Weather Impacts on Unmanned Aerial Systems


Unmanned aerial vehicles (UAVs) are rapidly becoming an important component of the national airspace, especially at low altitudes (less than about 1,500 ft or 450 m AGL).  To cope with the ever-increasing UAV traffic demands NASA is developing the Unmanned Aerial Vehicle Traffic Management System (UTM), which includes separation management, scheduling, demand capacity imbalance, contingency management, trajectory definition and prediction, and importantly, wind and weather.  Since many UAVs are very small in size they are quite susceptible to turbulence- and gust-induced loss-of-control and trajectory excursions.  Therefore, these weather-induced excursions are an important weather factor to consider in the management system is atmospheric turbulence.

Over the past two years RAL scientists have worked under a NASA-funded program aimed at (1) establishing  automated means for forecasting turbulence and gust levels in the atmosphere that affect UAVs, and (2) developing and testing translation algorithms to provide a turbulence hazard metric given the state of atmospheric turbulence. 

Task 1 involves modifying NCAR’s Graphical Turbulence Guidance (GTG) (Sharman et al. 2006, 2017) which has been operationally available since 2003 on NOAA’s Aviation Digital Data Service (ADDS), hosted by the National Weather Service’s Aviation Weather Center (AWC) and is available 24 x7 at   Uniquely, the GTG product provide forecasts and nowcasts of atmospheric turbulence intensity levels in terms of a well-known turbulence intensity metric known as the energy (or eddy) dissipation rate (EDR).  EDR is the International Civil Aviation Organization (ICAO) mandated metric for atmospheric turbulence, but computing EDR for operational use requires input from numerical weather prediction (NWP) models, which are too coarse to properly resolve atmospheric eddies that affect UAVs.  So the major emphasis has been on developing techniques to properly quantify EDR at low levels from relatively coarse NWP model output. 

Task 2 takes the output EDR from Task1 and maps this atmospheric metric to an aircraft (i.e., UAV) specific response.  For traditional winged systems, given information about the vehicle characteristics (e.g., stability derivatives, or size, weight, airspeed, and wing surface area), the turbulence response can be predicted using standard aerodynamic modeling methods.

FY2017 Accomplishments: Task 1   

With the goal of improving low-level turbulence (LLT) forecast capabilities of the Graphical Turbulence Guidance (GTG) model, efforts have focused on: 1) Development of an observational database of LLT energy (or eddy) dissipation rates (EDR) for model validation and development, 2) In-depth analysis of WRF mesoscale capabilities to forecast EDR in the lowest 300 m, and 3) Development of a prototype GTG-LLT algorithm with a specific focus on the near-surface region where UAVs operate.

In order to better understand LLT behavior for UTM applications, sonic anemometer data from the XPIA campaign (Lundquist et al. 2016) was employed, corresponding to the three months of the campaign (March-May 2015). During the XPIA campaign, the Boulder Atmospheric Observatory (BAO) tower was enhanced to have a total of 12 three-dimensional sonic anemometers located every 50 m from 50 to 300 m height above ground level (AGL).  Probability distributions of EDR from the three months of the XPIA campaign showed distinctly different behavior of atmospheric boundary layer turbulence during stable and convective conditions.  During the nighttime, the distribution of EDR bears a log-normal distribution at all heights, but daytime distributions are skewed toward higher EDR values, and fits well to a log-Weibull distribution.

The current GTG implementation (Sharman et al. 2017) is calibrated using the WRF RAP forecast product, which has a horizontal grid spacing of ~13 km assuming a log-normal distribution of EDR.  However for this application we used the High-Resolution Rapid Refresh (HRRR) NWP product, with a horizontal resolution of 3 ~km. Since the great majority of the turbulent indices depend upon spatial gradients of the NWP forecast, a change in the base NWP model required re-calibration of the turbulent indices, and takes into account the differing day and night conditions.

Task 2 Accomplishments

UAS longitudinal response to wind fields – including an autopilot model

The open loop longitudinal vehicle equations of motion (EOM) from the earlier work were updated to include an autopilot model. This autopilot is an altitude hold model, with inner loops controlling both pitch and pitch rate.

Figure 1. Open and closed loop UAS pitch time history response to unit-step elevator change.

Figure 1. Open and closed loop UAS pitch time history response to unit-step elevator change.

Figure 1 shows UAS open (black curve) and closed (red curve) loop time history response for pitch angle due to a unit step change in the elevator angle (green line). The large amplitude oscillatory behavior of open loop response is mainly due to the aforementioned phugoid mode, and it can be seen how the pitch and pitch rate autopilot does an excellent job of damping that response.

Figure 2. Open and closed loop UAS height time history response to 5 m/s vertical wind gust.

Figure 2. Open and closed loop UAS height time history response to 5 m/s vertical wind gust.

Figure 2 is similar to 1, but now showing a time history of height response to a 5 m/s vertical wind gust. It can be seen that, without an autopilot, the UAS quickly changes height by around 25 m, and then shows a slowly damping phugoid oscillation around that that height. On the other hand, the UAS with autopilot quickly damps the height change and returns to its starting value. This case is of interest as it sheds some light on an updraft incident during one of the NASA UTM field tests. A UAS, whose only height-following mode was based on thrust, (i.e., increasing or decreasing lift by changing the propeller speed), encountered an updraft (or is assumed to have done that) and quickly rose well above its assigned altitude. The problem was that the updraft overwhelmed the vehicle’s ability to use thrust to maintain altitude; and hence, it responded as if there was no control system, i.e., the thrust went to zero and still the vehicle rose tens of meters. This reinforces the issue of the need to model vehicle response with the inclusion of whatever control system is in place.

Proposed Work for FY18

Develop an automated wind, wind gust and turbulence forecast/nowcast system for UAVs.  We will continue to improve and validate turbulence forecast models with observational data to determine the balance between accuracy, precision, and execution time.

UAS impact metrics and translation algorithms Develop relevant UAV impact metrics based on steady and unsteady winds for various platforms and verify with UAV developers. The intention of this task is to provide NASA with translation algorithms which take as input the HRRR fields, turbulence diagnostics (GTG), and small-scale modeling results from Task 1 and outputs vehicle-specific impact products. Verification using field test data will be performed if such data is available.

Develop an initial UTM Weather Concepts document. This is a user interaction task, which focuses on the development of an initial UTM Weather Concepts document.  This will be developed in collaboration with a UTM Weather Advisory group and the NASA UTM group. The advisory group will contain a cross-section of members from the UTM community, including public organizations, private companies, and research organizations.