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.
Highlights
Technological development with respect to instrumentation systems for weather sampling increasingly demands the means to provide greater reliability of the data collected
The presented simulation results show the feasibility of the framework and the inverse dynamic model processing (IDMP) technique on measurements taken with a Unmanned Aerial Systems (UAS)
To begin exploring the mitigation of slow sensor dynamics and sensor noise for realistic 420 UAS flights, the IDMP was applied on real data collected using the CopterSonde rotary-wing UAS (rwUAS) (Segales et al, 2020) from the University of Oklahoma (OU)
Summary
Technological development with respect to instrumentation systems for weather sampling increasingly demands the means to provide greater reliability of the data collected. Researchers have been looking for ways to increase the reliability and accuracy of weather measurements, like Mahesh et al (1997) who successfully implemented a simple method to correct thermal lags from measurements taken with a radiosonde in strong inversions. UAS have paved the way for the development of new strategies for sampling the atmosphere in the past few years. It is well known that the planetary boundary layer (PBL) is quite under-sampled and that observational gaps limit the ability to accurately estimate the state of the atmosphere; UAS are seen as new opportunities to fill the gap (Bell et al, 2020). UAS are able to sample regions of the atmosphere that were either not feasible or not possible with other conventional meteorological instruments
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