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William A. Gallus Jr., Jamie Wolff, John Halley Gotway, Michelle Harrold, Lindsay Blank, and Jeff Beck


A well-known problem in high-resolution ensembles has been a lack of sufficient spread among members. Modelers often have used mixed physics to increase spread, but this can introduce problems including computational expense, clustering of members, and members that are not all equally skillful. Thus, a detailed examination of the impacts of using mixed physics is important. The present study uses two years of Community Leveraged Unified Ensemble (CLUE) output to isolate the impact of mixed physics in 36-h forecasts made using a convection-permitting ensemble with 3-km horizontal grid spacing. One 10-member subset of the CLUE used only perturbed initial conditions (ICs) and lateral boundary conditions (LBCs) while another 10-member ensemble used the same mixed ICs and LBCs but also introduced mixed physics. The cases examined occurred during NOAA’s Hazardous Weather Testbed Spring Forecast Experiments in 2016 and 2017. Traditional gridpoint metrics applied to each member and the ensemble as a whole, along with object-based verification statistics for all members, were computed for composite reflectivity and 1- and 3-h accumulated precipitation using the Model Evaluation Tools (MET) software package. It is found that the mixed physics increases variability substantially among the ensemble members, more so for reflectivity than precipitation, such that the envelope of members is more likely to encompass the observations. However, the increased variability is mostly due to the introduction of both substantial high biases in members using one microphysical scheme, and low biases in other schemes. Overall ensemble skill is not substantially different from the ensemble using a single physics package.

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Jamie K. Wolff, Michelle Harrold, Tressa Fowler, John Halley Gotway, Louisa Nance, and Barbara G. Brown


While traditional verification methods are commonly used to assess numerical model quantitative precipitation forecasts (QPFs) using a grid-to-grid approach, they generally offer little diagnostic information or reasoning behind the computed statistic. On the other hand, advanced spatial verification techniques, such as neighborhood and object-based methods, can provide more meaningful insight into differences between forecast and observed features in terms of skill with spatial scale, coverage area, displacement, orientation, and intensity. To demonstrate the utility of applying advanced verification techniques to mid- and coarse-resolution models, the Developmental Testbed Center (DTC) applied several traditional metrics and spatial verification techniques to QPFs provided by the Global Forecast System (GFS) and operational North American Mesoscale Model (NAM). Along with frequency bias and Gilbert skill score (GSS) adjusted for bias, both the fractions skill score (FSS) and Method for Object-Based Diagnostic Evaluation (MODE) were utilized for this study with careful consideration given to how these methods were applied and how the results were interpreted. By illustrating the types of forecast attributes appropriate to assess with the spatial verification techniques, this paper provides examples of how to obtain advanced diagnostic information to help identify what aspects of the forecast are or are not performing well.

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