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Dustan M. Wheatley and Robert J. Trapp

Abstract

This study examines the structure and evolution of quasi-linear convective systems (QLCSs) within complex mesoscale environments. Convective outflows and other mesoscale features appear to affect the rotational characteristics and associated dynamics of these systems. Thus, real-data numerical simulations of two QLCS events have been performed to (i) identify and characterize the various ambient mesoscale features that modify the structure and evolution of simulated QLCSs; and then to (ii) determine the nature of interaction of such features with the systems, with an emphasis on the genesis and evolution of low-level mesovortices.

Significant low-level mesovortices develop in both simulated QLCSs as a consequence of mechanisms internal to the system—consistent with idealized numerical simulations of mesovortex-bearing QLCSs—and not as an effect of system interaction with external heterogeneity. However, meso-γ-scale (order of 10 km) heterogeneity in the form of a convective outflow boundary is sufficient to affect mesovortex strength, as air parcels populating the vortex region encounter enhanced convergence at the point of QLCS–boundary interaction. Moreover, meso-β-scale (order of 100 km) heterogeneity in the form of interacting air masses provides for along-line variations in the distributions of low- to midlevel vertical wind shear and convective available potential energy. The subsequent impact on updraft strength/tilt has implications on the vortex stretching experienced by leading-edge mesovortices.

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Dustan M. Wheatley and David J. Stensrud

Abstract

Surface pressure observations are assimilated into a Weather Research and Forecast ensemble using an ensemble Kalman filter (EnKF) approach and the results are compared with observations for two severe weather events. Several EnKF experiments are performed to evaluate the relative impacts of two very different pressure observations: altimeter setting (a total pressure field) and 1-h surface pressure tendency. The primary objective of this study is to determine the surface pressure observation that is most successful in producing realistic mesoscale features, such as convectively driven cold pools, which often play an important role in future convective development. Results show that ensemble-mean pressure analyses produced from the assimilation of surface temperature, moisture, and winds possess significant errors in regard to mesohigh strength and location. The addition of surface pressure tendency observations within the assimilation yields limited ability to constrain such errors, while the assimilation of altimeter setting yields accurate depictions of the mesoscale pressure patterns associated with mesoscale convective systems. The mesoscale temperature patterns produced by all the ensembles are quite similar and tend to reproduce the observed features. Results suggest that even though surface pressure observations can have large cross covariances with temperature and the wind components, the resulting analyses fail to improve upon the EnKF temperature and wind analyses that exclude the surface pressure observations. Ensemble forecasts following the assimilation period show the potential to improve short-range forecasting of surface pressure.

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Dustan M. Wheatley, Robert J. Trapp, and Nolan T. Atkins

Abstract

This study examines damaging-wind production by bow-shaped convective systems, commonly referred to as bow echoes. Recent idealized numerical simulations suggest that, in addition to descending rear inflow at the bow echo apex, low-level mesovortices within bow echoes can induce damaging straight-line surface winds. In light of these findings, detailed aerial and ground surveys of wind damage were conducted immediately following five bow echo events observed during the Bow Echo and Mesoscale Convective Vortex (MCV) Experiment (BAMEX) field phase. These damage locations were overlaid directly onto Weather Surveillance Radar-1988 Doppler (WSR-88D) images to (i) elucidate where damaging surface winds occurred within the bow-shaped convective system (in proximity to the apex, north of the apex, etc.), and then (ii) explain the existence of these winds in the context of the possible damaging-wind mechanisms.

The results of this study provide clear observational evidence that low-level mesovortices within bow echoes can produce damaging straight-line winds at the ground. When present in the BAMEX dataset, mesovortex winds produced the most significant wind damage. Also in the BAMEX dataset, it was observed that smaller-scale bow echoes—those with horizontal scales of tens of kilometers or less—produced more significant wind damage than mature, extensive bow echoes (except when mesovortices were present within the larger-scale systems).

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Dustan M. Wheatley, Nusrat Yussouf, and David J. Stensrud

Abstract

A Weather Research and Forecasting Model (WRF)-based ensemble data assimilation system is used to produce storm-scale analyses and forecasts of the 4–5 July 2003 severe mesoscale convective system (MCS) over Indiana and Ohio, which produced numerous high wind reports across the two states. Single-Doppler observations are assimilated into a 36-member, storm-scale ensemble during the developing stage of the MCS with the ensemble Kalman filter (EnKF) approach encoded in the Data Assimilation Research Testbed (DART). The storm-scale ensemble is constructed from mesoscale EnKF analyses produced from the assimilation of routinely available observations from land and marine stations, rawinsondes, and aircraft, in an attempt to better represent the complex mesoscale environment for this event. Three EnKF simulations were performed using the National Severe Storms Laboratory (NSSL) one- and two-moment and Thompson microphysical schemes. All three experiments produce a linear convective segment at the final analysis time, similar to the observed system at 2300 UTC 4 July 2003. The higher-order schemes—in particular, the Thompson scheme—are better able to produce short-range forecasts of both the convective and stratiform components of the observed bowing MCS, and produce the smallest temperature errors when comparing surface observations and dropsonde data to corresponding model data. Only the higher-order microphysical schemes produce any appreciable rear-to-front flow in the stratiform precipitation region that trailed the simulated systems. Forecast performance by the three microphysics schemes is discussed in context of differences in microphysical composition produced in the stratiform precipitation regions of the rearward expanding MCSs.

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Corey K. Potvin, Elisa M. Murillo, Montgomery L. Flora, and Dustan M. Wheatley

Abstract

Observational and model resolution limitations currently preclude analysis of the smallest scales important to numerical prediction of convective storms. These missing scales can be recovered if the forecast model is integrated on a sufficiently fine grid, but not before errors are introduced that subsequently grow in scale and magnitude. This study is the first to systematically evaluate the impact of these initial-condition (IC) resolution errors on high-resolution forecasts of organized convection. This is done by comparing high-resolution supercell simulations generated using identical model settings but successively coarsened ICs. Consistent with the Warn-on-Forecast paradigm, the simulations are initialized with ongoing storms and integrated for 2 h. Both idealized and full-physics experiments are performed in order to examine how more realistic model settings modulate the error evolution.

In all experiments, scales removed from the IC (wavelengths < 2, 4, 8, or 16 km) regenerate within 10–20 min of model integration. While the forecast errors arising from the initial absence of these scales become quantitatively large in many instances, the qualitative storm evolution is relatively insensitive to the IC resolution. It therefore appears that adopting much finer forecast (e.g., 250 m) than analysis (e.g., 3 km) grids for data assimilation and prediction would improve supercell forecasts given limited computational resources. This motivates continued development of mixed-resolution systems. The relative insensitivity to IC resolution further suggests that convective forecasting can be more readily advanced by improving model physics and numerics and expanding extrastorm observational coverage than by increasing intrastorm observational density.

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Dustan M. Wheatley, David J. Stensrud, David C. Dowell, and Nusrat Yussouf

Abstract

An ensemble-based data assimilation system using the Weather Research and Forecasting Model (WRF) has been used to initialize forecasts of prolific severe weather events from springs 2007 to 2009. These experiments build on previous work that has shown the ability of ensemble Kalman filter (EnKF) data assimilation to produce realistic mesoscale features, such as drylines and convectively driven cold pools, which often play an important role in future convective development. For each event in this study, severe weather parameters are calculated from an experimental ensemble forecast started from EnKF analyses, and then compared to a control ensemble forecast in which no ensemble-based data assimilation is performed. Root-mean-square errors for surface observations averaged across all events are generally smaller for the experimental ensemble over the 0–6-h forecast period. At model grid points nearest to tornado reports, the ensemble-mean significant tornado parameter (STP) and the probability that STP > 1 are often greater in the experimental 0–6-h ensemble forecasts than in the control forecasts. Likewise, the probability of mesoscale convective system (MCS) maintenance probability (MMP) is often greater with the experimental ensemble at model grid points nearest to wind reports. Severe weather forecasts can be sharpened by coupling the respective severe weather parameter with the probability of measurable rainfall at model grid points. The differences between the two ensembles are found to be significant at the 95% level, suggesting that even a short period of ensemble data assimilation can yield improved forecast guidance for severe weather events.

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Patrick S. Skinner, Louis J. Wicker, Dustan M. Wheatley, and Kent H. Knopfmeier

Abstract

Two spatial verification methods are applied to ensemble forecasts of low-level rotation in supercells: a four-dimensional, object-based matching algorithm and the displacement and amplitude score (DAS) based on optical flow. Ensemble forecasts of low-level rotation produced using the National Severe Storms Laboratory (NSSL) Experimental Warn-on-Forecast System are verified against WSR-88D single-Doppler azimuthal wind shear values interpolated to the model grid. Verification techniques are demonstrated using four 60-min forecasts issued at 15-min intervals in the hour preceding development of the 20 May 2013 Moore, Oklahoma, tornado and compared to results from two additional forecasts of tornadic supercells occurring during the springs of 2013 and 2014.

The object-based verification technique and displacement component of DAS are found to reproduce subjectively determined forecast characteristics in successive forecasts for the 20 May 2013 event, as well as to discriminate in subjective forecast quality between different events. Ensemble-mean, object-based measures quantify spatial and temporal displacement, as well as storm motion biases in predicted low-level rotation in a manner consistent with subjective interpretation. Neither method produces useful measures of the intensity of low-level rotation, owing to deficiencies in the verification dataset and forecast resolution.

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Nusrat Yussouf, David C. Dowell, Louis J. Wicker, Kent H. Knopfmeier, and Dustan M. Wheatley

Abstract

As part of NOAA’s Warn-on-Forecast (WoF) initiative, a multiscale ensemble-based assimilation and prediction system is developed using the WRF-ARW model and DART assimilation software. To evaluate the capabilities of the system, retrospective short-range probabilistic storm-scale (convection allowing) ensemble analyses and forecasts are produced for the 27 April 2011 Alabama severe weather outbreak. Results indicate that the storm-scale ensembles are able to analyze the observed storms with strong low-level rotation at approximately the correct locations and to retain the supercell structures during the 0–1-h forecasts with reasonable accuracy. The system predicts the low-level mesocyclones of significant isolated tornadic supercells that align well with the locations of radar-derived rotation. For cases with multiple interacting storms in close proximity, the system tends to produce more variability in mesocyclone forecasts from one initialization time to the next until the observations show the dominance of one of the cells. The short-range ensemble probabilistic forecasts obtained from this continuous 5-min storm-scale 6-h-long update system demonstrate the potential of a frequently updated, high-resolution NWP system that could be used to extend severe weather warning lead times. This study also demonstrates the challenges associated with developing a WoF-type system. The results motivate future work to reduce model errors associated with storm motion and spurious cells, and to design storm-scale ensembles that better represent typical 1-h forecast errors.

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Robert J. Trapp, Dustan M. Wheatley, Nolan T. Atkins, Ronald W. Przybylinski, and Ray Wolf

Abstract

Postevent damage surveys conducted during the Bow Echo and Mesoscale Convective Vortex Experiment demonstrate that the severe thunderstorm wind reports in Storm Data served as a poor characterization of the actual scope and magnitude of the surveyed damage. Contrasting examples are presented in which a few reports grossly underrepresented a significant event (in terms of property damage and actual areal coverage of damage), while a large number of reports overrepresented a relatively less significant event. Explanations and further discussion of this problem are provided, as are some of the implications, which may include a skewed understanding of how and when systems of thunderstorms cause damage. A number of recommendations pertaining to severe wind reporting are offered.

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Nusrat Yussouf, Edward R. Mansell, Louis J. Wicker, Dustan M. Wheatley, and David J. Stensrud

Abstract

A combined mesoscale and storm-scale ensemble data-assimilation and prediction system is developed using the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW) and the ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed (DART) software package for a short-range ensemble forecast of an 8 May 2003 Oklahoma City, Oklahoma, tornadic supercell storm. Traditional atmospheric observations are assimilated into a 45-member mesoscale ensemble over a continental U.S. domain starting 3 days prior to the event. A one-way-nested 45-member storm-scale ensemble is initialized centered on the tornadic event at 2100 UTC on the day of the event. Three radar observation assimilation and forecast experiments are conducted at storm scale using a single-moment, a semi-double-moment, and a full double-moment bulk microphysics scheme. Results indicate that the EAKF initializes the supercell storm into the model with good accuracy after a 1-h-long radar observation assimilation window. The ensemble forecasts capture the movement of the main supercell storm that matches reasonably well with radar observations. The reflectivity structure of the supercell storm using a double-moment microphysics scheme appears to compare better to the observations than that using a single-moment scheme. In addition, the ensemble system predicts the probability of a strong low-level vorticity track of the tornadic supercell that correlates well with the observed rotation track. The rapid 3-min update cycle of the storm-scale ensemble from the radar observations seems to enhance the skill of the ensemble and the confidence of an imminent tornado threat. The encouraging results obtained from this study show promise for a short-range probabilistic storm-scale forecast of supercell thunderstorms, which is the main goal of NOAA's Warn-on-Forecast initiative.

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