2.5 Impact of sUAS Data on CAM Supercell Forecasts

Monday, 22 October 2018: 12:15 PM
Pinnacle room (Stoweflake Mountain Resort )
Jason M. Keeler, Central Michigan Univ., Mount Pleasant, MI; and A. L. Houston and A. B. Mills

Field studies in recent years have demonstrated the capability of sUAS to sample the thermodynamic and kinematic state in the vicinity of supercell thunderstorms. While it is clear that these data provide insight into a storm’s structure at a given time, it is less clear what impact the assimilation of data from one or more aircraft could have on the prediction of a given storm’s evolution via short-term model forecasts (i.e., over a 1-2 hour period). This talk will quantify this impact using the Observing System Simulation Experiment (OSSE) framework. In this method synthetic observations are “collected” by simulated sUAS flights through an atmospheric simulation (termed the nature run) with sufficient realism to represent the true atmospheric state. These data are then assimilated into relatively coarse-resolution “experiment” simulations. Comparison of the nature run predictions (truth) to the experimental predictions allows for evaluation of the impact UAS data could have on forecasts.

The nature run for this experiment was developed using the First-Generation Pennsylvania State University/National Center for Atmospheric Research Cloud Model (CM1). Based on prior work in ensemble sensitivity analysis, an aircraft model was developed to sample three regions in the storm’s vicinity: the rear flank gust front, forward flank, and inflow to the southeast of the storm’s updraft. These regions are targeted using simulated transect flights (between storm-relative waypoints) or simulated flights that loiter in a storm-relative location. Flight characteristics in the aircraft model are consistent with those of fixed-wing aircraft (e.g., Tempest or TTwistor). Characteristics of synthetic data sampled using the aircraft model, consideration of methods for assimilation of high spatiotemporal resolution data into CAMs (Convection Allowing Models), and the impact of those data on hypothetical forecasts will be discussed.

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