Modulationof The Recirculation Region Within Two Inline Square Rib Due toMagnetohydrodynamic FlowUsingComputational Fluid Dynamics

This study presents the turbulent flow around two square ribs. The numerical computations performed using k–ε model at Reynolds number (Re) = 60000 to quantify the turbulent transport measures. The magnetic field has been applied by Hartmann number (Ha) to generate the magnetohydrodynamic (MHD) flow. The reduction of recirculation region was observed due to the application of magnetic field in the flow. The weak recirculation has an effect on small vorticity generation, which leads to small turbulent kinetic energy (TKE) and TKE dissipation rate generation in the wake region behind the rib. The induced magnetic field is capable of controlling the vortex structures around the square ribs. Results in decreasing magnitude of turbulence level around and between the spaces of the square ribs. It may be hypothesized that by introducing magnetic field, the unwanted vibrations (due to vortex shedding behind the rib) of fully submerged structures can be controlled.

Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?

Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ϵ) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that the widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ϵ. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that the models eliminate the bias MYNN currently shows in representing ϵ, while also reducing the average error by up to almost 40 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ϵ, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.

Towards improved turbulence estimation with Doppler wind lidar velocity-azimuth display (VAD) scans

The retrieval of turbulence parameters with profiling Doppler wind lidars (DWLs) is of high interest for boundary layer meteorology and its applications. DWLs provide wind measurements above the level of meteorological masts while being easier and less expensive to deploy. Velocity-azimuth display (VAD) scans can be used to retrieve the turbulence kinetic energy (TKE) dissipation rate through a fit of measured azimuth structure functions to a theoretical model. At the elevation angle of 35.3∘ it is also possible to derive TKE. Modifications to existing retrieval methods are introduced in this study to reduce errors due to advection and enable retrievals with a low number of scans. Data from two experiments are utilized for validation: first, measurements at the Meteorological Observatory Lindenberg–Richard-Aßmann Observatory (MOL-RAO) are used for the validation of the DWL retrieval with sonic anemometers on a meteorological mast. Second, distributed measurements of three DWLs during the CoMet campaign with two different elevation angles are analyzed. For the first time, the ground-based DWL VAD retrievals of TKE and its dissipation rate are compared to in situ measurements of a research aircraft (here: DLR Cessna Grand Caravan 208B), which allows for measurements of turbulence above the altitudes that are in range for sonic anemometers. From the validation against the sonic anemometers we confirm that lidar measurements can be significantly improved by the introduction of the volume-averaging effect into the retrieval. We introduce a correction for advection in the retrieval that only shows minor reductions in the TKE error for 35.3∘ VAD scans. A significant bias reduction can be achieved with this advection correction for the TKE dissipation rate retrieval from 75∘ VAD scans at the lowest measurement heights. Successive scans at 35.3 and 75∘ from the CoMet campaign are shown to provide TKE dissipation rates with a good correlation of R>0.8 if all corrections are applied. The validation against the research aircraft encourages more targeted validation experiments to better understand and quantify the underestimation of lidar measurements in low-turbulence regimes and altitudes above tower heights.

Assessing the turbulent kinetic energy budget in an energetic tidal flow from measurements of coupled ADCPs

Two coupled four-beam acoustic Doppler current profilers were used to provide simultaneous and independent measurements of the turbulent kinetic energy (TKE) dissipation rate ε and the TKE production rate P over a 36 h long period at a highly energetic tidal energy site in the Alderney Race. The eight-beam arrangement enabled the evaluation of the six components of the Reynolds stress tensor which allows for an improved estimation of the TKE production rate. Depth-time series of ε, P and the Reynolds stresses are provided. The comparison between ε and P was performed by calculating individual ratios of ε corresponding to P . The depth-averaged ratio ε / P averaged over whole flood and ebb tide were found to be 2.2 and 2.8 respectively, indicating that TKE dissipation exceeds TKE production. It is shown that the term of diffusive transport of TKE is significant. As a result, non-local transport is important to the TKE budget and the common assumption of a local balance, i.e. a balance between production and dissipation, is not valid at the measurement site. This article is part of the theme issue ‘New insights on tidal dynamics and tidal energy harvesting in the Alderney Race’.

Can machine learning improve the model representation of TKE dissipation rate in the boundary layer for complex terrain?

Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ε) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that a widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ε. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that multivariate polynomial regressions and random forests can eliminate the bias MYNN currently shows in representing ε, while also reducing the average error by up to 30 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ε, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.

Evaluation of an E–ε and Three Other Boundary Layer Parameterization Schemes in the WRF Model over the Southeast Pacific and the Southern Great Plains

To accurately simulate the atmospheric state within the planetary boundary layer (PBL) by PBL parameterization scheme in different regions with their dominant weather/climate regimes is important for global/regional atmospheric models. In this study, we introduce the turbulence kinetic energy (TKE) and TKE dissipation rate (ε) based 1.5-order closure PBL parameterization (E–ε, EEPS) in the Weather Research and Forecasting (WRF) Model. The performances of the newly implemented EEPS scheme and the existing Yonsei University (YSU) scheme, the University of Washington (UW) scheme, and Mellor–Yamada–Nakanishi–Niino (MYNN) scheme are evaluated over the stratocumulus dominated southeast Pacific (SEP) and over the Southern Great Plains (SGP) where strong PBL diurnal variation is common. The simulations by these PBL parameterizations are compared with various observations from two field campaigns: the Variability of American Monsoon Systems Project (VAMOS) Ocean–Cloud–Atmosphere–Land Study (VOCALS) in 2008 over the SEP and the Land–Atmosphere Feedback Experiment (LAFE) in 2017 over the SGP. Results show that the EEPS and YSU schemes perform comparably over both regions, while the MYNN scheme performs differently in many aspects, especially over the SEP. The EEPS (MYNN) scheme slightly (significantly) underestimates liquid water path over the SEP. Compared with observations, the UW scheme produces the best PBL height over the SEP. The MYNN produces too high PBL height over the western part of the SEP while both the YSU and EEPS schemes produce too low PBL and cloud-top heights. The differences among the PBL schemes in simulating the PBL features over the SGP are relatively small.

Towards improved turbulence estimation with Doppler wind lidar VAD scans

The retrieval of turbulence parameters with profiling Doppler wind lidars (DWL) is of high interest for boundarylayer meteorology and its applications. The DWL measurements extend beyond the observations with meteorological masts and are comparably flexible in their installation. Velocity-azimuth display (VAD) type scans can be used to retrieve turbulence kinetic energy (TKE) dissipation rate through a fit of measured azimuth structure functions to a theoretical model. At the elevation angle of 35.3° it is also possible to derive TKE. We show in this study how modifications to existing methods allow to retrieve TKE and its dissipation rate even with a small number of scans, how a simple correction for advection improves the results at low altitudes and that VAD scans at different elevation angles with the same instrument provide comparable results of TKE dissipation rate after all filters and corrections. For this purpose, data of two experiments are utilized: First, measurements at the Observatory Lindenberg – Richard-Aßmann Observatory (MOL-RAO) are used for validation of the DWL retrieval with sonic anemometers on a meteorological mast. Second, distributed measurements of three DWL during the CoMet campaign are analyzed and compared to in-situ measurements of the DLR Cessna Grand Caravan 208B. The comparison to in-situ instruments shows that the methods to improve turbulence retrievals from VAD scans introduced in this study are effective, especially at low altitudes and for narrow cone angles, but it also shows the limits of turbulence measurement with state-ofthe-art DWL in low turbulence regimes.

A wavelet-entropy based segmentation of turbulence measurements from a moored shear probe near the wavy sea surface

In this study, we explore the applicability of a wavelet-entropy based segmentation technique in reduction of motion-induced contaminations in time-domain from subsurface turbulence measurements made by a moving shear probe. After the quality screening of data, the Shannon entropy procedure is combined with a time-dependent adaptive wavelet thresholding method to split each 60-s long shear segment into a number of motion-reduced subblocks. The wavelet-entropy strategy leads to preventing the false detection effect caused by applying either wavelet de-noising or Shannon entropy alone for conditions where the turbulence (strongly) overlap with scales induced by waves or platform motions. The longest stationary subblock, with a size greater than 16-s, is then used to extract the Turbulent Kinetic Energy (TKE) dissipation rate, $$\varepsilon$$ε. Efficiency of the proposed method is verified by comparing with $$\varepsilon$$ε measurements made by a nearby free-falling microstructure profiler. While the quality of observations is constrained by a number of factors such as sensors’ angle of attack, and the wave kinematical and dynamical effects, results demonstrate significant improvements, by approximately a factor of 5–10, compared with $$\varepsilon$$ε measurements from each 60-s segment using the Goodman et al. [13] method. Furthermore, the magnitudes of the motion-corrected $$\varepsilon$$ε using the proposed method is largely consistent with the scaling suggested by Terray et al. [30].