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Domingo Muñoz-Esparza and Branko Kosović

Abstract

Realistic multiscale simulations involve coupling of mesoscale and large-eddy simulation (LES) models, thus requiring efficient generation of turbulence in nested LES domains. Herein, we extend our previous work on the cell perturbation (CP) method to nonneutral atmospheric boundary layers (ABLs). A modified Richardson number scaling is proposed to determine the amplitude of the potential temperature perturbations in stable ABLs, with −1.0 overall providing optimum turbulence transition to a fully developed state (fetch reduced by a factor of 4–5, compared to the unperturbed cases). In the absence of perturbations, turbulence onset is triggered by a Kelvin–Helmholtz instability, typically occurring in the vicinity of the low-level jet maximum. It is found that a turbulent length scale can be used to more accurately estimate the optimum , where q is the turbulence kinetic energy, and N is the Brunt–Väisälä frequency. In convective ABLs, a perturbation amplitude based on mixed layer temperature variance scaling is proposed: . For that criterion to be optimum, the ratio , where is the wind speed at the top of the capping inversion, and is the convective velocity scale, needs to be incorporated: . This allows us to account for the competing roles of the surface thermal instability and the mean flow advection. For 10, the development fetch is reduced by a factor of 6, while when 3, the use of the CP method does not provide a significant advantage in the ability to generate turbulence, provided a smooth mesoscale inflow.

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Domingo Muñoz-Esparza and Robert Sharman

Abstract

A low-level turbulence (LLT) forecasting algorithm is proposed and implemented within the Graphical Turbulence Guidance (GTG) turbulence forecasting system. The LLT algorithm provides predictions of energy dissipation rate (EDR; turbulence dissipation to the one-third power), which is the standard turbulence metric used by the aviation community. The algorithm is based upon the use of distinct log-Weibull and lognormal probability distributions in a statistical remapping technique to represent accurately the behavior of turbulence in the atmospheric boundary layer for daytime and nighttime conditions, respectively, thus accounting for atmospheric stability. A 1-yr-long GTG LLT calibration was performed using the High-Resolution Rapid Refresh operational model, and optimum GTG ensembles of turbulence indices for clear-air and mountain-wave turbulence that minimize the mean absolute percentage error (MAPE) were determined. Evaluation of the proposed algorithm with in situ EDR data from the Boulder Atmospheric Observatory tower covering a range of altitudes up to 300 m above the surface demonstrates a reduction in the error by a factor of approximately 2.0 (MAPE = 55%) relative to the current operational GTG system (version 3). In addition, the probability of detection of typical small and large EDR values at low levels is increased by approximately 15%–20%. The improved LLT algorithm is expected to benefit several nonconventional turbulence-prediction sectors such as unmanned aerial systems and wind energy.

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Domingo Muñoz-Esparza, Robert D. Sharman, and Julie K. Lundquist

Abstract

A better understanding and prediction of turbulence dissipation rate ε in the atmospheric boundary layer (ABL) is important for many applications. Herein, sonic anemometer data from the Experimental Planetary boundary layer Instrumentation Assessment (XPIA) field campaign (March–May 2015) are used to derive energy dissipation rate (EDR; =) within the first 300 m above the ground employing second-order structure functions. Turbulence dissipation rate is found to be strongly driven by the diurnal evolution of the ABL, presenting a distinct statistical behavior between daytime and nighttime conditions that follows log–Weibull and lognormal distributions, respectively. In addition, the vertical structure of EDR is characterized by a decrease with height above the surface, with the largest gradients occurring within the surface layer (z < 50 m). Convection-permitting mesoscale simulations were carried out with all of the 1.5-order turbulent kinetic energy (TKE) closure planetary boundary layer (PBL) schemes available in the Weather Research and Forecasting (WRF) Model. Overall, the three PBL schemes capture the observed diurnal evolution of EDR as well as the statistical behavior and vertical structure. However, the Mellor–Yamada-type schemes underestimate the large EDR levels during the bulk of daytime conditions, with the quasi-normal scale elimination (QNSE) scheme providing the best agreement with observations. During stably stratified nighttime conditions, Mellor–Yamada–Janjić (MYJ) and QNSE tend to exhibit an artificial “clipping” to their background TKE levels. A reduction in the model constant in the dissipation term for the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme did not have a noticeable impact on EDR estimates. In contrast, application of a postprocessing statistical remapping technique reduced the systematic negative bias in the MYNN results by 75%.

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Domingo Muñoz-Esparza, Robert D. Sharman, and Wiebke Deierling

Abstract

We explore the use of machine learning (ML) techniques, namely, regression trees (RT), for the purpose of aviation turbulence forecasting at upper levels [20–45 kft (~6–14 km) in altitude]. In particular, we develop a series of RT-based algorithms that include random forests (RF) and gradient-boosted regression trees (GBRT) methods. Numerical weather prediction model prognostic variables and derived turbulence diagnostics based on 6-h forecasts from the 3-km High-Resolution Rapid Refresh model are used as features to train these data-driven models. Training and evaluation are based on turbulence estimates of eddy dissipation rate (EDR) obtained from automated in situ aircraft reports. Our baseline RF model, consisting of 100 trees with 30 layers of maximum depth, significantly reduces forecast errors for EDR < 0.1 m2/3 s−1 (which corresponds roughly to null and light turbulence) when compared with a simple regression model, increasing the probability of detection and in turn reducing the number of false alarms. Model complexity reduction via GBRT and feature-relevance analyses is performed, indicating that considerable execution speedups can be achieved while maintaining the model’s predictive skill. Overall, the ML models exhibit enhanced performance in discriminating the EDR forecast among the light, moderate, and severe turbulence categories. In addition, these artificial intelligence techniques significantly simplify the generation of new NWP and grid-spacing specific turbulence forecast products.

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

Abstract

Mesoscale numerical weather prediction (NWP) models are routinely exercised at kilometer-scale horizontal grid spacings (Δx). Such fine grids will usually allow at least partial resolution of small-scale gravity waves and turbulence in the upper troposphere and lower stratosphere (UTLS). However, planetary boundary layer (PBL) parameterization schemes used with these NWP model simulations typically apply explicit subgrid-scale vertical diffusion throughout the entire vertical extent of the domain, an effect that cannot be ignored. By way of an example case of observed widespread turbulence over the U.S. Great Plains, we demonstrate that the PBL scheme’s mixing in NWP model simulations of Δx = 1 km can have significant effects on the onset and characteristics of the modeled UTLS gravity waves. Qualitatively, PBL scheme diffusion is found to affect not only background conditions responsible for UTLS wave activity, but also to control the local vertical mixing that triggers or hinders the onset and propagation of these waves. Comparisons are made to a reference large-eddy simulation with Δx = 250 m to statistically quantify these effects. A significant and systematic overestimation of resolved vertical velocities, wave-scale fluxes, and kinetic energy is uncovered in the 1-km simulations, both in clear-air and in-cloud conditions. These findings are especially relevant for upper-level gravity wave and turbulence simulations using high-resolution kilometer-scale NWP models.

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Domingo Muñoz-Esparza, Jeremy A. Sauer, Rodman R. Linn, and Branko Kosović

Abstract

Mesoscale models are considered to be the state of the art in modeling mountain-wave flows. Herein, the authors investigate the role and accuracy of planetary boundary layer (PBL) parameterizations in handling the interaction between large-scale mountain waves and the atmospheric boundary layer. To that end, recent large-eddy simulation (LES) results of mountain waves over a symmetric two-dimensional bell-shaped hill are used and compared to four commonly used PBL schemes. It is found that one-dimensional PBL parameterizations produce reasonable agreement with the LES results in terms of vertical wavelength, amplitude of velocity, and turbulent kinetic energy distribution in the downhill shooting-flow region. However, the assumption of horizontal homogeneity in PBL parameterizations does not hold in the context of these complex flow configurations. This inappropriate modeling assumption results in a vertical wavelength shift, producing errors of approximately 10 m s−1 at downstream locations because of the presence of a coherent trapped lee wave that does not mix with the atmospheric boundary layer. In contrast, horizontally integrated momentum flux derived from these PBL schemes displays a realistic pattern. Therefore, results from mesoscale models using ensembles of one-dimensional PBL schemes can still potentially be used to parameterize drag effects in general circulation models. Nonetheless, three-dimensional PBL schemes must be developed in order for mesoscale models to accurately represent complex terrain and other types of flows where one-dimensional PBL assumptions are violated.

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Stanley B. Trier, Robert D. Sharman, Domingo Muñoz-Esparza, and Todd P. Lane

Abstract

A large midlatitude cyclone occurred over the central United States from 0000 to 1800 UTC 30 April 2017. During this period, there were more than 1100 reports of moderate-or-greater turbulence at commercial aviation cruising altitudes east of the Rocky Mountains. Much of this turbulence was located above or, otherwise, outside the synoptic-scale cloud shield of the cyclone, thus complicating its avoidance. In this study we use two-way nesting in a numerical model with finest horizontal spacing of 370 m to investigate possible mechanisms producing turbulence in two distinct regions of the cyclone. In both regions, model-parameterized turbulence kinetic energy compares well to observed turbulence reports. Despite being outside of hazardous large radar reflectivity locations in deep convection, both regions experienced strong modification of the turbulence environment as a result of upper-tropospheric/lower-stratospheric (UTLS) convective outflow. For one region, where turbulence was isolated and short lived, simulations revealed breaking of ~100-km horizontal-wavelength lower-stratospheric gravity waves in the exit region of a UTLS jet streak as the most likely mechanism for the observed turbulence. Although similar waves occurred in a simulation without convection, the altitude at which wave breaking occurred in the control simulation was strongly affected by UTLS outflow from distant deep convection. In the other analyzed region, turbulence was more persistent and widespread. There, overturning waves of much shorter 5–10-km horizontal wavelengths occurred within layers of gradient Richardson number < 0.25, which promoted Kelvin–Helmholtz instability associated with strong vertical shear in different horizontal locations both above and beneath the convectively enhanced UTLS jet.

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Katelyn A. Barber, Wiebke Deierling, Gretchen Mullendore, Cathy Kessinger, Robert Sharman, and Domingo Muñoz-Esparza

Abstract

Convectively induced turbulence (CIT) is an aviation hazard that continues to be a forecasting challenge as operational forecast models are too coarse to resolve turbulence affecting aircraft. In particular, little is known about tropical maritime CIT. In this study, a numerical simulation of a tropical oceanic CIT case where severe turbulence was encountered by a commercial aircraft is performed. The Richardson number (Ri), subgrid-scale eddy dissipation rate (EDR), and second-order structure functions (SF) are used as diagnostics to determine which may be used for CIT related to developing and mature convection. Model-derived subgrid-scale EDR in past studies of midlatitude continental CIT was shown to be a good diagnostic of turbulence but underpredicted turbulence intensity and areal coverage in this tropical simulation. SF diagnosed turbulence with moderate to severe intensity near convection and agreed most with observations. Further, SF were used to diagnose turbulence for developing convection. Results show that the areal coverage of turbulence associated with developing convection is less than mature convection. However, the intensity of turbulence in the vicinity of developing convection is greater than the turbulence intensity in the vicinity of mature convection highlighting developing convection as an additional concern to aviation.

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Gwang-Jin Lee, Domingo Muñoz-Esparza, Chaeyeon Yi, and Hi Jun Choe

Abstract

With the continuous increase in computing capabilities, large-eddy simulation (LES) has recently gained popularity in applications related to flow, turbulence, and dispersion in the urban atmospheric boundary layer (ABL). Herein, we perform high-resolution building-scale LES over the Seoul, South Korea, city area to investigate the impact of inflow turbulence on the resulting turbulent flow field in the urban ABL. To that end, LES using the cell perturbation method for inflow turbulence generation is compared to a case where no turbulence fluctuations in the incoming ABL are present (unperturbed case). Validation of the model results using wind speed and wind direction observations at 3 m above ground level reveals minimal differences irrespective of the presence of incoming ABL turbulence. This is due to the high density of building structures present at the surface level that create shear instabilities in the flow field and therefore induce local turbulence production. In the unperturbed case, turbulent fluctuations are found to slowly propagate in the vertical direction with increasing fetch from the inflow boundaries, creating an internal boundary layer that separates the turbulent region near the building structures and the nonturbulent flow aloft that occupies the rest of the ABL. Analysis of turbulence quantities including energy spectra, velocity correlations, and passive scalar fluxes reveals significant underpredictions that rapidly grow with increasing height within the ABL. These results demonstrate the need for realistic inflow turbulence in building-resolving LES modeling to ensure proper interactions within the ABL.

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Yujue Liu, Yubao Liu, Domingo Muñoz-Esparza, Fei Hu, Chao Yan, and Shiguang Miao

Abstract

A multiscale modeling study of a real case has been conducted to explore the capability of the large-eddy simulation version of the Weather Research and Forecasting Model (WRF-LES) over Xiaohaituo Mountain (a game zone for the Beijing, China, 2022 Winter Olympic Games). In comparing WRF-LES results with observations collected during the Mountain Terrain Atmospheric Observations and Modeling (MOUNTAOM) field campaign, it is found that at 37-m resolution with LES settings, the model can reasonably capture both large-scale events and microscale atmospheric circulation characteristics. Employing the Shuttle Radar Topography Mission 1 arc s dataset (SRTM1; ~30 m) high-resolution topographic dataset instead of the traditional USGS_30s (~900 m) dataset effectively improves the model capability for reproducing fluctuations and turbulent features of surface winds. Five sensitivity experiments are conducted to investigate the impact of different PBL treatments, including YSU/Shin and Hong (SH) PBL schemes and LES with 1.5-order turbulence kinetic energy closure model (1.5TKE), Smagorinsky (SMAG), and nonlinear backscatter and anisotropy (NBA) subgrid-scale (SGS) stress models. In this case, at gray-zone scales, differences between YSU and SH are negligible. LES outperform two PBL schemes that generate smaller turbulence kinetic energy and increase the model errors for mean wind speed, energy spectra, and probability density functions of velocity. Another key finding is that wind field features in the boundary layer over complex terrain are more sensitive to the choice of SGS models than above the boundary layer. With the increase of model resolution, the effects of the SGS model become more significant, especially for the statistical characteristics of turbulence. Among these three SGS models, NBA has the best performance. Overall, this study demonstrates that WRF-LES is a promising tool for simulating real weather flows over complex terrain.

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