Tuesday, 4 November 2014
Capitol Ballroom AB (Madison Concourse Hotel)
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. In this study, low-level observations are collected by a simulated aircraft within a nature run (100 m horizontal grid spacing) of an idealized supercell. The observations are assimilated using DART into a degraded, coarse run (1 km horizontal grid spacing) of the same idealized supercell, to approximate models that will be used in early warn-on-forecast systems. Ensemble sensitivity theory predicts a linear relationship between the expected forecast response by perturbing the model simulation and the actual response from assimilating observations. Although this relationship appears to be valid for synoptic scale phenomena, studies have not demonstrated that ensemble sensitivity theory is appropriate for mesoscale phenomena such as tropical cyclones. This study aims to test the validity of this theory in supercell thunderstorms as well as determining which regions of a supercell should be sampled by aircraft in order to provide the greatest improvements to model forecasts. Analysis of preliminary results are underway and final results of the simulations and their implications on targeting aircraft observations in supercells will be presented at the conference.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner