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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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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Policy implications of net-zero emissions: A multi-model analysis of United States emissions and energy system impacts

Many countries, subnational jurisdictions, and companies are setting net-zero emissions goals; however, questions remain about strategies to reach these targets, policy measures, technology gaps, and economic impacts. Here, we investigate the potential policy implications of reaching economy-wide net-zero CO 2 emissions across the United States by 2050 using results from a multi-model comparison with 14 energy-economic models. Model results suggest that achieving net-zero CO 2 targets depends on policies that accelerate deployment of zero- and low-emitting technologies that have seen rapid cost reductions in recent years (including wind, solar, battery storage, and electric vehicles) as well as relatively nascent options (including carbon capture and storage, advanced biofuels, low-carbon hydrogen, advanced nuclear, and long-duration energy storage). While net-zero policies are likely to lower fossil fuel consumption, including considerable coal and petroleum reductions, achieving net-zero emissions does not necessarily mean phasing out all fossil fuels. Model results indicate that the Inflation Reduction Act’s energy and climate provisions amplify near-term decarbonization but that net-zero policies have larger impacts on long-run outcomes. Stringent climate policy can have large fiscal impacts on tax revenue and government spending—revenues from carbon pricing and subsidies for carbon removal range from 0.1 % to 3.7 % of GDP in 2050 across models. Each dollar per metric ton carbon price leads to a 0.06 % to 0.31 % reduction in economy-wide CO 2 emissions relative to a reference scenario with current policies. Spending on energy across the economy decreases relative to today for many models under reference and net-zero policies, especially as a share of GDP, due primarily to end-use electrification and energy efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Adopting Biophysics Methods in Pursuit of Biogeophysical Research: Advancing the measurement and modeling of electrical signatures of microbe-mineral transformations impacting contaminant transport

This exploratory project involved laboratory experiments to investigate three hypotheses: (H1) Physics-based modeling of low-frequency dispersions (henceforth referred to as alpha) measured in broadband dielectric spectroscopy (DS) data can quantify pore-scale geometric changes impacting contaminant transport resulting from biomineralization; (H2) Physics-based modeling of high-frequency dispersions (henceforth referred to as beta) measured in broadband dielectric spectroscopy data can quantify rates of mineral growth in/on the cell wall; (H3) Application of this measurement and modeling approach can enhance geophysical interpretation of bioremediation experiments conducted at the RIFLE IFC by providing constraints on bioremediation efficiency (biomass concentration, mineral uptake within the cell wall, biomineralization rate). We tested H1 by performing DS measurements (alpha and beta range) on iron (Fe) particles of dimensions similar to microbial cells, dispersed within agar gels over a range of Fe concentrations. We have tested the ability of the physics-based modeling to predict volume concentrations of the Fe particles by assuming that the Fe particles are polarizable inclusions within an otherwise nonpolarizable medium. We evaluated the smallest volume concentration that can be detected with the DS method. Similar experiments and modeling have been performed on the sulfate-reducing bacteria D. vulgaris. Synchrotron x-ray absorption measurements were conducted to determine the local structure of biominerals coatings on D. vulgaris which were grown in the presence of different Fe concentrations. We imaged the mineral growth on cell wall using SEM. We used dielectric spectroscopy to differentiate between iron sulfide precipitates of biotic and abiotic nature. Biotic measurements were made on D. vulgaris bacteria grown in the presence of different concentrations of iron to form different thicknesses of iron sulfide precipitates around themselves and abiotic measurements were made on different concentrations of pyrrhotite particles suspended in agar. Results show a decrease in dielectric permittivity as a function of frequency for biotic minerals and an opposite trend is observed for abiotic minerals. Our results suggest that dielectric spectroscopy offers a noninvasive and fast approach for distinguishing between abiotic and biotic mineral precipitates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Adopting Biophysics Methods in Pursuit of Biogeophysical Research: Advancing the Measurement and Modeling of Electrical Signatures of Microbe-Mineral Transformations Impacting Contaminant Transport (Final Report)

This exploratory project involved laboratory experiments to investigate three hypotheses: (H1) Physics-based modeling of low-frequency dispersions (henceforth referred to as alpha) measured in broadband dielectric spectroscopy (DS) data can quantify pore-scale geometric changes impacting contaminant transport resulting from biomineralization; (H2) Physics-based modeling of high-frequency dispersions (henceforth referred to as beta) measured in broadband dielectric spectroscopy data can quantify rates of mineral growth in/on the cell wall; (H3) Application of this measurement and modeling approach can enhance geophysical interpretation of bioremediation experiments conducted at the RIFLE IFC by providing constraints on bioremediation efficiency (biomass concentration, mineral uptake within the cell wall, biomineralization rate). We tested H1 by performing DS measurements (alpha and beta range) on iron (Fe) particles of dimensions similar to microbial cells, dispersed within agar gels over a range of Fe concentrations. We have tested the ability of the physics-based modeling to predict volume concentrations of the Fe particles by assuming that the Fe particles are polarizable inclusions within an otherwise nonpolarizable medium. We evaluated the smallest volume concentration that can be detected with the DS method. Similar experiments and modeling have been performed on the sulfate-reducing bacteria D. vulgaris. Synchrotron x-ray absorption measurements were conducted to determine the local structure of biominerals coatings on D. vulgaris which were grown in the presence of different Fe concentrations. We imaged the mineral growth on cell wall using SEM. We used dielectric spectroscopy to differentiate between iron sulfide precipitates of biotic and abiotic nature. Biotic measurements were made on D. vulgaris bacteria grown in the presence of different concentrations of iron to form different thicknesses of iron sulfide precipitates around themselves and abiotic measurements were made on different concentrations of pyrrhotite particles suspended in agar. Results show a decrease in dielectric permittivity as a function of frequency for biotic minerals and an opposite trend is observed for abiotic minerals. Our results suggest that dielectric spectroscopy offers a noninvasive and fast approach for distinguishing between abiotic and biotic mineral precipitates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Simulation of Columbia River Floods in the Hanford Reach

Columbia River water elevations and flows in the Hanford Reach affect the environment and facilities along the shoreline, including movement of contaminants in groundwater, fish habitat, and infrastructure subject to flooding. This report describes the hydraulic simulation of hypothetical flood flows using the best available topographic and bathymetric data for the Hanford Reach and the Modular Aquatic Simulation System in 1 Dimension (MASS1) hydrodynamic model. The MASS1 model of the Hanford Reach was previously calibrated to field measurements of water surface elevations. The current model setup can be used for other studies of flow, water levels, and temperature in the Reach. The existing MASS1 channel geometry and roughness and other model configuration inputs for the Hanford Reach were used for this study, and previous calibration and validation results for the model are reprinted here for reference. The flood flows for this study were simulated by setting constant flow rates obtained from the U.S. Army Corps of Engineers (USACE) for the Columbia, Snake, and Yakima Rivers, and a constant water level at McNary Dam, and then running the model to steady state. The discharge levels simulated were all low-probability events; for example, a 100-year flood is one that would occur on average every 100 years, or put another way, in any given year there is a 1% chance that a discharge of that level or higher will occur. The simulated floods and their corresponding Columbia River discharges were 100-year (445,000 cfs), 500-year (520,000 cfs), and the USACE-defined Standard Project Flood (960,000 cfs). The resulting water levels from the steady-state floods can be viewed as “worst case” outcomes for the respective discharge levels. The MASS1 output for water surface elevations was converted to the North American Vertical Datum of 1988 and projected across the channel and land surface to enable mapping of the floodplain for each scenario. Floodplain maps show that for the 100-year and 500-year discharge levels, flooding is mainly confined to the topographic trench that is the river channel. The flooded area for the Standard Project Flood extends out of the channel area in some places, particularly in the 100-F Area. All of the output from the simulations have been archived and are available for future investigations in the Hanford Reach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Masses and Ages for 230,000 LAMOST Giants, via Their Carbon and Nitrogen Abundances

We measure carbon and nitrogen abundances to a precision of ≲0.1 dex for 450,000 giant stars from their low-resolution (R∼1800) LAMOST DR2 survey spectra. We use these [C/M] and [N/M] measurements, together with empirical relations based on the APOKASC sample, to infer stellar masses and implied ages for 230,000 of these objects to 0.08 dex and 0.2 dex respectively. We use The Cannon, a data-driven approach to spectral modeling, to construct a predictive model for LAMOST spectra. Our reference set comprises 8125 stars observed in common between the APOGEE and LAMOST surveys, taking seven APOGEE DR12 labels (parameters) as ground truth: T{sub eff}, log g, [M/H], [α/M], [C/M], [N/M], and A{sub k}. We add seven colors to the Cannon model, based on the g, r, i, J, H, K, W1, W2 magnitudes from APASS, 2MASS, and WISE, which improves our constraints on T{sub eff} and log g by up to 20% and on A{sub k} by up to 70%. Cross-validation of the model demonstrates that, for high-S/N objects, our inferred labels agree with the APOGEE values to within 50 K in temperature, 0.04 mag in A{sub k}, and <0.1 dex in log g, [M/H], [C/M], [N/M], and [α/M]. We apply the model to 450,000 giants in LAMOST DR2 that have not been observed by APOGEE. This demonstrates that precise individual abundances can be measured from low-resolution spectra and represents the largest catalog to date of homogeneous stellar [C/M], [N/M], masses, and ages. As a result, we greatly increase the number and sky coverage of stars with mass and age estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Extending accurate spectral induced polarization measurements into the kHz range: modelling and removal of errors from interactions between the parasitic capacitive coupling and the sample holder

SUMMARY Spectral induced polarization (SIP), describing the measurement of the frequency domain electrical impedance magnitude and phase of porous materials, has been widely used to characterize subsurface hydrological/biogeochemical properties and processes. SIP data collected at frequencies higher than 100 Hz are expected to describe the polarization of small particles providing insights into the physicochemical properties of clays, nanoparticles and microorganisms. However, the phase measurements at these high frequencies are often contaminated by errors due to the parasitic capacitive coupling of the SIP instrument, especially for lower conductivity samples. We developed a model showing the measured phase is the sum of the true sample phase and an error term ωCinZx, where ω is the angular frequency; Cin is the instrument input capacitance and Zx is a measurable impedance function related to the sample holder properties and the reference resistor. Based on this model, a new phase correction method is proposed that results in highly accurate SIP data up to 20 kHz as well as the determination of Cin. We tested the correction method using electric circuits, NaCl fluids and three unconsolidated samples (sand, sand-clay and sand-pyrite mixtures). The corrected phase for the circuit and NaCl fluid experiments showed excellent agreement with the theoretical phase response across the studied frequency range (errors <1 mrad). For unconsolidated samples, removal of errors results in phase spectra more consistent with expected polarization mechanisms, as based on phase peaks recorded for small pyrite and clay particles at high frequencies. These phase peaks could not be identified in the uncorrected data. Our approach can substantially enhance the value of the SIP method for the characterization of fine-grained sediments and rocks.

58 GEOSCIENCES↗
DOE OSTI2012

An Enhanced Engineering Perspective of Global Climate Systems and Statistical Formulation of Terrestrial CO2 Exchanges

This paper designs a comprehensive approach based on the engineering machine/system concept, to model, analyze, and assess the level of CO2 exchange between the atmosphere and terrestrial ecosystems, which is an important factor in understanding changes in global climate. The focus of this article is on spatial patterns and on the correlation between levels of CO2 fluxes and a variety of influencing factors in eco-environments. The engineering/machine concept used is a system protocol that includes the sequential activities of design, test, observe, and model. This concept is applied to explicitly include various influencing factors and interactions associated with CO2 fluxes. To formulate effective models of a large and complex climate system, this article introduces a modeling technique that will be referred to as Stochastic Filtering Analysis of Variance (SFANOVA). The CO2 flux data observed from some sites of AmeriFlux are used to illustrate and validate the analysis, prediction and globalization capabilities of the proposed engineering approach and the SF-ANOVA technology. The SF-ANOVA modeling approach was compared to stepwise regression, ridge regression, and neural networks. The comparison indicated that the proposed approach is a valid and effective tool with similar accuracy and less complexity than the other procedures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Counterflow ignition of n-butanol at atmospheric and elevated pressures

Vital to the development of predictive combustion models is a robust understanding of the coupled effects of chemical kinetics and convective–diffusive transport at both atmospheric and elevated pressures. The current study describes a new variable-pressure non-premixed counterflow ignition experiment designed to address the need for well-characterized reference data to validate such models under conditions sensitive to both chemical and transport processes. A comprehensive characterization of system behavior is provided to demonstrate boundary condition and ignition quality as well as adherence to the assumption of quasi-one-dimensionality, and suggest limitations and best practices for counterflow ignition experiments. This effort reveals that the counterflow ignition experiment requires special attention to ignition location in order to ensure that the assumption of quasi-one-dimensionality is valid, particularly at elevated pressures. This experimental tool is then applied to the investigation of n-butanol for pressures of 1–4 atm, pressure-weighted strain rates of 200–400 s -1 , and fuel mole fractions of 0.05–0.25. Results are simulated using two n-butanol models available in the literature and used to validate and assess model performance. Comparison of experimental and numerical ignition results for n-butanol demonstrates that while existing models largely capture the trends observed with varying pressure, strain rate, and fuel loading, the models universally over-predict experimental ignition temperatures. While several transport coefficients are found to exhibit order-of-magnitude or greater sensitivities relative to reaction rates, variation of transport parameters is not able to account for the large deviations observed between experimental and numerical results. Additional comparison of ignition kernel structure and fuel breakdown pathways between two literature models suggests that an under-prediction in the radical pool growth with respect to temperature variation may be responsible for both the deviation from the experimental results and the discrepancy in ignition temperature results observed between models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

ECAR-7932 Rev 0 Large Eddy Simulation of MARVEL Reactor Core Subchannel to Evaluate Model Uncertainty of Reynolds-Averaged Navier-Stokes Equation Based Computational Fluid Dynamics Analysis

In the previous work (ECAR-7210), the peak cladding temperature of the MARVEL microreactor has been evaluated by steady-state Reynolds-Averaged Navier-Stokes (RANS) based computational fluid dynamics (CFD) simulations. Although numerical uncertainties of RANS-based CFD simulations has been assessed in ECAR-7210, the model uncertainty of RANS turbulence models must be investigated to resolve the issues related to inaccurate prediction of turbulent heat flux and flow pulsation in a tight lattice rod bundle using the steady-state RANS simulations. Consequently, this ECAR conducted a high-fidelity CFD analysis utilizing Large Eddy Simulation (LES) to generate reference data and investigated the model uncertainty of RANS-based CFD simulations.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2021

Deep Reinforcement Learning for Automatic Generation Control of Wind Farms

This paper provides a model-free framework for real-time control of wind farms to accurately track a power reference signal. This problem requires tractable dynamical models for capturing the aerodynamic interaction between wind turbines and controllers that can make decisions in realtime given varying atmospheric conditions. In this paper, we propose a deep reinforcement learning framework to provide real-time yaw control of a wind farm. Modifications have been made to FLOw Redirection and Induction in Steady State (FLORIS), a modeling tool that incorporates transient wake behavior. The control problem is formulated to track a synthetic power reference signal based on historical atmospheric (wind speed and direction) information, price signals, and regulation deployment data from U.S. regional transmission operators. Results indicate that a wind farm, with this control paradigm, can achieve good tracking performance when tested with real atmospheric data.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2017

International Energy Agency Ocean Energy Systems Task 10 Wave Energy Converter Modeling Verification and Validation: Preprint

This is the first joint reference paper for the Ocean Energy Systems (OES) Task 10 Wave Energy Converter modeling verification and validation group. The group is established under the OES Energy Technology Network program under the International Energy Agency. OES was founded in 2001 and Task 10 was proposed by Bob Thresher (National Renewable Energy Laboratory) in 2015 and approved by the OES Executive Committee EXCO in 2016. The kickoff workshop took place in September 2016, wherein the initial baseline task was defined. Experience from similar offshore wind validation/verification projects (OC3-OC5 conducted within the International Energy Agency Wind Task 30) [1], [2] showed that a simple test case would help the initial cooperation to present results in a comparable way. A heaving sphere was chosen as the first test case. The team of project participants simulated different numerical experiments, such as heave decay tests and regular and irregular wave cases. The simulation results are presented and discussed in this paper.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Task 5 Field Scale Stress Modeling Scripts and Data

Project data and Matlab scripts for Task 5 of the project “A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO2 Storage”. Project includes the static earth models, grids and other data used for simulation and calibration of the two case studies addressed in the project, referred to herein as the “FutureGen2” model (Illinois) and the “Perch” model (Otsego county, Michigan). Large Dataset, please contact EDXSupport@netl.doe.gov

FutureGen2↗
DOE OSTI2022

Multiple Phase Screen Electromagnetic Propagation Simulations of Starfish Prime Event

It has been decided at this point to pause development of the trans-ionospheric model, and the main goal of this document is to record the current status and maintain a list of references for the future. The 2D model appears to work: however, to truly gain meaningful information about the environment, the scintillation model should be integrated into an end-to-end communication link simulation, which includes much more phenomenology. It would be beneficial at this point to specify a particular link or system and import a HANE environments into existing simulations. Multiple realizations of the noise could then be applied, and communication signal budgets could be analyzed for particular systems.

42 ENGINEERING↗
DOE OSTI2018

A Multi-scale Mathematical Model of Growth and Coalescence of Bubbles Beneath the Anode in an Aluminum Reduction Cell

Modeling of bubble shapes is challenging because of the wide range of length scales in aluminum reduction cells. A 3D multi-scale mathematical model was developed to understand the nucleation, growth and coalescence of bubbles beneath the anode and to investigate the transition of bubbles from the micro- to macro level. The motion of micro-bubbles is examined using the discrete bubble model (DBM) within a Lagrangian reference. An algorithm for the transition from discrete micro-bubbles to large bubbles, which are fully resolved by the volume of fluid (VOF) approach, is achieved using a user-defined function. The two-way coupling between discrete bubbles and continuous fluids is achieved by inter-phase momentum exchange. The model involves three kinds of bubble coalescence: micro-bubble coalescence is taken into account by the DBM; large bubbles swallowing up micro-bubbles are solved by the discrete-continuum transition model; the coalescence between large bubbles is handled by the VOF method. Numerical results show that the coverage and thickness of bubbles agree well with the experimental data in the literature. Bubble thickness stops increasing when the bubble elongates with the anode bottom, which is 4.0 to 4.5 mm. Meanwhile, two asymmetrical thick heads can be observed under the anode. Bubble release frequency increases with increasing current density.

36 MATERIALS SCIENCE↗
DOE OSTI2025

LLMs for Mfg.—On the State of Large Language Models and Applications to Manufacturing

Additive Manufacturing (AM), referred to as 3D printing, has emerged as a key pillar of Industry 4.0 enabling layer-by-layer fabrication of intricate geometries from CAD models. In parallel, Large Language Models (LLMs), deep learning models for natural language generation trained on vast text corpora, have demonstrated unprecedented capabilities in understanding and generating human-like text. The convergence of these trends opens new opportunities at the intersection of AM and AI/ML, where LLMs can assist engineers and researchers in design, manufacture planning, and knowledge discovery. Recent academic work has begun to explore LLM applications in AM and adjacent fields, such as material science, mechanical engineering, and design for additive manufacturing. This exploration ranges from intelligent process planning to domain-specific knowledge retrieval. This survey provides a comprehensive review of current developments, focusing on peer-reviewed literature contributions that apply, adapt, and advance LLMs in general and domain-specific domains. We analyze state-of-the-art (SOTA) techniques, such as fine-tuning foundational models for specific domains, retrieval-augmented generation (RAG) pipelines, knowledge graph integration, and delve into the architectures and evaluation methods employed. The goal of this survey is to inform researchers and practitioners of the current capabilities and limitations of LLMs in general and in domain-specific applications, and to outline how these models are being tailored to meet the requirements of these applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluation of Model Summertime Boundary Layer Cloud Development over Complex Terrain in New York State

Weather forecasting over complex terrain with diverse land cover is challenging. Utilizing the high-resolution observations from New York State Mesonet (NYSM), we are able to evaluate the surface processes of the Weather Research Forecast (WRF) Model in a detailed, scale-dependent manner. Here, in the study, possible impacts of land–atmosphere interaction on surface meteorology and boundary layer cloud development are investigated with different model resolutions, land surface models (LSMs), and planetary boundary layer (PBL) physical parameterizations. The High-Resolution Rapid Refresh, version 3 (HRRR), forecasting model is used as a reference for the sensitivity evaluation. Results show that over complex terrain, the high-resolution simulations (1 km × 60 vertical levels) generally perform better compared to low-resolution (3 km × 50 levels) in both surface meteorology and cloud fields. LSMs play a more important role in surface meteorology compared to PBL schemes. The NoahMP land surface model exhibits daytime warmer and drier biases compared to the Rapid Update Cycle (RUC) due to better prediction of the Bowen ratio in RUC. The PBL schemes would affect the convective strength in the boundary layer. The Shin–Hong (SH) scale-aware scheme tends to produce the strongest convective strength in the PBL, while the ACM2 PBL scheme rarely resolved convection even at 1-km resolution. By considering the radiation effect of subgrid-scale (SGS) clouds, the Mellor–Yamada–Nakanishi–Niino eddy diffusivity mass flux (MYNN-EDMF) predicted the highest cloud coverage and lowest surface solar radiation bias. The configuration of SGS clouds in MYNN-EDMF would not only significantly reduce shortwave radiation bias, but also affect the convection behaviors through land surface–cloud–radiation interaction.

54 ENVIRONMENTAL SCIENCES↗