Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model types”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Model America - data and models of every U.S. building

The 5-year goal of the 'Model America' concept was to generate a model of every building in the United States. This data repository delivers on that goal. Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,714,640 buildings detected in the United States and this dataset contains 122,930,327 (97.8%) buildings which resulted in a successful simulation. Future, annual updates have been proposed that may include additional buildings, data improvements, or other algorithmic enhancements. This dataset of 122.9 million buildings includes: Models (state_county.zip) - OpenStudio (v3.1.0) and EnergyPlus (v9.4) building energy models. Please note that the download requires the free Globus Connect Personal (https://www.globus.org/globus-connect-personal); Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus(v9.4) 2,784-page Input/Output Reference Guide (https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf) for everything that can be retrieved or simulated from these models. These models were derived from the following metadata, which is not included in this dataset: 1. ID - unique building ID 2. County - county name 3. State - state name 4. CZ - ASHRAE Climate Zone designation 5. Clim_Zone - text label of climate zone 6. est_year - estimated year of construction 7. est_commercial - estimated building type (0=residential, 1=commercial) 8. Centroid - building center location in latitude/longitude (from Footprint2D) 9. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 10. Height - building height (meters) 11. Area2D - footprint area (ft2) 12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 14. NumFloors - number of floors (above-grade) 15. Area - estimate of total conditioned floor area (ft2) 16. Standard - building vintage. These models are made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy's (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Please cite as: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). 'Model America - data and models of every U.S. building. [Data set].' Constellation, doi.ccs.ornl.gov/ui/doi/339, April 14, 2021

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Modeling and simulation of multiphase flows

Multiphase flow can be found in a wide range of natural and industrial systems, such as sediment flow in rivers and coasts to pipeline transport, and reactors of all types. Computational fluid dynamics (CFD) modeling provides a rigorous physics-based method to visualize and quantify these flows and to assess their influence on their surroundings. This presentation will highlight computational research on two varied technologies in the energy sector: carbon capture and wellbore acoustics.

acoustic analysis↗
DOE OSTI2024

Modeling and Power-Hardware-in-the-Loop Validation of Synchronous Wind: An Inverterless Grid-Forming Wind Power Plant: Preprint

Grid-forming (GFM) control of Type-3 and Type-4 wind turbine generators has attracted substantial attention in power systems research; however, the limited over-current capability of power electronics converters continues to deteriorate the grid strength of the evolving power systems. Synchronous wind, also known as Type-5 wind turbine generator (WTG), offers a unique GFM solution to address grid integration and grid strength issues by keeping the grid largely synchronous at very high penetration levels of renewable generation. A Type-5 WTG interfaces to the electric grid via a synchronous generator (SG) driven by a variable-speed hydraulic torque converter; hence, the wind rotor operates in variable-speed mode for maximum power generation and the generator shaft remains synchronous to the grid. This paper developed and tested a high-fidelity model of Type-5 WTG under power-hardware-in-the-loop (PHIL) testing environment. The PHIL demonstration showed that a Type-5 WTGs inherently behaves as a GFM unit and can obtain similar performance in terms of power responses, wind rotor dynamics, and efficiency compared to Type-3 WTG in high wind conditions. The developed model also provides further insight on how Type-5 WTGs can benefit the smooth transition to power systems with high integration level of inverter-based resources.

grid strength↗
DOE OSTI2025

MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain

Language models are transforming materials-aware naturallanguage processing by enabling the extraction of dynamic, context-rich information from unstructured text, thus, moving beyond the limitations of traditional information-extraction methods. Moreover, small language models are on the rise because some of them can perform better than large language models (LLMs) when given domain-specific questionanswer tasks, especially about an application area that relies on a highly specialized vernacular, such as materials science. We therefore present a new class of MechBERT language models for understanding mechanical stress and strain in materials. These employ Bidirectional Encoder Representations for transformer (BERT) architectures. We showcase four MechBERT models, all of which were pretrained on a corpus of documents that are textually rich in chemicals and their stress–strain properties and were fine-tuned on question-answering tasks. We evaluated the level of performance of our models on domain-specific as well as general English-language question-answer tasks and also explored the influence of the size and type of BERT architectures on model performance. We find that our MechBERT models outperform BERT-based models of the same size and maintain relevancy better than much larger BERT-based models when tasked with domain-specific question-answering tasks within the stress–strain engineering sector. These small language models also enable much faster processing and require a much smaller fraction of data to pretrain them, affording them greater operational efficiency and energy sustainability than LLMs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

elm-diagnostics

elm-diagnostics is a Python package for computing diagnostic analyses and visualizations for the E3SM Land Model (ELM) component and is meant to support new feature development in ELM. The tool reads model history files and performs quantitative analyses including budget-closure checking, variable transformations, temporal aggregations, and statistical summaries to support model evaluation, validation, and scientific interpretation. The framework is designed for extensibility, with modular architecture enabling straightforward addition of new diagnostic methods, derived variables, analysis types, visualization approaches, and model-specific adaptations

Hoffman, Matt [Los Alamos National Laboratory]↗
DOE OSTI2023

Dissolved gas recovery from water using a sidestream hollow-fiber membrane module: First principles model synthesis and steady-state validation

This paper presents a first-principles model for the recovery of dissolved gases from liquids using a sidestream hollow-fiber membrane module. The model avoids the use of new empirical coefficients, thus providing a parametric understanding of the process behavior for future design and optimization of membrane modules. This type of first-principles model could be particularly useful when gas recovery is beneficial to biological or chemical reactions of interest, such as the acetogenesis reactions in two-stage anaerobic digesters. The steady-state behavior of the model was validated against both new experimental data for the recovery of H 2 , CH 4 and H 2 –CH 4 mixtures from pure water, as well as existing published data. The modeled gas recovery predictions agreed with experimental data to an absolute average error of 13%, and an average R value of 0.98. Parametric analysis of mixed-gas recovery suggests possible key transition points in the composition of the recovered gases. For example, at 40 °C, increasing trans-membrane pressure while keeping hydraulic residence time (HRT) under 0.5 s will result in an increase in the ratio of H 2 to CH 4 recovered. Otherwise, increasing trans-membrane pressure will instead decrease the ratio of H 2 to CH 4 recovered. The model has potential to be extended to transient analysis, but has yet to be validated with transient experimental data. Furthermore, this model was successfully implemented in both Python and MATLAB, and provides valuable insights for future net-energy optimization for anaerobic digestion systems with in-situ gas recovery.

Anaerobic Digestion↗
DOE OSTI2024

Flavor-violating Higgs and Z boson decays at a future circular lepton collider

Recent advances in b , c , and s quark tagging coupled with novel statistical analysis techniques will allow future high energy and high statistics electron-positron colliders, such as the FCC-ee, to place phenomenologically relevant bounds on flavor violating Higgs and Z decays to quarks. We assess the FCC-ee reach for Z / h → b s , c u decays as a function of jet tagging performance. We also update the standard model (SM) predictions for the corresponding branching ratios, as well as the indirect constraints on the flavor violating Higgs and Z couplings to quarks. Using the type III two Higgs doublet model as an example of beyond the standard model physics, we show that the searches for h → b s , c u decays at FCC-ee can probe new parameter space not excluded by indirect searches. We also reinterpret the FCC-ee reach for Z → b s , c u in terms of the constraints on models with vectorlike quarks. Published by the American Physical Society 2024

Kamenik, Jernej F. (ORCID:0000000151838637)↗
DOE OSTI2023

Search for $t\bar{t}H/A$ → $t\bar{t}t\bar{t}$ production in the multilepton final state in proton–proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for a new heavy scalar or pseudo-scalar Higgs boson ($H/A$) produced in association with a pair of top quarks, with the Higgs boson decaying into a pair of top quarks ($H/A$ → $t\bar{t}$) is reported. The search targets a final state with exactly two leptons with same-sign electric charges or at least three leptons. The analysed dataset corresponds to an integrated luminosity of 139 fb –1 of proton–proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Two multivariate classifiers are used to separate the signal from the background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of a type-II two-Higgs-doublet model. The observed (expected) upper limits at 95% confidence level on the $t\bar{t}$$H/A$ production cross-section times the branching ratio of $H/A$ → $t\bar{t}$ range between 14 (10) fb and 6 (5) fb for a heavy Higgs boson with mass between 400 GeV and 1000 GeV, respectively. Assuming that only one particle, either the scalar $H$ or the pseudo-scalar $A$, contributes to the $t\bar{t}t\bar{t}$ final state, values of tan $β$ below 1.2 or 0.5 are excluded for a mass of 400 GeV or 1000 GeV, respectively. These exclusion ranges increase to tan $β$ below 1.6 or 0.6 when both particles are considered.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Biodiversity and Stoichiometric Plasticity Increase Pico‐Phytoplankton Contributions to Marine Net Primary Productivity and the Biological Pump

Earth System Models generally predict increasing upper ocean stratification from 21st century warming, which will cause a decrease in the vertical nutrient flux forcing declines in marine net primary productivity (NPP) and carbon export. Recent advances in quantifying marine ecosystem carbon to nutrient stoichiometry have identified large latitudinal and biome variability, with low-latitude oligotrophic systems harboring pico-sized phytoplankton exhibiting large phosphorus to carbon cellular plasticity. The climate forced changes in nutrient flux stoichiometry and phytoplankton community composition are thus likely to alter the ocean's biogeochemical response and feedback with the carbon-climate system. We have added three pico-phytoplankton functional types within the Biogeochemical Elemental Cycling component of the Community Earth System Model while incorporating variable cellular phosphorus to carbon stoichiometry for all represented phytoplankton types. The model simulates Prochlorococcus and Synechococcus populations that dominate the productivity and sinking carbon export of the tropical and subtropical ocean, and pico-eukaryote populations that contribute significantly to productivity and export within the subtropical to mid-latitude transition zone, with the western subtropical regions of each basin supporting the most P-poor stoichiometries. Subtropical gyre recirculation regions along the poleward flanks of surface western boundary currents are identified as regional hotspots of enhanced carbon export exhibiting C-rich/P-poor stoichiometry, preferentially inhabited by pico-eukaryotes and diatoms. Collectively, pico-phytoplankton contribute ∼58% of global NPP and ∼46% of global particulate organic carbon export below 100 m through direct and ecosystem processing pathways. Biodiversity and cellular nutrient plasticity in marine pico-phytoplankton combine to increase their contributions to ocean productivity and the biological carbon pump.

Environmental Sciences & Ecology↗
DOE OSTI2022

Modeling of vapor-liquid interactions in condensing ejectors

Ejectors are compact mechanical devices that utilize the expansion of a high-pressure primary fluid to entrain and compress a low-pressure secondary fluid by means of momentum transfer between the two streams of fluid. Condensing ejectors feature both momentum and heat transfer through the interaction between the vapor and liquid streams. The goal of this paper is to develop and validate a one-dimensional slug flow model to simulate the vapor–liquid interactions in two types of condensing ejectors – one with primary liquid and secondary vapor flows (Type I), and the other with primary vapor and secondary liquid flows (Type II). Control volume analysis of the mass, momentum, and energy balance in each phase and across the interface was conducted for the ejectors. The slug flow models for both types of ejectors are validated with published results in the literature. The friction coefficient on the inner wall of the ejectors and the interfacial heat transfer coefficient are identified as controlling parameters for the ejector performance in terms of pressure and temperature distributions along the axis. The detailed parametric study shows that the liquid inlet velocity and mixing tube diameter have a significant impact on the performance of the Type I ejectors, and the performance of the Type II ejectors is mainly controlled by the secondary liquid flow inlet temperature.

42 ENGINEERING↗
DOE OSTI2025

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models

Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images from diverse sources—such as varying physical groundings or data acquisition systems—and to learn spatio-temporal correlations using transformer architectures. However, tokenizing and aggregating images can be compute-intensive, a challenge not fully addressed by current distributed methods. In this work, we introduce the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach designed for datasets with a large number of channels across image modalities. Our method is compatible with any model-parallel strategy and any type of vision transformer architecture, significantly improving computational efficiency. We evaluated D-CHAG on hyperspectral imaging and weather forecasting tasks. When integrated with tensor parallelism and model sharding, our approach achieved up to a 75% reduction in memory usage and more than doubled sustained throughput on up to 1,024 AMD GPUs on the Frontier Supercomputer.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗
DOE OSTI2023

Technical Report on Waveform Fit Metrics for Global Models

The new WAVEFORMS Initiative in the Ground-based Nuclear Detonation Detection (GNDD) program includes an increased emphasis on the development of Earth models and methods to predict entire seismic and acoustic waveforms more accurately. In general, this increased emphasis is predicated on the need to better characterize seismic events and provide improved model-based discrimination between event types including earthquakes and explosions. More specifically, while current moment tensor inversion methods tend to work well for larger events (M>~4) using tuned 1-D Earth models, the development of state-of-the-art 3-D models and methods is required for the prediction of shorter period waves over large areas for discrimination of smaller events. There is no standard metric for model-based waveform prediction accuracy used in the waveform modeling/inversion community. However, there are several popular waveform misfit definitions; and minimizing the corresponding objective functions is the goal of waveform inversion. Some example misfit definitions employed for adjoint waveform tomography include measures of simple travel time differences (e.g. Tape et al., 2010), cross-correlation travel time differences (e.g. Luo and Schuster, 1991), multi-taper frequency dependent methods (e.g. Lei et al., 2020), time-frequency phase misfit functions (e.g. Fichtner 2010; Rodgers et al., 2022), normalized cross-correlation methods (e.g. Tao et al., 2018), and others. In some cases, these misfit definitions also involve complicated weighting schemes and summations over multiple frequency bands making it difficult to duplicate the misfit measurement with alternative models and datasets. Although each of the misfit definitions mentioned above are useful for developing waveform models, the actual misfit values are not usually meaningful outside of a given project, model, and/or dataset. Therefore, it is difficult to understand and communicate model performance for predicting waveforms and comparing to other models and/or new model iterations with a different dataset. Therefore, there is a need for a generalized method for evaluating overall model performance that is independent from the specific misfit chosen to develop the waveform models that is also intuitive and meaningful. In this report, we describe a new metric we refer to as ‘Percent of Correlated Signal’. The following sections describe and demonstrate the metric with a case study event and a more rigorous test using a random selection of globally distributed events. While the focus here is on global tomography models, the metric is meant to applicable to regional ‘wiggle-for-wiggle’ waveform models/studies as well.

58 GEOSCIENCES↗
DOE OSTI2023

Temperature Rise on Liner Surfaces of Fuel Cell Electric Vehicle Tanks during Fueling Process

Herein, the temperature of the inner tank walls, or plastic liners, of composite pressure tanks in fuel cell electric vehicles during fueling using three‐dimensional computational fluid dynamics (CFD) models is evaluated. The liner materials are limited to 85.0 °C to prevent thermal stress causing material failure that would result in a hydrogen leak. Therefore, the temperature of hydrogen gas must be limited to below 85.0 °C during the fueling process as dictated by the current fueling protocol. However, there are limited experimental or simulation data confirming that the temperature changes do not exceed the threshold. Herein, the liner temperatures with CFD tank models for two sizes of type IV tanks representative of the upper and lower system bounds that are close to the SAE J2601 fueling protocol (36.0 and 244.0 L) are evaluated. First, each model's reliability is validated with experimental data and then analyzed, and the data are used to evaluate the maximum hydrogen and liner temperatures under real‐world fueling conditions. The evaluation shows that the maximum liner surface temperature of each tank model is at least 7 °C lower than that of the hydrogen. Additionally, there is at least 12 °C difference found between the upper limit and actual liner temperatures.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Assessment of tritium effluent from Prototype Generation IV Sodium-cooled Fast Reactor

We report a tritium transport model for a pool-type sodium cooled fast reactor was developed for estimation of tritium effluents from the Prototype Generation IV Sodium-cooled Fast Reactor (PGSFR). For this purpose, the transport model developed for Experimental Breeder Reactor II and Fast Flux Test Facility was updated and validated. The tritium production rate from ternary fission and activations in PGSFR were calculated by the Argonne Reactor Computation (ARC) code suite for fast reactor analysis, and due to the potential uncertainties in source terms and variations of cold trap performance and material permeability during reactor operation, the tritium distributions and tritium effluents were estimated for several bounding cases. The upper bounding gaseous and liquid tritium release rates from PGSFR are similar to ~440.7 Ci/year and 14.7 Ci/year, respectively. The gaseous tritium effluent is about few orders of magnitude smaller than the regulatory constraint of the tritium concentration in the air. The peak tritium concentration in the steam-generator feedwater is similar to ~1.1x10 -3 mu Ci/g, which is required to be diluted prior to releasing as tritiated water. The accumulated tritium in the cold trap after five cycle operations is ~ 52,000 Ci. Because of significant conservatism in these upper bounding estimations, it is expected that the tritium effluent at the nominal case with plausible source terms would be about 2 orders of magnitude smaller than that of the upper bounding case.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

One-to-one aeroservoelastic validation of operational loads and performance of a 2.8 MW wind turbine model in OpenFAST

Abstract. This article presents a validation study of the popular aeroservoelastic code suite OpenFAST leveraging weeks of measurements obtained during normal operation of a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate one-to-one turbulent flow fields (i.e., comparing simulation to measurement in 10 min increments, or bins) through unconstrained and constrained assimilation methods using the kinematic turbulence generators TurbSim and PyConTurb. A total of 253 bins of 10 min of normal turbine operation were selected for analysis, and a statistical comparison in terms of performance and loads is presented. We show that successful validation of the model was not strongly dependent on the type of inflow assimilation method used for mean quantities of interest, which had median modeling errors per wind-speed interval generally within 5 %–10 % of the measurement. The type of inflow assimilation method did have a larger effect on the fatigue predictions for blade-root flapwise and tower-base fore–aft quantities, which surprisingly saw larger errors from the assumed higher-fidelity assimilation methods. Avenues for further work are discussed and include possible improvements to the aerodynamic, structural, and controller modeling that may offer insight on the origin of the up to ∼ 40 % median overprediction of fatigue for these quantities.

17 WIND ENERGY↗
DOE OSTI2022

Dataset for "Bioaerosols are the dominant source of warm-temperature immersion-mode INPs and drive uncertainties in INP predictability

Ice nucleating particles (INPs) are a rare subset of atmospheric aerosol that can initiate primary ice formation and thus trigger cloud glaciation. The re is a significant gap between our ability to measure INPs and to predict their concentrations and variability in large-scale weather and climate models. Accurate simulation of I NPs requires simulation of their major particle sources, as well as representative parameterizations of IN efficiency. Thus, there is a need for measurements of INP concentrations , delineated by particle type, to validate and improve model prediction of INP concentrations. Here we present a novel method for speciating INP concentrations into the relative c ontributions from dust, sea spray aerosol (SSA), and bioaerosol using single particle measurements. In a field campaign at Bodega Bay (coastal California), we find that bioaerosol s were the primary source of INPs between -12 and -20 ?C, while dust was a relatively minor source and SSA did not contribute significantly. We show that recent INP parameterizati ons for dust and SSA accurately predict ambient INP concentrations for these particle types. Finally, we use the speciated INP concentrations to evaluate the simulation of INPs at Bodega Bay, using a Lagrangian approach to connect the locally-observed aerosol with regionally-widespread emissions parameterizations. We find that we can skillfully simulate du st and SSA INPs, but not bioaerosol INPs. This points to a need for additional research to identify the major factors controlling the emissions and INP efficiency of bioaerosol IN Ps in order to develop improved parameterizations and enable their improved representation in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation

Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Furthermore, our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The winter central Arctic surface energy budget: A model evaluation using observations from the MOSAiC campaign

This study evaluates the simulation of wintertime (15 October, 2019, to 15 March, 2020) statistics of the central Arctic near-surface atmosphere and surface energy budget observed during the MOSAiC campaign with short-term forecasts from 7 state-of-the-art operational and experimental forecast systems. Five of these systems are fully coupled ocean-sea ice-atmosphere models. Forecast systems need to simultaneously simulate the impact of radiative effects, turbulence, and precipitation processes on the surface energy budget and near-surface atmospheric conditions in order to produce useful forecasts of the Arctic system. This study focuses on processes unique to the Arctic, such as, the representation of liquid-bearing clouds at cold temperatures and the representation of a persistent stable boundary layer. It is found that contemporary models still struggle to maintain liquid water in clouds at cold temperatures. Given the simple balance between net longwave radiation, sensible heat flux, and conductive ground flux in the wintertime Arctic surface energy balance, a bias in one of these components manifests as a compensating bias in other terms. This study highlights the different manifestations of model bias and the potential implications on other terms. Three general types of challenges are found within the models evaluated: representing the radiative impact of clouds, representing the interaction of atmospheric heat fluxes with sub-surface fluxes (i.e., snow and ice properties), and representing the relationship between stability and turbulent heat fluxes.

54 ENVIRONMENTAL SCIENCES↗