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Hyperlocal weather predictions are often necessary in order to determine whether a particular sUAS route will be safe to fly. The General Urban area Microclimate Predictions tool (GUMP) seeks to provide such predictions through the use of machine learning (ML) models and computational fluid dynamics (CFD) simulations. The computed wind flow field is converted into an intuitive risk map for sUAS operators through the use of appropriate thresholds on wind velocities.

Adverse weather conditions, particularly, high winds, can have a highly adverse impact on small uncrewed aircraft system (sUAS) operations. These conditions can vary significantly within a small area (particularly, in an urban environment); thus, hyperlocal weather predictions are often necessary in order to determine whether a particular sUAS route will be safe to fly. The General Urban area Microclimate Predictions tool (GUMP) seeks to provide such predictions through the use of machine learning (ML) models and computational fluid dynamics (CFD) simulations. Specifically, ML models are trained to ingest mesoscale forecasts from the National Oceanic and Atmospheric Administration (NOAA) and output refined forecasts for some specific location, typically, a weather station that serves as a source of ground truth data during training. At the same time, CFD simulations over 3D models of structures (e.g., buildings) are utilized to extend the refined forecast to other points within the area of interest surrounding the location. Because it is difficult to perform such simulations in real-time, they are executed offline under a wide range of boundary conditions, generating a broad set of resulting wind flow fields. During deployment, GUMP retrieves the wind flow field that is most consistent with the ML model’s forecast. The wind flow field can be converted into an intuitive risk map for sUAS operators through the use of appropriate thresholds on wind velocities.

Funding Agency: National Aeronautics and Space Administration

The LADME projects seek to investigate sources of convective initiation and suppression within the orographic planetary boundary layer over Arizona during the North American Monsoon using a unique suite of meteorological instruments mounted onto both uncrewed and crewed Embry-Riddle aircraft. This low-altitude drone monsoon experiment (LADME) will obtain focused meteorological quantities in locations of known thunderstorm initiation and sample the fine-scale atmospheric flows that precondition the atmosphere and promote updrafts that trigger convection. This research features interdisciplinary research between Embry-Riddles Daytona Beach and Prescott campuses.

Project Lead: Dr. Ronny Schroeder, Embry‑Riddle Aeronautical University, Prescott, AZ

Uncrewed Aircraft Systems (UAS) have become prevalent in a wide variety of meteorological investigations. UAS affords the ability to fill an important atmospheric observational gap, namely observations in the domain between the reach of ground-based sensors and the altitudes that crewed aircraft can safely operate at. Fixed-wing UAS offer an opportunity to cover vast horizontal and vertical distances in a continuous manner with high spatial resolution. Multirotor UAS possess the ability to launch and recover in small spaces, fly at slow airspeeds, hover, accomplish vertical profiles, and probe obstacle-laden environments while making spatially dense observations. Each of these UAS categories offers a new observational strategy that is efficient, reusable, durable, repeatable; has a much lower cost barrier; requires minimal infrastructure; and renders superior spatial flexibility, range and resolution.

Swarms of meteorologically instrumented UAS provide an opportunity to further capitalize on these advantages. However, any given UAS flight must remain within the visual line of sight (VLOS) of the remote pilot. Therefore, in order to observe a large geographical area that spans beyond VLOS, multiple UAS must be simultaneously flown. Likewise, to accomplish more spatially dense observations in an immediate area, multiple UAS must undertake concurrent observations. Each of these strategies increases the complexity of the operation and presents a challenge in tying together disparate measurements.

To assist in multi-vehicle data collection, as previously described, the open-source, publicly available Mobility Virtual Environment (MoVE) has been developed. This software is designed to first rehearse multi-vehicle scenarios in simulation and then collect real data in real-time using a cellular network. In simulation, MoVE can be used to select waypoint routes that ensure safety, are appropriate for the objectives of the atmospheric investigation, and fit within the performance envelope of the involved pilots and uncrewed aircraft (UA). Once observational strategies are reasonably well prepared in the virtual environment, real pilots with real UA can rehearse or undertake flight plans with uninstrumented or instrumented UA. Through MoVE, all data is brought together in time and geo-tagged at suitable frequencies making it easy to combine individual UA data together into a single data set. In this presentation, the ability of MoVE to streamline the planning, execution, post-processing and visualization of data in multi-vehicle field campaigns is explored. The benefits MoVE affords the atmospheric science community can also translate to the broader scientific and engineering communities.

Project Lead: Dr. Marc Compere, Embry‑Riddle Aeronautical University

Urban air mobility (UAM) is expected to be an integral component of cities of the future. However, the urban environment is a new setting for sustained aviation operations. The lower mass, more limited thrust and slower speeds of these vehicles increase their sensitivity to the spatially and temporally dynamic urban environment. Exacerbating this situation is the fact that traditional aviation weather products for observations and forecasts on the outskirts of a metropolitan area do not necessarily translate well to the urban setting. The initial and continuing costs associated with a dense meteorological observation network, required for the heterogeneous nature of the urban environment, make the creation of one in every participating metropolitan area across the country unrealistic. This project explores a variety of potential data sources and proposes a cyber-physical system (CPS) architecture, including an incentive-based crowdsensing application, for real-time aviation observations.

Project Lead: Dr. M. Ilhan Akbas, Embry‑Riddle Aeronautical University

The WMO Uncrewed Aircraft Systems (UAS) Demonstration Campaign (UAS-DC) aims at demonstrating the potential capability of UAS to play a role as an operational component of the WMO Integrated Global Observing System (WIGOS) under the Global Basic Observing Network (GBON).

MicroSCALES explore the effects of urban landscapes by sampling urban heat island impacts and localized variations in winds and turbulence in support of high-fidelity, large eddy simulation improvement efforts and the WMO UAS Demonstration Campaign.

Embry-Riddle Lead: Dr. Kevin A. Adkins, Embry‑Riddle Aeronautical University

The synergy between moderate-resolution satellite imagery and fine-resolution drone imagery, LiDAR data, and meteorological data, along with generally available GIS data, must be identified and optimized. These data will be integrated to produce a variety of products that help identify what tools, inputs, and management strategies most effectively contribute to an increase in the productivity and resilience of an important agricultural system to a major weather or climate-related disturbance.

Satellite imagery has been used in agriculture for some time and the increasing implementation of drones into agriculture and agriculture science holds unique promise. However, the synergy between moderate-resolution satellite imagery, fine-resolution drone imagery, fine-resolution LiDAR (Light Detection And Ranging) data, fine-resolution meteorological data and generally available GIS (Geographic Information Systems) data must be identified and optimized. To be most useful, this fusion of data should help provide estimates in the health and yield of agriculture systems as well as insight into the microclimate and ecosystem variation within a farm site. These data will be integrated to produce a variety of fine-resolution maps that can be analyzed to identify what tools, inputs, and management strategies most effectively contribute to an increase in productivity, agroecological system health, and resiliency or restoration (typically in response to weather or climatic disturbance) of a given farming operation and site. This research will apply these data science methods and tools to varying farm types in Puerto Rico. We expect new insight into how the fusing of a multitude of data can be effectively integrated into an agriculture operation and, subsequently, determine which outputs are most valuable to the varied farm types, practices, and locations. This investigation will also provide critical information on the resistance and resilience of an important agricultural system to major weather or climate-related disturbances and, subsequently, inform management decisions related to climate change adaptation.

Funding Agency: United States Department of Agriculture

Simulation and modeling have shown that wind farms have an impact on the near-surface atmospheric boundary layer as turbulent wakes generated by the turbines enhance vertical mixing. While a few observational data sets that focus on near-surface temperature changes exist, these studies lack high spatial resolution and neglect the combined effect of these temperature changes with an altered humidity profile. With a large portion of wind farms hosted within an agricultural context, changes to humidity can potentially have secondary impacts, such as on the productivity of crops. The goal of this study was to gather high-resolution in situ field measurements in the wake of wind turbines in order to differentially map downstream changes to humidity. Measurements, obtained by instrumented uncrewed aerial systems, are complemented by numerical experiments conducted using large-eddy simulation. Observations and numerical results are in good general agreement and show that downstream relative humidity is differentially altered in all directions.

Project Lead: Dr. Kevin A. Adkins, Embry‑Riddle Aeronautical University

The complexity of wildfires dictates the use of semi-empirical physically-based models to predict wildfire spread rates and direction. It has long been recognized that processes in the lower atmospheric boundary layer (ABL) significantly influence the behavior of wildfires, such as modifying their rate of spread (Schroeder, 1970; Beer, 1991). Recent laboratory and field experiments, along with numerical simulation, have shown that fine-scale atmospheric processes, such as sweeps and ejections, play an important role in fire spread (Bebieva et al., 2021). Although the National Weather Service (NWS) provides regular weather forecasts, including enhanced model development (such as the High-Resolution Rapid Refresh Ensemble, HRRRE, Kalina et al., 2021), it is still incapable of capturing all of the small physical processes that determine the fire spread rates. In the absence of fires, a recent study evaluating weather nowcasting against Doppler LiDAR data showed that winds generated by surface heating are poorly predicted by the model (Banta et al., 2021). In the presence of fires, buoyant flame dynamics induce extra wind (i.e., the wind generated by the fires themselves) that is impossible to predict using the weather forecasting models. Yet, this extra wind controls the convective heating process that ignites fuel ahead and yields fire spread (Finney et al., 2015).

The new generation of wildfire models combines combustion fire models (either physical or empirical) together with a numerical weather prediction (NWP) model or a computational fluid dynamics (CFD) model (see Bakhshaii and Johnson, 2019, for the full review). One of the important parameters in the weather model is wind characteristics. Currently, there are no observations of fire-generated wind patterns around fire fronts and, consequently, a new observational system is needed. The overarching goal of this project is to develop a fully automated wind observing system, ”Windtl”, that allows a better prediction of wind and turbulence events in the lower ABL. At the heart of this system is an algorithm that estimates local wind velocity based on aircraft state measurements without the need for hosted wind sensors.

Funding Agency: Improving Aviation, Ms. Rocio Frej Vitalle

Harmful algal blooms (HABs) are a great threat to South Florida’s environment, society and economy. Over recent decades, HAB events have led to widespread die offs of seagrass, fis, and aquatic mammals. HABs are also a public health hazard, causing skin irritation and respiratory complications among vulnerable human communities. Also important, HABs have caused great impacts to local economies around the lake and canals, which depend highly on fishing and tourism. Therefore, HABs impacting South Florida warrant deep understanding and rigorous analysis via sophisticated experimental and numerical tools.

Addressing this need, and in alignment with the goals of the EPA 2022-2026 Strategic Plan, specifically the goal of ensuring clean and safe water for all communities, the proposed research seeks to:

• Develop automated UA surface water sampling. The use of UA for this purpose will allow water sample collection to take place at a higher spatiotemporal resolution more efficiently across a vast area, including hard-to-reach regions, without disturbing and mixing water samples. Traditional hand collection from crewed boats is a proven method of sampling but has limitations. UAS borne systems can enhance sampling with automation, precision flight and machine-level consistency in sampling.
• Execute UA-based remote sensing to include multispectral, hyperspectral, visible light and thermal infrared systems deployed on short-range multirotor UA and long-range beyond-visual-line-of-sight (BVLOS) systems. Deployment of these sensors with a UA platform provides an opportunity for very high spatiotemporal resolution of algal bloom formation and dynamics, as well as for monitoring of submerged aquatic vegetation (SAV) recovery and loss. These technologies have the promise to fill a niche that provides significantly higher spatial resolution than satellite missions, while also greatly expanding coverage area as compared to in situ monitoring. Employment of these sensors will be purposefully planned to coincide with satellite overflights, thus creating the opportunity to create fused data products.
• Develop an airborne particle monitoring system that is hosted on a UA that will provide new insight into the aerosolization and transport of airborne particles over HABs. While little is known about the transport, and vertical distribution, of airborne toxins associated with HABs, the availability of this information can help in better understanding the associated threats. This can help to better understand HABs, mitigate and manage risks, and facilitate making informed health advisories for communities that are located on and around impacted bodies of water. The airborne particle monitoring system will consist of an optical particle counter (OPC) and impinging device (ID).
• Integrate these newly developed datasets (water and airborne) into well-tested, management-oriented numerical models which have been co-developed with management agencies and local stakeholders.

Funding Agency: Environmental Protection Agency (EPA)

Project Lead: Dr. Kevin A. Adkins, Embry‑Riddle Aeronautical University

The goal of this work is to (1) improve the accessibility of air quality sensors, (2) increase the number of air quality observations, and (3) increase the spatial resolution of air quality sensors using low-cost sensors (LCSs). Particulate matter (PM) has been recognized as a detrimental pollutant in the atmosphere causing several adverse impacts on human health as well as on the environment. In the United States, similar to many other countries, concentrations of PM are monitored and enforced by regulations based on the National Ambient Air Quality Standards (NAAQS). However, measuring atmospheric PM using the existing infrastructure is designed to measure PM pollution on urban/regional scales. Nonetheless, these conventional monitoring technologies are limited in capturing the spatial and temporal variations in atmospheric PM concentrations at fine scales. Additionally, observations from these technologies possess inherent limitations due to relatively high cost and size. Hence, these constraints bring about the need for more sustainable methods to monitor air pollution in order to devise preventive and/or corrective measures to protect the environment in highly polluted urban, where monitoring is not spatially sufficient, as well as remote areas, where monitoring is often underestimated. This project is designed to characterize and compare the performance of three different commercially available PM LCSs. The innovative aspect of this proposed design is our ability to measure horizontal and vertical profiles of PM in the atmosphere using three different multi-dimensional modes of operation: (1) stationary (1D), (2) mobile on crewed vehicles (2D), and (3) mobile on an uncrewed aerial vehicles (3D). This study has the potential to produce viable systems to be used by the public and systems to be implemented in crewed and uncrewed vehicles. As a renowned aeronautical university, Embry‑Riddle Aeronautical University (ERAU) has extensive experience with crewed and uncrewed vehicles, and more recently, the university has been giving special attention to research in air quality and sustainability as part of its strategic plan.

Project Lead: Dr. Marwa El-Sayed, Sustainability and Environmental Engineering Lab (SEEL), Embry‑Riddle Aeronautical University

• Implement comprehensive, decadal scenarios of land cover change associated with sea-level rise
• Develop suitability assessments and spatial optimizations/prioritizations for implementing green infrastructure within the IRL watershed
• Deploy multi-sensor UAS missions on near-term priority sites identified in Objective 2 for the purpose of developing high-resolution site condition assessments of hydrologic connectivity, extant vegetation quality, and associated feasibility for sustainable utilization in a green infrastructure mosaic
• Development of specific policy and, as appropriate, project recommendations for potential implementation by governmental and non-governmental entities, with a special focus on underserved and underrepresented communities
• Provide frequent and diverse opportunities for direct public engagement and technical outreach, including with elected officials and historically underrepresented communities
• Provide professional development support for undergraduate students and a post-doctoral researcher through a project that provides technical advancement, public outreach, and concrete policy outcomes

Project Lead: Stetson University; Dr. Dan Macchiarella, Embry‑Riddle Aeronautical University