Convection Scheme Research Articles

The Galactic chemical evolution of phosphorus observed with IGRINS

Context. Phosphorus (P) is considered to be one of the key elements for life, making it an important element to look for in the abundance analysis of spectra of stellar systems. Yet, only a select number of spectroscopic studies exist to estimate the phosphorus abundances and investigate its trend across a range of metallicities. This is due to the lack of good phosphorus lines in the optical wavelength region and the requirement of careful manual analysis of the blended phosphorus lines in near-infrared H-band spectra obtained with individual observations and surveys such as the Apache Point Observatory Galactic Evolution Experiment (APOGEE). Aims. Based on a consistent and systematic analysis of high-resolution, near-infrared Immersion GRating INfrared Spectrograph (IGRINS) spectra of 38 K giant stars in the Solar neighborhood, we present and investigate the phosphorus abundance trend in the metallicity range of −1.2 dex < [Fe/H] < 0.4 dex. Furthermore, we compare this trend with the available chemical evolution models to shed some light on the origin and evolution of phosphorus. Methods. We have observed full H- and K-band spectra at a spectral resolving power of R = 45 000 with IGRINS mounted on the Gemini South telescope, the Discovery Channel Telescope, and the Harlan J Smith Telescope at McDonald Observatory. Abundances were determined from spectral lines by modeling the synthetic spectrum that best matches the observed spectrum by χ2 minimization. For this task, we used the Spectroscopy Made Easy (SME) tool in combination with one-dimensional (1D) Model Atmospheres in a Radiative and Convective Scheme (MARCS) stellar atmosphere models. The investigated sample of stars have reliable stellar parameters estimated using optical FIber-fed Echelle Spectrograph (FIES) spectra obtained in a previous study of a set of stars called Giants in the Local Disk (GILD). In order to determine the phosphorus abundances from the 16482.92 Å phosphorus line, we needed to take special care blending the CO(v = 7−4) line. With the stellar parameters known, we thus determined the C, N, and O abundances from atomic carbon and a range of nonblended molecular lines (CO, CN, and OH) which are plentiful in the H-band region of K giant stars, assuring an appropriate modeling of the blending CO(v = 7−4) line. Results. We present the [P/Fe] versus [Fe/H] trend for K giant stars in the metallicity range of −1.2 dex < [Fe/H] < 0.4 dex and enhanced phosphorus abundances for two metal-poor s-rich stars. We find that our trend matches well with the compiled literature sample of prominently dwarf stars and the limited number of giant stars. Our trend is found to be higher by ~0.05−0.1 dex compared to the theoretical chemical evolution trend resulting from the core collapse supernova (type II) of massive stars with the phosphorus yields arbitrarily increased by a factor of 2.75. Thus the enhancement factor might need to be ~0.05−0.1 dex higher to match our trend. We also find an empirically determined primary behavior for phosphorus. Furthermore, the phosphorus abundance is found to be elevated by ~0.6−0.9 dex in the two s-enriched stars compared to the theoretical chemical evolution trend.

Positivity‐preserving scheme without separated method for 2D convection–diffusion equations on polygonal meshes

AbstractWe present a finite volume scheme for 2D steady convection–diffusion equations on arbitrary convex polygonal meshes. The method uses auxiliary unknowns on the cell‐edge and cell‐node of the mesh, and the continuous flux is computed to be a two‐point nonlinear flux. Since the transpose of the coefficient matrix is an M‐matrix, it is guaranteed that the scheme is positive. We propose a new strategy, which aims that providing a scheme without separated method to compute the value on the cell‐edge without previous reconstruct. Numerical results show that our scheme is positive on polygonal meshes for both diffusion‐dominated and convection‐dominated problems and has second‐order accuracy for solutions.

Extension of an exponential discretization scheme to multidimensional convection–diffusion problems

ENATE (Enhanced Numerical Approximation of a Transport Equation) is a high-order exponential scheme for convection–diffusion problems, such as those that govern the transport of fluid properties in a flow field. The scheme was intended to be employed in fluid-related transport equations, although it can be used for any inhomogeneous second-order ordinary differential equation. The value of a variable ϕ at a generic point is related to those of adjacent nodes via an algebraic equation. Thus, a three-point stencil is associated to each node. The coefficients of this equation contain integrals of some fluid and flow parameters. One important property is that the scheme allows to obtain a machine-accurate solution of an inhomogeneous transport equation if these integrals can be obtained analytically. As the scheme is essentially one-dimensional, getting the machine-accurate solution of multidimensional problems is not guaranteed even in cases where ENATE integrals are analytic along each coordinate. In this regard the paper presents a simple way of getting solutions in multidimensional problems while still using the one-dimensional formulation. Moreover, if the problem is such that the solution is machine-accurate in the one-dimensional problem along coordinate lines, it will also be for the multidimensional domain. Two different methods of evaluating those terms that come out of the discretization will be explained and compared in various cases.

Sensitivity analysis of convective and PBL parameterization schemes for Luban and Titli tropical cyclones

Sensitivity analysis of convective and PBL parameterization schemes for Luban and Titli tropical cyclones

Data and scripts for "Modelling water isotopes using a global non-hydrostatic model with an explicit convection scheme: Comparison with a gridded dataset and site observations"

Data and scripts for "Modelling water isotopes using a global non-hydrostatic model with an explicit convection scheme: Comparison with a gridded dataset and site observations"

Effect of convective schemes in wall-resolved and wall-modeled LES of compressible wall turbulence

The current study discusses how numerical schemes and discretization approaches affect wall-resolved and wall-modeled LES outcomes. A turbulent boundary layer setup over a flat plate in both super- and hypersonic conditions is used to illustrate the effect of different numerical discretization strategies. In particular, six convective methods are examined, as well as various degrees of hybridization between shock-capturing and centered approaches: The former introducing non-negligible numerical viscosity, the latter being virtually dissipation-free. The analysis reveals that injected numerical viscosity due to upwinding procedures considerably alters wall dynamics for both wall-resolved and especially wall-modeled arrangements. In particular, if low-order or pure shock-capturing schemes are used, wall modeling fails in heading the system dynamics due to a strong modulation of main turbulent features. Conversely, realistic turbulence patterns are recovered if hybrid and/or high-order shock-capturing methods are employed. Thus, the paper establishes criteria for selecting a suitable numerical setup in wall-modeled LES, providing suggestions for grid resolution levels and convective scheme selection/hybridization. An overview perspective concerning numerical diffusion coupling with turbulent stresses in wall-resolved and wall-modeled LES is also provided.

Quantification of model uncertainty in sub-daily extreme precipitation projections

Sub-daily precipitation is projected to intensify more rapidly with global warming, that in turn may lead to frequent disasters such as flash floods, landslides, etc. However, the precipitation projections from Global Climate Model (GCMs) are known to be highly uncertain, with an increasing magnitude at finer spatio-temporal scales. Hence, realistic quantification of the inherent uncertainty is vital to improve the confidence in these projections, for the mitigation/adaptation applications. In the present study, the model uncertainty in sub-daily (3-hourly) precipitation extremes is estimated, by considering 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, during four different seasons for the near (2026–45) and far-future (2081–99) periods. An uncertainty quantification approach using “mutual information based independence weight” is proposed in this study, to account the linear and nonlinear dependence between GCMs along with the equitability property. The CMCC-CM model is found to be the most independent model having highest independence weight (0.064) in the ensemble. The uncertainty in northern hemisphere is found to be more during June to August period, whereas in southern hemispheres during December to February period. Overall, there is an increase in model uncertainty in far-future, especially over the equatorial regions. However, the behaviour of different indices is not in agreement with each other. For example, extreme characteristics such as monthly maximum 3hourly-precipitation has increasing uncertainty; whereas the mean characteristic like, simple hourly-precipitation intensity index possesses unchanging uncertainty. Model uncertainty is most sensitive to the precipitation characteristics of the regions, followed by elevation, a significant geophysical factor. The study also highlighted the need for model improvement over the grids having elevation <2 km and above 4 km. The study identifies the possible regions, seasons, factors etc. where model improvement can be made. Moreover, it is anticipated that improvement of resolution, updation of convective parameterization schemes and land surface parameterization schemes in GCMs may enhance the skill of GCMs and can improve the confidence in the projection of precipitation on sub-daily scale. • Seasonal model uncertainty is estimated to assess the reliability in the projections of sub-daily precipitation extremes. • Mutual information-based weighting technique is used to establish the independence weight of models. • Different aspects of sub-daily precipitation exhibit differing uncertainty, in terms of magnitude and also temporal evolution. • Precipitation intensity remains the dominant factor behind model uncertainty, but geophysical influences cannot be ignored. • Reliability on the sub-daily mean characteristics improves with time; whereas that on extreme characteristics declines.

Impacts of Cumulus Convection and Turbulence Parameterizations on the Convection-Permitting Simulation of Typhoon Precipitation

Abstract Convection-permitting resolutions, which refer to kilometer-scale horizontal grid spacings, have become increasingly popular in regional numerical weather prediction and climate studies. However, this resolution range is in the gray zone for the simulation of convection, where conventional cumulus convection and subgrid-scale (SGS) turbulence parameterizations are inadequate for such grid spacings due to invalid assumptions and simplifications. Recent studies demonstrated that the magnitudes of SGS fluxes of momentum and scalars are comparable to those of resolved fluxes at convection-permitting resolutions and that horizontal SGS components are as important as the vertical SGS component. Thus, it appears necessary to adapt available schemes to model the SGS effects of convective motions for the gray zone. Here, we investigated the efficacy of separately parameterizing the vertical and horizontal SGS effects in improving the convection-permitting simulation of Typhoon Vicente (2012). To represent the vertical SGS turbulence effect, we evaluated the Grell-3, Tiedtke, and multiscale Kain–Fritsch (MSKF) schemes in the Weather Research and Forecasting (WRF) Model; the MSKF scheme is scale adaptive, whereas the other two are conventional cumulus schemes. For horizontal SGS turbulence, we evaluated the effects of the traditional Smagorinsky scheme and our newly developed reconstruction and nonlinear anisotropy (RNA) model, which models not only downgradient diffusion but also backscatter. We found that the simulation combining the MSKF and RNA schemes exhibits the best skill in predicting precipitation, especially rainfall extremes. The advantages are rooted in the MSKF scheme’s scale-awareness and parameterized cloud–radiation feedback and in the backscatter-enabling capability of the RNA model. Significance Statement Operational numerical weather prediction and some climate simulations have approached kilometer-scale horizontal resolutions, called convection-permitting resolutions. However, details of convective storms are not well represented at these resolutions, and small-scale fluid motions can potentially impact the overall simulation performance. In practice, the effects of such unresolved turbulent eddies were once neglected. We suggest representing these effects in the vertical and horizontal directions with an adaptive cumulus convection parameterization and an advanced turbulence model, respectively, which significantly improve the simulation of tropical cyclones. This framework allows us to adapt convection schemes developed by the mesoscale modeling community and turbulence schemes studied by large-eddy simulation groups for representing three-dimensional turbulence in the convection-permitting regime.

A virtual element method‐based positivity‐preserving conservative scheme for convection–diffusion problems on polygonal meshes

AbstractIn this paper, we propose a positivity‐preserving conservative scheme based on the virtual element method (VEM) to solve convection–diffusion problems on general meshes. As an extension of finite element methods to general polygonal elements, the VEM has many advantages such as substantial mathematical foundations, simplicity in implementation. However, it is neither positivity‐preserving nor locally conservative. The purpose of this article is to develop a new scheme, which has the same accuracy as the VEM and preserves the positivity of the numerical solution and local conservation on primary grids. The first step is to calculate the cell‐vertex values by the lowest‐order VEM. Then, the nonlinear two‐point flux approximations are utilized to obtain the nonnegativity of cell‐centered values and the local conservation property. The new scheme inherits both advantages of the VEM and the nonlinear two‐point flux approximations. Numerical results show that the new scheme can reach the optimal convergence order of the virtual element theory, that is, the second‐order accuracy for the solution and the first‐order accuracy for its gradient. Moreover, the obtained cell‐centered values are nonnegative, which demonstrates the positivity‐preserving property of our new scheme.

Optimizing simulation of summer precipitation by weather research and forecasting model over the mountainous southern Tibetan Plateau

The mountainous regions of southern Tibetan Plateau and surroundings (hereafter mountainous southern TP), mainly including Himalayas, Gangdise, Nyainqentanglha and Hengduan Mountains, are characterized by high mountains and a complex terrain, and play a crucial role in the redistribution of monsoon water vapor and consequently determine the spatial distribution of precipitation over the TP. However, numerical simulations generally have large precipitation deviation in this focus area. Therefore, optimizing simulation of summer precipitation by Weather Research and Forecasting (WRF) model with two-way nested domains was carried out. A matrix of 11 model experiments, including four cumulus convection (CU) schemes, four microphysics (MP) schemes and four planetary boundary layer (PBL) schemes, were designed. In situ observations from 44 stations, TRMM 3B42 v7 precipitation product, and statistical and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods were utilized for the model evaluation. The results showed that physical parameterization schemes, Kain-Fritsch-Cumulus Potential scheme (CU), Thompson scheme (MP), Mellor-Yamada-Janjic scheme (PBL), Unified Noah land surface model, and Eta similarity surface layer scheme, greatly reduced the summer precipitation simulation error of newly High Asia Refined analysis (HAR v2) over the mountainous southern TP, with the better reproduction of the spatiotemporal distribution and vertical variability of precipitation. Compared to NCEP-FNL reanalysis data, the performance of this model setup forced by ERA5 was notably improved, especially with perspective to the vertical variability of precipitation. Our findings suggested that summer precipitation simulation was more sensitive to the CU scheme, and the choice of Kain-Fritsch-Cumulus Potential scheme (ERA5 as initial conditions) significantly improved the precipitation simulation, especially with the lowest MAE (2.00 mm/d), RMSE (2.89 mm/d) and highest Rs (0.74). Further analysis implied that accurate description of shallow convection and proper suppression of deep convection should be considered in precipitation simulations over the TP, especially in mountainous regions. This study could provide a firm foundation for the production of more realistic dynamic downscaling precipitation dataset over the TP.

On algebraically stabilized schemes for convection–diffusion–reaction problems

An abstract framework is developed that enables the analysis of algebraically stabilized discretizations in a unified way. This framework is applied to a discretization of this kind for convection–diffusion–reaction equations. The definition of this scheme contains a new limiter that improves a standard one in such a way that local and global discrete maximum principles are satisfied on arbitrary simplicial meshes.

Benchmark simulation of the flow-induced vibrations for nuclear applications

This study investigates the computational modelling of flow-induced vibrations of cantilever rods subjected to turbulent axial flow at operating conditions relevant to those of fuel rods of pressurized-water-cooled (PWR) nuclear reactors. The aim is to assemble all the modelling elements needed for a cost-effective and thus URANS-based (Unsteady Reynolds Averaged Navier–Stokes) modelling strategy, employing high-Reynolds-number models of turbulence. This objective is pursued through three stages. First, we investigate the numerical FSI (Fluid–Structure Interaction) strategy adopted, through the computation of flow-induced vibration of an elastic plate subjected to axial laminar flow. We subsequently assess the suitability of URANS models through computations of turbulent flow over a forward-facing step, for which measurements of the fluctuating wall pressure are available. On the numerical side, these explorations led to adopting a two-way FSI strategy, using a single finite-volume solver, with the Arbitrary Lagrangian–Eulerian (ALE) approach, high order convective discretization schemes and Laplacian smoothing for the displacement of the mesh in the fluid domain. On the physical modelling side, they resulted in the use of high-Reynolds-number Reynolds stress transport models. The resulting modelling strategy is subsequently validated against the experimentally investigated case of a steel cantilever rod caused to vibrate through exposure to turbulent axial flow. The resulting comparisons show that for the first time, to our knowledge, both the frequency and the amplitude of the flow induced vibrations of this case, have been reproduced with good accuracy.

Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation

The scale-aware convective parameterization for high resolution global climate models must satisfy the requirement that the parameterized subgrid convective transport diminishes as the model resolution increases to convection-resolving resolutions. A major assumption in current scale-aware convection schemes is that the differences between convective cloud properties and their environmental counterparts are independent of cloud fraction. This study examines convective cloud vertical velocity, moist static energy (MSE), moisture, and the vertical eddy transport of MSE and moisture for different averaging subdomain sizes and fractional convective cloudiness using a cloud resolving model simulation of a midlatitude mesoscale convective system. Results show that convective cloud fraction, mass flux, and vertical transport of MSE and moisture increase with decreasing subdomain size. The differences between convective cloud properties in both updrafts and downdrafts and their environment depend on both cloud fraction and the averaging subdomain size. For a given subdomain size, the differences increase with cloud fraction, in contrast to the assumption used in current scale-aware convection parameterization schemes. A consequence of this is that the parameterized convective eddy transport reaches maximum at a higher cloud fraction than believed in previous studies. This has implications on how fast the subgrid convective transport should diminish as GCM resolution increases.

Ensemble‐Based Experimental Design for Targeting Data Acquisition to Inform Climate Models

AbstractData required to calibrate uncertain general circulation model (GCM) parameterizations are often only available in limited regions or time periods, for example, observational data from field campaigns, or data generated in local high‐resolution simulations. This raises the question of where and when to acquire additional data to be maximally informative about parameterizations in a GCM. Here we construct a new ensemble‐based parallel algorithm to automatically target data acquisition to regions and times that maximize the uncertainty reduction, or information gain, about GCM parameters. The algorithm uses a Bayesian framework that exploits a quantified distribution of GCM parameters as a measure of uncertainty. This distribution is informed by time‐averaged climate statistics restricted to local regions and times. The algorithm is embedded in the recently developed calibrate‐emulate‐sample framework, which performs efficient model calibration and uncertainty quantification with only model evaluations, compared with evaluations typically needed for traditional approaches to Bayesian calibration. We demonstrate the algorithm with an idealized GCM, with which we generate surrogates of local data. In this perfect‐model setting, we calibrate parameters and quantify uncertainties in a quasi‐equilibrium convection scheme in the GCM. We consider targeted data that are (a) localized in space for statistically stationary simulations, and (b) localized in space and time for seasonally varying simulations. In these proof‐of‐concept applications, the calculated information gain reflects the reduction in parametric uncertainty obtained from Bayesian inference when harnessing a targeted sample of data. The largest information gain typically, but not always, results from regions near the intertropical convergence zone.

Numerical Analysis of High Speed Flow Applications using Various Flux Schemes

Numerical analysis using computational fluid dynamics is an affordable and effective method with increasing relevance every day in research and engineering problems. Choosing an appropriate convective discretization scheme is paramount importance for obtaining more accurate solutions using CFD methods. The present study compares two commonly used numerical convective schemes, the upwind scheme of Advection Upstream Splitting Method (AUSM) and the central schemes of Kurganov-Noelle-Petrova and Kurganov-Tadmor scheme (K-T) have been made to find the better central scheme. These two schemes are validated with inviscid 1D and 2D problems such as Sod’s shock tube, forward facing step in a Mach 3 tunnel, 15 Degree ramp in a Mach 2 supersonic tunnel, the Mach-reflection problem and scramjet engine operating under high Mach number. The AUSM and K-T schemes are robust enough to capture all the features of compressible flows. Comparison of results obtained by these 2 schemes is presented using density contours and surface property changes. It has been observed that both the schemes are robust enough to capture the flow features of the problems and that they have better accuracy for different conditions.&#x0D; HIGHLIGHTS&#x0D; &#x0D; This paper focuses on two convective flux discretization techniques i.e. the upwind scheme of AUSM and the central schemes of K-T and KNP&#x0D; Inviscid simulations for various problems in the supersonic as well as hypersonic regime were used in Sod’s 1-Dimensional Shock Tube, flow over forward facing step in supersonic wind tunnel and Mach 5 flow thorough scramjet engine&#x0D; The AUSM and K-T schemes are robust enough to capture all the features of compressible flows. It has been observed that both the schemes are robust enough to capture the flow features of the problems and that they have better accuracy for different conditions&#x0D; &#x0D; GRAPHICAL ABSTRACT

Diabatic processes modulating the vertical structure of the jet stream above the cold front of an extratropical cyclone: sensitivity to deep convection schemes

Abstract. The effect of deep convection parameterisation on the jet stream above the cold front of an explosive extratropical cyclone is investigated in the global numerical weather prediction model ARPEGE, operational at Météo-France. Two hindcast simulations differing only in the deep convection scheme used are systematically compared with each other, with (re)analysis datasets and with NAWDEX airborne observations. The deep convection representation has an important effect on the vertical structure of the jet stream above the cold front at 1-d lead time. The simulation with the less active scheme shows a deeper jet stream, associated with a stronger potential vorticity (PV) gradient in the middle troposphere. This is due to a larger deepening of the dynamical tropopause on the cold air side of the jet and a higher PV destruction on the warm air side, near 600 hPa. To better understand the origin of this stronger PV gradient, Lagrangian backward trajectories are computed. On the cold air side of the jet, numerous trajectories undergo a rapid ascent from the boundary layer to the mid-levels in the simulation with the less active deep convection scheme, whereas they stay at mid-levels in the other simulation. This ascent explains the higher PV noted on that side of the jet in the simulation with the less active deep convection scheme. These ascending air masses form mid-level ice clouds that are not observed in the microphysical retrievals from airborne radar-lidar measurements. On the warm air side of the jet, in the warm conveyor belt ascending region, the Lagrangian trajectories with the less active deep convection scheme undergo a higher PV destruction due to a stronger heating occurring in the lower and middle troposphere. In contrast, in the simulation with the most active deep convection scheme, both the heating and PV destruction extend further up into the upper troposphere.

The Transition from Practical to Intrinsic Predictability of Midlatitude Weather

Abstract This study investigates the transition from current practical predictability of midlatitude weather to its intrinsic limit. For this purpose, estimates of the current initial condition uncertainty of 12 real cases are reduced in several steps from 100% to 0.1% and propagated in time with a global numerical weather prediction model (ICON at 40 km resolution) that is extended by a stochastic convection scheme to better represent error growth from unresolved motions. With the provision that the perfect model assumption is sufficiently valid, it is found that the potential forecast improvement that could be obtained by perfecting the initial conditions is 4–5 days. This improvement is essentially achieved with an initial condition uncertainty reduction by 90% relative to current conditions, at which point the dominant error growth mechanism changes: With respect to physical processes, a transition occurs from rotationally driven initial error growth to error growth dominated by latent heat release in convection and due to the divergent component of the flow. With respect to spatial scales, a transition from large-scale up-amplitude error growth to a very rapid initial error growth on small scales is found. Reference experiments with a deterministic convection scheme show a 5%–10% longer predictability, but only if the initial condition uncertainty is small. These results confirm that planetary-scale predictability is intrinsically limited by rapid error growth due to latent heat release in clouds through an upscale-interaction process, while this interaction process is unimportant on average for current levels of initial condition uncertainty. Significance Statement Weather predictions provide high socioeconomic value and have been greatly improved over the last decades. However, it is widely believed that there is an intrinsic limit to how far into the future the weather can be predicted. Using numerical simulations with an innovative representation of convection, we are able to confirm the existence of this limit and to demonstrate which physical processes are responsible. We further provide quantitative estimates for the limit and the remaining improvement potential. These results make clear that our current weather prediction capabilities are not yet maxed out and could still be significantly improved with advancements in atmospheric observation and simulation technology in the upcoming decades.

Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution

In recent studies, proxy XCH4 retrievals from the Japanese Greenhouse gases Observing SATellite (GOSAT) have been used to constrain top-down estimation of CH4 emissions. Still, the resulting interannual variations often show significant discrepancies over some of the most important CH4 source regions, such as China and Tropical South America, by causes yet to be determined. This study compares monthly CH4 flux estimates from two parallel assimilations of GOSAT XCH4 retrievals from 2010 to 2019 based on the same Ensemble Kalman Filter (EnKF) framework but with the global chemistry transport model (GEOS-Chem v12.5) being run at two different spatial resolutions of 4° × 5° (R4, lon × lat) and 2° × 2.5° (R2, lon × lat) to investigate the effects of resolution-related model errors on the derived long-term global and regional CH4 emission trends. We found that the mean annual global methane emission for the 2010s is 573.04 Tg yr−1 for the inversion using the R4 model, which becomes about 4.4 Tg yr−1 less (568.63 Tg yr−1) when a finer R2 model is used, though both are well within the ensemble range of the 22 top-down results (2008–17) included in the current Global Carbon Project (from 550 Tg yr−1 to 594 Tg yr−1). Compared to the R2 model, the inversion based on the R4 tends to overestimate tropical emissions (by 13.3 Tg yr which is accompanied by a general underestimation (by 8.9 Tg yr−1) in the extratropics. Such a dipole reflects differences in tropical-mid-latitude air exchange in relation to the model’s convective and advective schemes at different resolutions. The two inversions show a rather consistent long-term CH4 emission trend at the global scale and over most of the continents, suggesting that the observed rapid increase in atmospheric methane can largely be attributed to the emission growth from North Africa (1.79 Tg yr−2 for R4 and 1.29 Tg yr−2 for R2) and South America Temperate (1.08 Tg yr−2 for R4 and 1.21 Tg yr−2 for R2) during the first half of the 2010s, and from Eurasia Boreal (1.46 Tg yr−2 for R4 and 1.63 Tg yr−2 for R2) and Tropical South America (1.72 Tg yr−2 for R4 and 1.43 Tg yr−2 for R2) over 2015–19. In the meantime, emissions in Europe have shown a consistent decrease over the past decade. However, the growth rates by the two parallel inversions show significant discrepancies over Eurasia Temperate, South America Temperate, and South Africa, which are also the places where recent GOSAT inversions usually disagree with one other.

Modeling and optimization of non-isothermal convective drying process of Lavandula × allardii

Non-isothermal convective drying schemes were examined for Lavandula × allardii leaves and inflorescences. Drying process parameters were optimized using response surface methodology (RSM) to ensure the peak operational performance. The effects of temperature increase rate (2–4 °C/h) and the airflow velocity (1–3 m/s) on the essential oil yield, drying duration and consumption, were investigated. A face-centered central composite design was deployed and the experimental data was adapted to the most suitable polynomial models, as determined by the regression analysis. Analysis of variance was applied to assess the effects of the process variables, their interactions and the statistical significance of the examined models. Both factors of temperature increase rate and airflow velocity had a significant impact on the drying duration. Airflow velocity had a greater effect on leaves’ essential oil yield and inflorescences’ process energy consumption, whereas the rates of temperature increase had a greater influence on the inflorescences’ essential oil yield and leaves’ energy consumption. The minimum drying duration and energy consumption were obtained for the maximum temperature increasing rate at 3 and 1 m/s airflow velocities respectively; and the highest essential oil yield was obtained for the least rate of temperature increase and airflow velocity for both leaves and inflorescences. Numerical optimization was performed for minimizing drying duration and energy consumption by maximizing the essential oil yield. The rate of temperature increases of 4 °C/h and the airflow velocity of 1 m/s, were proposed as the optimum non-isothermal drying conditions for both leaves and inflorescences of Lavandula × allardii. Predicted values of essential oil content have been 1.387/3.05 mL/g, 4.21/4.18 h drying time and 0.809/0.732 kWh energy consumption at the optimum operation point for leaves and inflorescences, respectively. The resulted optimized non-stationary temperature scheme considerably improved the drying kinetics and the process consumption by achieving a similar essential oil recovery with the standard low-temperature convective drying. The present study aimed to eliminate the preexisting gap of the optimum selection of the process parameters for the particular type of the examined non-isothermal drying schemes. Previous findings could be utilized for designing dryers and drying schedules aiming to retain the qualitative attributes, by reducing the cost and duration of the drying operations.

Computational Fluid Dynamics of the Right Atrium: A Comparison of Modeling Approaches in a Range of Flow Conditions

Abstract The right atrium (RA) combines flows from the inferior (IVC) and superior vena cava (SVC). Here RA mixing is simulated using computational fluid dynamics, comparing four modeling approaches. A patient-averaged model (11 M cells) was created from four volunteers. We compared: (1) unsteady k–ω Reynolds-averaged Navier–Stokes (URANS) (2) implicit large eddy simulation with second-order upwind convection scheme (iLES-SOU) (3) iLES with bounded-central difference convection scheme (iLES-BCD) and (4) LES with wall-adapting local eddy-viscosity (LES-WALE). A constant inlet flow rate of 6 L/min was applied with both IVC/SVC contributions ranging from 30–70%. A higher density mesh (37 M cells) was also simulated for models 2 and 4 (equal IVC/SVC flow) to assess the accuracy of models 1–4. Results from the 11 M cell LES-WALE model showed good agreement with the 37 M cell meshes. All four 11 M cell models captured the same large-scale flow structures. There were local differences in velocity, time-averaged wall shear stress, and IVC/SVC mixing when compared to LES-WALE, particularly at high SVC flow. Energy spectra and velocity animations from the LES-WALE model suggest the presence of transitional flow. For the general flow structures, all four methods provide similar results, though local quantities can vary greatly. On coarse meshes, the convection scheme and subgrid-scale (SGS) model have a significant impact on results. For RA flows, URANS should be avoided and iLES models are sensitive to convection scheme unless used on a highly resolved grid.