Convective Scale Research Articles

The effect of pressure on lean premixed hydrogen-air flames

The effect of pressure on the propagation of lean premixed hydrogen-air flames is investigated numerically with a hierarchy of canonical configurations including direct numerical simulations of turbulent flames in homogeneous isotropic turbulence. Thermo-diffusive instability and the ratio of turbulent to laminar burning velocity are found to be clearly amplified for increasing pressure. The observed amplification results from the combined effects of multi-dimensional geometrical features of the reaction front induced by stretch, chemical kinetics and flame speed / thickness sensitivity to preferential diffusion induced by the flame geometry. High-pressure flames exhibit stronger sensitivity to fast molecular diffusion of hydrogen into the reaction front of a reference freely-propagating flame that is nominally weaker at high pressure due to the greater relative importance of chain-termination. In this context, two-dimensional flames subjected to monochromatic velocity perturbations demonstrate locally enhanced flame speeds and reduced flame thickness at high pressure in regions of curvature that are convex towards the reactants. Furthermore, it is found that a Peclet number definition, based on the ratio of diffusive to convective time scales, provides a satisfactory global representation of pressure effects upon thermo-diffusive instabilities of freely-propagating flames. Finally, employing a series of carefully designed direct numerical simulations, it is empirically demonstrated that very similar flame responses can be obtained at atmospheric and pressurized conditions by approximately matching a selection of four key parameters (Leeff, Ze, Ma, Pe). This key finding demonstrates that the flame speed enhancement often observed at elevated pressure is not a pressure effect per se, but rather determined by the sensitivity of the flame to preferential diffusion (Pe) versus its own reactivity (Ze), with nominally ‘weaker’ flames showing stronger sensitivity.

A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors

AbstractNumerical weather prediction centers increasingly make use of cloudy and rainy microwave radiances. Currently, the high microwave frequencies are simulated using simplified assumptions regarding the radiative properties of frozen hydrometeors. In particular, one single particle shape is often used for all precipitating frozen particles, all over the globe, and for all cloud types. In this paper, a multi‐SSP (single scattering properties) approach for 1D Bayesian inversions is examined. Two experiments were set up: (1) one with three SSPs and (2) one with the previous SSPs plus one which leads to very cold brightness temperature distributions. For that purpose, we used observations from the GPM Microwave Imager radiometer over 2 months period and forecasts from the Météo‐France convective scale AROME model. The results showed that mixtures of SSP are chosen by the inversion method for meteorological conditions with low scattering and that a single particle is chosen for those with high scattering to perform the inversions. Despite the fact that no specific weather scenes were found to be associated with a particular SSP the most efficient scattering particles can be favored for some of them.

Latitudinal regionalization of rotating spherical shell convection

Convection occurs ubiquitously on and in rotating geophysical and astrophysical bodies. Prior spherical shell studies have shown that the convection dynamics in polar regions can differ significantly from the lower latitude, equatorial dynamics. Yet most spherical shell convective scaling laws use globally-averaged quantities that erase latitudinal differences in the physics. Here we quantify those latitudinal differences by analysing spherical shell simulations in terms of their regionalized convective heat-transfer properties. This is done by measuring local Nusselt numbers in two specific, latitudinally separate, portions of the shell, the polar and the equatorial regions, $Nu_p$ and $Nu_e$ , respectively. In rotating spherical shells, convection first sets in outside the tangent cylinder such that equatorial heat transfer dominates at small and moderate supercriticalities. We show that the buoyancy forcing, parameterized by the Rayleigh number $Ra$ , must exceed the critical equatorial forcing by a factor of ${\approx }20$ to trigger polar convection within the tangent cylinder. Once triggered, $Nu_p$ increases with $Ra$ much faster than does $Nu_e$ . The equatorial and polar heat fluxes then tend to become comparable at sufficiently high $Ra$ . Comparisons between the polar convection data and Cartesian numerical simulations reveal quantitative agreement between the two geometries in terms of heat transfer and averaged bulk temperature gradient. This agreement indicates that rotating spherical shell convection dynamics is accessible both through spherical simulations and via reduced investigatory pathways, be they theoretical, numerical or experimental.

The Time Scale of Shallow Convective Self-Aggregation in Large-Eddy Simulations Is Sensitive to Numerics.

Numerical simulations of the tropical mesoscales often exhibit a self-reinforcing feedback between cumulus convection and shallow circulations, which leads to the self-aggregation of clouds into large clusters. We investigate whether this basic feedback can be adequately captured by large-eddy simulations (LESs). To do so, we simulate the non-precipitating, cumulus-topped boundary layer of the canonical "BOMEX" case over a range of numerical settings in two models. Since the energetic convective scales underpinning the self-aggregation are only slightly larger than typical LES grid spacings, aggregation timescales do not converge even at rather high resolutions (<100m). Therefore, high resolutions or improved sub-filter scale models may be required to faithfully represent certain forms of trade-wind mesoscale cloud patterns and self-aggregating deep convection in large-eddy and cloud-resolving models, and to understand their significance relative to other processes that organize the tropical mesoscales.

Entrainment Rates and Their Synoptic Dependence on Wind Speed Aloft in California's Central Valley

Daytime atmospheric boundary layer (ABL) dynamics—including potential temperature budgets, water vapour budgets, and entrainment rates—are presented from in situ flight data taken on six afternoons near Fresno in the San Joaquin Valley (SJV) of California during July/August 2016. The flights took place as a part of the California Baseline Ozone Transport Study aimed at investigating transport pathways of air entering the Central Valley from offshore and mixing down to the surface. Midday entrainment velocity estimates ranged from 0.8 to 5.4 cm s−1 and were derived from a combination of continuously determined ABL heights during each flight and model-derived subsidence rates, which averaged -2.0 cm s−1 in the flight region. A strong correlation was found between entrainment velocity (normalized by the convective velocity scale) and an inverse bulk ABL Richardson number, suggesting that wind shear at the ABL top plays a significant role in driving entrainment. Similarly, we found a strong correlation between the entrainment efficiency (the ratio of entrainment to surface heat fluxes with an average of 0.23 ± 0.15) and the wind speed at the ABL top. We explore the synoptic conditions that generate higher winds near the ABL top and propose that warm anomalies in the southern Sierra Nevada mountains promote increased entrainment. Additionally, a method is outlined to estimate turbulence kinetic energy, convective velocity scale (w*), and the surface sensible heat flux in the ABL from a slow, airborne wind measurement system using mixed-layer similarity theory.

Non-Darcian Displacement of Oil by a Micellar Solution in Fractal Porous Media

A Buckley–Leverett analysis with capillary pressure to model the oil displacement in fractal porous media is herein presented. The effective permeability for a non-Newtonian micellar fluid is calculated by a constitutive equation used to describe the rheological properties of a displacement fluid. The main assumption of this model involves a bundle of tortuous capillaries with a size distribution and tortuosity that follow fractal laws. The BMP model predicts two asymptotic (Newtonian) regions at low and high shear and a power-law region between the two Newtonian regions corresponding to a stress plateau. Both the stress at the wall and the fluidity are calculated using an imposed pressure gradient in order to determine the mobility of the solution. We analyze different mobility ratios to describe the behavior of the so-called self-destructive surfactants. Initially, the viscosity of the displacing fluid (micellar solution) is high; however, interactions with the porous media lead to a breakage process and degradation of the surfactant, producing low viscosity. This process is simulated by varying the applied pressure gradient. The resulting equation is of the reaction–diffusion type with various time scales; a shock profile develops in the convective time scale, as in the traditional Buckley-Leverett analysis, while at longer times diffusion effects begin to affect the profile. Predictions include shock profiles and compressive waves. These results may find application when selecting surfactants for enhanced oil recovery processes in oilfields.

Impact of Adaptively Thinned GOES-16 Cloud Water Path in an Ensemble Data Assimilation System

Assimilation of cloud properties in the convective scale ensemble data assimilation system is one of the prime topics of research in recent years. Satellites can retrieve cloud properties that are important sources of information of the cloud and atmospheric state. The Advance Baseline Imager (ABI) aboard the GOES-16 geostationary satellite brings an opportunity for retrieving high spatiotemporal resolution cloud properties, including cloud water path over continental United States. This study investigates the potential impacts of assimilating adaptively thinned GOES-16 cloud water path (CWP) observations that are assimilated by the ensemble-based Warn-on-Forecast System and the impact on subsequent weather forecasts. In this study, for CWP assimilation, multiple algorithms have been developed and tested using the adaptive-based thinning method. Three severe weather events are considered that occurred on 19 July 2019, 7 May and 21 June 2020. The superobbing procedure used for CWP data smoothed from 5 to 15 km or more depending on thinning algorithm. The overall performance of adaptively thinned CWP assimilation in the Warn-on-Forecast system is assessed using an object-based verification method. On average, more than 60% of the data was reduced and therefore not used in the assimilation system. Results suggest that assimilating less than 40% of CWP superobbing data into the Warn-on-Forecast system is of similar forecast quality to those obtained from assimilating all available CWP observations. The results of this study can be used on the benefits of cloud assimilation to improve numerical simulation.

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity

AbstractBased on the idealized supercell and real case studies in Part I and II, the purpose of this subsequent study is to further investigate the impact of assimilating Geostationary Operational Environmental Satellites‐16 (GOES‐16) derived atmospheric motion vectors (AMVs) in addition to WSR_88D Doppler radar observations on convective scale numerical weather prediction. Five high‐impact weather events that occurred in spring 2018 and 2019 are analyzed using the National Severe Storms Laboratory three‐dimensional variational data assimilation (DA) system. Four types of experiments are implemented and compared: (a) the control experiment (NoDA) without assimilating any observation, (b) the radar DA experiment (RAD), (c) the GOES‐16 AMV DA experiment (AMV), and (d) the experiment assimilating AMVs together with radar data (AMV_RAD). Score metrics aggregated over all cases indicate that AMV_RAD performs slightly better than RAD in 0–3 hr reflectivity and precipitation forecasts especially at higher thresholds, suggesting the added value of GOES‐16 AMVs on radar data. Detailed case examinations also show that AMV_RAD generally exhibits slightly more skillful storm forecast in terms of the areal coverage, storm mode, and storm orientations, owing to improvements in the analysis of boundary locations and localized enhanced divergence signatures. In spite of encouraging objective and subjective evaluation results, AMV_RAD has difficulty in adjusting the moisture gradient associated with dryline and tends to underpredict the associated weak discrete storms.

Ensemble Cloud Model Application in Simulating the Catastrophic Heavy Rainfall Event

An attempt has been made in the present research to simulate a deadly flash-flood event over the City of Skopje, Macedonia on 6 August 2016. A cloud model ensemble forecast method is developed to simulate a super-cell storm's initiation and evolutionary features. Sounding data are generated using an ensemble approach, that utilizes a triple-nested WRF model. A three-dimensional (3-D) convective cloud model (CCM) with a very fine horizontal grid resolution of 250-m is initialized, using the initial representative sounding data, derived from the WRF 1-km forecast outputs. CCM is configured and run with an open lateral boundary conditions LBC, allowing explicit simulation of convective scale processes. This preliminary study showed that the ensemble approach has some advantages in the generation of the initial data and the model initialization. The applied method minimizes the uncertainties and provides a more qualitative-quantitative assessment of super-cell storm initiation, cell structure, evolutionary properties, and intensity. A high-resolution 3-D run is capable to resolve detailed aspects of convection, including high-intensity convective precipitation. The results are significant not only from the aspect of the cloud model's ability to provide a qualitative-quantitative assessment of intense precipitation but also for a deeper understanding of the essence of storm development, its vortex dynamics, and the meaning of micro-physical processes for the production and release of large amounts of precipitation that were the cause of the catastrophic flood in an urban area. After a series of experiments and verification, such a system could be a reliable tool in weather services for very short-range forecasting (nowcasting) and early warning of weather disasters.

Toward instrument combination for boundary layer classification

AbstractTo handle the complexity of the atmospheric boundary layer (ABL) and make accurate feature detection (top height, low‐level jets, inversions, etc.), a prior necessary step is to identify the type of boundary layer. This study proposes a new method to identify the boundary layer type through unsupervised classification and the synergistic use of ground‐based remote sensing. Unsupervised classification is used to lighten the human supervision. The new classification was applied to a 1‐day case study collected during wintertime in the Arve River valley near Chamonix–Mont‐Blanc during the Passy‐2015 field experiment. The ABL classification obtained from microwave radiometer and ceilometer observations (ground‐based remote sensors [GBReS]) combination is compared with high‐frequency radiosoundings (RS) data and the French convective scale AROME model outputs. Classifications from RS and GBReS broadly agree, demonstrating the good behavior of the method, AROME leading to different results at night. The difference of AROME is likely due to the different nature of the data (model fields are smoother and include forecasting errors). The results show the ability of unsupervised classification to segment relevant objects in the boundary layer and the benefit to use a combination of GBReS.

Asymptotic behaviour of rotating convection-driven dynamos in the plane layer geometry

Dynamos driven by rotating convection in the plane layer geometry are investigated numerically for a range of Ekman number ( $E$ ), magnetic Prandtl number ( $Pm$ ) and Rayleigh number ( $Ra$ ). The primary purpose of the investigation is to compare results of the simulations with previously developed asymptotic theory that is applicable in the limit of rapid rotation. We find that all of the simulations are in the quasi-geostrophic regime in which the Coriolis and pressure gradient forces are approximately balanced at leading order, whereas all other forces, including the Lorentz force, act as perturbations. Agreement between simulation output and asymptotic scalings for the energetics, flow speeds, magnetic field amplitude and length scales is found. The transition from large-scale dynamos to small-scale dynamos is well described by the magnetic Reynolds number based on the small convective length scale, $\widetilde {Rm}$ , with large-scale dynamos preferred when $\widetilde {Rm} \lesssim O(1)$ . The magnitude of the large-scale magnetic field is observed to saturate and become approximately constant with increasing Rayleigh number. Energy spectra show that all length scales present in the flow field and the small-scale magnetic field are consistent with a scaling of $E^{1/3}$ , even in the turbulent regime. For a fixed value of $E$ , we find that the viscous dissipation length scale is approximately constant over a broad range of $Ra$ ; the ohmic dissipation length scale is approximately constant within the large-scale dynamo regime, but transitions to a $\widetilde {Rm}^{-1/2}$ scaling in the small-scale dynamo regime.

Can We Integrate Spatial Verification Methods into Neural Network Loss Functions for Atmospheric Science?

Abstract In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise verification. However, neural networks (NN) in atmospheric science are almost always trained to optimize pixelwise loss functions, even when ultimately assessed with SV methods. This establishes a disconnect between model verification during versus after training. To address this issue, we develop spatially enhanced loss functions (SELF) and demonstrate their use for a real-world problem: predicting the occurrence of thunderstorms (henceforth, “convection”) with NNs. In each SELF we use either a neighborhood filter, which highlights convection at scales larger than a threshold, or a spectral filter (employing Fourier or wavelet decomposition), which is more flexible and highlights convection at scales between two thresholds. We use these filters to spatially enhance common verification scores, such as the Brier score. We train each NN with a different SELF and compare their performance at many scales of convection, from discrete storm cells to tropical cyclones. Among our many findings are that (i) for a low or high risk threshold, the ideal SELF focuses on small or large scales, respectively; (ii) models trained with a pixelwise loss function perform surprisingly well; and (iii) nevertheless, models trained with a spectral filter produce much better-calibrated probabilities than a pixelwise model. We provide a general guide to using SELFs, including technical challenges and the final Python code, as well as demonstrating their use for the convection problem. To our knowledge this is the most in-depth guide to SELFs in the geosciences. Significance Statement Gridded predictions, in which a quantity is predicted at every pixel in space, should be verified with spatially aware methods rather than pixel by pixel. Neural networks (NN), which are often used for gridded prediction, are trained to minimize an error value called the loss function. NN loss functions in atmospheric science are almost always pixelwise, which causes the predictions to miss rare events and contain unrealistic spatial patterns. We use spatial filters to enhance NN loss functions, and we test our novel spatially enhanced loss functions (SELF) on thunderstorm prediction. We find that different SELFs work better for different scales (i.e., different-sized thunderstorm complexes) and that spectral filters, one of the two filter types, produce unexpectedly well calibrated thunderstorm probabilities.

Atmospheric Boundary Layers over an Oceanic Eddy

Abstract Imagery and numerical modeling show an abundance of submesoscale oceanic eddies in the upper ocean. Large-eddy simulation (LES) is used to elucidate eddy impacts on the atmospheric boundary layer (ABL) forced by winds, convection, and an eddy with varying radius; the maximum azimuthal eddy speed is 1 m s−1. Simulations span the unstable regime −1/L = [0, ∞], where L is the Monin–Obukhov (M–O) stability parameter. A linearized Ekman model and the LES couple ABL winds to an eddy through rough-wall M–O boundary conditions. The eddy currents cause a surface stress anomaly that induces Ekman pumping in a dipole horizontal pattern. The dipole is understood as a consequence of surface winds aligned or opposing surface currents. In free convection a vigorous updraft is found above the eddy center and persists over the ABL depth. Heterogeneity in surface temperature flux is responsible for the full ABL impact. With winds and convection, current stress coupling generates a dipole in surface temperature flux even with constant sea surface temperature. Wind, pressure, and temperature anomalies are sensitive to an eddy under light winds. The eddy impact on ABL secondary circulations is on the order of the convective velocity scale but grows with increasing current speed, decreasing wind, or increasing convection. Flow past an isolated eddy develops a coherent ABL “wake” and secondary circulations for at least five eddy radii downwind. Kinetic energy exchanges by wind work indicate an eddy-killing effect on the oceanic eddy current, but only a spatial rearrangement of the atmospheric wind work.

Exploring the Role of Deep Moist Convection in the Wavenumber Spectra of Atmospheric Kinetic Energy and Brightness Temperature

Abstract Through a series of global convection-permitting simulations and geostationary satellite observations, this study investigates the role of deep moist convection in atmospheric kinetic energy (KE) and brightness temperature (BT) spectra in a realistic framework. The control simulation was produced on a quasi-uniform 3-km global mesh, which allowed the explicit representation of deep convection. To assess the impact of deep moist convection, a fake-dry simulation was performed with latent heating–cooling feedback in the microphysics removed for comparison. The impacts of deep moist convection on mesoscale KE spectrum are concentrated on energizing the mesoscale at the upper troposphere and the lower stratosphere through buoyancy production. BT spectra for the control simulation have a similar shallow slope in the mesoscale as that for the observations. The greater spectral power of BT for the control simulation compared to the observed is attributed to the dislocation and higher intensity of simulated convection. The observed BT spectra exhibit a large diurnal variability due to the diurnal variation of the intensity of convection. The simulated BT spectrum is dependent on convective systems at different scales. Deep convection in the intertropical convergence zone (ITCZ) and shallow convection in the North Pacific storm-track region play an important role in energizing the convective scale of the BT spectrum. In the mesoscale, the BT spectrum is mainly energized by mesoscale convective systems (MCSs) in the ITCZ. Tropical equatorial waves and baroclinic waves in the southern midlatitudes are critical in producing the shallow slope near −5/3 and providing energy in the BT spectrum at the synoptic scale. Significance Statement We further explore the role of deep moist convection in kinetic energy and brightness temperature spectra through high-resolution radiance observations and convection-permitting simulations. Moist processes can energize the mesoscale of kinetic energy. Brightness temperature spectra show dependence on convective systems at different scales. These results point the way toward a new approach to evaluate the predictability of convective systems, and future development of model dynamics and parameterization.

Future Changes in African Heatwaves and Their Drivers at the Convective Scale

Abstract The future change in dry and humid heatwaves is assessed in 10-yr pan-African convective-scale (4.5 km) and parameterized convection (25 km) climate model simulations. Compared to reanalysis, the convective-scale simulation is better able to represent humid heatwaves than the parameterized simulation. Model performance for dry heatwaves is much more similar. Both model configurations simulate large increases in the intensity, duration, and frequency of heatwaves by 2100 under RCP8.5. Present-day conditions that occur on 3–6 heatwave days per year will be normal by 2100, occurring on 150–180 days per year. The future change in dry heatwaves is similar in both climate model configurations, whereas the future change in humid heatwaves is 56% higher in intensity and 20% higher in frequency in the convective-scale model. Dry heatwaves are associated with low rainfall, reduced cloud, increased surface shortwave heating, and increased sensible heat flux. In contrast, humid heatwaves are predominately controlled by increased humidity, rainfall, cloud, longwave heating, and evaporation, with dry-bulb temperature gaining more significance in the most humid regions. Approximately one-third (32%) of humid heatwaves commence on wet days. Moist processes are known to be better represented in convective-scale models. Climate models with parameterized convection, such as those in CMIP, may underestimate the future change in humid heatwaves, which heightens the need for mitigation and adaptation strategies and indicates there may be less time available to implement them to avoid future catastrophic heat stress conditions than previously thought. Significance Statement Temperatures are higher in dry heatwaves, but humid heatwaves can be more dangerous, as the ability to cool by sweating is limited. We found that dry heatwaves are caused by decreased cloud, allowing the sun to heat the surface, whereas humid heatwaves are caused by increased cloud, rainfall, and evaporation from the surface. We found that a state-of-the-art very high-resolution climate model predicts a larger future change in humid heatwaves compared to a more traditional global climate model. Previous estimates of the prevalence of humid heatwaves in the future may therefore be underestimated. If we do not cut emissions of greenhouse gases, present-day African heatwave conditions could be experienced on up to half of all days of the year by 2100.

A dataset of temperature, humidity, and liquid water path retrievals from a network of ground-based microwave radiometers dedicated to fog investigation

The database presented in this study has been acquired during the SOuth west FOGs 3D (SOFOG3D) experiment for processes study. This international campaign led by Météo-France during the winter 2019–2020 aimed at deploying a unique network of both in situ and remote sensing measurements in order to document spatial and temporal variabilities of fog events. To support this scientific objective but also to conduct first data assimilation experiments within the French convective scale model AROME, an un-precedented network of 8 ground-based microwave radiometers (MWR) has been deployed in 7 different locations known to be prone to fog occurrences. The database gives access to vertical profiles of temperature and humidity (both absolute and relative) from the surface up to 10 km altitude as well as integrated water vapor and liquid water path estimates. The retrieved profiles offer a very large database that can be exploited for several scientific purposes: fog process studies at specific location, documentation on the variability of fog properties at the regional scale, better understanding of the atmospheric boundary layer (ABL) height and dynamics during wintertime conditions, development of nowcasting products dedicated to fog alerts, data assimilation experiments to improve fog forecasts, development of synergetical advanced products, and evaluation of new model configurations with advanced parameterization or resolutions.

Effects of different momentum control variables in radar data assimilation on the analysis and forecast of strong convective systems under the background of northeast cold vortex

Based on the WRF-3DVar system, different momentum control variables in radar data assimilation are investigated for their effects on the analysis and forecast of strong convective weather systems in the study. The single observation assimilation tests are performed besides three cycling data assimilation experiments based on U-wind/V-wind (UV) and stream-function and velocity potential (ψχ) that are scalar representations of flow fields for their irrotational and divergence components. The wind field increments for both control variables in single observation tests show that when UV are the control variables, the wind field increments have a smaller range of influence that it better reflects the characteristics of the observed information itself at the convective scale than ψχ. When ψχ are used as control variables, negative increments are generated in the analyzed wind field because ψχ have the property of maintaining the integrated value of the wind field. In addition, radar data from 5 Doppler radars in northeast China are assimilated. The cycling assimilation experiments show that CV_UV produces enhanced forecast than CV_ψχ in terms of radar reflectivity and precipitation. Another case also proves this conclusion. In particular, CV_UV improves the dynamics field and water vapor convergence, which lead to better intensity and structure prediction of the strong convection process. In contrast, CV_ψχ yields relative discontinuous wind field structure, which tends to reduce the organization of the forecast strong echoes. Another set of experiments are further designed with only radar reflectivity assimilated with CV_UV. The results show that assimilating radial velocity is helpful to improve the forecasting skill.

Experimental Analysis of Pressure Shielding Mechanisms in Bioinspired Unidirectional Canopies

Previous studies have demonstrated that treatments such as a canopy or finlets placed within a boundary layer can shield surfaces from unsteady pressure fluctuations without substantially compromising the aerodynamic performance. This paper describes research into fundamental mechanisms of this phenomenon known as pressure shielding. Unidirectional canopy is an idealized surface treatment which consists of a streamwise array of rods cantilevered at the downstream end, inspired from the downy coating on owls’ wings. Experiments show that such a canopy attenuates the surface pressure in two distinct frequency ranges. At low frequencies associated with convective scales much greater than the canopy height, the attenuation spectra show scaling on the Strouhal number based on canopy height. At high frequencies, associated with convective scales of the order of the canopy height or lower, a dissipation-type frequency scaling appears more appropriate. The ratio of streamwise distance over the height is an important parameter at the low-frequency regions of attenuation, while the open-area ratio controls the broadband magnitude of attenuation spectra. Spatial and temporal correlations further shed light on the effects of the canopy in reducing the larger, energetic turbulent structures associated with the wall jet unsteady surface pressure fluctuations.

Dense mantle flows periodically spaced below ocean basins

Understanding mantle flow is key to elucidate how deep Earth dynamics relate to tectonics at the global scale. The convective mass transport is reflected in lateral variations of the gravity field, seismic velocities, as well as deformations of the Earth's surface. Yet, upper to mid-mantle dynamics have been difficult to constrain at the medium scales of thousands of km. Here, we analyze the second-order horizontal derivatives of seafloor topography and of the gravity potential over the Pacific and Northern Indian ocean basins, and provide evidence for periodic undulations of 1600-2000 km wavelength in both signals, elongated along the direction of absolute plate motion. We investigate potential crustal and lithospheric sources and show that at least part of this signal must originate below the lithosphere, with alignments of sub-lithospheric upper mantle mass excess below seafloor lows. Furthermore, we find that these alignments coincide geographically over wide areas with similarly periodic slow seismic velocity fingers located at upper mantle depths. These two fields may thus record an intermediate scale of mantle convection below ocean basins, which cannot be explained by purely thermal convection and requires instead lateral variations in composition in the upper mantle. Elucidating the nature of the detected mass excess sources coincident with the slow seismic velocities calls for a joint dynamical modeling of all observations in a thermo-chemical context.

Experimental Investigation of Fluid-Structure Interaction in Mach 2 Flow Using Simultaneous High-Speed PIV and DIC

The fluid-structure interaction (FSI) of a compliant panel under a compression-ramp-induced shock-wave/boundary-layer interaction (SBLI) has been studied in Mach 2 flow. Simultaneous high-speed measurements of the velocity field and the panel displacement were conducted using 50 kHz particle image velocimetry (PIV) and 5 kHz stereoscopic digital image correlation (DIC). The mean effect of the panel displacement has been evaluated by monitoring the change in velocity profiles along the streamwise direction (x), upstream of the separated flow region. Streamwise (u) velocity near the panel surface has been shown to change its magnitude in response to the wall shape. Furthermore, the strong cross-correlation between fluctuations of the wall-normal panel displacement and the transverse (v) velocity can be explained by the flow remaining tangent to the wall surface as the panel deforms. This latter result is consistent with the panel motion being sufficiently low frequency compared to flow convective time scales that the flow is quasi-steady. In addition, assessment of the correlation between the separation shock position and panel displacement seems to suggest that when the panel is bulged down (concave up) at the downstream end of the panel, a larger separated flow is generated and the shock moves upstream. This observation remains speculative, but is consistent with the flow undergoing greater compression for the bulged down case.