Grid Spacing Research Articles

The Impact on Simulated Bow Echoes of Changing Grid Spacing from 3 km to 1 km in the WRF Model

Abstract Ten bow echo events were simulated using the Weather Research and Forecasting (WRF) Model with 3- and 1-km horizontal grid spacing with both the Morrison and Thompson microphysics schemes to determine the impact of refined grid spacing on this often poorly simulated mode of convection. Simulated and observed composite reflectivities were used to classify convective mode. Skill scores were computed to quantify model performance at predicting all modes, and a new bow echo score was created to evaluate specifically the accuracy of bow echo forecasts. The full morphology score for runs using the Thompson scheme was noticeably improved by refined grid spacing, while the skill of Morrison runs did not change appreciably. However, bow echo scores for runs using both schemes improved when grid spacing was refined, with Thompson runs improving most significantly. Additionally, near storm environments were analyzed to understand why the simulated bow echoes changed as grid spacing was changed. A relationship existed between bow echo production and cold pool strength, as well as with the magnitude of microphysical cooling rates. More numerous updrafts were present in 1-km runs, leading to longer intense lines of convection which were more likely to evolve into longer-lived bow echoes in more cases. Large-scale features, such as a low-level jet orientation more perpendicular to the convective line and surface boundaries, often had to be present for bow echoes to occur in the 3-km runs.

RaFSIP: Parameterizing Ice Multiplication in Models Using a Machine Learning Approach

AbstractAccurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms.

Atmospheric flow simulation over stationary waves with various steepness using WRF-LES

We investigated the capability of the Weather Research and Forecasting (WRF) model to perform large-eddy simulations (LESs) of the structure of the marine atmospheric boundary layer and turbulence exchange between the sea surface and the atmosphere. The WRF-LES model results were compared to results from Sullivan (2008) [1] for two cases: flow over a flat surface and over an idealized stationary sinusoidal surface. We also investigated the sensitivity of the WRF-LES model in simulating turbulence through the marine atmosphere under a range of horizontal grid spacings and wave steepnesses. All simulations were performed in a conventionally neutral atmosphere with a geostrophic wind of 5 m s−1, aligned with the direction of wave propagation. The simulated wind vertical profiles from the WRF-LES model exhibit similar behavior to those of Sullivan (2008) over both wavy and flat surfaces, but the differences in Sullivan’s vertical wind profiles for the two cases are considerably larger in comparison to those derived from WRF-LES. Furthermore, spectral analysis clearly shows the effect of horizontal grid spacing. The impact of wave steepness on turbulence distribution is clearly noticeable in the vertical profiles of resolved velocity variances and covariances. The impact is largest close to the surface, but it is important throughout the bulk of the boundary layer.

A stably-stratified atmospheric inflow assimilated to measured mean profiles interacting with a wind park

In this paper, a simulation chain is presented. This method comprises the precursor large-eddy simulation (LES) of a stably-stratified atmospheric boundary layer (ABL), the assimilation of the simulated mean velocities to measured wind profiles, and finally a simulation of the generated turbulent ABL flow passing through two wind turbines in a row. The high-resolved precursor simulation with a horizontal grid spacing of 3 m accounts for the characteristic turbulence of the stably-stratified ABL. Using an assimilation technique, the horizontal velocities are adapted accurately to the measured mean wind profiles for the WiValdi wind farm site at Krummendeich. The wakes of two wind turbines in a row with the assimilated inflow is successfully computed. With the simulation chain presented, it is possible to generate realistic atmospheric inflows for wind-turbine simulations with manageable computational effort.

Evaluation of the simulation performance of WRF-Solar for a summer month in China using ground observation network data

Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels. However, the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction. Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization. This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022, to evaluate the accuracy and performance of the simulated solar radiation across China. The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network. The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China, but there is still an overestimation of solar radiation, and the model underestimates the total cloud cover. The root-mean-square error ranged from 92.83 to 188.13 Wm−2 and the mean bias (MB) ranged from 21.05 to 56.22 Wm−2. The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau, while the largest MB was observed in Southeast China. To enhance the accuracy of solar radiation simulation, the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.摘要准确模拟和预测太阳辐射及其变化对于优化太阳能利用至关重要. 本研究使用WRF-Solar模式对中国2022 年 6 月 25 日至 7 月 25 日的太阳辐射情况进行了深入模拟, 模式网格水平分辨率为27 km, 通过与中国生态系统研究网络 (CERN) 的地面观测网络数据的37个观测站点进行对比, 以评估模式性能. 结果表明: WRF-Solar模式能较好地捕捉到中国上空的辐照度GHI (Global Horizontal Irradiance) 的时空分布特征, 但存在高估太阳辐射量, 以及低估总云量的情况. 全国的总辐射模拟均方根误差范围为92.83–188.13 W m−2, 平均偏差范围为21.05–56.22 W m−2. 青藏高原拉萨站的偏差最小, 在中国东南部的偏差最大. 为了进一步提升太阳辐射模拟的精确度, 本研究还对比了FARMS与RRTMG辐射方案, 发现FARMS方案的模拟精度更高.

A Conceptual Model for the Development of Tornadoes in the Complex Orography of the Po Valley

Abstract The Po Valley in northern Italy is a hotspot for tornadoes in Europe in spite of being surrounded by two mountain ridges: the Alps in the north and the Apennines in the southwest. The research focuses on the case study of 19 September 2021, when seven tornadoes (four of them rated as F2) developed in the Po Valley in a few hours. The event was analyzed using observations and numerical simulations with the convection-permitting Modello Locale in Hybrid Coordinates (MOLOCH) model. Observations show that during the event in the Po Valley, there were two surface boundaries that created a triple point: an outflow boundary generated by convection triggered in the Alpine foothills and a dryline generated by downslope winds from the Apennines, while warm and moist air advected westward from the Adriatic Sea east (ahead) of the boundaries. Tornadoes developed about 20 km northeast of the triple point. Numerical simulations with 500-m grid spacing suggest that the development of supercells and drylines in the Po Valley was sensitive to the elevation of the Apennines. Simulated vertical profiles show that the best combination of instability and wind shear for the development of tornadoes was attained within a narrow area located ahead of the dryline. A conceptual model for the development of tornadoes in the Po Valley is proposed, and the differences between tornado environments over a flat terrain and over a region with complex terrain are discussed. Significance Statement The Po Valley is a highly populated area where some of the most violent tornadoes in Europe have developed. We investigated a tornado outbreak that occurred on 19 September 2021 in this region, in order to identify its main environmental characteristics. High-resolution numerical simulations revealed that values of instability and wind shear were compatible with the development of several tornadoes only in a narrow area close to the intersection of two surface boundaries (a triple point). Moreover, the atmospheric environment during the tornado outbreak was strongly influenced by the presence of mountain ridges surrounding the plain. We have summarized our results in a conceptual model that can potentially be used for forecasting applications.

A Numerical Study of Smoke Concentration Distribution near Low-Intensity Fires: Sensitivity to Vertical Canopy Structure and Fire Heat Source Strength

Abstract Broadly speaking, prediction of the negative impacts of prescribed fire on air quality is limited by gaps in our understanding of the underlying fire, fuels, and atmospheric processes. These knowledge gaps hinder our ability to accurately predict smoke concentration distributions, leading to unintended smoke intrusions into nearby communities and subsequent threats to public health and safety. In this study, numerical simulations are performed using the Flexible Particle Weather Research and Forecasting (FLEXPART-WRF) Model, a Lagrangian particle dispersion model, with particle motion driven by output from a full-physics atmospheric model with a forest canopy submodel and 10-m horizontal grid spacing [Advanced Regional Prediction System (ARPS)-CANOPY], to address two research questions. First, what is the relationship between near-fire (within ∼50–150 m of fire) smoke concentration distribution and (i) vertical canopy structure and (ii) fire heat source strength? Second, what roles do mean transport (i.e., transport by the mean wind) and turbulent diffusion play in shaping the near-fire smoke concentration distribution? To address these questions, simulations are run with 25 combinations of plant area density profile and fire sensible heat flux magnitude, and smoke is represented by particles with diameters ≤ 2.5 μm (PM2.5). Results show that near-fire PM2.5 concentration distribution is primarily controlled by vertical canopy structure, with fire heat source strength primarily controlling the PM2.5 concentration magnitude. Analysis of the underlying ARPS-CANOPY variables driving the FLEXPART-WRF particle dispersion helps elucidate the roles of mean transport and turbulent diffusion. In total, the study findings suggest that the vertical distribution of canopy vegetation and fire heat source strength are important factors influencing PM2.5 dispersion and concentration distribution near low-intensity fires.

Comparing Gravity Waves in a Kilometer‐Scale Run of the IFS to AIRS Satellite Observations and ERA5

AbstractAtmospheric gravity waves (GWs) impact the circulation and variability of the atmosphere. Sub‐grid scale GWs, which are too small to be resolved, are parameterized in weather and climate models. However, some models are now available at resolutions at which these waves become resolved and it is important to test whether these models do this correctly. In this study, a GW resolving run of the European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), run with a 1.4 km average grid spacing (TCo7999 resolution), is compared to observations from the Atmospheric Infrared Sounder (AIRS) instrument, on NASA's Aqua satellite, to test how well the model resolves GWs that AIRS can observe. In this analysis, nighttime data are used from the first 10 days of November 2018 over part of Asia and surrounding regions. The IFS run is resampled with AIRS's observational filter using two different methods for comparison. The ECMWF ERA5 reanalysis is also resampled as AIRS, to allow for comparison of how the high resolution IFS run resolves GWs compared to a lower resolution model that uses GW drag parametrizations. Wave properties are found in AIRS and the resampled models using a multi‐dimensional S‐Transform method. Orographic GWs can be seen in similar locations at similar times in all three data sets. However, wave amplitudes and momentum fluxes in the resampled IFS run are found to be significantly lower than in the observations. This could be a result of horizontal and vertical wavelengths in the IFS run being underestimated.

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

First spiral arm detection using dynamical mass measurements of the Milky Way disk

We applied the vertical Jeans equation to the Milky Way disk in order to study non-axisymmetric variations in the thin disk surface density. We divided the disk plane into area cells with a 100 pc grid spacing and used four separate subsets of the Gaia DR3 stars, defined by cuts in absolute magnitude, that reach distances up to 3 kpc. The vertical Jeans equation is informed by the stellar number density field and the vertical velocity field; for the former, we used maps produced via Gaussian process regression; for the latter, we used Bayesian neural network radial velocity predictions, which allowed us to utilise the full power of the Gaia DR3 proper motion sample. For the first time, we find evidence of a spiral arm in the form of an over-density in the dynamically measured disk surface density, detected in all four data samples, which agrees very well with the spiral arm as traced by stellar age and chemistry. We fitted a simple spiral arm model to this feature and infer a relative over-density of roughly 20% and a width of roughly 400 pc. We also infer a thin disk surface density scale length of 3.3–4.2 kpc when restricting the analysis to stars within a distance of 2 kpc.

Landslide Warning Area Delineation through Seismic Signals and Landslide Characteristics: Insights from the Silabaku Landslide in Southern Taiwan

Abstract Publishing timely warnings of areas potentially affected by landslides and subsequent events remains challenging in hazard assessment. Recently, seismic monitoring has emerged as a promising technique for landslide detection and location determination. However, the effectiveness of this approach is limited by location errors. To overcome this problem, we propose a hybrid method (a three-step process) through seismic analysis and consideration of prior geomorphological characteristics of areas and aspects in a landslide inventory to constrain the potential landslide locations. The first analysis entails a grid-based single-force inversion of 0.02–0.05 Hz seismic signals by spatial grid spacings of 0.2° and 0.05° to retrieve the single-force direction, the estimated landslide area, and a wide search range for a given source. The second analysis involves two location methods for high-frequency seismic signals (>1 Hz) with a finer spatial grid spacing of 0.01°, gradually reducing the landslide search region. By integrating seismology-determined information with a landslide inventory, potential landslide locations and warning areas could be identified effectively. Then we apply the proposed the three-step analysis method to the 2021 Silabaku landslide in southern Taiwan. The results showed that the seismic techniques we used progressively condensed the initial search range from 1831 to 770 km2, converging to a small area of 99 km2. The estimated landslide area was 0.80±0.04 km2, and the force direction was 154.09° ± 6.76°. Subsequently, according to the earlier information, we identified three possible landslide source locations from the 2020 landslide inventory. These findings have implications for warning for two road sections, one of which was subsequently damaged by debris flow after the Silabaku landslide. This study highlights the effectiveness of the hybrid three-step algorithm in constraining areas potentially affected by landslides, facilitating timely assessments for warnings in southern Taiwan.

Influence of alloy solidification path on melt pool behavior in additive manufacturing

Numerical models used to study transport phenomena in laser-powder bed fusion processes often rely on assumptions and simplifications to reduce their computational expense. One common simplification is in the description of latent heat evolution during the solid-liquid phase change (i.e., the solidification pathway), justified by the fact that the mushy zone thickness is similar to the numerical grid spacing used for continuum transport models. The lack of resolution of transport phenomena in the mushy zone motivates the use of computationally convenient solidification paths such as linear or sigmoidal relationships over pathways derived from fundamental solidification theory such as equilibrium or Scheil models. In the present work, an uncertainty quantification (UQ) framework is used to analyze the influence of solidification pathway selection on the solidification dynamics and melt pool geometries in laser based additive manufacturing (AM) of IN625. Results show the solidification pathway has a quantifiable influence on the cooling rate at the liquidus isotherm, mushy zone thickness, and solidification time. Due to similarities in the latent heat evolution at the beginning of solidification, the equilibrium and Scheil models predict similar cooling rates near the liquidus isotherm, however the wider freezing range of Scheil leads to a wider mushy zone compared to equilibrium. The non-physical latent heat release profiles of sigmoidal and linear paths lead to significant overpredictions of cooling rates at the liquidus isotherm compared to equilibrium and Scheil. These results indicate that careful consideration should be given to the choice of solidification pathway to ensure reliable model predictions.

Surface drag on deformed topographic boundaries: Tests using a semi-idealized model

Abstract The physics of the surface drag (or surface stress) boundary condition is explored in the context of semi-idealized flows past realistic terrain. Numerical experiments are presented to explore the impact of the drag condition on flows past a region of complex topography, with a particular focus on the dependence on terrain geometry. Arguments are presented to show that the drag condition depends on the geometry of the terrain in two respects: (i) a dependence on terrain slope, as represented by a normal gradient term; and (ii) a dependence on the curvature, which appears in the drag condition as a Dirichlet term. The dependence on the geometry is illustrated through a series of numerical experiments in which simulations using the full form of the drag condition are compared to companion simulations using one of two widely used approximations: (a) the normal gradient condition, which accounts for the terrain slope but neglects curvature; and (b) the flat boundary assumption, which neglects both slope and curvature. The results show that the role of the terrain geometry in the drag condition is strongly dependent on grid spacing, with more highly resolved topography leading to a stronger dependence on the slope and curvature. For sufficiently high resolutions, the dependence on the geometry becomes significant, to the extent that simulations using the approximate drag conditions fail to capture important aspects of the flow. Some basic implications of these results for the problem of high resolution wind energy forecasting are discussed.

Urban boundary‐layer flows in complex terrain: Dynamic interactions during a hot and dry summer season in Phoenix, Arizona

AbstractAnthropogenic modification of natural landscapes to urban environments impacts land–atmosphere interactions in the boundary layer. Ample research has demonstrated the effect of such landscape transitions on development of the urban heat island (UHI), but considerably less attention has been given to impacts on regional wind flow. Here, we use a set of high‐resolution (1 km grid spacing) regional climate modeling simulations with the Weather Research and Forecasting model coupled to a multilayer urban canopy scheme to investigate the dynamical interaction between the urban boundary layer of the Phoenix metro (United States) area and the thermal circulation of the complex terrain it resides within. We conduct paired simulations for the extremely hot and dry summer of 2020, using a contemporary urban representation and a pre‐settlement landscape representation to examine the effect of the built environment on local to regional‐scale wind flow. Analysis of our simulation results shows that, during the summer of 2020, (a) the thermo‐topographical circulation dominates over both urban and rural areas for a majority of the diurnal cycle; (b) the built environment obstructs wind flow in the inertial sublayer during the late afternoon and the nighttime, whereas more intense daytime urban sensible heat flux dampens the urban‐roughness‐induced drag effect through a deeper urban boundary layer and vigorous mixing; (c) the Phoenix metro UHI does not result in a well‐developed and clearly discernible induced circulation as observed in other urban areas and described in the scientific literature; (d) shortly before dawn, the local UHI is able to affect the local thermo‐topographical circulation through flow intensity modulation that results in an ~10 km eastward shift of the center of mass convergence. Our results highlight the need for future research—both observational and simulation based—into urbanizing regions where multiscale flows are dominant.

A generalized curvilinear solver for spherical shell Rayleigh-Bénard convection

Summary A three-dimensional finite-difference solver has been developed and implemented for Boussinesq convection in a spherical shell. The solver transforms any complex curvilinear domain into an equivalent Cartesian domain using Jacobi transformation and solves the governing equations in the latter. This feature enables the solver to account for the effects of the non-spherical shape of the convective regions of planets and stars. Apart from parallelization using MPI, implicit treatment of the viscous terms using a pipeline alternating direction implicit scheme and HYPRE multigrid accelerator for pressure correction makes the solver efficient for high-fidelity direct numerical simulations. We have performed simulations of Rayleigh-Bénard convection at two Rayleigh numbers Ra = 105 and 107 while keeping the Prandtl number fixed at unity (Pr = 1). The average radial temperature profile and the Nusselt number match very well, both qualitatively and quantitatively, with the existing literature. Closure of the turbulent kinetic energy budget, apart from the relative magnitude of the grid spacing compared to the local Kolmogorov scales, ensures sufficient spatial resolution.

Uncertainty quantification of the lattice Boltzmann method focussing on studies of human-scale vascular blood flow

Uncertainty quantification is becoming a key tool to ensure that numerical models can be sufficiently trusted to be used in domains such as medical device design. Demonstration of how input parameters impact the quantities of interest generated by any numerical model is essential to understanding the limits of its reliability. With the lattice Boltzmann method now a widely used approach for computational fluid dynamics, building greater understanding of its numerical uncertainty characteristics will support its further use in science and industry. In this study we apply an in-depth uncertainty quantification study of the lattice Boltzmann method in a canonical bifurcating geometry that is representative of the vascular junctions present in arterial and venous domains. These campaigns examine how quantities of interest—pressure and velocity along the central axes of the bifurcation—are influenced by the algorithmic parameters of the lattice Boltzmann method and the parameters controlling the values imposed at inlet velocity and outlet pressure boundary conditions. We also conduct a similar campaign on a set of personalised vessels to further illustrate the application of these techniques. Our work provides insights into how input parameters and boundary conditions impact the velocity and pressure distributions calculated in a simulation and can guide the choices of such values when applied to vascular studies of patient specific geometries. We observe that, from an algorithmic perspective, the number of time steps and the size of the grid spacing are the most influential parameters. When considering the influence of boundary conditions, we note that the magnitude of the inlet velocity and the mean pressure applied within sinusoidal pressure outlets have the greatest impact on output quantities of interest. We also observe that, when comparing the magnitude of variation imposed in the input parameters with that observed in the output quantities, this variability is particularly magnified when the input velocity is altered. This study also demonstrates how open-source toolkits for validation, verification and uncertainty quantification can be applied to numerical models deployed on high-performance computers without the need for modifying the simulation code itself. Such an ability is key to the more widespread adoption of the analysis of uncertainty in numerical models by significantly reducing the complexity of their execution and analysis.

Team stress: Grasping physiological stress dynamics in small teams through state space grids.

Team stress: Grasping physiological stress dynamics in small teams through state space grids.

Is a direct numerical simulation (DNS) of Navier-Stokes equations with small enough grid spacing and time-step definitely reliable/correct?

Turbulence is strongly associated with the vast majority of fluid flows in nature and industry. Traditionally, results given by the direct numerical simulation (DNS) of Navier-Stokes (NS) equations that relate to a famous millennium problem are widely regarded as ‘reliable’ benchmark solutions of turbulence, as long as grid spacing is fine enough (i.e. less than the minimum Kolmogorov scale) and time-step is small enough, say, satisfying the Courant-Friedrichs-Lewy condition (Courant number < 1). Is this really true? In this paper a two-dimensional sustained turbulent Kolmogorov flow driven by an external body force governed by the NS equations under an initial condition with a spatial symmetry is investigated numerically by the two numerical methods with detailed comparisons: one is the traditional DNS, the other is the ‘clean numerical simulation’ (CNS). In theory, the exact solution must have a kind of spatial symmetry since its initial condition is spatially symmetric. However, it is found that numerical noises of the DNS are quickly enlarged to the same level as the ‘true’ physical solution, which finally destroy the spatial symmetry of the flow field. In other words, the DNS results of the turbulent Kolmogorov flow governed by the NS equations are badly polluted mostly. On the contrary, the numerical noise of the CNS is much smaller than the ‘true’ physical solution of turbulence in a long enough interval of time so that the CNS result is very close to the ‘true’ physical solution and thus can remain symmetric, which can be used as a benchmark solution for comparison. Besides, it is found that numerical noise as a kind of artificial tiny disturbances can lead to huge deviations at large scale on the two-dimensional Kolmogorov turbulence governed by the NS equations, not only quantitatively (even in statistics) but also qualitatively (such as spatial symmetry of flow). This highly suggests that fine enough spatial grid spacing with small enough time-step alone could not guarantee the validity of the DNS of the NS equations: it is only a necessary condition but not sufficient. All of these findings might challenge some of our general beliefs in turbulence: for example, it might be wrong in physics to neglect the influences of small disturbances to NS equations. Our results suggest that, from physical point of view, it should be better to use the Landau-Lifshitz-Navier-Stokes (LLNS) equations, which consider the influence of unavoidable thermal fluctuations, instead of the NS equations, to model turbulent flows.

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (&lt; 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.