Microphysical and Resolution Influences on WRF Forecasts of Convective Morphology Evolution for Nocturnal MCSs in Weakly Forced Environments

The use of convection-allowing models has allowed reproduction of detailed convective morphologies in model forecasts with some degree of success, however, some recent studies have found major shortcomings in forecasts of some modes, especially bow echos and squall lines. For both a sample of 14 nocturnal convective system events from 2010 to 2013 where the low-level jet (LLJ) was present with weak synoptic forcing, and additional cases from the 2015 PECAN field experiment, this study utilizes Advanced Research WRF (WRF-ARW) simulations to examine the predictability of convective morphology through a 10-category classification scheme. For the 14 cases, a six-member ensemble was run at 3 km horizontal grid spacing utilizing two different microphysical schemes (Thompson and WSM6) and three different planetary boundary layer schemes (YSU, MYJ, MYNN). Additionally, a subset of these runs was conducted at 1 km horizontal grid spacing to allow for investigation into the effects of increased resolution.

Preliminary results from the 2010-2013 case set show an overall underprediction of linear convective modes during the initiation to 0600 UTC time period with both microphysical schemes in the 3 km runs, which agrees with the results of previous studies at similar resolutions. However, the 1 km runs simulate more linear modes, albeit without significant improvement in mean verification score. This score, which uses a weighted scale based on either exact classification match or cellular/linear/non-linear group matches, also showed no significant difference in mean score between the two microphysics schemes. However, scores for the runs utilizing Thompson microphysics varied much more among cases than those utilizing WSM6 microphysics. Additionally, the ensemble was used to generate probabilistic forecasts of convective morphology. Analysis of the Divergence Skill Score (which is based on the Kullback-Leibler Divergence) of these forecasts showed decreasing skill over time compared to the study climatology, with less skill overall compared to a previous study not restricted to nocturnal systems (Carlberg et. al., submitted to Wea. Forecasting, 2017).

Similarly configured WRF runs have begun for several PECAN mesoscale convective system cases. Preliminary results from these cases show similar deficiencies in predicting linear convective modes and a similar decrease of skill in probabilistic forecasts over time. The effects of finer grid spacing are also being explored for the PECAN case set. In the one case simulated with 1km grid spacing so far, a similar improvement has been noted in the depiction of linear modes.