scholarly journals A Case Study of Clear-Air Turbulence at Upper Levels

2021 ◽  
Author(s):  
Léo Rogel ◽  
Didier Ricard ◽  
Eric Bazile ◽  
Irina Sandu
Keyword(s):  
Turbulent Flow ◽  
Partial Information ◽  
Weather Prediction ◽  
Turbulent Fluxes ◽  
Small Scale ◽  
Mixing Length ◽  
Vertical Resolution ◽  
Stable Conditions ◽  
Study Results

<p>Because of the technical difficulties of achieving measurements at high altitudes, it is not clear how well turbulent phenomena are represented in the upper levels of current Numerical Weather Prediction (NWP) operational models.<br>Indeed, turbulence in strongly stable conditions near the tropopause is known to be particularly difficult to correctly parameterize. The constraining buoyancy forces on the vertical lead to anisotropic turbulence, potentially inhibiting turbulent production in NWP models.<br>Partial information for high altitude turbulence events is nonetheless available in the form of in-situ measurements from aircrafts. However, it only allows for qualitative comparisons with model outputs.<br>This study focuses on a turbulent episode induced by a winter upper-level jet above east Belgium on January 27, 2018, for which in-situ EDR (Eddy Dissipation Rate) reports indicate moderate-or-greater turbulence levels. Numerical simulations are performed with the Météo-France operational model AROME, and with the mesoscale research model MesoNH (Laero/CNRM), at the same horizontal grid resolution (1.3km). These two models also use the eddy-diffusivity turbulence scheme of Cuxart et al (2000), a 1.5 order closure scheme based on a prognostic Turbulent Kinetic Energy (TKE) evolution equation, with a diagnostic computation of the mixing length.<br>TKE budgets, as well as stability indices and gradient-based quantities (Richardson number, vertical wind shear) are computed from the model outputs, and qualitative comparison with in-situ data is presented. Time evolution of the turbulent event over Belgium is well captured by both models, agreeing with EDR data.<br>Several sensitivity tests on the vertical resolution, on the mixing length formulation and on the parameters of the TKE equation are then performed. Most notably, the use of an increased vertical resolution near the tropopause greatly enhances the turbulent fluxes in both operational and research models. Secondly, comparison of various expressions of the mixing length shows that the Bougeault and Lacarrere (1989) formulation produces the higher amount of subgrid TKE and turbulent mixing. A decreased turbulent dissipation parameter also significantly increases the amount of subgrid TKE. On the contrary, the use of a 3D turbulence scheme appears to have very limited impacts on the turbulent flow at this kilometer-scale horizontal resolution.<br>On a second part of this study, results from ongoing Large Eddy Simulations (LES) will be presented. These simulations aim at representing small-scale features of the turbulent flow. They will be used as a reference for the computation of turbulent fluxes at kilometer-scale resolution using a coarse-graining method, allowing for a comparison with the parameterized fluxes from the turbulence scheme. In particular, the dissipation term of the TKE equation will be examined. These results are expected to give insight on the leading turbulent mechanisms for which the current turbulence parameterization can be improved in stable conditions.</p>

scholarly journals Does the Representation of Flow Structure and Turbulence at a Cold Front Converge on Multiscale Observations with Model Resolution?

2017 ◽  
Vol 145 (11) ◽  
pp. 4345-4363 ◽  
Author(s):  
Ben Harvey ◽  
John Methven ◽  
Chloe Eagle ◽  
Humphrey Lean
Keyword(s):  
Flow Structure ◽  
Cold Front ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Turbulent Fluxes ◽  
Numerical Weather Prediction Model ◽  
Shear Instability ◽  
Small Scale ◽  
Convective Boundary ◽  
Direct Evaluation

In situ aircraft observations are used to interrogate the ability of a numerical weather prediction model to represent flow structure and turbulence at a narrow cold front. Simulations are performed at a range of nested resolutions with grid spacings of 12 km down to 100 m, and the convergence with resolution is investigated. The observations include the novel feature of a low-altitude circuit around the front that is closed in the frame of reference of the front, thus allowing the direct evaluation of area-average vorticity and divergence values from circuit integrals. As such, the observational strategy enables a comparison of flow structures over a broad range of spatial scales, from the size of the circuit itself ([Formula: see text]100 km) to small-scale turbulent fluctuations ([Formula: see text]10 m). It is found that many aspects of the resolved flow converge successfully toward the observations with resolution if sampling uncertainty is accounted for, including the area-average vorticity and divergence measures and the narrowest observed cross-frontal width. In addition, there is a gradual handover from parameterized to resolved turbulent fluxes of moisture and momentum as motions in the convective boundary layer behind the front become partially resolved in the highest-resolution simulations. In contrast, the parameterized turbulent fluxes associated with subgrid-scale shear-driven turbulence ahead of the front do not converge on the observations. The structure of frontal rainbands associated with a shear instability along the front also does not converge with resolution, indicating that the mechanism of the frontal instability may not be well represented in the simulations.

scholarly journals A Diagnosis of Excessive Mixing in Smagorinsky Subfilter-Scale Turbulent Kinetic Energy Models

2017 ◽  
Vol 74 (5) ◽  
pp. 1495-1511 ◽  
Author(s):  
Stephan R. de Roode ◽  
Harm J. J. Jonker ◽  
Bas J. H. van de Wiel ◽  
Victor Vertregt ◽  
Vincent Perrin
Keyword(s):  
Kinetic Energy ◽  
Prandtl Number ◽  
Turbulent Kinetic Energy ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Constant Length ◽  
Similarity Relations ◽  
Stable Conditions ◽  
Mixing Functions

Abstract Large-eddy simulation (LES) models are widely used to study atmospheric turbulence. The effects of small-scale motions that cannot be resolved need to be modeled by a subfilter-scale (SFS) model. The SFS contribution to the turbulent fluxes is typically significant in the surface layer. This study presents analytical solutions of the classical Smagorinsky SFS turbulent kinetic energy (TKE) model including a buoyancy flux contribution. Both a constant length scale and a stability-dependent one as proposed by Deardorff are considered. Analytical expressions for the mixing functions are derived and Monin–Obukhov similarity relations that are implicitly imposed by the SFS TKE model are diagnosed. For neutral and weakly stable conditions, observations indicate that the turbulent Prandtl number (PrT) is close to unity. However, based on observations in the convective boundary layer, a lower value for PrT is often applied in LES models. As a lower Prandtl number promotes a stronger mixing of heat, this may cause excessive mixing, which is quantified from a direct comparison of the mixing function as imposed by the SFS TKE model with empirical fits from field observations. For a strong stability, the diagnosed mixing functions for both momentum and heat are larger than observed. The problem of excessive mixing will be enhanced for anisotropic grids. The findings are also relevant for high-resolution numerical weather prediction models that use a Smagorinsky-type TKE closure.

scholarly journals Vertical Velocity Variance Measurements from Wind Profiling Radars

2016 ◽  
Author(s):  
Katherine McCaffrey ◽  
Laura Bianco ◽  
Paul Johnston ◽  
James M. Wilczak
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Planetary Boundary Layer ◽  
Vertical Velocity ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Stable Conditions ◽  
Sonic Anemometers ◽  
Velocity Variance

Abstract. Observations of turbulence in the planetary boundary layer are critical for developing and evaluating boundary layer parameterizations in mesoscale numerical weather prediction models. These observations, however, are expensive, and rarely profile the entire boundary layer. Using optimized configurations for 449 MHz and 915 MHz wind profiling radars during the eXperimental Planetary boundary layer Instrumentation Assessment, improvements have been made to the historical methods of measuring vertical velocity variance through the time series of vertical velocity, as well as the Doppler spectral width. Using six heights of sonic anemometers mounted on a 300-m tower, correlations of up to R2 = 0.74 are seen in measurements of the large-scale variances from the radar time series, and R2 = 0.79 in measurements of small-scale variance from radar spectral widths. The total variance, measured as the sum of the small- and large-scales agrees well with sonic anemometers, with R2 = 0.79. Correlation is higher in daytime, convective boundary layers than nighttime, stable conditions when turbulence levels are smaller. With the good agreement with the in situ measurements, highly-resolved profiles up to 2 km can be accurately observed from the 449 MHz radar, and 1 km from the 915 MHz radar. This optimized configuration will provide unique observations for the verification and improvement to boundary layer parameterizations in mesoscale models.

scholarly journals A comparison of vertical velocity variance measurements from wind profiling radars and sonic anemometers

2017 ◽  
Vol 10 (3) ◽  
pp. 999-1015 ◽  
Author(s):  
Katherine McCaffrey ◽  
Laura Bianco ◽  
Paul Johnston ◽  
James M. Wilczak
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Planetary Boundary Layer ◽  
Vertical Velocity ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Stable Conditions ◽  
Sonic Anemometers ◽  
Velocity Variance

Abstract. Observations of turbulence in the planetary boundary layer are critical for developing and evaluating boundary layer parameterizations in mesoscale numerical weather prediction models. These observations, however, are expensive and rarely profile the entire boundary layer. Using optimized configurations for 449 and 915 MHz wind profiling radars during the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA), improvements have been made to the historical methods of measuring vertical velocity variance through the time series of vertical velocity, as well as the Doppler spectral width. Using six heights of sonic anemometers mounted on a 300 m tower, correlations of up to R2 = 0. 74 are seen in measurements of the large-scale variances from the radar time series and R2 = 0. 79 in measurements of small-scale variance from radar spectral widths. The total variance, measured as the sum of the small and large scales, agrees well with sonic anemometers, with R2 = 0. 79. Correlation is higher in daytime convective boundary layers than nighttime stable conditions when turbulence levels are smaller. With the good agreement with the in situ measurements, highly resolved profiles up to 2 km can be accurately observed from the 449 MHz radar and 1 km from the 915 MHz radar. This optimized configuration will provide unique observations for the verification and improvement to boundary layer parameterizations in mesoscale models.

scholarly journals Improving High-Impact Numerical Weather Prediction with Lidar and Drone Observations

2020 ◽  
Vol 101 (7) ◽  
pp. E1036-E1051 ◽  
Author(s):  
Daniel Leuenberger ◽  
Alexander Haefele ◽  
Nadja Omanovic ◽  
Martin Fengler ◽  
Giovanni Martucci ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Positive Impact ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
High Impact ◽  
Vertical Resolution ◽  
Numerical Weather ◽  
Temperature And Humidity ◽  
Ground Based Lidar

Abstract The current atmospheric observing systems fail to provide a satisfactory amount of spatially and temporally resolved observations of temperature and humidity in the planetary boundary layer (PBL) despite their potential positive impact on numerical weather prediction (NWP). This is particularly critical for humidity, which exhibits a very high variability in space and time or for the vertical distribution of temperature, determining the atmosphere’s stability. Novel ground-based lidar remote sensing technologies and in situ measurements from unmanned aerial vehicles can fill this observational gap, but operational maturity was so far lacking. Only recently, commercial lidar systems for temperature and humidity profiling in the lower troposphere and automated observations on board of drones have become available. Raman lidar can provide profiles of temperature and humidity with high temporal and vertical resolution in the troposphere. Drones can provide high-quality in situ observations of various meteorological variables with high temporal and vertical resolution, but flights are complicated in high-wind situations, icing conditions, and can be restricted by aviation activity. Both observation systems have shown to considerably improve analyses and forecasts of high-impact weather, such as thunderstorms and fog in an operational, convective-scale NWP framework. The results of this study demonstrate the necessity for and the value of additional, high-frequency PBL observations for NWP and how lidar and drone observations can fill the gap in the current operational observing system.

scholarly journals On Estimating Fluxes due to Small-Scale Turbulent Convection in a Rotating Star

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
D. O. Gough
Keyword(s):  
Large Scale ◽  
Convection Zone ◽  
Turbulent Fluxes ◽  
Turbulent Convection ◽  
Small Scale ◽  
The Sun ◽  
Mixing Length ◽  
Large Scale Flow ◽  
Rotating Star ◽  
The Way

The way in which turbulent fluxes are usually represented in computations of large-scale flow in the convection zones of the sun and other stars is briefly described. A model of an ensemble of eddies that is capable of generalization to circumstances more complicated than the usual essentially spherically symmetrical convection zone is outlined. Generalization usually requires the introduction of new postulates, and, in so doing, also lays bare some of the assumptions, often implicit, in the usual mixing-length formalisms.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals Upscaling THM modeling from small-scale to full-scale in-situ experiments in the Callovo-Oxfordian claystone

2021 ◽  
Vol 144 ◽  
pp. 104582
Author(s):  
D.M. Seyedi ◽  
C. Plúa ◽  
M. Vitel ◽  
G. Armand ◽  
J. Rutqvist ◽  
...  
Keyword(s):  
Full Scale ◽  
Small Scale ◽  
In Situ Experiments

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals E–ε Model of Spray-Laden Near-Sea Atmospheric Layer in High Wind Conditions

2014 ◽  
Vol 44 (2) ◽  
pp. 742-763 ◽  
Author(s):  
Yevgenii Rastigejev ◽  
Sergey A. Suslov
Keyword(s):  
Turbulent Flow ◽  
Drag Coefficient ◽  
Turbulent Mixing ◽  
Numerical Solutions ◽  
Turbulent Energy ◽  
Wave Crest ◽  
Mixing Length ◽  
High Wind ◽  
Close Proximity ◽  
Ocean Spray

Abstract In-depth understanding and accurate modeling of the interaction between ocean spray and a turbulent flow under high wind conditions is essential for improving the intensity forecasts of hurricanes and severe storms. Here, the authors consider the E–ε closure for a turbulent flow model that accounts for the effects of the variation of turbulent energy and turbulent mixing length caused by spray stratification. The obtained analytical and numerical solutions show significant differences between the current E–ε model and the lower-order turbulent kinetic energy (TKE) model considered previously. It is shown that the reduction of turbulent energy and mixing length above the wave crest level, where the spray droplets are generated, that is not accounted for by the TKE model results in a significant suppression of turbulent mixing in this near-wave layer. In turn, suppression of turbulence causes an acceleration of flow and a reduction of the drag coefficient that is qualitatively consistent with field observations if spray is fine (even if its concentration is low) or if droplets are large but their concentration is sufficiently high. In the latter case, spray inertia may become important. This effect is subsequently examined. It is shown that spray inertia leads to the reduction of wind velocity in the close proximity of the wave surface relative to the reference logarithmic profile. However, at higher altitudes the suppression of flow turbulence by the spray still results in the wind acceleration and the reduction of the local drag coefficient.

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