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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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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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Roger M. Wakimoto, Zachary Wienhoff, Dylan Reif, Howard B. Bluestein, and David C. Lewellen

Abstract

Mobile, polarimetric radar data were collected on a series of tornadoes that occurred near Dodge City, Kansas. A poststorm survey revealed a series of tornadic debris swaths in several dirt fields and high-resolution pictures of the tornado documented the visual characteristics of the tornado and the lofted debris cloud. The main rotational couplet associated with the tornado was identified in the single-Doppler velocities; however, no secondary rotational couplets were resolved in the low-level data performed during two consecutive volume scans. Numerical simulations have suggested that cycloidal damage swaths can result when debris is deposited as the low-level inflow turns upward in the corner region of the updraft annulus of the tornado core. This mechanism can dominate even when suction vortices are present in the simulations and can produce these swaths in the absence of these smaller-scale vortices. It is hypothesized that the observed cycloidal damage swaths were a result of the low-level inflow in the corner region of the tornado and not by the existence of suction vortices. Polarimetric data were combined with photographs of the tornado in order to document the lofted debris cloud and its relationship with the funnel. This analysis provided an opportunity to investigate whether recent findings describing the cross-correlation coefficient ρ hv and differential reflectivity Z DR signatures of the lofted debris cloud could be replicated. Regions of low ρ hv at the periphery of the funnel cloud suggesting high debris loading and a column of negative Z DR centered on the tornado believed to be produced by common debris alignment were noted.

Significance Statement

It is well known that some tornadoes produce smaller-scale vortices that rotate around the central axis of the main circulation. In addition, numerous aerial photographs have documented cycloidal debris marks within tornado damage tracks that traverse open fields. The prevailing theory shown in numerous textbooks is that these marks are produced by these vortices. The current study suggests that this widely accepted model for producing these marks may be incorrect. It is suggested that these cycloidal marks are produced by the main tornado circulation and not by the smaller-scale vortices in this case.

Open access
Mikhail Ovchinnikov, Jerome D. Fast, Larry K. Berg, William I. Gustafson Jr., Jingyi Chen, Koichi Sakaguchi, and Heng Xiao

Abstract

Atmospheric properties in a convective boundary layer vary over a wide range of spatial scales and are commonly studied using large-eddy simulations (LES) in various configurations. We examine how the boundary layer depth and distribution of variability across scales are affected by LES grid spacing, domain size, inhomogeneity of surface properties, and external forcing. Two different setups of the Weather Research and Forecasting (WRF) Model are analyzed. A semi-idealized configuration uses a periodic domain, flat surface, prescribed homogeneous surface heat fluxes, and horizontally uniform profiles of large-scale advective tendencies. A nested LES setup employs a larger domain and realistic initial and boundary conditions, including an interactive land surface model with representative topography and vegetation and soil types. Subdomains of identical size are analyzed for all simulations. Characteristic structure sizes are quantified using the variability scales L 50 and L 95, defined such that features smaller than that contain 50% and 95% of the total variance, respectively. Progressive increase in L 50 from vertical velocity to temperature and moisture structures is systematically reproduced in all simulation configurations. This dependence of L 50 on the considered variable complicates the development of scale-aware parameterizations for models with grid spacing in the “terra incognita.” In simulations using a larger domain with heterogeneous surface properties, the development of internal mesoscale patterns significantly affects variance distributions inside analyzed subdomains. Sizes of boundary layer structures also strongly depend on the LES grid spacing and, in case of heterogeneous surface and topography, on location of the subdomain inside a larger computational domain.

Open access
Brian J. Gaudet, G. García Medina, R. Krishnamurthy, W. J. Shaw, L. M. Sheridan, Z. Yang, R. K. Newsom, and M. Pekour

Abstract

From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long-term measurements of hub-height wind speed in the marine environment. We performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) Model, to see if simulated hub-height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a stand-alone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 8 May 2015 and the cold frontal case of 25 March 2016, but not the nor’easter of 18 January 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 March 2016 but worsening them for 8 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the Virginia buoy, largely due to greater self-consistency between wind and wave fields.

Significance Statement

Using atmospheric models to forecast winds in the environments of offshore wind turbines will be critical in the new energy economy. The models used are imperfect, however, being sometimes too coarse, and may not properly represent the wind field at typical turbine hub heights of 90 m, for which we have limited observations in the marine environment. To help address this gap, two buoys equipped with lidars that measured hub-height winds continuously were deployed off the U.S. East Coast from 2014 to 2017. We used the lidar buoy data to show the benefits of a relatively high-resolution atmospheric model over existing reanalysis products, as well as including both the impacts of waves on winds and vice versa.

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Chidong Zhang, Aaron F. Levine, Muyin Wang, Chelle Gentemann, Calvin W. Mordy, Edward D. Cokelet, Philip A. Browne, Qiong Yang, Noah Lawrence-Slavas, Christian Meinig, Gregory Smith, Andy Chiodi, Dongxiao Zhang, Phyllis Stabeno, Wanqiu Wang, Hong-Li Ren, K. Andrew Peterson, Silvio N. Figueroa, Michael Steele, Neil P. Barton, Andrew Huang, and Hyun-Cheol Shin

Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

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