Christopher Kerr-February 7 Colloquium

Analysis of environmental modifications by deep convection during MPEX

The Mesoscale Predictability Experiment (MPEX; 2013) included frequent coordinated sampling of near-storm environments via upsondes.  These unique observations were taken to better understand the upscale effects of deep convection on the environment, and are used to validate the accuracy of convection-allowing (3-km grid spacing) model ensemble analyses. A 36-member ensemble was created with physics diversity using the WRF model, and Doppler radar and conventional observations were assimilated via the Data Assimilation Research Testbed using an ensemble adjustment Kalman filter. No MPEX upsonde observations were assimilated. They are used to verify the accuracy of the analyses of the near-storm environments. A total of 81 upsondes were released over the four-day period, sampling different regions of near-storm environments including storm inflow, outflow, downstream, and anvil. The observations reveal modest analysis errors overall when considering all samples, although specific environmental regions reveal larger errors in some state fields.  The ensemble analyses underestimate cold pool depth, and storm inflow meridional winds have a pronounced northerly bias which results from an under-prediction of inflow.  These ensemble analyses reveal how convection perturbs the surrounding environment.  The ensemble sensitivity analysis method is used to show the dependence of convection evolution on the near-storm environment.

Mr. Kerr is a Ph.D. candidate within the OU’s School of Meteorology (SoM).  He earned his B.S. in physics from Clemson University and completed his M.S. in meteorology at the SoM in 2013.  In 2015 he received the Yoshi Sasaki Award for Best M.S. Publication. His research interests include convective-scale data assimilation and short-term predictability of convective storms