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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A coupled fluid-mechanical workflow to simulate the directed energy deposition additive manufacturing process

Simulation of additive manufacturing processes can provide essential insight into material behavior, residual stress, and ultimately, the performance of additively manufactured parts. In this work, we describe a new simulation based workflow utilizing both solid mechanics and fluid mechanics based formulations within the finite element software package SIERRA (Sierra Solid Mechanics Team in Sierra/Solid Mechanics 4.52 User’s Guide SAND2019-2715. Technical report, Sandia National Laboratories, 2011) to enable integrated simulations of directed energy deposition (DED) additive manufacturing processes. In this methodology, a high-fidelity fluid mechanics based model of additive manufacturing is employed as the first step in a simulation workflow. This fluid model uses a level set field to track the location of the boundary between the solid material and background gas and precisely predicts temperatures and material deposition shapes from additive manufacturing process parameters. Next, the resulting deposition shape and temperature field from the fluid model are then mapped into a solid mechanics formulation to provide a more accurate surface topology for radiation and convection boundary conditions and a prescribed temperature field. Solid mechanics simulations are then conducted to predict the evolution of material stresses and microstructure within a part. By combining thermal history and deposition shape from fluid mechanics with residual stress and material property evolutions from solid mechanics, additional fidelity and precision are incorporated into additive manufacturing process simulations providing new insight into complex DED builds.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗
DOE OSTI2023

Cloud‐Surface Coupling Alters the Morning Transition From Stable to Unstable Boundary Layer

Abstract Due to surface heating, the morning boundary layer transits from stable to neutral or convective conditions, exerting critical influences on low tropospheric thermodynamics. Low clouds closely interact with the boundary layer development, yet their interactions bear considerable uncertainties. Our study reveals that cloud‐surface coupling alters the morning transition from stable to unstable boundary layer and thus notably affects the diurnal variation of the boundary layer. Specifically, due to the reduction in surface fluxes, decoupled clouds can delay the process of eroding nocturnal inversion by 0.8‐hr and even prevent the transition of the boundary layer from happening for 12% of decoupled cases, keeping the boundary layer in a stable state during the noontime. On the other hand, when clouds are coupled with the surface, cloud‐top radiative cooling can directly cool the upper boundary layer to facilitate sub‐cloud convection, leading to an unstable boundary layer in the earlier morning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC

Abstract Models struggle to accurately simulate observed sea ice thickness changes, which could be partially due to inadequate representation of thermodynamic processes. We analyzed co‐located winter observations of the Arctic sea ice from the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate for evaluating and improving thermodynamic processes in sea ice models, aiming to enable more accurate predictions of the warming climate system. We model the sea ice and snow heat conduction for observed transects forced by realistic boundary conditions to understand the impact of the non‐resolved meter‐scale snow and sea ice thickness heterogeneity on horizontal heat conduction. Neglecting horizontal processes causes underestimating the conductive heat flux of 10% or more. Furthermore, comparing model results to independent temperature observations reveals a ∼5 K surface temperature overestimation over ice thinner than 1 m, attributed to shortcomings in parameterizing surface turbulent and radiative fluxes rather than the conduction. Assessing the model deficiencies and parameterizing these unresolved processes is required for improved sea ice representation.

Geology↗
DOE OSTI2026

Evaluating Atmospheric River Impacts on Energy and Moisture Transport in the Arctic Using Different Detection Algorithms

Atmospheric rivers (ARs) significantly impact the Arctic climate system by enhancing atmospheric heat and moisture transport and altering the local energy budget. Developing AR detection tools (ARDTs) is critical yet challenging. This study evaluates 12 ARDTs in the Arctic to assess their performance in representing atmospheric heat (represented by moist static energy) and moisture transport, as well as surface downward longwave radiation (LWD) and precipitation impacts, spanning 2000 to 2019 using ERA5 reanalysis. We find that AR occurrence frequency in the Arctic varies widely, from less than 1% to over 13%, depending on the ARDT. This variability leads to differences in contributions to poleward atmospheric heat (<1%–33%) and moisture (<1%–49%) transport. The highest AR frequency, and corresponding contributions to atmospheric heat and moisture transport, occurs over the Atlantic sector during non-summer seasons for most ARDTs. This region aligns with the primary poleward moisture pathway and the end of climatological mid-latitude storm tracks, highlighting strong connections between Arctic ARs and mid-latitude cyclones. ARs induce significant LWD anomalies, largest in winter, smallest in summer, and also substantially contribute to the seasonal precipitation. Global ARDTs detect fewer ARs with larger anomalies (>100 W m−2 in higher Arctic), but contribute <1% to seasonal climatological LWD and precipitation. In contrast, polar-specific ARDTs detect higher AR occurrences and account for up to 16% of seasonal LWD and 41% precipitation. This suggests that algorithms emphasizing extreme events with large anomalies do not necessarily indicate a large climate radiative and precipitation impact.

Zhang, Chen [University of Colorado, Boulder, CO (↗
DOE OSTI2023

More frequent atmospheric rivers slow the seasonal recovery of Arctic sea ice

In recent decades, Arctic sea ice coverage experienced a drastic decline in winter, when sea ice is expected to recover following the melting season. It is unclear to what extent atmospheric processes such as atmospheric rivers (ARs), intense corridors of moisture transport, contribute to this reduced recovery of sea ice. Here, in this study, using observations and climate model simulations we find a robust frequency increase in ARs in early winter over the Barents-Kara Seas and the central Arctic (ABK) between 1979 and 2021. The moisture carried by more frequent ARs has intensified surface downward longwave radiation and rainfall, caused stronger melting of thin, fragile ice cover, and slowed the seasonal recovery of sea ice, accounting for 34% of the sea ice cover decline in ABK. A series of model ensemble experiments suggests that, in addition to a uniform AR increase in response to anthropogenic warming, tropical Pacific variability also contributes to the observed Arctic AR changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Summertime low clouds mediate the impact of the large-scale circulation on Arctic sea ice

The rapid Arctic sea ice retreat in the early 21 st century is believed to be driven by several dynamic and thermodynamic feedbacks, such as ice-albedo feedback and water vapor feedback. However, the role of clouds in these feedbacks remains unclear since the causality between clouds and these processes is complex. Here, we use NASA CERES satellite products and NCAR CESM model simulations to suggest that summertime low clouds have played an important role in driving sea ice melt by amplifying the adiabatic warming induced by a stronger anticyclonic circulation aloft. The upper-level high pressure regulates low clouds through stronger downward motion and increasing lower troposphere relative humidity. The increased low clouds favor more sea ice melt via emitting stronger longwave radiation. Then decreased surface albedo triggers a positive ice-albedo feedback, which further enhances sea ice melt. Considering the importance of summertime low clouds, accurate simulation of this process is a prerequisite for climate models to produce reliable future projections of Arctic sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Detecting tropospheric composition and climate responses to US air pollution controls in the context of internally-arising variability

Since the 1970s, air pollutant emissions controls in the United States (US) have lowered concentrations of ozone (O 3 ) and aerosols, which have opposing radiative effects on surface temperature. Using a pair of initial-condition ensembles generated by a fully-coupled chemistry-climate model, we simulate the “world avoided” by US air pollution controls. In this counterfactual world, we find tropospheric column O 3 increases, robust to natural internal variability, extending across the Northern Hemisphere. Robust aerosol increases, dominated by sulfate, remain localized near the US. Wintertime Northwest Atlantic cloud droplet number concentration is particularly sensitive to US aerosol. While an ensemble mean US surface cooling signal (−0.4 °C) implies that aerosol-driven cooling prevails over any O 3 -induced warming, we find that large regional internal variability will confound its detection in any single transient realization. Larger signal-to-noise ratios for composition versus climate variables underscore the greater detectability of emissions-driven changes in tropospheric composition compared to their associated climate impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mid-latitude clouds contribute to Arctic amplification via interactions with other climate feedbacks

Abstract Traditional feedback analyses, which assume that individual climate feedback mechanisms act independently and add linearly, suggest that clouds do not contribute to Arctic amplification. However, feedback locking experiments, in which the cloud feedback is disabled, suggest that clouds, particularly outside of the Arctic, do contribute to Arctic amplification. Here, we reconcile these two perspectives by introducing a framework that quantifies the interactions between radiative feedbacks, radiative forcing, ocean heat uptake, and atmospheric heat transport. We show that including the cloud feedback in a comprehensive climate model can result in Arctic amplification because of interactions with other radiative feedbacks. The surface temperature change associated with including the cloud feedback is amplified in the Arctic by the surface-albedo, Planck, and lapse-rate feedbacks. A moist energy balance model with a locked cloud feedback exhibits similar behavior as the comprehensive climate model with a disabled cloud feedback and further indicates that the mid-latitude cloud feedback contributes to Arctic amplification via feedback interactions. Feedback locking in the moist energy balance model also suggests that the mid-latitude cloud feedback contributes substantially to the intermodel spread in Arctic amplification across comprehensive climate models. These results imply that constraining the mid-latitude cloud feedback will greatly reduce the intermodel spread in Arctic amplification. Furthermore, these results highlight a previously unrecognized non-local pathway for Arctic amplification.

58 GEOSCIENCES↗
DOE OSTI2020

Nanometer-Scale Imaging of Inhomogeneous Active Charge Carriers in Arsenic-Doped CdTe Thin Films

We report nanometer-scale imaging of active carrier distribution of As-doped CdTe films by scanning capacitance microscopy (SCM). We developed SCM sample preparation for CdTe by ion-milling followed by thermal processing. The nanometer-resolution carrier delineation for CdTe was validated by imaging on a CdTe cross-section sample made by a molecular beam epitaxy layer stack with As-doping concentrations of 10^15 ~10^18 /cm 3 . We found that the carrier distribution in As-doped films was significantly nonuniform, with inhomogeneity ranging from sub-um to a few um and concentration variation of one order of magnitude (low 10^16 to low 10^17 /cm 3 ). This nonuniformity is distributed randomly, independent of grain structure and grain boundary (GB). We used Kelvin probe force microscopy (KPFM) and cathodoluminescence (CL) to further map the surface potential and radiative illumination on the same area as the SCM image. Higher potential and lower CL intensity were found on GBs but not on SCM contrast, illustrating positive GB charging and GB recombination but not GB-distinguished doping. The overall KPFM potential image is in rough agreement with the SCM carrier distribution, in terms of Fermi-level position relative to the bandgap edge - thus resulting in the band-edge potential fluctuation. Nonuniform carrier concentration, potential fluctuation, and defect recombination can all together cause the Voc deficit of the As-doped CdTe device.

14 SOLAR ENERGY↗
DOE OSTI2023

Atmosphere–Ocean Coupled Energy Budgets of Tropical Convective Discharge–Recharge Cycles

An energy budget combining atmospheric moist static energy (MSE) and upper ocean heat content (OHC) is used to examine the processes impacting day-to-day convective variability in the tropical Indian and western Pacific Oceans. Feedbacks arising from atmospheric and oceanic transport processes, surface fluxes, and radiation drive the cyclical amplification and decay of convection around suppressed and enhanced convective equilibrium states, referred to as shallow and deep convective discharge–recharge (D–R) cycles, respectively. The shallow convective D–R cycle is characterized by alternating enhancements of shallow cumulus and stratocumulus, often in the presence of extensive cirrus clouds. The deep convective D–R cycle is characterized by sequential increases in shallow cumulus, congestus, narrow deep precipitation, wide deep precipitation, a mix of detached anvil and altostratus and altocumulus, and once again shallow cumulus cloud types. Transitions from the shallow to deep D–R cycle are favored by a positive “column process” feedback, while discharge of convective instability and OHC by mesoscale convective systems (MCSs) contributes to transitions from the deep to shallow D–R cycle. Variability in the processes impacting MSE is comparable in magnitude to, but considerably more balanced than, variability in the processes impacting OHC. Variations in the quantity of atmosphere–ocean coupled static energy (MSE + OHC) result primarily from atmospheric and oceanic transport processes, but are mainly realized as changes in OHC. MCSs are unique in their ability to rapidly discharge both lower-tropospheric convective instability and OHC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measurements and Model Improvement: Insight into NWP Model Error Using Doppler Lidar and Other WFIP2 Measurement Systems

Abstract Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s −1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Characterization of oceanic post-cold frontal clouds and their model representation. Final report

Low-level clouds are ubiquitous over the oceans, and their impact on the Earth’ radiative budget is important. However, general circulation models (GCMs) that are used to represent our climate and its evolution still experience problems to represent their correct amount and radiative impact. In the extratropical latitudes (~30-60 N/S), cloud properties are strongly modulated by the occurrence of anti-cyclones (high pressure systems) and cyclones (low pressure systems). While deep clouds dominate in cyclones near the center and along the cold and warm fronts, low-level clouds populate the colder regions of the cyclones, and more specifically the region in the wake of the cold fronts, the post-cold frontal region (PCF). The GCMs mentioned above were found to underestimate the amount of these PCF clouds, and this caused errors in their representation of the amount of solar radiation at the surface of the southern hemisphere oceans, causing an excess in absorption and errors in long-term predictions. To better understand which processes are ill-represented in the GCMs to cause such issues, we used ground-based observations from the Eastern North Atlantic (ENA) ARM site and a high resolution regional model (called WRF) to examine the properties of PCF clouds. With the WRF model, we simulated the passage of a cold front at the ENA site using theoretically distinct representations of convection and the physics of the boundary layer. While these physical representations had little impact on the timing, structure and circulation of the cold front, we found a high sensitivity to how the large scale information was fed into the model. Errors rapidly developed if the incoming flow information was injected too far from the site, but 1000 km was found to be an optimal distance between the outer boundary and the ENA site for a realistic cold front passage. With this model, we then conducted a series of experiment where convection and boundary layer schemes were changed, and revealed that 1) the clouds were more sensitive to the convection than boundary layer schemes and 2) the interaction between the two has a large impact. Furthermore, changing the representation of the physics in the model had greater impacts on PCF clouds than changing the initial conditions. As large-scale models increase their spatial resolution, convection will eventually be resolved but it remains that their representation of the physics of the boundary layer will matter for the correct representation of shallow (low-level) clouds. Observations were used to find which large-scale information had a clear relationship with PCF cloud and precipitation properties, in view of helping with GCM parameterizations and evaluation. Both lower troposphere stability and surface forcing were found to be important for PCF clouds that experience more vigorous dynamics than their tropical or subtropical counterparts. For cloud base and top heights, a simple measure of the change in potential temperature between the surface and the 800 hPa was found to be a good predictor. This relationship was found to hold as well when using data from campaigns recently conducted in the southern ocean south of Tasmania. A recent GCM was found to succeed in representing similar relations. For cloud depth, water content, and different characteristics of precipitation both the temperature contrast between the surface and near-air and the near-surface wind speed had a strong relationship. To summarize, through observational constraints and regional model experiments, the project has provided guidance to evaluate GCMs and tools to improve their representation of low-level clouds in the extratropics. It has also highlighted differences in behavior between these clouds and those typically found in the subtropics, thus opening new avenues of research for shallow clouds in general.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cloud Processing of Aerosol during SAIL Field Campaign Report

Globally, on average, the release rate of aerosols from clouds is estimated to be about 6,000 Tg/yr and an aerosol particle is estimated to experience about three cloud cycles. Cloud processing of aerosol produces aerosols that have dramatically different physicochemical properties compared to their original form. Cloud processing may transform particles to efficient cloud condensation nuclei (CCN) or ice nucleating particles (INP) that can further participate in cloud formation. We investigated cloud processing of aerosol during the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign using multi-modal offline analysis techniques available at DOE’s Environmental Molecular Sciences Laboratory (EMSL). The main scientific aim of this project was to understand the processes that transform aerosols (chemically, and physically), and influence aerosol-cloud interactions. Aerosol particles undergo significant modifications due to warm (cloud droplets) and cold cloud (ice crystals) processing. Cloud processing of aerosol has important effects on the atmospheric aerosol number and mass concentration, size distribution, phase separation, and chemical composition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

How Snow Drives the Seasonal Evolution of Land and Sea Surface Albedos in the Alaskan High Arctic: Final Technical Report

The purpose of the project was to observe and quantify temporal variation in snow albedo and snow characteristics across the Arctic coastal landscape as winter transitioned into spring and snowmelt occurred, both on tundra and sea ice. This transition is bounded by fully snow-covered landscapes with broadband albedos of approximately 0.8 and snow-free landscapes with albedos of 0.15 (tundra or ponded sea ice). For these landscapes, we monitored the spring surface characteristics and radiative properties nearly daily at three locations on or near the Department of Energy ARM North Slope of Alaska (NSA) User Facility in Utqiagvik, Alaska: the central NSA Facility (hereafter called ARM), one near NSA-E12 (BEO), and one on the sea ice of Elson Lagoon (ICE) during three melt seasons (2019, 2022, and 2024). The field campaign component of this award was named SALVO ( S now AL bedo E VO lution). Typical field seasons began in mid-April and lasted until mid-June. Main measurements included snow depth (at 1-m intervals), broadband albedo (at 5-m intervals), spectral albedo (at 5- m intervals), and multiple digital images. Orthomosaics were converted into binary images to determine the snow-covered fraction over time across various landscapes. Additional measurements included basic weather data and snow-ground (or ice) interface temperatures. Sky conditions were observed and photographed to help assess albedo values.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Optimization and Prediction of Spectral Response of Metasurfaces Using Artificial Intelligence

Hot-electron generation has been a topic of intense research for decades for numerous applications ranging from photodetection and photochemistry to biosensing. Recently, the technique of hot-electron generation using non-radiative decay of surface plasmons excited by metallic nanoantennas, or meta-atoms, in a metasurface has attracted attention. These metasurfaces can be designed with thicknesses on the order of the hot-electron diffusion length. The plasmonic resonances of these ultrathin metasurfaces can be tailored by changing the shape and size of the meta-atoms. One of the fundamental mechanisms leading to generation of hot-electrons in such systems is optical absorption, therefore, optimization of absorption is a key step in enhancing the performance of any metasurface based hot-electron device. Here we utilized an artificial intelligence-based approach, the genetic algorithm, to optimize absorption spectra of plasmonic metasurfaces. Using genetic algorithm optimization strategies, we designed a polarization insensitive plasmonic metasurface with 90% absorption at 1550 nm that does not require an optically thick ground plane. We fabricated and optically characterized the metasurface and our experimental results agree with simulations. Finally, we present a convolutional neural network that can predict the absorption spectra of metasurfaces never seen by the network, thereby eliminating the need for computationally expensive simulations. Our results suggest a new direction for optimizing hot-electron based photodetectors and sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

The Fuzzball Paradigm

We describe the puzzles that arise in the quantum theory of black holes, and explain how they are resolved in string theory. We review how the Bekenstein entropy is obtained through the count of brane bound states. We describe the fuzzball construction of black hole microstates. These states have no horizon and radiate from their surface like a normal body, so there is no information puzzle. We explain how the semiclassical approximation is violated in gravitational collapse even though curvatures are low at the classical horizon. This violation happens because the collapse leads to a stretching of space that is {\it fast}: light does not have time to travel across the collapsing region to establish the `vecro' correlations needed in the quantum gravitational vacuum. These vecro correlations arise from the existence of virtual fuzzball fluctuations in the gravitational vacuum, and are significant because of the large degeneracy of fuzzball states implied by the Bekenstein entropy. It is plausible that similar effects of fast expansion may be responsible for effects like dark energy and the Early Dark Energy postulated to explain the Hubble tension.

FOS: Physical sciences↗
DOE OSTI2026

Improving thermodynamic nudging in the E3SM Atmosphere Model version 2 (EAMv2): strategy and hindcast skills on weather systems

Nudging techniques are commonly employed to constrain atmospheric simulations toward observed states, facilitating model evaluation and sensitivity studies. However, if applied improperly – particularly to thermodynamic variables such as temperature and humidity – nudging can distort physical processes and introduce spurious biases, undermining the credibility of the simulations. This study presents an improved nudging implementation that applies vertically modulated tendencies to reduce adverse impacts on model physics. The framework is tested in version 2 of the Energy Exascale Earth System Model (EAMv2) using a suite of hindcast simulations nudged toward ERA5 reanalysis. We systematically evaluate the individual and combined effects of nudging wind, temperature, and humidity fields on the model's ability to represent large-scale atmospheric states and high-impact weather systems. Results show that the revised strategy – particularly when nudging temperature and humidity at selected levels – enhances hindcast skill by improving agreement with ERA5 without degrading the hydrological cycle or precipitation processes. Additional improvements in surface temperature, outgoing longwave radiation, and precipitation biases are achieved through targeted nudging of land-surface variables. The proposed approach strengthens the representation of large-scale conditions relevant to tropical cyclones, atmospheric rivers, and extratropical cyclones in the low-resolution EAMv2. These findings demonstrate that carefully designed thermodynamic nudging, especially of temperature and humidity, improves the realism of constrained simulations and broadens the utility of nudged EAMv2 for atmospheric modeling, machine learning, and high-impact weather research.

Atmospheric river↗