was only around 2 meters per second, a significantly tighter and more reliable result.
Looking ahead
Looking to the future, Nick sees broader applications for the WindSonic ultrasonic anemometer:
“I’d like to install the Gill ultrasonic anemometer on a new static weather station we’re about to deploy out at a State Park because one of the challenges with traditional propeller anemometers is that they require recalibration every three years”.
That recalibration process is time-consuming and equipment-heavy. The sensors must be removed from the field, placed in a wind tunnel, and tested at calibrated speeds and directions to verify their accuracy. Mechanical components like bearings can wear out, and the internal sensor that tracks the propeller’s rotation can warp or bend over time – introducing error into wind measurements.
With the ultrasonic sensors, there aren’t any moving parts that can wear down. That makes them especially useful in high-wind environments or places where birds might land on them. There’s just less that can go wrong.
By deploying advanced instruments like ultrasonic anemometers in the field, the research team is able to gather precise data that can be used to refine predictive models.
Recommended wind sensors
Gill WindSonic is a low-cost 2-axis ultrasonic anemometer, providing wind speed (0-60m/s) and direction data in a robust housing. This anemometer has no moving parts, offering maintenance-free operation in a wide range of applications.
WindUltra is Gill’s smallest and lightest anemometer. It has an extremely robust design, has been aggressively tested to IP69k, provides high accuracy measurement, and is easy to install and use.