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Research

Wake vortices from large aircraft are a major factor limiting landing and take-off capacity at commercial airports. This has implications for cost, fuel efficiency, and airport operations. One way to increase landing capacity is to reduce the distance between departing and arriving aircraft. Such decrease in distance comes with the risk of unsafe runway conditions in the case that the wake vortices from a preceding aircraft have not sufficiently dissipated. In current airport operations, there are no operational systems for measuring wake vortices. Rather, air traffic control separates aircraft based on rules specified by local and regional aviation administrations.

Wake vortex from airplane

Climate change is increasing the frequency and intensity of rain and wind producing storms with measurable impacts on infrastructure, water resource management, warning communications, and public safety. These challenges can be mitigated by new multi-sensor, high-resolution sensing that can identify short-, medium-, and long-term risks and communicate these new kinds of risks effectively for public safety and economy.  Using a combination of nowcasting and machine learning methods, we are partnering with industry to combine high resolution pressure, temperature, and humidity fields with high-resolution radar data to provide geographically targeted forecasts of rain intensity and high winds.

Car in flooded underpass

CityWarn is a context-aware warning and prediction platform that links high-resolution data to warning, response, and impact developed through NSF funding.

Interface showing alerts for UAV demo flight

Among various considerations that affect path planning, weather may have the most significant
impact, as Unmanned Aerial Vehicles (UAVs) are sensitive to weather conditions such as wind, temperature, and precipitation. Our objective is to address this need by developing a decision support system for UAV path planning under the consideration of stochastic weather evolution. 

Our proposed model is dynamic and data-driven, and allows for safe and effective path planning while also minimizing any involved costs during each mission.

Drone in weather

The Dallas Fort Worth Living Lab is a research platform for advancing severe weather warning systems through active collaboration and partnerships with emergency managers, stormwater managers, and the National Weather Service forecasters in the Dallas Fort Worth metroplex. The lab operates a sensors-to-people severe weather warning system that functions simultaneously as a real time warning system and as a platform for advancing interdisciplinary research.  The Living Lab is centered on a network of 7 CASA X-band radars, but includes rain gauges, disdrometers, and infrasound sensors.   Over the last 10 years, the lab has been operated through an innovative multisector partnership, among University of Massachusetts Amherst, Colorado State University, the North Central Texas Council of Governments, the National Weather Service,  emergency managers, and industry.  The North Texas based partners fund the operation of the radar network, while the academic partners leverage the living lab for grants that advance the technology and its application.  

DFW Living Lap Map

Climate research holds immense significance due to its direct impact on various aspects of our lives and the environment. As a result, there has been a consistent rise in the deployment of atmospheric sensors (e.g., weather radars, satellites, etc.), leading to a substantial increase in data volumes that need to be efficiently distributed to the cloud. This project will investigate

the implementation of distributed machine learning for climate modeling and weather forecasting. It is our goal to better support climate researchers with scientific workflows that will enable them to develop better weather warning and forecast systems.

Nowcasting Sequence