Sensitivity of 24 h Forecast Dryline Position and Structure to Boundary Layer Parameterizations in Convection-allowing WRF Model Simulations

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Thursday, 6 February 2014: 9:00 AM
Room C202 (The Georgia World Congress Center )
Adam J. Clark, CIMMS/Univ. of Oklahoma and NSSL/NOAA, Norman, OK; and M. Coniglio and B. E. Coffer

A major emphasis of recent NOAA/Hazardous Weather Testbed Spring Forecasting Experiments (SFEs) has involved examining vertical temperature/moisture profiles and low-level thermodynamic and kinematic fields in WRF model simulations with identical configurations except for their boundary layer parameterization. In fact, each year since 2010, the Center for Analysis and Prediction of Storms (CAPS) has configured at least three members of their Storm Scale Ensemble Forecast (SSEF) system specifically for examination of sensitivities to boundary layer parameterizations, including the schemes MYJ, QNSE, ACM2, YSU, and MYNN. In post-experiment analyses of these runs, significant systematic differences in forecast boundary layer structure and evolution have been observed. These differences are also reflected in forecast dryline position and structure, which is not surprising because vertical mixing processes in the boundary layer drive dryline movement and dynamics.

The goals of this study include quantifying differences in 24 h forecast dryline position in the simulations run by CAPS during Spring 2010-2012 with different boundary layer schemes, as well as documenting systematic differences in dryline structure and dryline-induced vertical circulations from these runs. The method for identifying drylines in forecasts and observations follows the manual procedure utilized in our recently published study documenting forecast dryline position errors in an experimental version of the WRF model run by the National Severe Storms Laboratory.

Results show that all schemes have a systematic eastward error in 24 h forecast dryline location. The smallest eastward errors, which averaged about 0.5 degrees longitude, were associated with MYJ. The largest eastward errors, which averaged nearly 1.0 degrees longitude, were associated with MYNN and ACM2. The large eastward errors in MYNN were somewhat counter-intuitive because recent work evaluating forecasts from the same dataset shows that MYNN depicts the boundary layer temperature and moisture profiles very well. Causes for the eastward biases will be investigated further.