Weather Impacts on Emerging Modes of Aerial Transportation

BACKGROUND

Unmanned aerial systems (UAS) and urban air mobility (UAM) are emerging as new and innovative modes of air transportation. Small UAS (weighing less than 55 lbs.) are increasingly performing all manner of commercial operations, including delivery of medical supplies, inspection of pipelines and railway tracks, surveilling mining operations, crop monitoring, search and rescue, public safety and numerous emerging applications. UAM is gaining attention as a futuristic mode of aerial ride-sharing to cross metropolitan areas through the airspace, thus avoiding congestion on roads.

The sensitivity of aviation to weather hazards increases as the size of an aircraft decreases. Moreover, particular challenges loom for these emerging modes of aerial transportation in complex terrain and areas of strong surface characteristic changes (e.g., land/sea contrasts), near thunderstorms, and in urban environments. NCAR is helping the FAA, NASA and industry leaders to appreciate the weather challenges that UAS and UAM operations are facing, and we’re developing relevant micro-weather prediction capabilities to effectively guide such operations, ultimately geared towards safe integration of UAS and UAM aerial operations into the National Airspace System (NAS).

This summary describes activities related to evolving unmanned aerial transportation through an improved understanding of potential weather impacts and improvements in their prediction over what is currently possible with today’s operational weather prediction systems. The following sections report on the development of new systems and products to support small UAS operations, the translation of turbulence into impacts on small UAS, and work to provide weather products to support UAM development efforts.

MICRO-SCALE WEATHER PREDICTION FOR UNMANNED AERIAL SYSTEMS

A key component for integrating UAS into the NAS is to ensure and demonstrate their safe flight during hazardous weather conditions. To meet this goal, under NASA and NSF funding, NCAR is coupling the latest weather prediction capabilities and data assimilation techniques to generate predictions of winds, turbulence, and other aviation weather hazards (fog, icing) at scales relevant to support UAS operations that will ultimately feed into Unmanned Traffic Management (UTM) systems.  Coupled with the impacts-translation modeling discussed below, these forecasts will provide critical information for flight planning, as well as in-flight decision making.  

FY2019 Accomplishments

Work proceeded on a number of fronts this past year in developing weather forecast guidance to support UAS flight planning and operations.  RAL’s Graphical Turbulence Guidance (GTG) product was adapted to use HRRR data as input to provide short-term predictions of low-level turbulence (LLT) for NASA-UTM testbeds in Reno, San Francisco, and South Texas. RAL’s realtime mesoscale-to-microscale prediction system, which was conducted over the San Luis Valley of Colorado during ISARRA LAPSE-RATE in July 2018, continues to undergo rigorous evaluation with preliminary results in various stages of publication.  In addition to using UAS data collected to evaluate the prediction system, studies are underway to assess the value of assimilating UAS data to improve the simulations.

The GTG-LLT algorithm was implemented within the framework of the GTG model. A one-yr-long GTG LLT calibration was performed using the High-Resolution Rapid Refresh operational (HRRR) model, and optimum GTG ensembles of turbulence indices for clear-air and mountain-wave turbulence that minimize the mean absolute percentage error (MAPE) were determined. Also included were new turbulence indices specific to atmospheric boundary-layer turbulence. The results from this work have been published in Muñoz-Esparza and Sharman (2018). This algorithm was implemented and run in realtime to translate HRRR data into realistic estimates of turbulence intensity. A user-friendly display was developed that allowed users to view GTG-LLT turbulence forecasts. Figure 1 shows a screenshot displaying Eddy Dissipation Rate (EDR) forecasts for each of the NASA UTM test regions (Reno, San Francisco, and South Texas). The data, along with other relevant meteorological variables provided from the HRRR (e.g., wind speed, wind direction, wind gust, ceiling, visibility, flight category, precipitation rate, and ground elevation) aided in UTM planning and decision-making.

Figure 1:  Screenshot of EDR at 150 m AGL obtained from the realtime GTG-LLT display developed to support NASA UTM field campaigns at three NASA UTM Test Sites: Reno, San Francisco and South Texas.
Figure 1:  Screenshot of EDR at 150 m AGL obtained from the realtime GTG-LLT display developed to support NASA UTM field campaigns at three NASA UTM Test Sites: Reno, San Francisco and South Texas.

While HRRR-based guidance gives a good depiction of the mesoscale environment and associated potential for turbulence, much finer-scale simulations are needed to provide more accurate guidance on winds and turbulence relevant to small UAS flight planning. The Figure 2 provides an example of the information gain possible with mesoscale to microscale coupling. In the case shown below, the HRRR model is not able to capture fine-scale variations in low-level winds associated with nocturnal drainage flows that formed on a daily basis during the summer 2018 LAPSE-RATE field experiment that took place in the San Luis Valley of Colorado. The HRRR is also unable to capture the full dynamic range of wind speeds on this day when compared with the microscale simulation (Figure 2). These issues are of critical importance in flight planning for small UAS.

Figure 2. Wind speed and direction obtained from (left) HRRR and (right) WRF-LES using 100-m grid spacing for the San Luis Valley of Colorado. The AWOS station 04V marks the location of UAS observations shown in Figure X3.
Figure 2. Wind speed and direction obtained from (left) HRRR with 3 km grid spacing and (right) WRF-LES using 100-m grid spacing for the San Luis Valley of Colorado. The AWOS station 04V marks the location of UAS observations shown in Figure 3.

While these comparisons reveal the sensitivity of key atmospheric quantities to model resolution, the fidelity of the fine-scale simulations is still under investigation. UAS observations obtained during the drainage flow IOP were used to perform a detailed evaluation of the characteristics of the drainage flow (Figure 3). It is evident that after a one-hour model adjustment period, the modeled drainage flow timing and depth is similar to that observed by UAS with some notable differences. The narrow layer of light winds is not well resolved by the model. Additionally, the modeled drainage flow was a bit deeper and wind speeds a bit stronger than observed. At the same time, the timing of the wind shift and strength/depth of the up-canyon flow was well captured by the model.

Figure 3. Comparison of wind speeds at 04V from (left) meso-to-micro model and (right) two UAS operated by the University of Kentucky.
Figure 3. Comparison of wind speeds at 04V from (left) meso-to-micro model and (right) two UAS operated by the University of Kentucky.

Finally, EnKF data assimilation experiments have been performed using NCAR’s Data Assimilation Research Testbed (DART) to develop a UAS data assimilation capabilities. Studies are being performed to evaluate a number of aspects involved in UAS data assimilation including appropriate sampling rates, radius of influence of UAS data and treating observation error. EnKF was chosen to capture flow-dependent error covariances while concurrently providing a means for estimating forecast uncertainty. Initial evaluations have demonstrated the value of assimilating UAS data as shown in Figure 4. In this example, assimilation of UAS data collected by the University of Kentucky (see Fig. 3), significantly improved the predicted strength and depth of the cold pool observed in the exit region of Saguache Canyon as independently measured with a coptersonde operated by the University of Oklahoma.

Figure 4. (left) Observed potential temperature obtained from the University of Oklahoma Coptersonde compared with ensemble mean modeled values obtained (middle) with UAS data assimilation and (right) without UAS data assimilation. Model data was obtained using EnKF data assimilation and a 40 member ensemble.
Figure 4. (left) Observed potential temperature obtained from the University of Oklahoma Coptersonde compared with ensemble mean modeled values obtained (middle) with UAS data assimilation and (right) without UAS data assimilation. Model data was obtained using a 40 member ensemble run with 1 km grid-spacing.

FY2020 Plans

Future work will focus on performing more in-depth analysis of the model performance and evaluating the potential of UAS data assimilation to improve short term prediction of winds and turbulence. Both of these efforts will continue to mine the suite of observations collected during the July 2018 LAPSE rate experiment resulting in multiple publications.

WEATHER IMPACTS ON UNMANNED AERIAL SYSTEMS

To predict the likelihood of mission success, given a predicted flow field, it is critical to understand how small UAS will respond to variations in turbulence intensity. In this NASA-sponsored study we are developing a full non-linear, six-degree-of-freedom flight-simulation capability for both fixed-wing and multirotor UAS. The key aspect of this work is to include the effects of arbitrary wind fields, via the induced aerodynamic forces and moments. In order to actuate these forces and moments for fixed-wing vehicles a “four point method” is employed.  With this approach, the aerodynamic effect of turbulent flow on a UAS is calculated by assuming the turbulent flow can be approximated by linear functions that are evaluated at four points on the vehicle.  Note that modeling the response of a fixed wing UAS is much different than modeling the response of a small multirotor aircraft. A key aspect of this work has been the development of a sub-grid scale wind model that provides three-dimensional turbulence representation for wind field simulations at a resolution required to simulate impacts on small UAS.

FY2019 Accomplishments

Intuitively, it is clear that small UAVs, flying at relatively low airspeeds, will be more sensitive to small-scale wind structures than larger faster aircraft. Figure 5 provides a concrete example, showing the vertical acceleration response of a small fixed-wing UAV (black) and a mid-size commercial transport aircraft (red) to the vertical wind component. Both vehicles are at low altitudes and slow (relative) airspeeds. It can be seen that the wavelengths of the winds that are most important to the UAV are in the meters to tens of meters range while impacts on a manned transport aircraft peak at a scale of 500 m. This implies that analyzing the impact of small-scale wind structures on UAVs quantitatively, it is imperative to model the winds accurately at these small scales.

Figure 5. Vertical acceleration response due to vertical wind for UAS (black) and mid-sized transport aircraft (red) – as a function of input wind wavelength.
Figure 5. Vertical acceleration response due to vertical wind for a fixed-wing UAS (black) and mid-sized transport aircraft (red) – as a function of input wind wavelength.

As described above, NCAR/RAL has developed the capability to model realistic wind fields via LES methods and synthetic turbulence fields via analytic/numerical methods. To enforce computational stability, numerical weather models typically filter out the smallest scales of the flow. Moreover, subgrid-scale processes are parameterized.  Therefore, in reality weather models can only fully resolve processes that are 5 to 10 times as large as the grid spacing. To overcome this issue, an empirical method was developed to merge information from  25 m LES wind fields (can resolve eddies of 250 m or greater) with synthetic meter-scale isotropic turbulence.  By matching the energy from a spectral decomposition of LES winds at the lower frequencies with that for isotropic turbulence at higher frequencies we can create a merged spectrum of turbulence energy. This matching information can then be used to produce the space- and time-varying winds at scales relevant for modeling impacts on a small UAS as illustrated in Figure 6. It can be seen that the LES-alone data provides the larger-scale variation in the winds, while the merged wind field provides much finer scale and realistic variability that is consistent with the larger-scale variations obtained with the LES.

The LES-alone and merged subgrid/LES winds were then used as input to a three degree of freedom (airspeed, height and pitch), small UAS flight simulation. Figure 7 shows the results for the height and acceleration response of the small, fixed-wing UAS flown though the merged wind field shown in Figure 6. The red curve is the height/acceleration response to the LES-alone vertical wind component, and the black curve is that for the merged LES/subgrid wind data. From these figures, it is clear that the LES-alone wind component is insufficient for simulating the magnitude of vertical variations along a planned UAS flight path intending to remain level.

Figure 6. Vertical wind component from LES (red) and merged subgrid turbulence and LES (black).
Figure 6. Vertical wind component from LES (red) and merged subgrid turbulence and LES (black).
Figure 7. Small UAS (left) height and (right) acceleration response to flight through LES-alone vertical wind component (red) and merged subgrid turbulence/LES (black).
Figure 7. Small UAS (left) height and (right) acceleration response to flight through LES-alone vertical wind component (red) and merged subgrid turbulence/LES (black).

Finally, as part of this project, we have also convened a UTM Weather Users Group which consists of approximately 30 individuals from a broad range of backgrounds and interests. The goal of this user group is to develop an initial UTM Weather Concepts document based on discussion and presentations made in a bi-monthly teleconference. The Concepts document will be developed in collaboration with a UTM Weather Advisory group and the NASA UTM group. The advisory group contains a cross-section of members from the UTM community, including public organizations, private companies, and research organizations. A number of questions has arisen regarding the implementation of weather data and products within UTM. In order to answer these questions we will need to baseline what is used now by the UAS community and identify gaps.

FY2020 Plans

Future work will focus on completing the implementation and verification of the fixed-wing and multirotor vehicle simulation and three-dimensional LES-subgrid turbulence merging. We will also complete the sequence of telecons, the last being on the subject of “UTM Supplemental Data Service Providers.” The information from these telecons will be used to develop an initial UTM Weather Concepts document.

WEATHER CHALLENGES FOR URBAN AIR MOBILITY

One of the critical elements that might limit more widespread use of UAM is weather. For commercial aviation, currently 25% of the aircraft get delayed and 75% of these delays are caused by weather. It is important to understand the implications of weather on UAM to determine its resiliency. Thus far, detailed low-altitude weather information was not as critical for the aviation industry, but with the emergence of small UAS and in anticipation of passenger carrying electric/hybrid vertical take-off and landing aerial vehicles (eVTOLs), it has become significantly more important to understand the implications of different types of weather on such operations.

FY2019 Accomplishments

This past year, an in-depth analysis was conducted to assess the observing infrastructure and routinely available weather guidance with regard to their adequacy to support emerging modes of transportation, like UAS and UAM. The focus was primarily on the Dallas / Fort Worth (DFW) metropolitan area, which has been selected as a testbed for early adoption of eVTOL flight operations. Also, fine-scale building-resolving simulations of the wind and turbulence characteristics in the DFW downtown area were conducted to anticipate operational UAM challenges and limitations.

Figure 8. Monthly wind roses based on METAR reports from the Dallas/Fort Worth (KDFW) ASOS site. Wind speed ranges are marked in colors and values are in knots.
Figure 8. Monthly wind roses based on METAR reports from the Dallas/Fort Worth (KDFW) ASOS site. Wind speed ranges are marked in colors and values are in knots.
Figure 9. Vertical cross section of fine-resolution wind flow through urban canopy.
Figure 9. Vertical cross section of fine-resolution wind flow through urban canopy.

The current weather observing infrastructure to support UAM over DFW was documented, capturing both in situ and remote sensing capabilities. Moreover, a wide range of routinely generated weather guidance products was identified that includes analysis products as well as forecast products. The core of the effort focused on climatological analyses of relevant weather characteristics (e.g., Fig. 8), including a discussion of high-impact weather scenarios. In the example shown, winds from the south exceeding 15 knots can occur frequently from March through June at DFW. This type of climatological information will be critical when planning eVTOL operations and routing structures. Based on these analyses, gaps were identified in both the observing infrastructure and the available weather guidance that may limit support of the emerging modes of aerial transportation. Opportunities were sought for enhancing the present weather capabilities beyond the current shortcomings.

The most challenging issue identified was to appropriately capture the potentially very dynamic situation of winds and turbulence that can occur within an urban landscape (Fig. 9) to enable safe, efficient and reliable UAM operations in the future. Moreover, in order to achieve operational reliability, serious consideration needs to go into enabling all-weather operations, including dealing with low-visibilities, impacts from thunderstorms (e.g., sporadic winds, heavy rain, hail and lightning) and wintry conditions (snow and in-flight icing).

FY2020 Plans

The above observational analyses will be expanded to include two dozen major cities across the United States. The fine-scale model simulations will be enhanced and digested with a specific focus on impacts on operations between select takeoff and landing spots in the DFW cityscape.

Citations

Muñoz-Esparza and Sharman (2018). An Improved Algorithm for Low-Level Turbulence Forecasting, Journal of Applied Meteorology and Climatology, 57, 1249 – 1263.