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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Assessing the Sensitivity of the Tropical Cyclone Boundary Layer to the Parameterization of Momentum Flux in the Community Earth System Model

Recent studies have demonstrated that high-resolution (~25 km) Earth System Models (ESMs) have the potential to skillfully predict tropical cyclone (TC) occurrence and intensity. However, biases in ESM TCs still exist, largely due to the need to parameterize processes such as boundary layer (PBL) turbulence. Building on past studies, we hypothesize that the depiction of the TC PBL in ESMs is sensitive to the configuration of the PBL parameterization scheme, and that the targeted perturbation of tunable parameters can reduce biases. The Morris one-at-a-time (MOAT) method is implemented to assess the sensitivity of the TC PBL to tunable parameters in the PBL scheme in an idealized configuration of the Community Atmosphere Model, version 6 (CAM6). The MOAT method objectively identifies several parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBB) scheme that appreciably influence the structure of the TC PBL. We then perturb the parameters identified by the MOAT method within a suite of CAM6 ensemble simulations and find a reduction in model biases compared to observations and a high-resolution, cloud-resolving model. Importantly, we demonstrate that the high-sensitivity parameters are tied to PBL processes that reduce turbulent mixing and effective eddy diffusivity, and that in CAM6 these parameters alter the TC PBL in a manner consistent with past modeling studies. In this way, we provide an initial identification of process-based input parameters that, when altered, have the potential to improve TC predictions by ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High temporal frequency data from a four turbine, blade-resolved wind farm simulation with ExaWind

The data was generated with ExaWind (https://github.com/Exawind) which couples AMR-Wind (https://github.com/Exawind/amr-wind/), Nalu-Wind (https://github.com/Exawind/nalu-wind), TIOGA (https://github.com/Exawind/tioga), and OpenFAST (https://github.com/OpenFAST/openfast). This is a large-scale simulation of a blade-resolved wind farm using the ExaWind software stack. ExaWind couples together a background flow solver, AMR-Wind, and a near-body solver, Nalu-Wind, through an overset technique from the TIOGA application. Another application, OpenFAST, handles the structural dynamics of the turbine blades and towers, which informs the fluid-structure interaction of the wind turbines with the flow solvers. This particular simulation includes four blade-resolved wind turbines operating in a turbulent atmospheric boundary layer. The AMR-Wind solver uses 500 million cells and is being solved on 256 AMD GPUs of the Oakridge Leadership Computing Facility Frontier supercomputer. Each turbine is assigned its own Nalu-Wind solver with over 13 million elements per turbine and solved using 448 CPU cores, for a total of 1792 CPU cores. For each node, 56 cores contain Nalu-Wind, while 8 cores correspond to AMR-Wind operations on the GPUs. Consequently, ExaWind is entirely utilizing the CPUs and the GPUs of the nodes concurrently. The data used in the visualization is full flow field data output from the simulation. It is lossy-compressed to a specific accuracy using ZFP and written to disk every 16 time-steps to enable real-time flow visualization. The flow fields are sampled at a high temporal frequency to enable real-time, 24fps visualization. The flow fields are sampled every 12 simulation time steps (every 0.04132s).

17 WIND ENERGY↗
DOE OSTI2025

Building partnerships for development of sustainable energy systems with atmospheric measurements

Atmospheric dynamics often play a critical role in the sustainability and reliability of diverse forms of energy production. This is especially true for the growing number of renewable energy deployments that harness aspects of the environment for power production. While the University of Memphis has a strong research background in energy systems, we have little experience working with the Earth and Environmental Systems Science Division (EESSD) and their associated User Facilities. Of particular interest to us is the Atmospheric Science Research and the Atmospheric Radiation Measurement (ARM) user facility to address surface-boundary layer interactions and physical phenomena. One of the major challenges for understanding and developing energy systems and management platforms is accurate modeling/forecasting of atmospheric conditions across disparate spatial and temporal scales. These conditions are often required to understand the lowest levels of the atmospheric boundary layer, but are also important to understand higher atmospheric conditions where aerosols affect cloud development. The objective of this work was to develop partnerships with national laboratories for collaboration on environmental science and its intersection with sustainable energy systems, as well as to leverage the ARM user facility data repositories to enhance our research capabilities in energy systems and their inter-dependence on environmental systems for future engagement with EESSD. Specifically, we accomplished these objectives by (1) developing collaborations with Oakridge National Laboratory ARM Data Science and Integration Group which resulted in student internships, (2) employed ARM data to develope modeling of the atmospheric boundary layer optical turbulence, and (3) optimally-sized large-scale renewable energy systems and their associated energy storage systems with ARM repository data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Ocean Surface Flux Algorithm Effects on Earth System Model Energy and Water Cycles

Earth system models parameterize ocean surface fluxes of heat, moisture, and momentum with empirical bulk flux algorithms, which introduce biases and uncertainties into simulations. We investigate the atmosphere and ocean model sensitivity to algorithm choice in the Energy Exascale Earth System Model (E3SM). Flux differences between algorithms are larger in atmosphere simulations (where wind speeds can vary) than ocean simulations (where wind speeds are fixed by forcing data). Surface flux changes lead to global scale changes in the energy and water cycles, notably including ocean heat uptake and global mean precipitation rates. Compared to the control algorithm, both COARE and University of Arizona (UA) algorithms reduce global mean precipitation and top of atmosphere radiative biases. Further, UA may slightly reduce biases in ocean meridional heat transport. We speculate that changes seen here, especially in the ocean, could be even larger in coupled simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Biomass-burning smoke's properties and its interactions with marine stratocumulus clouds in WRF-CAM5 and southeastern Atlantic field campaigns

Abstract. A large part of the uncertainty in climate projections comes from uncertain aerosol properties and aerosol–cloud interactions as well as the difficulty in remotely sensing them. The southeastern Atlantic functions as a natural laboratory to study biomass-burning smoke and to constrain this uncertainty. We address these gaps by comparing the Weather Research and Forecasting with Chemistry Community Atmosphere Model (WRF-CAM5) to the multi-campaign observations ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), CLARIFY (CLoud–Aerosol–Radiation Interaction and Forcing), and LASIC (Layered Atlantic Smoke Interactions with Clouds) in the southeastern Atlantic in August 2017 to evaluate a large range of the model's aerosol chemical properties, size distributions, processes, and transport, as well as aerosol–cloud interactions. Overall, while WRF-CAM5 is able to represent smoke properties and transport, some key discrepancies highlight the need for further analysis. Observations of smoke composition show an overall decrease in aerosol mean diameter as smoke ages over 4–12 d, while the model lacks this trend. A decrease in the mass ratio of organic aerosol (OA) to black carbon (BC), OA:BC, and the OA mass to carbon monoxide (CO) mixing ratio, OA:CO, suggests that the model is missing processes that selectively remove OA from the particle phase, such as photolysis and heterogeneous aerosol chemistry. A large (factor of ∼2.5) enhancement in sulfate from the free troposphere (FT) to the boundary layer (BL) in observations is not present in the model, pointing to the importance of properly representing secondary sulfate aerosol formation from marine dimethyl sulfide and gaseous SO2 smoke emissions. The model shows a persistent overprediction of aerosols in the marine boundary layer (MBL), especially for clean conditions, which multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes several model features, such as not representing observed shifts towards smaller aerosol diameters, inaccurate concentration ratios of carbon monoxide and black carbon, underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. The average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (∼0.65), while it decreases substantially in observations from ORACLES (∼0.78) to CLARIFY (∼0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLD= 400–500 cm−3 and overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10. We expect these results to motivate similar evaluations of other modeling systems and promote model development to reduce critical uncertainties in climate simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Magnitude and timescale of liquid water path adjustments to cloud droplet number concentration perturbations for nocturnal non-precipitating marine stratocumulus

Cloud liquid water path (L) adjusts to perturbations in cloud droplet number concentration (N) over time. We explore the magnitude and timescale of this adjustment in nocturnal non-precipitating marine stratocumuli using large eddy simulations of baseline conditions and aerosol seeding experiments for 22 meteorological conditions. The results confirm that the L adjustment (δL) slope (k) is more negative for simulation pairs with relatively low N and less negative for high N. Overall, k is unlikely to be lower than −0.4 within 24 h of seeding start, meaning the L adjustment is unlikely to fully offset the brightening due to the Twomey effect. After seeding, the δL becomes increasingly negative, which can be characterized by an exponential convergence. This evolution is governed by a short timescale around 5 h and lasts for around 8–12 h. It is driven by the feedback between entrainment, L, and boundary layer (BL) turbulence. Other processes, including radiation, surface fluxes, and subsidence, respond to the seeding weakly. This short timescale is insensitive to the amount of seeding, making the evolution of δL and some other deviations similar for different seeding amounts after appropriate scaling. The timescale of k evolution is closely related to the δL timescale and hence also short, while it could also be affected by the δN evolution. The results are most relevant to conditions where seeding is applied to a large area of marine stratocumulus in well-mixed and overcast BL where shear is not a primary source of turbulence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

17 WIND ENERGY↗
DOE OSTI2022

Turbulence Structures in the Very Stable Boundary Layer Under the Influence of Wind Profile Distortion

Abstract In very stable boundary layers (VSBL), a “cocktail” of submeso motions routinely result in elevated mean wind speed maxima above the ground, acting as a new source of turbulence generation. This new source of turbulent kinetic energy enhances turbulent mixing and causes mean wind profile distortion (WPD). As a results, this transient distortion in the wind profile adjusts the classical log‐law. Addressing how WPD‐induced turbulence regulates flow structures, turbulent fluxes, and transitions in stability regimes across layers remains a challenge. Eddy covariance data measured at four levels on a 62‐m tower are employed to address these questions. It is shown that the WPD initiates large turbulent eddies that penetrate downward, leading to enhanced vertical mixing and comparable turbulent transport efficiencies across layers. As a consequence, turbulence intensity and fluxes are increased. As the WPD is intensified, turbulent fluxes and turbulent flux transport caused by large eddies are also enhanced, leading to a transition from very stable to weakly stable regimes. Due to the influence of WPD‐induced large eddies, the large‐eddy turbulent Prandtl number does not deviate appreciably from unity and the partitioning between turbulent kinetic and potential energies is linearly related to the gradient Richardson number.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Boundary Layer Climatology at ARM Southern Great Plains

Operational since 1992, the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, USA, has become a reference research site for meteorological studies. Because of an open data policy the ARM data are used by researchers all over the world. In this report, we review the long-term climatology of the atmospheric boundary layer, the SGP instrumentation, the site and some site-specific atmospheric conditions, which potentially affect wind turbines in the region. Because the atmospheric boundary layer is bounded and influenced by the land surface, observations of surface radiation components and heat fluxes are crucial to understanding land–atmosphere interactions. The entrainment of air, updrafts, downdrafts, and boundary layer height characteristics is needed for understanding the structure and growth of the atmospheric boundary layer. Therefore, measurements from both ground surface in situ and remote-sensing observations at the SGP site provide an overall climatology and their interactions from ground surface up to the boundary layer. Measurements from a 60 m meteorological tower, surface flux stations, disdrometers, soil temperature and moisture flux plates, coherent Doppler lidar, Raman lidar, radiosondes, and satellite data at the SGP central facility were analyzed. All the measurements were generally made within a few square kilometers of each other at the central facility. This report focuses on data from January 2010 to June 2020 at the SGP central facility. The various sections describe the ARM SGP site and surrounding wind turbines; in situ and remotesensing instrumentation used in the report; mathematical equations to analyze fluxes, turbulence, and other boundary layer parameters; a climatological analysis of surface winds, fluxes and thermodynamic parameters for several years; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory (MOST); an analysis of the boundary layer winds and direction from a Doppler lidar; multi-year turbulence estimates through the boundary layer from a Doppler lidar; atmospheric boundary layer water vapor and relative humidity profiles from Raman lidar; cloud base height and boundary layer height from multiple sensors and satellite data; and finally site-specific atmospheric conditions, such as nocturnal low-level jets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Simulating Real Atmospheric Boundary Layers at Gray-Zone Resolutions: How Do Currently Available Turbulence Parameterizations Perform?

Recent computational and modeling advances have led a diverse modeling community to experiment with atmospheric boundary layer (ABL) simulations at subkilometer horizontal scales. Accurately parameterizing turbulence at these scales is a complex problem. The modeling solutions proposed to date are still in the development phase and remain largely unvalidated. This work assesses the performance of methods currently available in the Weather Research and Forecasting (WRF) model to represent ABL turbulence at a gray-zone grid spacing of 333 m. We consider three one-dimensional boundary layer parameterizations (MYNN, YSU and Shin-Hong) and coarse large-eddy simulations (LES). The reference dataset consists of five real-case simulations performed with WRF-LES nested down to 25 m. Results reveal that users should refrain from coarse LES and favor the scale-aware, Shin-Hong parameterization over traditional one-dimensional schemes. Overall, the spread in model performance is large for the cellular convection regime corresponding to the majority of our cases, with coarse LES overestimating turbulent energy across scales and YSU underestimating it and failing to reproduce its horizontal structure. Despite yielding the best results, the Shin-Hong scheme overestimates the effect of grid dependence on turbulent transport, highlighting the outstanding need for improved solutions to seamlessly parameterize turbulence across scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimating scalar turbulent fluxes with slow-response sensors in the stable atmospheric boundary layer

Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable atmospheric conditions are evaluated, with a focus on gases for which fast sensors are not readily available. First, the relaxed eddy accumulation (REA) classical approach and a recently proposed mixing length parameterization, labeled A22, are tested against eddy-covariance computations. Using high-frequency measurements collected from two contrasting sites (the frozen tundra near Utqiaġvik, Alaska, and a sparsely vegetated grassland in Wendell, Idaho, during winter), it is shown that the REA and A22 models outperform the conventional Monin–Obukhov similarity theory (MOST) utilized widely to infer fluxes from mean gradients. Second, scenarios where slow trace gas sensors are the only viable option in field measurements are investigated using digital filtering applied to fast-response sensors to simulate their slow-response counterparts. With a filtered scalar signal, the observed filtered eddy-covariance fluxes are referred to here as large-eddy-covariance (LEC) fluxes. A virtual eddy accumulation (VEA) approach, akin to the REA model but not requiring a mechanical apparatus to separate the gas flows, is also formulated and tested. A22 outperforms VEA and LEC in predicting the observed unfiltered (total) eddy-covariance (EC) fluxes; however, VEA can still capture the LEC fluxes well. This finding motivates the introduction of a sensor response time correction into the VEA formulation to offset the effect of sensor filtering on the underestimated net averaged fluxes. The only needed parameter for this correction is the mean velocity at the instrument height, a surrogate of the advective timescale. The VEA approach is very suitable and simple to use with gas sensors of intermediate speed (∼ 0.5 to 1 Hz) and with conventional open- or closed-path setups.

58 GEOSCIENCES↗
DOE OSTI2021

The diurnal cycle of entrainment and detrainment in LES of the Southern Ocean driven by observed surface fluxes and waves

Empirical rules for both entrainment and detrainment are developed from LES of the Southern Ocean boundary layer when the turbulence, stratification, and shear cannot be assumed to be in equilibrium with diurnal variability in surface flux and wave (Stokes drift) forcing. A major consequence is the failure of downgradient eddy viscosity, which becomes more serious with Stokes drift and is overcome by relating the angle between the stress and shear vectors to the orientations of Lagrangian shear to the surface and of local Eulerian shear over 5 m. Thus, the momentum flux can be parameterized as a stress magnitude and this empirical direction. In addition, the response of a deep boundary layer to sufficiently strong diurnal heating includes boundary layer collapse and the subsequent growth of a morning boundary layer, whose depth is empirically related to the time history of the forcing, as are both morning detrainment and afternoon entrainment into weak diurnal stratification. Below the boundary layer, detrainment rules give the maximum buoyancy flux and its depth, as well a specific stress direction. Another rule relates both afternoon and nighttime entrainment depth and buoyancy flux to surface layer turbulent kinetic energy production integrals. These empirical relationships are combined with rules for boundary layer transport to formulate two parameterizations; one based on eddy diffusivity and viscosity profiles and another on flux profiles of buoyancy and of stress magnitude. Evaluations against LES fluxes show the flux profiles to be more representative of the diurnal cycle, especially with Stokes drift.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Characterization of turbulence under different stability conditions using lidar scanning data

The spatiotemporal scales of turbulence in the boundary layer flow vary with the static stability of the atmosphere. For example, turbulence structures in unstable conditions are often found to be large and coherent, whereas the turbulent structures in stable conditions are relatively small and sporadic. To better understand the behavior of such turbulent structures under different atmospheric stability conditions in the lower atmosphere, herein, we use scanning lidar data collected over several months during the US Department of Energy's second Wind Forecast Improvement Project (WFIP 2) in 2016. The radial velocity data from scanning lidar were gathered along the horizontal plane (PPI mode) and were analyzed using proper orthogonal decomposition (POD) and spectral energy methods. The results from these analyses show that the turbulent structures in daytime unstable conditions are different from those present during nighttime stable conditions in terms of amount of energy and coherent structures. The POD energy is distributed more evenly in all the higher spatial modes in the daytime unstable case compared to the nighttime stable case. Besides characterizing the turbulence in the flow, the lidar data were also used to compare the simulated flow field generated from the WRF-LES framework (Weather Research and Forecasting - large-eddy simulation) with the scanning lidar data. The results from both simulated and lidar scanned data show similar structures orienting the streak-like structure along the mean wind. This indicates that the streak-like structures can exist in the real atmosphere during unstable atmospheric condition cases.

Rai, Raj K.↗
DOE OSTI2020

Detection of Extreme Phenomena in the Stable Boundary Layer over the Amazonian Forest

We apply different methods for detection of extreme phenomena (EP) in air-turbulent time series measured in the nocturnal boundary layer above the Amazon forest. The methods used were: (a) a Morlet complex wavelet transform, which is often used in analysis of non-linear application processes. Through the use of the wavelet, it is possible to observe a phase singularity that involves a strong interaction between an extensive range of scales; (b) recurrence plot tests, which were used to identify a sudden change between different stable atmospheric states. (c) statistical analysis of early-warning signals, which verify simultaneous increases in the autocorrelation function and in the variance in the state variable; and (d) analysis of wind speed versus turbulent kinetic energy to identify different turbulent regimes in the stable boundary layer. We found it is adequate to use a threshold to classify the cases of strong turbulence regime, as a result of the occurrence of EP in the tropical atmosphere. All methods used corroborate and indicate synergy between events that culminate in what we classify as EP of the stable boundary layer above the tropical forest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sensitivity Analysis of Wind and Turbulence Predictions With Mesoscale‐Coupled Large Eddy Simulations Using Ensemble Machine Learning

Abstract Coupling between mesoscale models and large‐eddy simulation (LES) models is increasingly used to more realistically represent the wide range of scales of atmospheric motions affecting boundary layer winds and turbulence that need to be simulated accurately for applications such as wind energy. However, such mesoscale‐to‐microscale coupled modeling frameworks are potentially affected by a large number of uncertain closure parameters. Here, we investigate the sensitivity associated with six closure parameters related to a 1.5‐order subgrid‐scale turbulence closure for an ensemble of mesoscale‐coupled LES. The simulations are performed using the Weather Research and Forecasting model nested from horizontal resolutions of greater than a kilometer down to tens of meters. Closure parameters are varied to generate perturbed parameter ensembles for two case studies of highly sheared, convective boundary layers observed in the Columbia Basin of Oregon and Washington during the Second Wind Forecast Improvement Project. Machine learning algorithms are used to explore the sensitivity of LES predictions, considering the effects of the perturbed physical parameters alongside categorical factors such as the case study identity, measurement location, and LES resolution. For the conditions we examine, a single parameter, the eddy viscosity coefficient, is the dominant source of parametric sensitivity and its importance is comparable to the categorical factors for several of the simulation response variables we examine.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Zooming in: SCREAM at 100 m using regional refinement over the San Francisco Bay Area

Pushing global climate models to large-eddy simulation (LES) scales over complex terrain has remained a major challenge. This study presents the first known implementation of a global model – SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) – at 100 m horizontal resolution using a regionally refined mesh (RRM) over the San Francisco Bay Area. Two hindcast simulations were conducted to test performance under both strong synoptic forcing and weak, boundary-layer-driven conditions. We demonstrate that SCREAM can stably run at LES scales while realistically capturing topography, surface heterogeneity, and coastal processes. The 100 m SCREAM-RRM substantially improves near-surface wind speed, temperature, humidity, and pressure biases compared to the baseline 3.25 km simulation, and better reproduces fine-scale wind oscillations and boundary-layer structures. These advances leverage SCREAM's scale-aware SHOC turbulence parameterization, which transitions smoothly across scales without tuning. Performance tests show that while CPU-only simulations remain costly, GPU acceleration with SCREAMv1 on NERSC's Perlmutter system enables two-day hindcasts to complete in under two wall-clock days. Our results open the door to LES-scale studies of orographic flows, boundary-layer turbulence, and coastal clouds within a fully comprehensive global modeling framework.

Geosciences↗