ALBERT

Description: In this study, the dynamical processes contributing to warm-core meso-β-scale vortex formation associated with the 8 May 2009 “super derecho” are examined utilizing two complementary quasi-Lagrangian approaches—a circulation budget and backward trajectory analyses—applied to a fortuitous numerical simulation of the event. Warm-core meso-β-scale vortex formation occurs in a deeply moist, potentially stable environment that is conducive to the development of near-surface rotation and is somewhat atypical compared to known derecho-supporting environments. Air parcels in the vicinity of the developing vortex primarily originate near the surface in the streamwise vorticity-rich environment, associated with the vertical wind shear of the low-level jet, immediately to the east of the eastward-moving system. Cyclonic vertical vorticity is generated along inflowing air parcels primarily by the ascent-induced tilting of streamwise vorticity and amplified primarily by ascent-induced vortex tube stretching. Descent-induced tilting of crosswise vorticity contributes to cyclonic vertical vorticity generation for the small population of air parcels in the vicinity of the developing vortex that originate to its north and west. No consistent source of preexisting vertical vorticity is present within the environment. Cyclonic circulation on the scale of the warm-core meso-β-scale vortex increases in the lower troposphere in response to the mean vortex-scale convergence of cyclonic absolute vorticity and the local expulsion of eddy anticyclonic vertical vorticity generated within the system’s cold pool. Increased cyclonic circulation is partially offset by the system-scale tilting of horizontal vorticity associated with the low-level jet, rear-inflow jet, environmental vertical wind shear, and rotational flow of the warm-core vortex itself.

Description: Convection-permitting Weather Research and Forecasting (WRF) Model forecasts with 3-km horizontal grid spacing were produced for a 50-member ensemble over a domain spanning three-quarters of the contiguous United States between 25 May and 25 June 2012. Initial conditions for the 3-km forecasts were provided by a continuously cycling ensemble Kalman filter (EnKF) analysis–forecast system with 15-km horizontal grid length. The 3-km forecasts were evaluated using both probabilistic and deterministic techniques with a focus on hourly precipitation. All 3-km ensemble members overpredicted rainfall and there was insufficient forecast precipitation spread. However, the ensemble demonstrated skill at discriminating between both light and heavy rainfall events, as measured by the area under the relative operating characteristic curve. Subensembles composed of 20–30 members usually demonstrated comparable resolution, reliability, and skill as the full 50-member ensemble. On average, deterministic forecasts initialized from mean EnKF analyses were at least as or more skillful than forecasts initialized from individual ensemble members “closest” to the mean EnKF analyses, and “patched together” forecasts composed of members closest to the ensemble mean during each forecast interval were skillful but came with caveats. The collective results underscore the need to improve convection-permitting ensemble spread and have important implications for optimizing EnKF-initialized forecasts.

Description: Herein, an analysis of a 3-km explicit convective simulation of an unusually intense bow echo and associated mesoscale vortex that were responsible for producing an extensive swath of high winds across Kansas, southern Missouri, and southern Illinois on 8 May 2009 is presented. The simulation was able to reproduce many of the key attributes of the observed system, including an intense [~100 kt (51.4 m s−1) at 850 hPa], 10-km-deep, 100-km-wide warm-core mesovortex and associated surface mesolow associated with a tropical storm–like reflectivity eye. A detailed analysis suggests that the simulated convection develops north of a weak east–west lower-tropospheric baroclinic zone, at the nose of an intensifying low-level jet. The system organizes into a north–south-oriented bow echo as it moves eastward along the preexisting baroclinic zone in an environment of large convective available potential energy (CAPE) and strong tropospheric vertical wind shear. Once the system moves east of the low-level jet and into an environment of weaker CAPE and weaker vertical wind shear, it begins an occlusion-like phase, producing a pronounced comma-shaped reflectivity echo with an intense warm-core mesovortex at the head of the comma. During this phase, a deep strip of cyclonic vertical vorticity located on the backside of the bow echo consolidates into a single vortex core. A notable weakening of the low-level convectively generated cold pool also occurs during this phase, perhaps drawing parallels to theories of tropical cyclogenesis wherein cold convective downdrafts must be substantially mitigated for subsequent system intensification.

Description: Since 2003 the National Center for Atmospheric Research (NCAR) has been running various experimental convection-allowing configurations of the Weather Research and Forecasting Model (WRF) for domains covering a large portion of the central United States during the warm season (April–July). In this study, the skill of 3-hourly accumulated precipitation forecasts from a large sample of these convection-allowing simulations conducted during 2004–05 and 2007–08 is compared to that from operational North American Mesoscale (NAM) model forecasts using a neighborhood-based equitable threat score (ETS). Separate analyses were conducted for simulations run before and after the implementation in 2007 of positive-definite (PD) moisture transport for the NCAR-WRF simulations. The neighborhood-based ETS (denoted 〈ETS〉r) relaxes the criteria for “hits” (i.e., correct forecasts) by considering grid points within a specified radius r. It is shown that 〈ETS〉r is more useful than the traditional ETS because 〈ETS〉r can be used to diagnose differences in precipitation forecast skill between different models as a function of spatial scale, whereas the traditional ETS only considers the spatial scale of the verification grid. It was found that differences in 〈ETS〉r between NCAR-WRF and NAM generally increased with increasing r, with NCAR-WRF having higher scores. Examining time series of 〈ETS〉r for r = 100 and r = 0 km (which simply reduces to the “traditional” ETS), statistically significant differences between NCAR-WRF and NAM were found at many forecast lead times for 〈ETS〉100 but only a few times for 〈ETS〉0. Larger and more statistically significant differences occurred with the 2007–08 cases relative to the 2004–05 cases. Because of differences in model configurations and dominant large-scale weather regimes, a more controlled experiment would have been needed to diagnose the reason for the larger differences that occurred with the 2007–08 cases. Finally, a compositing technique was used to diagnose the differences in the spatial distribution of the forecasts. This technique implied westward displacement errors for NAM model forecasts in both sets of cases and in NCAR-WRF model forecasts for the 2007–08 cases. Generally, the results are encouraging because they imply that advantages in convection-allowing relative to convection-parameterizing simulations noted in recent studies are reflected in an objective neighborhood-based metric.

Description: This study assesses forecasts of the preconvective and near-storm environments from the convection-allowing models run for the 2008 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) spring experiment. Evaluating the performance of convection-allowing models (CAMs) is important for encouraging their appropriate use and development for both research and operations. Systematic errors in the CAM forecasts included a cold bias in mean 2-m and 850-hPa temperatures over most of the United States and smaller than observed vertical wind shear and 850-hPa moisture over the high plains. The placement of airmass boundaries was similar in forecasts from the CAMs and the operational North American Mesoscale (NAM) model that provided the initial and boundary conditions. This correspondence contributed to similar characteristics for spatial and temporal mean error patterns. However, substantial errors were found in the CAM forecasts away from airmass boundaries. The result is that the deterministic CAMs do not predict the environment as well as the NAM. It is suggested that parameterized processes used at convection-allowing grid lengths, particularly in the boundary layer, may be contributing to these errors. It is also shown that mean forecasts from an ensemble of CAMs were substantially more accurate than forecasts from deterministic CAMs. If the improvement seen in the CAM forecasts when going from a deterministic framework to an ensemble framework is comparable to improvements in mesoscale model forecasts when going from a deterministic to an ensemble framework, then an ensemble of mesoscale model forecasts could predict the environment even better than an ensemble of CAMs. Therefore, it is suggested that the combination of mesoscale (convection parameterizing) and CAM configurations is an appropriate avenue to explore for optimizing the use of limited computer resources for severe weather forecasting applications.

Description: Sensitivity of 0–12-h warm-season precipitation forecasts to atmospheric initial conditions, including those from different large-scale model analyses and from rapid cycled (RC) three-dimensional variational data assimilations (3DVAR) with and without radar data, is investigated for a 6-day period during the International H2O Project. Neighborhood-based precipitation verification is used to compare forecasts made with the Advanced Research core of the Weather Research and Forecasting Model (ARW-WRF). Three significant convective episodes are examined by comparing the precipitation patterns and locations from different forecast experiments. From two of these three case studies, causes for the success and failure of the RC data assimilation in improving forecast skill are shown. Results indicate that the use of higher-resolution analysis in the initialization, rapid update cycling via WRF 3DVAR data assimilation, and the additional assimilation of radar observations each play a role in shortening the period of the initial precipitation spinup as well as in placing storms closer to observations, thus improving precipitation forecast skill by up to 8–9 h. Impacts of data assimilation differ for forecasts initialized at 0000 and 1200 UTC. The case studies show that the pattern and location of the forecasted precipitation were noticeably improved with radar data assimilation for the two late afternoon cases that featured lines of convection driven by surface-based cold pools. In contrast, the RC 3DVAR, both with and without radar data, had negative impacts on convective forecasts for a case of morning elevated convection associated with a midlatitude short-wave trough.

Description: Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have persistent problems with underdispersion. Representing initial and or lateral boundary condition uncertainty along with forecast model error provides a foundation for building a more dependable CPEFS, but the best practice for ensemble system design is not well established. Several configurations of CPEFSs are examined where ensemble forecasts are nested within a larger domain, drawing initial conditions from a downscaled, continuously cycled, ensemble data assimilation system that provides state-dependent initial condition uncertainty. The control ensemble forecast, with initial condition uncertainty only, is skillful but underdispersive. To improve the reliability of the ensemble forecasts, the control ensemble is supplemented with 1) perturbed lateral boundary conditions; or, model error representation using either 2) stochastic kinetic energy backscatter or 3) stochastically perturbed parameterization tendencies. Forecasts are evaluated against stage IV accumulated precipitation analyses and radiosonde observations. Perturbed ensemble forecasts are also compared to the control forecast to assess the relative impact from adding forecast perturbations. For precipitation forecasts, all perturbation approaches improve ensemble reliability relative to the control CPEFS. Deterministic ensemble member forecast skill, verified against radiosonde observations, decreases when forecast perturbations are added, while ensemble mean forecasts remain similarly skillful to the control.

Description: During the spring 2011 season, a real-time continuously cycled ensemble data assimilation system using the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed toolkit provided initial and boundary conditions for deterministic convection-permitting forecasts, also using WRF, over the eastern two-thirds of the conterminous United States (CONUS). In this study the authors evaluate the mesoscale assimilation system and the convection-permitting forecasts, at 15- and 3-km grid spacing, respectively. Experiments employing different physics options within the continuously cycled ensemble data assimilation system are shown to lead to differences in the mean mesoscale analysis characteristics. Convection-permitting forecasts with a fixed model configuration are initialized from these physics-varied analyses, as well as control runs from 0.5° Global Forecast System (GFS) analysis. Systematic bias in the analysis background influences the analysis fit to observations, and when this analysis initializes convection-permitting forecasts, the forecast skill is degraded as bias in the analysis background increases. Moreover, differences in mean error characteristics associated with each physical parameterization suite lead to unique errors of spatial, temporal, and intensity aspects of convection-permitting rainfall forecasts. Observation bias by platform type is also shown to impact the analysis quality.