Overview and preliminary results of mobile upsonde operations during the Mesoscale Predictability Experiment (MPEX)

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Tuesday, 4 February 2014: 3:45 PM
Room C201 (The Georgia World Congress Center )
Russ S. Schumacher, Colorado State University, Ft. Collins, CO; and M. C. Coniglio, R. J. Trapp, M. E. Baldwin, and D. J. Stensrud

From 15 May-15 June 2013, the Mesoscale Predictability Experiment (MPEX) was conducted. The research goals of MPEX were aimed at identifying the times and locations for collecting sounding observations that would most improve explicit-convection numerical forecasts of hazardous convective storms, and at understanding how deep convective storms alter their environments and how those alterations, in turn, affect the skill of convection-permitting forecasts on subsequent days. Two sets of sounding observations were collected during intensive observing periods: dropsondes over the western United States during the morning, and mobile ground-based radiosonde launches (upsondes) in the afternoon and evening.

This presentation will focus on the approximately 225 upsonde observations that were collected during MPEX. These observations were taken using a novel observing strategy involving three to four coordinated vehicles launching balloon-borne radiosondes in the environment both preceding the development of convective storms and in the convectively disturbed environment near supercell storms, mesoscale convective systems, and failed attempts at convection initiation. Soundings at high temporal and spatial resolution revealed the detailed structure of the environment both prior to and during the tornado outbreaks in Kansas and Oklahoma on 18, 19, 20, and 31 May 2013, non-tornadic supercells on 30 May 2013, and mesoscale convective systems on 23, 28, 29 May and 3 and 8 June 2013. The presentation will discuss the observing strategy during these events, along with preliminary insights into the environments in which these convective storms developed, the effects of the convection on its environment, and the potential importance of these mobile observations on improving numerical model forecasts.