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Jana Lesak Houser, Howard B. Bluestein, Kyle Thiem, Jeffrey Snyder, Dylan Reif, and Zachary Wienhoff

Abstract

This study builds upon recent rapid-scan radar observations of mesocyclonic tornadogenesis in supercells by investigating the formation of seven tornadoes (four from a single cyclic supercell), most of which include samples at heights < 100 m above radar level. The spatiotemporal evolution of the tornadic vortex signatures (TVSs), maximum velocity differentials across the vortex couplet, and pseudovorticity are analyzed. In general, the tornadoes formed following a non-descending pattern of evolution, although one case was descending over time scales O(<60) s and the evolution of another case was dependent upon the criteria used to define a tornado, and may have been associated with a rapidly occurring top-down process. Thus, it was determined that the vertical sense of evolution of a tornado can be sensitive to the criteria employed to define a TVS. Furthermore, multiple instances were found in which TVSs terminated at heights below 1.5 km, although vertical sampling above this height was often limited.

Significance Statement

It is generally well understood that tornadoes form over short time scales [i.e., O(∼60) s]. Despite this fact, detailed scientific measurements of tornado evolution during and just prior to genesis remains limited, particularly very near the ground and on time and space scales sufficient to observe tornado processes. Multiple recent studies have supported a non-descending evolution of rotation in supercell tornadoes, but the small number of analyzed cases is still insufficient for generalization. This study investigates seven new cases of tornadogenesis using high spatiotemporal resolution radar data that include near-ground level observations to examine the evolution of rotation with time and height. For the time scales observable by the radar platform [i.e., O(∼30) s], genesis occurred predominately following a non-descending manner in five out of the seven tornadoes studied, while the vertical evolution of two tornadoes were sensitive to the criterion used to define a “tornadic” vortex signature.

Open access
Frank P. Colby Jr., David Coe, Mathew Barlow, Ryan Brown, and Elizabeth Krajewski

Abstract

Snow squalls are sudden snow events that last less than 1 h, are characterized by low visibility and gusty winds, and can result in notable societal impacts. This analysis develops a climatology of non-lake-effect snow squall events in southern New England for 1994–2018 and investigates the synoptic environment and mesoscale factors conducive to their formation. National Weather Service surface observations were used to identify events; sea level pressure maps, composite radar charts, and a cell-tracking algorithm were used to determine their organization and movement; and ERA5 hourly reanalysis data were used to analyze the associated synoptic and infer mesoscale features, as well as convective and symmetric instability. A total of 100 events were identified and categorized into four distinct types on the basis of the direction of movement of the associated radar echoes, which is closely linked to characteristic synoptic structures and mesoscale factors. The four types are Classic (squall movement from the northwest; 72 events), Atlantic (from the southwest; 15 events), Northern (from the north; 9 events), and Special (varying; 4 events). All types have a 500-hPa trough over the Northeast but differ in the structure of the trough and its relation to lower-level flow, which accounts for the differences in movement of the squalls. The snow events occur in shallow, convective squall lines, and the ingredients for convection were present in all cases. Both upright and symmetric instability are typically present, all cases had at least one lower-tropospheric layer with cyclonic differential vorticity advection, and many cases were also associated with frontogenesis.

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Xiaohui Zhao and Ryan D. Torn

Abstract

Tropical cyclone (TC) intensity has been shown to have limited predictability in numerical weather prediction models; therefore, ensemble forecasting may be critical. An ensemble prediction system (EPS) should ideally cover all sources of uncertainty; however, most meso and convective-scale EPSs typically consider initial-condition uncertainty alone, with limited treatment of model uncertainty, even though the evolution of mesoscale features is highly dependent on uncertain parameterization schemes. The role of stochastic treatment of model error in the Hurricane Weather Research and Forecasting (HWRF) EPS is evaluated by applying independent stochastically perturbed parameterization (iSPPT) scheme to individual parameterization schemes for four TCs from 2017-2018. Experiments with Hurricane Irma (2017) indicate that TC intensity ensemble standard deviation is most sensitive to the amplitude of the stochastic perturbation field, with smaller impact from adjusting the decorrelation time scale and spatial length scale. Results from all four TC cases show that stochastic perturbations to the turbulent mixing scheme can increase the ensemble standard deviation in intensity metrics over a 72-h simulation without introducing significant differences in mean error or bias. By contrast, stochastic perturbations to the microphysics, radiation, and cumulus tendencies have negligible effects on intensity standard deviation.

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Timothy W. Juliano, Branko Kosović, Pedro A. Jiménez, Masih Eghdami, Sue Ellen Haupt, and Alberto Martilli

Abstract

Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Δx) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Δx ≈ 100–1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional mesoscale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation.

Significance Statement

The spatial resolution of weather models continues to increase at a rapid rate in accordance with the enhancement of computing power. As a result, smaller-scale atmospheric features become more explicitly resolved. However, most numerical models still ignore the impact of horizontal weather variations on boundary layer flows, which becomes more important at these smaller spatial scales. To address this issue, we have implemented a new modeling approach, using fundamental principles, which accounts for horizontal variability. Our results show that including three-dimensional effects of turbulence is necessary to achieve realistic boundary layer characteristics. This novel technique may be useful for many applications including complex terrain flows, pollutant dispersion, and surface–atmosphere interaction studies.

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Naveen Goutham, Riwal Plougonven, Hiba Omrani, Sylvie Parey, Peter Tankov, Alexis Tantet, Peter Hitchcock, and Philippe Drobinski

Abstract

Subseasonal forecasts of 100-m wind speed and surface temperature, if skillful, can be beneficial to the energy sector as they can be used to plan asset availability and maintenance, assess risks of extreme events, and optimally trade power on the markets. In this study, we evaluate the skill of the European Centre for Medium-Range Weather Forecasts’ subseasonal predictions of 100-m wind speed and 2-m temperature. To the authors’ knowledge, this assessment is the first for the 100-m wind speed, which is an essential variable of practical importance to the energy sector. The assessment is carried out on both forecasts and reforecasts over European domain gridpoint wise and also by considering several spatially averaged domains, using several metrics to assess different attributes of forecast quality. We propose a novel way of synthesizing the continuous ranked probability skill score. The results show that the skill of the forecasts and reforecasts depends on the choice of the climate variable, the period of the year, and the geographical domain. Indeed, the predictions of temperature are better than those of wind speed, with enhanced skill found for both variables in the winter relative to other seasons. The results also indicate significant differences between the skill of forecasts and reforecasts, arising mainly due to the differing ensemble sizes. Overall, depending on the choice of the geographical domain and the forecast attribute, the results show skillful predictions beyond 2 weeks, and in certain cases, up to 6 weeks for both variables, thereby encouraging their implementation in operational decision-making.

Open access
Yongjie Huang, Xuguang Wang, Andrew Mahre, Tian-You Yu, and David Bodine

Abstract

Phased-array radar (PAR) technology can potentially provide high-quality clear-air radial velocity observations at a high spatiotemporal resolution, usually ∼1 min or less. These observations are hypothesized to partially fill the gaps in current operational observing systems with relatively coarse-resolution surface mesonet observations and the lack of high-resolution upper-air observations especially in planetary boundary layer. In this study, observing system simulation experiments are conducted to investigate the potential value of assimilating PAR observations of clear-air radial velocity to improve the forecast of convection initiation (CI) along small-scale boundary layer convergence zones. Both surface-based and elevated CIs driven by meso-γ-scale boundary layer convergence are tested. An ensemble Kalman filter method is used to assimilate synthetic surface mesonet observations and PAR clear-air radial velocity observations. Results show that assimilating only surface mesonet observations fails to predict either surface-based or elevated CI processes. Assimilating clear-air radial velocity observations in addition to surface mesonet observations can capture both surface-based and elevated CI processes successfully. Such an improvement benefits from the better analyses of boundary layer convergence, resulting from the assimilation of clear-air radial velocity observations. Additional improvement is observed with more frequent assimilation. Assimilating clear-air radial velocity observations only from the one radar results in analysis biases of cross-beam winds and CI location biases, and assimilating additional radial velocity observations from the second radar at an appropriate position can reduce these biases while sacrificing the CI timing. These results suggest the potential of assimilating clear-air radial velocity observations from PAR to improve the forecast of CI processes along boundary layer convergence zones.

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Ján Mašek, Ivan Bašták Ďurán, and Radmila Brožková

Abstract

In this paper, we present a new and more stable numerical implementation of the two-energy configuration of the Third Order Moments Unified Condensation and N-dependent Solver (TOUCANS) turbulence scheme. The original time-stepping scheme in TOUCANS tends to suffer from spurious oscillations in stably stratified turbulent flows. Because of their high frequency, the oscillations resemble the so-called fibrillations that are caused by the coupling between turbulent exchange coefficients and the stability parameter. However, our analysis and simulations show that the oscillations in the two-energy scheme are caused by the usage of a specific implicit–explicit temporal discretization for the relaxation terms. In TOUCANS, the relaxation technique is used on source and dissipation terms in prognostic turbulence energy equations to ensure numerical stability for relatively long time steps. We present both a detailed linear stability analysis and a bifurcation analysis, which indicate that the temporal discretization is oscillatory for time steps exceeding a critical time-step length. Based on these findings, we propose a new affordable time discretization of the involved terms that makes the scheme more implicit. This ensures stable solutions with enough accuracy for a wider range of time-step lengths. We confirm the analytical outcomes in both idealized 1D and full 3D model experiments.

Significance Statement

The vertical turbulent transport of momentum, heat, and moisture has to be parameterized in numerical weather prediction models. The parameterization typically employs nonlinear damping equations, whose numerical integration can lead to unphysical, time-oscillating solutions. In general, a presence of such numerical noise negatively affects the model performance. In our work, we address numerical issues of the recently developed scheme with two prognostic turbulence energies that have more realism and physical complexity. Specifically, we detect, explain, and design a numerical treatment for a new type of spurious oscillations that is connected to the temporal discretization. The treatment suppresses the oscillations and allows us to increase the model time step more than 4 times while keeping an essentially non-oscillatory solution.

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Anthony W. Lyza, Matthew D. Flournoy, and Erik N. Rasmussen

Abstract

A historic outbreak of tornadoes impacted a large swath of the eastern United States on 26-28 April 2011. The most severe series of tornadoes was associated with numerous classic supercell thunderstorms that developed across the Southeast during the afternoon and evening of 27 April and continued into the predawn of 28 April. This study documents characteristics of these storms with respect to tornado production and mesocyclone strength during different periods of each storm’s lifecycle. The supercells initiated in four quasi-distinct spatiotemporal regions, with each cluster exhibiting slightly different evolutionary traits and tornado production. These included differences in the mean times between convection initiation and the time of first tornadogenesis for each supercell, as well as variations in overall and significant tornado production. This suggests that mesoscale environmental differences, such as proximity to a mesoscale boundary, and/or storm-scale events strongly influenced the variety of supercell evolutionary paths that were observed during this event, even in the presence of a synoptic-scale background environment extremely favorable for supercell and tornado production. The azimuthal shear products from the Multi-Year Reanalysis of Remotely Sensed Storms database perform well in discriminating between mesocyclones associated with ongoing weak, strong, and violent tornadoes during the event. Furthermore, mean azimuthal shear values during pretornadic (e.g., within 30 min of tornadogenesis) and tornadic phases are significantly larger than those during nontornadic phases. This warrants further study of azimuthal shear characteristics in different environments and its potential usefulness in aiding real-time forecasting efforts.

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Stanley B. Trier, Robert D. Sharman, Domingo Muñoz-Esparza, and Teddie L. Keller

Abstract

Two cases of observed widespread moderate-or-greater (MOG) clear-air turbulence (CAT) in different synoptic patterns are investigated using a nested high-resolution NWP model. Both of these cases occurred in confluent entrance regions of upper-tropospheric/lower-stratospheric (UTLS) jet streaks, where large-scale anticyclonic outflow from distant organized moist convection strengthened the UTLS jet. Both the strength and vertical sharpness of the resulting jet influence the altitudes of MOG turbulence and the details of simulated turbulence onset mechanisms. In a strong and narrow UTLS jet downstream of a weak synoptic ridge, MOG turbulence arises from Kelvin-Helmholtz (KH) waves that overturn in opposite directions on the vertical flanks of the jet. In broader UTLS jets, MOG turbulence arising from KH waves was favored in strong vertical shear layers beneath the wind maximum, but was inhibited above it due to static stability increases near the tropopause. However, vertically propagating internal gravity waves initiated above KH wave breaking beneath the UTLS jet amplify within the lower stratosphere above the jet, constituting another possible source of turbulence. Turbulence onset mechanisms were often apparent in simulations with minimum horizontal grid spacings of Δx = 1 km. However, amplitudes of the associated grid-resolved vertical motions were unreliable when compared with simulations having minimum horizontal grid spacings of Δx = 1/3 km. In spite of this, turbulence forecasting systems driven by input from coarser resolution operational NWP models are demonstrated to provide good diagnoses of this type of convectively-influenced CAT when the NWP model accurately forecasts upstream convection.

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Rumeng Li, Juanzhen Sun, Qinghong Zhang, and Anders A. Jensen

Abstract

Hailstones have large damage potential; however, their explicit prediction remains quite challenging. The uncertainty in model’s initial condition and microphysics are two of the significant contributors to the challenge. This two-part study aims to investigate the impacts of improved initial condition and microphysics on hail prediction for a moderate hailstorm occurred in Beijing on 10 June 2016. In the first part, the role of initial condition on hail prediction is explored by assimilating high-density observations into a numerical model with a lately developed explicit hail microphysics scheme. High-resolution and high-frequency observations from radar and surface networks are assimilated using the Weather Research and Forecasting (WRF) Model’s three-dimensional variational data assimilation (3DVAR) system. The role of initial condition in improving explicit hail prediction with two different planetary boundary layer (PBL) schemes, the Yonsei University (YSU) scheme and the Mellor-Yamada-Janjic (MYJ) scheme, is then examined. Results indicate that the data assimilation significantly improves the hail size and location prediction for both PBL schemes by reducing errors in surface wind, temperature and moisture fields. It is also shown that the improved analyses of low-level and mid-level vertical wind shears, resulting mainly from radar data assimilation, are pivotal to the improvement of hailstorm prediction with the YSU scheme while the improved analysis of thermodynamic field resulting from the assimilation of both radar and surface data plays a more important role with the MYJ scheme. The results of this work shed light on the influence of data assimilation and provide insights on explicit hail predictability with respect to model initial condition.

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