367 A Preliminary Investigation of the Conditional Practical Predictability of the 31 May 2013 Heavy-Rain-Producing Mesoscale Convective System

Monday, 8 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Aidan R. Kuroski, Univ. of Wisconsin, Milwaukee, WI; and C. Evans

During the local evening hours on 31 May 2013, a supercell thunderstorm responsible for a deadly tornado in El Reno, OK grew upscale into an intense mesoscale convective system (MCS). This MCS, which exhibited both quasi-stationary and backbuilding characteristics, resulted in significant flooding and multiple fatalities in the northwestern Oklahoma City, OK metropolitan area. Schumacher (2015, Mon. Wea. Rev.) showed that high-resolution forecasts of this MCS had large sensitivity to convection initiation (CI) fostered by model horizontal resolution and planetary boundary layer parameterization. MCSs can greatly impact society with heavy rain and damaging winds (Weisman 1992, J. Atmos. Sci.; Schumacher and Johnson 2005, Mon. Wea. Rev.), yet have somewhat limited predictability (e.g., Wandishin et al. 2010, Wea. Forecasting). However, most MCS predictability research either prescribes CI or implicitly assumes that it will occur, which likely overstates MCS predictability because CI is not inevitable and is associated with its own limited predictability (e.g., Burghardt et al. 2014, Wea. Forecasting, and references therein). Thus, this research seeks to quantify MCS predictability when CI is not assumed or prescribed, when the entire storm cycle can be evaluated from birth to determine the major factors contributing to uncertainties in CI.

To facilitate quantification of the conditional predictability of MCS formation, motion, and structure for this particular case, a fifty-member ensemble adjustment Kalman filter-based cycled data assimilation and numerical simulation forecast system is used. The lanai release of the Data Assimilation Research Testbed (DART) software and version 3.9 of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) numerical forecast model are used for data assimilation and cycling, respectively. Cycled data assimilation begins at 1200 UTC 26 May 2013 with further assimilation cycles conducted every 6 h until 0000 UTC 31 May 2013, at which time the cycling interval is reduced to 1 h until 1200 UTC 31 May 2013. The resulting ensemble of model initial conditions is used to initialize two-way-nested 15-/3-km numerical simulations that extend forward to 0000 UTC 2 June 2013. Results will be presented with a particular focus on quantifying the practical predictability of convection initiation preceding the MCS as well as the quasi-stationarity and backbuilding characteristics of the MCS itself.

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