Evaluation of Forecasts of a Convectively Generated Bore Using an Intensively Observed Case Study from PECAN

Aaron Johnson, Xuguang Wang, Kevin R. Haghi, David B. Parsons
2018 Monthly Weather Review  
This paper presents a case study from an intensive observing period (IOP) during the Plains Elevated Convection at Night (PECAN) field experiment that was focused on a bore generated by nocturnal convection. Observations from PECAN IOP 25 on 11 July 2015 are used to evaluate the performance of highresolution Weather Research and Forecasting Model forecasts, initialized using the Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter. The focus is on understanding model errors
more » ... ing model errors and sensitivities in order to guide forecast improvements for bores associated with nocturnal convection. Model simulations of the bore amplitude are compared against eight retrieved vertical cross sections through the bore during the IOP. Sensitivities of forecasts to microphysics and planetary boundary layer (PBL) parameterizations are also investigated. Forecasts initialized before the bore pulls away from the convection show a more realistic bore than forecasts initialized later from analyses of the bore itself, in part due to the smoothing of the existing bore in the ensemble mean. Experiments show that the different microphysics schemes impact the quality of the simulations with unrealistically weak cold pools and bores with the Thompson and Morrison microphysics schemes, cold pools too strong with the WDM6 and more accurate with the WSM6 schemes. Most PBL schemes produced a realistic bore response to the cold pool, with the exception of the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme, which creates too much turbulent mixing atop the bore. A new method of objectively estimating the depth of the near-surface stable layer corresponding to a simple twolayer model is also introduced, and the impacts of turbulent mixing on this estimate are discussed.
doi:10.1175/mwr-d-18-0059.1 fatcat:ztj7gslo3zfvdo4j2o2e7i72iq