The Representation of Mesoscale Atmospheric Phenomena by Unmanned Aircraft Systems

Unmanned aircraft are capable of collecting observations of atmospheric phenomena that conventional platforms cannot retrieve. In many cases, these observations require sensor deployments that are deemed to be too dull, dirty, or dangerous for manned in situ platforms. As such, there are many important scientific questions that cannot be answered without the data that only unmanned aircraft can collect. The aim of this work is to characterize the accuracy with which mesoscale atmospheric phenomena such as gust fronts, vortices, thunderstorms, and tropical cyclone structures are represented using unmanned aircraft systems (UAS), and what UAS configurations and flight plans are necessary to resolve those features satisfactorily. We hypothesize that the results are sensitive to the scale of the phenomena and the time constant of the sensors. Assessments are made through observing system simulation experiments (OSSEs). One class of OSSEs consists of highly idealized gust fronts and vortices without the presence of clouds or precipitation. A second set of OSSEs has been conducted with idealized supercells and tropical cyclones to simulate the aircraft in more realistic environments. Flight plans are varied based on the direction of the aircraft through the features and the number of aircraft used. Objective methods such as the root mean square error are used to quantify the difference between the nature runs and the analyses that incorporate simulated observations. Results from both sets of simulations will be presented.