Development and Calibration of Pressure-Temperature-Humidity (PTH) Probes for Distributed Atmospheric Monitoring Using Unmanned Aircraft Systems.

Small unmanned aircraft systems (UAS) are increasingly being used for meteorology and atmospheric monitoring. The ease of deployment makes distributed sensing of parameters such as barometric pressure, temperature, and relative humidity in the lower atmospheric boundary layer feasible. However, constraints on payload size and weight, and to a lesser extent power, limit the types of sensors that can be deployed. The objective of this work was to develop a miniature pressure-temperature-humidity (PTH) probe for UAS integration. A set of eight PTH probes were fabricated and calibrated/validated using an environmental chamber. An automated routine was developed to facilitate calibration and validation from a large set of temperature and relative humidity setpoints. Linear regression was used to apply temperature and relative humidity calibrations. Barometric pressure was calibrated using a 1-point method consisting of an offset. The resulting PTH probes were less than 4 g in mass and consumed less than 1 mA when operated from a 5 VDC source. Measurements were transmitted as a formatted string in ASCII format at 1 Hz over a 3.3 V TTL UART. Prior to calibration, measurements between individual PTH probes were significantly different. After calibration, no significant differences in temperature measurements across all PTH probes were observed, and the level of significance between PTH probes was reduced. Actual differences between calibrated PTH probes were likely to be negligible for most UAS-based applications, regardless of significance. RMSE across all calibrated PTH probes for the pressure, temperature, and relative humidity was less than 31 Pa, 0.13 °C, and 0.8% RH, respectively. The resulting calibrated PTH probes will improve the ability to quantify small variations in ambient conditions during coordinated multi-UAS flights.

Classifying Reflectance Targets under Ambient Light Conditions using Passive Spectral Measurements.

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient-light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two-step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%.

Intercomparison of Small Unmanned Aircraft System (sUAS) Measurements for Atmospheric Science during the LAPSE-RATE Campaign.

Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation-a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 ± 2 . 6 ∘ C and 0.22 ± 0 . 59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.

Development and Preliminary Evaluation of a Spray Deposition Sensing System for Improving Pesticide Application.

An electronic, resistance-based sensor array and data acquisition system was developed to measure spray deposition from hydraulic nozzles. The sensor surface consisted of several parallel tin plated copper traces of varying widths with varying gap widths. The system contained an embedded microprocessor to monitor output voltage corresponding to spray deposition every second. In addition, a wireless module was used to transmit the voltage values to a remote laptop. Tests were conducted in two stages to evaluate the performance of the sensor array in an attempt to quantify the spray deposition. Initial tests utilized manual droplet placement on the sensor surface to determine the effects of temperature and droplet size on voltage output. Secondary testing utilized a spray chamber to pass nozzles at different speeds above the sensor surface to determine if output varied based on different application rates or spray droplet classification. Results from this preliminary analysis indicated that manual droplets of 5 and 10 µL resulted in significantly different values from the sensors while temperature did not consistently affect output. Spray chamber test results indicated that different application rates and droplet sizes could be determined using the sensor array.

Unmanned aerial vehicles reveal the impact of a total solar eclipse on the atmospheric surface layer.

We use unmanned aerial vehicles to interrogate the surface layer processes during a solar eclipse and gain a comprehensive look at the changes made to the atmospheric surface layer as a result of the rapid change of insolation. Measurements of the atmospheric surface layer structure made by the unmanned systems are connected to surface measurements to provide a holistic view of the impact of the eclipse on the near-surface behaviour, large-scale turbulent structures and small-scale turbulent dynamics. Different regimes of atmospheric surface layer behaviour were identified, with the most significant impact including the formation of a stable layer just after totality and evidence of Kelvin-Helmholtz waves appearing at the interface between this layer and the residual layer forming above it. The decrease in surface heating caused a commensurate decrease in buoyant turbulent production, which resulted in a rapid decay of the turbulence in the atmospheric surface layer both within the stable layer and in the mixed layer forming above it. Significant changes in the wind direction were imposed by the decrease in insolation, with evidence supporting the formation of a nocturnal jet, as well as backing of the wind vector within the stable layer.