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Experimental field campaigns are an essential part of atmospheric research, as well as of university education in the field of atmospheric physics and meteorology. Experimental field observations are needed to improve the understanding of the surface-atmosphere interaction and atmospheric boundary layer (ABL) physics and develop corresponding model parameterizations. Information on the ABL wind profiles is essential for the interpretation of other observations. However, wind profile measurements above the surface layer remain challenging and expensive, especially for the field campaigns performed in remote places and harsh conditions. In this study, we consider the experience of using two low-cost methods for the wind profiling, which may be easily applied in the field studies with modest demands on logistical opportunities, available infrastructure, and budget. The first one is a classical and well-known method of pilot balloon sounding, i.e., when balloon is treated as a Lagrangian particle and tracked by theodolite observations of angular coordinates. Second one is based on a vertical sounding with a popular and relatively cheap mass-market quadcopter DJI Phantom 4 Pro and utilizes its built-in opportunity to restore the wind vector from quadcopter tilt angles. Both methods demonstrated reasonable agreement and applicability even in harsh weather conditions and complex terrain. Advantages and shortcomings of these methods, as well as practical recommendations for their use are discussed. For the drone-based wind estimation, the importance of calibration by comparison to high-quality wind observations is shown.
Experimental field campaigns are an essential part of atmospheric research, as well as of university education in the field of atmospheric physics and meteorology. Experimental field observations are needed to improve the understanding of the surface-atmosphere interaction and atmospheric boundary layer (ABL) physics and develop corresponding model parameterizations. Information on the ABL wind profiles is essential for the interpretation of other observations. However, wind profile measurements above the surface layer remain challenging and expensive, especially for the field campaigns performed in remote places and harsh conditions. In this study, we consider the experience of using two low-cost methods for the wind profiling, which may be easily applied in the field studies with modest demands on logistical opportunities, available infrastructure, and budget. The first one is a classical and well-known method of pilot balloon sounding, i.e., when balloon is treated as a Lagrangian particle and tracked by theodolite observations of angular coordinates. Second one is based on a vertical sounding with a popular and relatively cheap mass-market quadcopter DJI Phantom 4 Pro and utilizes its built-in opportunity to restore the wind vector from quadcopter tilt angles. Both methods demonstrated reasonable agreement and applicability even in harsh weather conditions and complex terrain. Advantages and shortcomings of these methods, as well as practical recommendations for their use are discussed. For the drone-based wind estimation, the importance of calibration by comparison to high-quality wind observations is shown.
Evaluation of thermal stratification and systematic monitoring of water temperature are required for lake management. Water temperature profiling requires temperature measurements through a water column to assess the level of thermal stratification which impacts oxygen content, microbial growth, and distribution of fish. The objective of this research was to develop and assess the functions of a water temperature profiling system mounted on a multirotor unmanned aerial vehicle (UAV). The buoyancy apparatus mounted on the UAV allowed vertical takeoff and landing on the water surface for in situ measurements. The sensor node that was integrated with the UAV consisted of a microcontroller unit, a temperature sensor, and a pressure sensor. The system measured water temperature and depth from seven pre-selected locations in a lake using autonomous navigation with autopilot control. Measurements at 100 ms intervals were made while the UAV was descending at 2 m/s until it landed on water surface. Water temperature maps of three consecutive depths at each location were created from the measurements. The average surface water temperature at 0.3 m was 22.5 °C, while the average water temperature at 4 m depth was 21.5 °C. The UAV-based profiling system developed successfully performed autonomous water temperature measurements within a lake.
Soil erosion monitoring is a pivotal exercise at macro through micro landscape levels, which directly informs environmental management at diverse spatial and temporal scales. The monitoring of soil erosion can be an arduous task when completed through ground-based surveys and there are uncertainties associated with the use of large-scale medium resolution image-based digital elevation models for estimating erosion rates. LiDAR derived elevation models have proven effective in modeling erosion, but such data proves costly to obtain, process, and analyze. The proliferation of images and other geospatial datasets generated by unmanned aerial systems (UAS) is increasingly able to reveal additional nuances that traditional geospatial datasets were not able to obtain due to the former’s higher spatial resolution. This study evaluated the efficacy of a UAS derived digital terrain model (DTM) to estimate surface flow and sediment loading in a fluvial aggregate excavation operation in Waukesha County, Wisconsin. A nested scale distributed hydrologic flow and sediment loading model was constructed for the UAS point cloud derived DTM. To evaluate the effectiveness of flow and sediment loading generated by the UAS point cloud derived DTM, a LiDAR derived DTM was used for comparison in consonance with several statistical measures of model efficiency. Results demonstrate that the UAS derived DTM can be used in modeling flow and sediment erosion estimation across space in the absence of a LiDAR-based derived DTM.
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