The influence of organized regions of deep convection on its environment in both space and time has been recognized for many years. For example, organized deep convective regions are known to enhance upper-level jet streaks through modification of the direct mass circulation in jet entrance regions through diabatic heating. Individual thunderstorms modify the nearby surrounding mass and momentum fields within a few hours, likely assisting in storm maintenance and influencing storm severity. While past observational and modeling studies have documented these nearby and more distant feedback effects, this research represents the first attempt to conduct a careful comparison of model-simulated convective feedbacks with those diagnosed from dropsonde and Microwave Temperature Profiling (MTP) observations taken during the Mesoscale Predictability Experiment (MPEX). The improved capability of numerical weather prediction (NWP) models at convection-allowing grid spacing (1-4 km), and the availability of the NCAR GV airborne observing systems, argues strongly that it is time to understand how deep convection modifies the surrounding environment in much greater detail.
A multi-institutional team with broad expertise has been assembled to pursue the fundamental scientific questions of convective storm-environmental feedbacks and predictability. In particular, the team will seek to: 1) quantify the observed environmental modifications and upscale feedbacks from deep convection, and relate these back to the characteristics of the convection; 2) evaluate model simulations of upscale feedbacks from deep convection with MPEX observations; and 3) explore the predictability of convectively disturbed atmospheres. These objectives will be met using various diagnostic approaches applied to the dropsonde observations, including calculation of heat and moisture budgets; numerical model simulations with ensemble Kalman filter data assimilation at convection-allowing resolutions; and careful comparisons between MPEX observations and model simulations.
Intellectual merit: Results from this project will lead to a much better understanding of the convective storm-environmental feedback. Upscale feedbacks from deep convection will be documented carefully for the first time with the unique MPEX observations that will surround the convective region. Model analyses, constrained via the ensemble Kalman filter approach, will allow for a novel assessment of the capability of a convection-allowing model simulation to reproduce these upscale feedbacks. Improved understanding of the predictability of convectively disturbed atmospheres will provide new insight into the rapid decrease of forecast skill in research and operational numerical weather prediction models of high-impact convective weather events.
Broader impacts: Results from this project will yield new information on the observations needed within and nearby convective regions to extend the predictability of numerical model forecasts of hazardous weather events. It will also provide insight on how well upscale feedbacks are represented in current model parameterizations of deep moist convection, and how this might affect predictions on seasonal and longer time scales. Research results will be integrated into teaching materials and published to reach broad audiences. Three graduate students will be trained through participation in MPEX data collection, research and teaching activities, and participation at conference and workshops.
