Preprints
https://doi.org/10.5194/amt-2021-316
https://doi.org/10.5194/amt-2021-316

  20 Nov 2021

20 Nov 2021

Review status: this preprint is currently under review for the journal AMT.

A Framework for Improving Data Quality of Thermo-Hygrometer Sensors aboard Unmanned Aerial Systems for Planetary Boundary Layer Research

Antonio R. Segales1,2,3, Phillip B. Chilson4, and Jorge L. Salazar-Cerreño1,3 Antonio R. Segales et al.
  • 1The University of Oklahoma School of Electrical and Computer Engineering, Norman, Oklahoma
  • 2Cooperative Institute for Severe and High-Impact Weather Research and Operations, The University of Oklahoma, Norman, Oklahoma
  • 3Advanced Radar Research Center, The University of Oklahoma, Norman, Oklahoma
  • 4Center for Autonomous Sensing and Sampling, The University of Oklahoma, Norman, Oklahoma

Abstract. Small Unmanned Aerial Systems (UAS) are becoming a good candidate technology for solving the observational gap in the planetary boundary layer (PBL). Additionally, the rapid miniaturization of thermodynamic sensors over the past years allowed for more seamless integration with small UASs and more simple system characterization procedures. However, given that the UAS alters its immediate surrounding air to stay aloft by nature, such integration can introduce several sources of bias and uncertainties to the measurements if not properly accounted for. If weather forecast models were to use UAS measurements, then these errors could significantly impact numerical predictions and, hence, influence the weather forecasters' situational awareness and their ability to issue warnings. Therefore, some considerations for sensor placement are presented in this study as well as flight patterns and strategies to minimize the effects of UAS on the weather sensors. Moreover, advanced modeling techniques and signal processing algorithms should be investigated to compensate for slow sensor dynamics. For this study, dynamic models were developed to characterize and assess the transient response of commonly used temperature and humidity sensors. Consequently, an inverse dynamic model processing (IDMP) algorithm that enhances signal restoration is presented and demonstrated on simulated data. A few real case studies are discussed that show a clear distinction between the rapid evolution of the PBL and sensor time response. The conclusions of this study provide information regarding the effectiveness of the overall process of mitigating undesired biases and distortions in the data sampled with a UAS and increase the data quality and reliability.

Antonio R. Segales et al.

Status: open (until 25 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Antonio R. Segales et al.

Antonio R. Segales et al.

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Short summary
The mitigation of undesired contamination, sensor characterization, and signal conditioning and restoration is crucial to improve the reliability of the weather UAS deliverables. This study presents an overview of the general framework and procedures for sensor slow response and uncertainty mitigation on temperature and humidity measurements collected using a UAS. The improved weather parameters accuracy can lead to better data assimilation into weather forecast models.