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Moti Segal, Eric A. Aligo, and William A. Gallus Jr.

Abstract

A conceptual evaluation and scaling of the potential impact of surface wetness on spring/summer midlatitude daytime surface cold front moisture convergence is presented. First, a simplified expression is derived, evaluating the effect of surface wetness on frontal moisture convergence due to a differential cloud-cover-induced thermal gradient perturbation. It indicates that wet surfaces may be conducive to enhanced moisture convergence compared with dry surfaces only for very high values of both the cross-frontal relative wind component and the frontal background vertical velocity. With increased background specific humidity in the warm sector, decreased cross-frontal relative wind speed, and a less stable early morning temperature lapse rate, dry surface conditions are significantly more conducive to enhanced frontal moisture convergence. When the daytime boundary layer thermal destabilization effects on the frontal updraft are considered, generally insignificant modifications of the above patterns of frontal moisture convergence are indicated. Overall, the evaluation suggests that typically dry surfaces better promote daytime frontal moisture convergence than wet surfaces, a result that is counterintuitive.

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Eric A. Aligo, William A. Gallus Jr., and Moti Segal

Abstract

Weather Research and Forecast (WRF) model exploratory sensitivity simulations were performed to determine the impact of vertical grid resolution (VGR) on the forecast skill of Midwest summer rainfall. Varying the VGR indicated that a refined VGR, while adopting the widely used North America Regional Reanalysis (NARR) for initial and lateral boundary conditions, does not necessarily result in a consistent improvement in quantitative precipitation forecasts (QPFs). When averaged over a variety of microphysical schemes in an illustrative case, equitable threat score (ETS) and bias values actually worsened with a greater overpredicted rainfall for half of the rainfall thresholds when the VGR was refined. Averaged over strongly forced cases, ETS values worsened for all rainfall thresholds while biases mostly increased, indicating a further overprediction of rainfall when the number of levels was increased. Skill improved, however, for all rainfall thresholds when the resolution above the melting level was increased. Skill also improved for most rainfall thresholds when the resolution in the surface layer was increased, which is attributed to better-resolved surface turbulent momentum and thermal fluxes. Likewise, a refined VGR resulted in improvements in weakly forced cases, which are governed mostly by thermodynamic forcing and are sensitive to vertical profiles of temperature and moisture. Application of the factor separation method suggested that the refined VGR more frequently had a negative impact on skill through the interaction between lower-atmospheric processes and microphysical processes above the melting level.

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Eric A. Aligo, Brad Ferrier, and Jacob R. Carley

Abstract

The Ferrier–Aligo (FA) microphysics scheme has been running operationally in the National Centers for Environmental Prediction (NCEP) North American Mesoscale Forecast System (NAM) since August 2014. It was developed to improve forecasts of deep convection in the NAM contiguous United States (CONUS) nest, and it replaces previous versions of the NAM microphysics. The FA scheme is the culmination of extensive microphysical scheme sensitivity experiments made over nearly a dozen warm- and cool-season severe weather cases, as well as an extensive real-time testing in a full, system-wide developmental version of the NAM. While the FA scheme advects each hydrometeor species separately, it was the mass-weighted rime factor (RF) that allowed rimed ice to be advected to very cold temperatures aloft and improved the vertical structure of deep convection. Rimed ice fall speeds were reduced in order to offset an increase in bias of heavy precipitation as a consequence of the mass-weighted RF advection. The FA scheme also incorporated findings from 3-km model runs using the Thompson scheme, including 1) improved closure assumptions for large precipitating ice that targeted the convective and anvil regions of storms, 2) a new diagnostic calculation of radar reflectivity from rimed ice in association with intense convection, and 3) a variable rain intercept parameter that reduced widespread spurious weak reflectivity from shallow boundary layer clouds and increased stratiform rainfall.

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Eric A. Aligo, William A. Gallus Jr., and Moti Segal

Abstract

The performance of an ensemble forecasting system initialized using varied soil moisture alone has been evaluated for rainfall forecasts of six warm season convective cases. Ten different soil moisture analyses were used as initial conditions in the ensemble, which used the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model at 4-km horizontal grid spacing with explicit rainfall. Soil moisture analyses from the suite of National Weather Service operational models—the Rapid Update Cycle, the North American Model (formerly known as the Eta Model), and the Global Forecasting System—were used to design the 10-member ensemble. For added insight, two other runs with extremely low and high soil moistures were included in this study. Although the sensitivity of simulated 24-h rainfall to soil moisture was occasionally substantial in both weakly forced and strongly forced cases, a U-shaped rank histogram indicated insufficient spread in the 10-member ensemble. This result suggests that ensemble forecast systems using soil moisture perturbations alone might not add enough variability to rainfall forecasts. Perturbations to both atmospheric initial conditions and land surface initial conditions as well as perturbations to other aspects of model physics may increase forecast spread. Correspondence ratio values for the 0.01- and 0.5-in. rainfall thresholds imply some spread in the soil moisture ensemble, but mainly in the weakly forced cases. Relative operating characteristic curves for the 10-member ensemble and for various rainfall thresholds indicate modest skill for all thresholds with the most skill associated with the lowest rainfall threshold, a result typical of warm season events.

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Jamie K. Wolff, Michelle Harrold, Tracy Hertneky, Eric Aligo, Jacob R. Carley, Brad Ferrier, Geoff DiMego, Louisa Nance, and Ying-Hwa Kuo

Abstract

A wide range of numerical weather prediction (NWP) innovations are under development in the research community that have the potential to positively impact operational models. The Developmental Testbed Center (DTC) helps facilitate the transition of these innovations from research to operations (R2O). With the large number of innovations available in the research community, it is critical to clearly define a testing protocol to streamline the R2O process. The DTC has defined such a process that relies on shared responsibilities of the researchers, the DTC, and operational centers to test promising new NWP advancements. As part of the first stage of this process, the DTC instituted the mesoscale model evaluation testbed (MMET), which established a common testing framework to assist the research community in demonstrating the merits of developments. The ability to compare performance across innovations for critical cases provides a mechanism for selecting the most promising capabilities for further testing. If the researcher demonstrates improved results using MMET, then the innovation may be considered for the second stage of comprehensive testing and evaluation (T&E) prior to entering the final stage of preimplementation T&E.

MMET provides initialization and observation datasets for several case studies and multiday periods. In addition, the DTC provides baseline results for select operational configurations that use the Advanced Research version of Weather Research and Forecasting Model (ARW) or the National Oceanic and Atmospheric Administration (NOAA) Environmental Modeling System Nonhydrostatic Multiscale Model on the B grid (NEMS-NMMB). These baselines can be used for testing sensitivities to different model versions or configurations in order to improve forecast performance.

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Corey K. Potvin, Jacob R. Carley, Adam J. Clark, Louis J. Wicker, Patrick S. Skinner, Anthony E. Reinhart, Burkely T. Gallo, John S. Kain, Glen S. Romine, Eric A. Aligo, Keith A. Brewster, David C. Dowell, Lucas M. Harris, Israel L. Jirak, Fanyou Kong, Timothy A. Supinie, Kevin W. Thomas, Xuguang Wang, Yongming Wang, and Ming Xue

Abstract

The 2016–18 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFE) featured the Community Leveraged Unified Ensemble (CLUE), a coordinated convection-allowing model (CAM) ensemble framework designed to provide empirical guidance for development of operational CAM systems. The 2017 CLUE included 81 members that all used 3-km horizontal grid spacing over the CONUS, enabling direct comparison of forecasts generated using different dynamical cores, physics schemes, and initialization procedures. This study uses forecasts from several of the 2017 CLUE members and one operational model to evaluate and compare CAM representation and next-day prediction of thunderstorms. The analysis utilizes existing techniques and novel, object-based techniques that distill important information about modeled and observed storms from many cases. The National Severe Storms Laboratory Multi-Radar Multi-Sensor product suite is used to verify model forecasts and climatologies of observed variables. Unobserved model fields are also examined to further illuminate important intermodel differences in storms and near-storm environments. No single model performed better than the others in all respects. However, there were many systematic intermodel and intercore differences in specific forecast metrics and model fields. Some of these differences can be confidently attributed to particular differences in model design. Model intercomparison studies similar to the one presented here are important to better understand the impacts of model and ensemble configurations on storm forecasts and to help optimize future operational CAM systems.

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