10.6 Fog-Prediction Errors Evaluated for Multiple Physical Parameterization Schemes in the AFWA Mesoscale Ensemble

Ryerson, William, Joshua Hacker, Mary Jordan, and Kurt Nielsen, Naval Postgraduate School

Skillful fog prediction relies heavily on skillful NWP predictions of cloud water mixing ratio in the boundary layer.  Motivated by ceiling and visibility predictions for AFWA, this work evaluates fog-related predictions from individual members of a 10-member WRF ensemble with 4-km horizontal resolution.  Each member receives unique initial and lateral boundary conditions and has varied land surface characteristics, but is also configured with a unique parameterization suite.  The 3-month wintertime evaluation period encompasses seven California sites representing distinct coastal, valley, and mountainous mesoclimates.  Results from the lowest model layer (roughly 20 m AGL) show that all physics combinations produce far too many forecasts of zero cloud water compared to observations, and that this bias is at the expense of cloud water values corresponding to visibilities of 1-7 mi, which are commonly observed but rarely predicted by the WRF runs.  Lack of cloud water is shown to be mostly due to a warm bias that is greatest at night and in the coastal region, and varies in magnitude depending on the WRF member.  Although cloud water predictions are not produced at the 2 m level, temperature and relative humidity predictions at 2 m have less bias, albeit with greater inter-member variability.