2A.2 Calibration and Validation of Weather Sensors for Rotary-Wing UAS: The Devil is in the Details

Thursday, 26 January 2017: 10:45 AM
Conference Center: Chelan 4 (Washington State Convention Center )
Phillip B. Chilson, Univ. of Oklahoma, Norman, OK; and R. Huck, C. Fiebrich, D. Cornish, T. Wawrzyniak, S. Mazuera, A. Dixon, E. Burns, and B. Greene

The prospects of using unmanned aircraft systems (UAS) for atmospheric observations is being increasingly explored as a means of helping to fill the data void that has faced meteorologists for so many years. The appeal of using these vehicles lies in part in the large number of small UAS (sUAS) currently available commercially to researchers and the fact that regulations governing the operations of sUAS are being relaxed. However, to collect meaningful observations of the thermodynamic and kinematic state of the atmosphere, one must fully understand the uncertainties and limitations of the sensors selected and how they perform during various flight operations. In this presentation we examine the performance of sensor packages produced by InterMet and Sparv Embedded (Windsond), both of which provide temperature, humidity, pressure, and GPS data. The evaluation consists of two steps. The first is calibration of the sensors against reference measurements provided by the Oklahoma Mesonet. The second step involves validation of the sensors during flight. The latter was achieved by operating the sUAS near an Oklahoma Mesonet tower and comparing observations from the on-board sensors against those from the calibrated instruments on the tower. Additionally, we are estimating the mean horizontal wind using an algorithm that relies on values of the Euler angles provided by the UAS autopilot system. The wind estimates from the sUAS were compared against measurements obtained from the tower’s cup and vane anemometer. In this presentation we provide a general evaluation of the sensors along with their performance characteristics, suggestions for sensor placements based on our experience, and an overview of the wind retrieval algorithm and the uncertainties of the resulting wind estimates.
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