In recent years, a mixed-physics ensemble approach has been investigated as a method to better predict mesoscale convective system (MCS) rainfall. For both mixed-physics ensemble design and interpretation, knowledge of the general impact of various physical schemes and their interactions on warm season MCS rainfall forecasts would be useful. Adopting the newly emerging Weather Research and Forecasting (WRF) model for this purpose would further emphasize such benefits. To pursue this goal, a matrix of 18 WRF model configurations, created using different physical scheme combinations, was run with 12-km grid spacing for eight International H2O Project (IHOP) MCS cases. For each case, three different treatments of convection, three different microphysical schemes, and two different planetary boundary layer schemes were used. Sensitivity to physics changes was determined using the correspondence ratio and the squared correlation coefficient. The factor separation method was also used to quantify in detail the impacts of the variation of two different physical schemes and their interaction on the simulated rainfall. Skill score measures averaged over all eight cases for all 18 configurations indicated that no one configuration was obviously best at all times and thresholds. The greatest variability in forecasts was found to come from changes in the choice of convective scheme, although notable impacts also occurred from changes in the microphysics and planetary boundary layer (PBL) schemes. Specifically, changes in convective treatment notably impacted the forecast of system average rain rate, while forecasts of total domain rain volume were influenced by choices of microphysics and convective treatment. The impact of interactions (synergy) of different physical schemes, although occasionally of comparable magnitude to the impacts from changing one scheme alone (compared to a control run), varied greatly among cases and over time, and was typically not statistically significant. KeywordsAgronomy, boundary layers, computer simulation, correlation methods, mathematical models, rain, mesoscale convective system (MCS) rainfall, planetary boundary layer, weather research and forecasting (WRF) model, weather forecasting, cloud microphysics, convective system, ensemble forecasting, forecasting method, parameterization ABSTRACTIn recent years, a mixed-physics ensemble approach has been investigated as a method to better predict mesoscale convective system (MCS) rainfall. For both mixed-physics ensemble design and interpretation, knowledge of the general impact of various physical schemes and their interactions on warm season MCS rainfall forecasts would be useful. Adopting the newly emerging Weather Research and Forecasting (WRF) model for this purpose would further emphasize such benefits. To pursue this goal, a matrix of 18 WRF model configurations, created using different physical scheme combinations, was run with 12-km grid spacing for eight International H 2 O Project (IHOP) MCS cases. For each case, three diffe...
A stochastic parameter perturbation (SPP) scheme consisting of spatially and temporally varying perturbations of uncertain parameters in the Grell–Freitas convective scheme and the Mellor–Yamada–Nakanishi–Niino planetary boundary scheme was developed within the Rapid Refresh ensemble system based on the Weather Research and Forecasting Model. Alone the stochastic parameter perturbations generate insufficient spread to be an alternative to the operational configuration that utilizes combinations of multiple parameterization schemes. However, when combined with other stochastic parameterization schemes, such as the stochastic kinetic energy backscatter (SKEB) scheme or the stochastic perturbation of physics tendencies (SPPT) scheme, the stochastic ensemble system has comparable forecast performance. An additional analysis quantifies the added value of combining SPP and SPPT over an ensemble that uses SPPT only, which is generally beneficial, especially for surface variables. The ensemble combining all three stochastic methods consistently produces the best spread–skill ratio and generally outperforms the multiphysics ensemble. The results of this study indicate that using a single-physics suite ensemble together with stochastic methods is an attractive alternative to multiphysics ensembles and should be considered in the design of future high-resolution regional and global ensembles.
Numerical prediction of precipitation associated with five cool-season atmospheric river events in northern California was analyzed and compared to observations. The model simulations were performed by using the Advanced Research Weather Research and Forecasting Model (ARW-WRF) with four different microphysical parameterizations. This was done as a part of the 2005-06 field phase of the Hydrometeorological Test Bed project, for which special profilers, soundings, and surface observations were implemented. Using these unique datasets, the meteorology of atmospheric river events was described in terms of dynamical processes and the microphysical structure of the cloud systems that produced most of the surface precipitation. Events were categorized as ''bright band'' (BB) or ''nonbright band'' (NBB), the differences being the presence of significant amounts of ice aloft (or lack thereof) and a signature of higher reflectivity collocated with the melting layer produced by frozen precipitating particles descending through the 08C isotherm.The model was reasonably successful at predicting the timing of surface fronts, the development and evolution of low-level jets associated with latent heating processes and terrain interaction, and wind flow signatures consistent with deep-layer thermal advection. However, the model showed the tendency to overestimate the duration and intensity of the impinging low-level winds. In general, all model configurations overestimated precipitation, especially in the case of BB events. Nonetheless, large differences in precipitation distribution and cloud structure among model runs using various microphysical parameterization schemes were noted. FIG. 4. Simulated (a) wind profile at BBY and (b) precipitation accumulation by the model run using the Lin microphysics for the period 0000 UTC 30 to 2200 UTC 31 Dec 2005. Wind flags, barbs, and isotachs as in Fig. 3. AUGUST 2009 J A N K O V E T A L . FIG. 8. Cloud water, cloud ice, rain, snow, and graupel mixing ratios (310 23 kg kg 21 ) averaged over five events and the American River basin for the four different microphysics schemes. AUGUST 2009 J A N K O V E T A L . FIG. 13. Snow (see color bar), rain (white contours) and graupel (black contours) mixing ratios (kg kg 21 ), and 08C temperature (red) and wet bulb temperature (green) lines for the 30 Dec 2005 event at CZD for model runs using the (a) Lin, (b) WSM6, (c) Thompson, and (d) Schultz microphysics. Melting level heights (asterisks) from available OAK soundings. Both shading and contours are scaled by a factor of 10 24 . AUGUST 2009 J A N K O V E T A L .
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