Unmanned Aerial System (UAS) Weather Research

Background

Figure 1. Predicted wind speed (overlaid on terrain height) and (right) turbulence over the Saguache River Canyon in the northwestern portion  of the San Luis Valley during the transition from stable to convective boundary layer on a clear, weakly forced weather day in July 2017. Obtained with 100 m grid spacing meso-to-microscale model simulation.
Figure 1. Predicted wind speed (overlaid on terrain height) and (right) turbulence over the Saguache River Canyon in the northwestern portion  of the San Luis Valley during the transition from stable to convective boundary layer on a clear, weakly forced weather day in July 2017. Obtained with 100 m grid spacing meso-to-microscale model simulation.

The age of unmanned systems is upon us, both in the skies and on our highways! The number of small (weighing between 0.55 and 55 lbs) commercial Unmanned Aerial Systems (UAS) operating over the U.S. is expected to increase exponentially with time, potentially exceeding 1.6 million by 2021. The Federal Aviation Administration (FAA), NASA, NCAR, and industry leaders are working toward enabling the safe integration of UAS into the National Airspace (NAS) in support of developing a UAS Traffic Management (UTM) system. A key component of integrating UAS into the NAS is to demonstrate their safe flight in the presence of hazardous weather conditions. To meet this goal, NCAR is coupling the latest weather technologies with new algorithms that translate detailed weather information into UAS-specific performance impacts. These new systems can be tailored for specific UAS applications to aid in both flight planning and in-flight decision making.

Because of their light weight, slower air speed and limited endurance, most small UAS are much more susceptible to variations in wind speed/direction and turbulence occurring on much finer scales than that impacting commercial aircraft.  Sudden changes in wind speed and direction can render small UAS unrecoverable if the resulting head winds exceed performance characteristics. At the same time, UAS trying to maintain level/smooth flight in areas of more intense turbulence can run out of power more quickly than anticipated. 

FY2018 Accomplishments

Figure 2. Comparison of the timeseries of (left) simulated winds and (right) winds obtained with a Doppler lidar wind profiler at Saguache Municipal Airport (04V) during ISARRA on 17 July 2018.
Figure 2. Comparison of the timeseries of (left) simulated winds and (right) winds obtained with a Doppler lidar wind profiler at Saguache Municipal Airport (04V) during ISARRA on 17 July 2018.

In an attempt to mitigate these operational constraints, we employed a fine-scale realtime modeling capability to provide guidance on the fine-scale variability of winds and the location, intensity and duration of turbulence as shown in Figure 1. The WRF-LES system was run in realtime on the NCAR supercomputer using 2124 processors to produce forecasts twice per day to support UAS flight planning during the week-long ISARRA (International Society for Atmospheric Research using Remotely-piloted Aircraft) LAPSE-RATE field project which took place in the San Luis Valley of Colorado in July 2018.

Figure 3. Comparison of (left) modeled wind speed (green) and observed wind speed (black) and (right) model (u – green, v – magenta) and UAS-observed wind components (u – black, v – red) at 100 m AGL. UAS winds were obtained with University of Kentucky fixed-wing UAS flying stacked profiles at the south end of the runway at Saguache Airport (04V) on 19 July 2018.
Figure 3. Comparison of (left) modeled wind speed (green) and observed wind speed (black) and (right) model (u – green, v – magenta) and UAS-observed wind components (u – black, v – red) at 100 m AGL. UAS winds were obtained with University of Kentucky fixed-wing UAS flying stacked profiles at the south end of the runway at Saguache Airport (04V) on 19 July 2018.

This system was adapted from RAL’s WRF-LES used to provide support for wildland fire management in Colorado. Output from WRF-LES was tailored to support UAS operations using the NCEP Unified Post Processor (UPP) to produce a set of variables (e.g., ceiling, visibility, TKE, wind gust strength) needed for decision-making by UAS operators. Data from the WRF-LES simulations, was interpolated to a set of flight levels with vertical resolution of roughly 50 m. A realtime data display was implemented using RAL-developed extensions to the OpenLayers mapping library. This display capability enables quick viewing of several key variables that were used in mission planning.

Atmospheric measurements obtained with both ground-based sensors and UAS during ISARRA LAPSE-RATE are being used to evaluate the WRF-LES forecasts. Drainage flows of varying intensity were observed on most nights during the week-long field experiment. An example comparison of modeled winds and those obtained with CU’s Doppler Lidar is shown in Figure 2 for a weak drainage flow event observed at the mouth of Saguache Canyon. In this example the duration, depth and strength of the modeled drainage event were all in excellent agreement with the observations. Wind and turbulence data collected during coordinated UAS flight missions are also being used to evaluate the model forecasts. An example comparison shown in Figure 3 reveals that the predicted drainage flow on 19 July 2018 was generally too strong but the predicted weakening trend and timing of wind reversal was well captured.

Figure 4. Planned flight path overlaid on wind speed (m s-1) and direction (arrows) obtained for test case run for planned UAS transit corridor in Upstate NY. Simulation results are from the inner grid of WRF-LES model with 100 m grid spacing and (right) expected winds and turbulence along planned flight path with an assessment of predicted battery life for a standard small UAS. Orange segments indicate times when the predicted turbulence increases the battery discharge rate. Current position of UAS on the simulated transect is denoted by the red triangles indicating the expected wind/turbulence and remaining battery charge at 1h25m into the flight.
Figure 4. Planned flight path overlaid on wind speed (m s-1) and direction (arrows) obtained for test case run for planned UAS transit corridor in Upstate NY. Simulation results are from the inner grid of WRF-LES model with 100 m grid spacing and (right) expected winds and turbulence along planned flight path with an assessment of predicted battery life for a standard small UAS. Orange segments indicate times when the predicted turbulence increases the battery discharge rate. Current position of UAS on the simulated transect is denoted by the red triangles indicating the expected wind/turbulence and remaining battery charge at 1h25m into the flight.

Finally, translation models are being developed to assess the impact of forecasted winds and turbulence on planned UAS missions as well as inflight. Figure 4 show timeseries of wind speed and turbulence that are predicted to occur along a planned UAS flight route. The aircraft’s planned position at 1h25m into the flight is depicted with a red triangle. In this example flight planning tool, the UAS’s ground speed is predicted using the modeled wind field and UAS’s typical airspeed. The UAS battery discharge rate is computed using standard relationships between power consumption and thrust with a simple model being used to relate the battery discharge rate required for aircraft trust responses to changes in atmospheric turbulence. As seen in Figure 4, battery discharge rate increases when the UAS flight plan intersects areas of more intense turbulence. This type of information can be used by operators for flight planning and by UTM to determine the most efficient routing structures and UAS spacing along congested flight routes. 

Plans for FY2019

Work will continue to utilize the myriad of observational datasets collected during ISARRA LAPSE-RATE to evaluate and improve WRF-LES modeling system. Work will be performed to develop UAS that are weather aware. That is, data from WRF-LES will be used by UAS for in-flight decision making. At the same time, analyses will be performed to evaluate the impact of assimilating UAS data on forecast skill. Finally, work will continue on assessing the impacts of turbulence and finescale variability in winds on UAS performance to aid in improving operating efficiency and safety.