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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Studying Aerosol, Clouds, and Air Quality in the Coastal Urban Environment of Southeastern Texas

A multi-agency succession of field campaigns was conducted in southeastern Texas during July 2021 through October 2022 to study the complex interactions of aerosols, clouds and air pollution in the coastal urban environment. As part of the Tracking Aerosol Convection interactions Experiment (TRACER), the TRACER- Air Quality (TAQ) campaign the Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) and the Convective Cloud Urban Boundary Layer Experiment (CUBE), a combination of ground-based supersites and mobile laboratories, shipborne measurements and aircraft-based instrumentation were deployed. These diverse platforms collected high-resolution data to characterize the aerosol microphysics and chemistry, cloud and precipitation micro- and macro-physical properties, environmental thermodynamics and air quality-relevant constituents that are being used in follow-on analysis and modeling activities. We present the overall deployment setups, a summary of the campaign conditions and a sampling of early research results related to: (a) aerosol precursors in the urban environment, (b) influences of local meteorology on air pollution, (c) detailed observations of the sea breeze circulation, (d) retrieved supersaturation in convective updrafts, (e) characterizing the convective updraft lifecycle, (f) variability in lightning characteristics of convective storms and (g) urban influences on surface energy fluxes. The work concludes with discussion of future research activities highlighted by the TRACER model-intercomparison project to explore the representation of aerosol-convective interactions in high-resolution simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Exposing and Reducing Biases of Simulating Mixed-Phase Clouds in the Convection-Permitting E3SM Atmosphere Model: Lessons From an Arctic Cold-Air Outbreak

Mixed-phase clouds modulate the water and energy cycles of high-latitude regions, yet their liquid-ice phase partitioning has long been poorly simulated in climate models. Here, simulations of Arctic mixed-phase clouds by the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) are assessed against large-eddy simulations, satellite data, and ground-based observations during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment field campaign. SCREAM simulates nearly completely frozen clouds, which is attributed largely to the unreasonably strong Wegener–Bergeron–Findeisen (WBF) process that converts liquid to ice excessively and partly to the early over-abundant ice production at cold temperatures from a temperature-deterministic deposition ice nucleation scheme. Assuming no subgrid variation for the WBF process in the original formulation particularly conflicts with the instantaneous saturation adjustment assumption in the condensation scheme that assumes subgrid variability, leading to exaggerated WBF process rates. A proposed simple physically-based improvement on the treatment of subgrid cloud overlap substantially increases supercooled liquid water content and notably improves cloud-top phase partitioning, aligning better with observations. Improvement of supercooled liquid water content also converges with increasing horizontal resolution. The deposition ice nucleation scheme is found responsible for a falsely-produced ice cloud aloft that is not observed, biasing the simulated cloud radiative effects and top-of-atmosphere radiative fluxes. This study identifies key deficiencies in cloud parameterizations that continue to challenge convection-permitting models.

Geosciences↗
DOE OSTI2024

Can general circulation models (GCMs) represent cloud liquid water path adjustments to aerosol–cloud interactions?

Abstract. General circulation models (GCMs), unlike other lines of evidence, indicate that anthropogenic aerosols cause a global-mean increase in cloud liquid water path (ℒ) and thus a negative adjustment to radiative forcing of the climate by aerosol–cloud interactions. In part 1 of this series of papers, we showed that this is true even in models that reproduce the negative correlation observed in present-day internal variability in ℒ and cloud droplet number concentration (Nd). We studied several possible confounding mechanisms that could explain the noncausal cloud–aerosol correlations in GCMs and that possibly contaminate observational estimates of radiative adjustments. Here, we perform single-column and full-atmosphere GCM experiments to investigate the causal model-physics mechanisms underlying the model radiative adjustment estimate. We find that both aerosol–cloud interaction mechanisms thought to be operating in real clouds – precipitation suppression and entrainment evaporation enhancement – are active in GCMs and behave qualitatively in agreement with physical process understanding. However, the modeled entrainment enhancement has a negligible global-mean effect. This raises the question of whether the GCM estimate is incorrect due to parametric or base-state representation errors or whether the process understanding gleaned from a limited set of canonical cloud cases is insufficiently representative of the diversity of clouds in the real climate. Regardless, even at limited resolution, the GCM physics appears able to parameterize the small-scale microphysics–turbulence interplay responsible for the entrainment enhancement mechanism. We suggest ways to resolve tension between current and future (storm-resolving) global modeling systems and other lines of evidence in synthesis climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Roadmap for a Lightning Modeling Grand Challenge

This document is a roadmap for building an interconnected model of the physical processes that produce a lightning discharge, and its observable optical and radio signals. We call this a Lightning Modeling Grand Challenge, recognizing that significant effort and coordination of human and financial resources is required to realize the capability. The roadmap serves to outline the coordination of resources necessary to enable stitching together existing knowledge and model components to make a lightning prediction, and to test these predictions with observations. Such a capability does not currently exist. The roadmap is motivated not only by a spirit of scientific inquiry, but by practical challenges faced by US Federal and societal stakeholders. Advancements in lightning observations have outpaced our tests of integrated understanding, leaving many stakeholders unsure how to design their missions to properly detect and discriminate lightning, and unsure how to apply the sometimes-disagreeing lightning signals from diverse instruments. The time is right to connect existing theories and models to support stakeholders in understanding the signals they observe, for needs as diverse as climate monitoring, national security, weather forecasting, public safety, and protection of natural and built environments. The roadmap’s two main technical sections describe the components of a linked physical model, followed by a description of models of lightning signals and sensors that are driven by outputs from the physical model. The goal is to predict the time-varying physical properties of lightning that are self-consistent with the thunderstorm’s structure and dynamics. These lightning signals then propagate through the storm, with realistic dispersion and attenuation, to receivers on the ground or in space. At a high level, the model begins with weather (cloud) model output, including explicit prediction of the electrification of cloud particles. The cloud’s electrical structure drives a model of lightning physics, from initiation, through channel development, and discharges along those channels. Key lightning parameters, such as the temperature and currents in the channel, and their space and time distribution, are then used to produce optical and electromagnetic signal sources that propagate to modeled receivers. This architecture therefore generates a dataset suitable for comparison to existing and envisioned observing systems. The need for additional measurements and field campaigns to support model development is described. In each model sub-component, inputs, outputs, uncertainties, evaluation methods, and next steps are summarized, interleaved with references to the scientific literature. Identifying boundaries between the model sub-components aids in segmenting an integrated, complex model into practical work packages and system sub-components, allowing a diverse team to contribute and maintain the system. We estimate that at least five years of effort and a $\$$10M initial investment is necessary to make a significant step forward. Mechanisms to facilitate community coordination, including annual workshops and open-source code repositories, are described.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Impact of Turbulence Representation on the Relationship Between Cloud Feedback and Aerosol‐Cloud Interaction in an E3SMv2 Perturbed Parameter Ensemble

Recent studies reveal an anti‐correlation between global cloud feedback (CF) and effective radiative forcing due to aerosol‐cloud interaction (ERFaci) in Earth system models, but the physical mechanisms underlying it remain uncertain. Here we investigate how different turbulence representations contribute to this relationship over the global ocean using an ensemble of Energy Exascale Earth System Model version 2 simulations with perturbed turbulence parameters. The anti‐correlation appears only in the tropical ascent regime. In the Northern Hemisphere midlatitude and high latitude regimes, there is no significant correlation, and in the tropical marine low cloud and Southern Ocean regimes, the correlation is positive. These opposite correlations are primarily driven by opposing CF responses to perturbed parameters. We find that the mean‐state turbulent mixing strength affects both CF and ERFaci, enabling strong correlations in certain regimes. This study highlights the complex linkages between CF and ERFaci through turbulent processes across diverse cloud regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

CoURAGE KAZR b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility supports atmospheric and earth system research through a comprehensive network of fixed and mobile observatories. These facilities provide long-term and intensive campaign-based observations of clouds, aerosols, precipitation, radiation, and meteorological state variables. ARM observations are designed to improve the physical understanding and numerical representation of atmospheric processes in earth system models, with particular emphasis on cloud-radiation interactions and precipitation processes. The Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) deploys one of the ARM Mobile Facilities (AMF) to the Mid-Atlantic region surrounding Baltimore, Maryland, for the period 1 December 2024 through 30 November 2025. This deployment focuses on characterizing atmospheric structure, cloud properties, and precipitation processes across strong land-use and surface heterogeneity gradients associated with urban, rural, and coastal (Chesapeake Bay) environments. The CoURAGE deployment complements the Baltimore Social-Environmental Collaborative (BSEC), a DOE Urban Integrated Field Laboratory (UIFL), by providing high-quality atmospheric observations needed to connect urban surface processes, emissions, and meteorology to cloud and precipitation responses. In addition to the central urban site, ancillary observing sites were deployed to rural Maryland northwest of Baltimore and to an island site in Chesapeake Bay. These measurements further complement a long-term atmospheric observatory operated in Beltsville, Maryland, by Howard University in collaboration with the Maryland Department of the Environment. Together, these assets form a four-node regional atmospheric observatory network representing Baltimore and its three primary surrounding environments—urban, rural, and coastal/bay. This coordinated observational strategy enables investigation of spatial gradients in boundary-layer structure, cloud occurrence, precipitation evolution, and aerosol-cloud interactions across complex surface regimes. Within this network, vertically pointing cloud radars play a critical role by providing continuous, high-resolution measurements of cloud and precipitation vertical structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗
DOE OSTI2024

minimpl (b1)

The micropulse lidar (MPL) is a ground-based, optical, remote-sensing system designed primarily to determine the altitude of clouds; however, it is also used for detection of atmospheric aerosols. The physical principle is the same as for radar. Pulses of energy are transmitted into the atmosphere; the energy scattered back to the transceiver is collected and measured as a time-resolved signal, thereby detecting clouds and aerosols in real time. From the time delay between each outgoing pulse and the backscattered signal, the distance to the scatterer is inferred. Post-processing of the lidar return characterizes the extent and properties of aerosols or other particles in a region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Lessons Learned for Responsible Use of Cloud in the Cirrus Project, Following the CrowdStrike Outage Event

A disruption in CrowdStrike’s Falcon cybersecurity platform on July 19th, 2024, caused worldwide chaos. This event highlights the imperative need for cloud security measures for networks that are critically reliant on cloud technology. This incident negatively impacted air travel, government networks, and critical infrastructure sectors such as hospitals and financial institutions. While no electric utilities had a physical impact, and few had an IT impact, there were issues created by loss of cloud services, and other interrelated industries. For utilities and energy distribution organizations, understanding and mitigating these risks is essential. The Cirrus tool offers a strategic solution engineered to weave cloud integration seamlessly into the fabric of operational management, thereby enhancing resilience and streamlining efficiency in the face of digital challenges.

25 ENERGY STORAGE↗
DOE OSTI2025

Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments.

Chemistry↗
DOE OSTI2024

Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations

Mesoscale convective systems (MCSs) play an important role in modulating the global hydrological cycle, general circulation, and radiative energy budget. In this study, we evaluate MCS simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25? horizontal resolution. We track MCSs consistently in the model and observations using the PyFLEXTRKR algorithm, which defines MCS based on both cloud-top brightness temperature (Tb) and surface precipitation. Results from using Tb only to define MCS, commonly used in previous studies, are also discussed. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in the tropics and contiguous United States. This is mainly attributed to the underestimated precipitation intensity in EAMv2. In contrast, the simulated MCS frequency becomes more comparable to observations if MCSs are defined only based on cloud-top Tb. The Tb-based MCS tracking method, however, includes many cloud systems with very weak precipitation which conflicts with the MCS definition. This result illustrates the importance of accounting for precipitation in evaluating simulated MCSs. We also find that the new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are generally not improved. This suggests that simulating MCSs will remain a challenge for the next version of E3SM.

Zhang, Meng↗
DOE OSTI2024

Modifying the Asynchronous Jacobi Method for Data Corruption Resilience

Moving scientific computation from high-performance computing (HPC) and cloud computing (CC) environments to devices on the edge, i.e., physically near instruments of interest, has received tremendous interest in recent years. Such edge computing environments can operate on data in situ, offering enticing benefits over data aggregation to HPC and CC facilities that include avoiding costs of transmission, increased data privacy, and real-time data analysis. Because of the inherent unreliability of edge computing environments, new fault-tolerant approaches must be developed before the benefits of edge computing can be realized. Motivated by algorithm-based fault tolerance, a variant of the asynchronous Jacobi (ASJ) method is developed that achieves resilience to data corruption by rejecting solution approximations from neighbor devices according to a bound derived from convergence theory. Numerical results on a two-dimensional Poisson problem show that the new rejection criterion, along with a novel approximation to the shortest path length on which the criterion depends, restores convergence for the ASJ variant in the presence of certain types data corruption. Numerical results are obtained for when the singular values in the analytic bound are approximated. Additional linear systems are also explored, one with a more dense sparsity pattern and one that includes advection. All results indicate that successful resilience to data corruption depends on whether the bound tightens fast enough to reject corrupted data before the iteration evolution deviates significantly from that predicted by the convergence theory defining the bound. This observation generalizes to future work on algorithm-based fault tolerance for other asynchronous algorithms, including upcoming approaches that leverage Krylov subspaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Measurement of the thermal effects in the dispersion relation of the dust acoustic wave in the presence of a magnetic field

This award supported an experimental study of the properties of the dust acoustic wave, low-frequency, l ongitudinal wave mode that propagates through the dust component of a dusty plasma system and is self-excited by the free energy from the ion streaming p ast the dust, in the presence of a high magnetic fiel d. In particular, this project measured the thermal state of dusty plasma system by measuring the dispersi on relation of the dust acoustic wave while varying (1) the strength and direction of the magnetic field relative to the propagation direction of the wave and (2) the neutral gas pressure. By exploiting the unique ability of the MDPX facility to generate a dusty plasma in a high magnetic field environment, it is possible to create a preferential ion flow along the magnetic field lines as the ions become magnetized. In this way, the magnetic field strength can be used to control t he strength of the ion flow through the dust cloud an d provide insight into the contribution that one of the physical mechanisms, a two-stream ion-dust instability, thought to be responsible for high temperatures that have been previously observed might have. This work was completed over two, one-week long runs o n the Magnetized Dusty Plasma eXperiment (MDPX) in the Magnetized Plasma Research Laboratory at Auburn University and supported by work at Wittenberg University (development of an electrode system that would allow for the wave mode to propagate in th e direction of gravity as the orientation of the magnetic field is changed by rotating the magnets on the M DPX device, analysis of data acquired during the experimental campaigns, testing alternative strategies to form a dust cloud based on what was learned the first experimental campaign, etc.).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Aircraft in-situ measurements from SOCRATES constrain the anthropogenic perturbations of cloud droplet number

Aerosol-cloud interactions (ACI) in warm clouds alter reflected shortwave radiation by influencing cloud microphysical and macrophysical properties. The variable of state controlling ACI is the cloud droplet number concentration (N d ). Here, we examine the perturbations in N d due to anthropogenic aerosols (ΔN d, PD-PI ) using a perturbed parameter ensemble (PPE) hosted in the sixth Community Atmosphere Model (CAM6). Surrogate models are created for the CAM6 PPE outputs and are used to generate 1 million model variants of CAM6 by sampling 45 sources of parameter uncertainty. The range of uncertain physical parameters related to ACI are constrained with observations of aerosol and cloud properties from SOCRATES. The likely range of uncertain parameters and the associated range of ΔN d, PD-PI are more strongly constrained with observations of N d relative to observations of cloud condensation nuclei. We conduct sensitivity tests of how constraints on ΔN d, PD-PI are affected by systematic uncertainties in observations and our limitations in our surrogate models created for CAM6 PPE outputs. Based on this, we provide guidance on the impact of reducing systematic uncertainty in airborne microphysical observations and in surrogate models.

Song, Ci [Univ. of Wyoming, Laramie, WY (United St↗
DOE OSTI2025

Long‐Term Large‐Scale Atmospheric Forcing Data From Three‐Dimensional Constrained Variational Analysis for the ARM SGP Site

Here, this study presents a long‐term three‐dimensional large‐scale forcing data set (VARANAL3D) derived from the three‐dimensional constrained variational analysis (3DCVA) method at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site from 2004 to 2018. Building on the same input data sets as the conventional continuous forcing data set (VARANAL), VARANAL3D maintains overall consistency in domain‐averaged fields while introducing spatial variability, offering critical insights into the influence of mesoscale synoptic systems on cloud‐related processes. Evaluations are conducted across four cloud and precipitation regimes: Clear‐sky, Shallow‐clouds, Afternoon‐precipitation, and Nocturnal‐precipitation, presenting high consistency of the domain‐mean forcing data sets while emphasizing the role of subdomain forcing variability particularly in precipitating regimes. Single column model (SCM) simulations demonstrate that subdomain VARANAL3D forcing improves cloud and precipitation representation, with the ensemble outperforming domain‐mean forcing in three cloudy and precipitating regimes. Overall, these results highlight VARANAL3D's value for investigating the impacts of spatial variability of large‐scale forcing on atmospheric processes. The VARANAL3D data set provides new opportunities for evaluating model physics, advancing the development of scale‐aware parameterizations and deepening our understanding of cloud and precipitation dynamics.

Environmental sciences↗
DOE OSTI2024

Development of the ARM Lagrangian Large-Scale Forcing Data (ARMLAGTRAJ) Value-Added Product Based on the lagtraj Framework

The Atmospheric Radiation Measurement (ARM) large-scale forcing data developed based on the constrained variational analysis (VARANAL) value-added product (VAP) (Zhang and Lin 1997, Zhang et al. 2001, Xie et al. 2004, Tang et al. 2019) has been widely used for single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LESs) to understand and improve physical processes in models. Recently, the U.S. Department of Energy (DOE) ARM user facility conducted several major field campaigns using ship-based moving observational platforms. For example, the Marine ARM GPCI Investigation of Clouds (MAGIC) field campaign focused on the role of subtropical marine-boundary layer (MBL) clouds, and the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign aimed to improve understanding of the coupled climate systems in the Arctic. Observations from moving platforms are critical to provide a comprehensive characterization of coupled-system processes associated with all stages of the cloud and/or sea-ice life cycle. Traditional ARM large-scale forcing data have been developed at fixed locations. They need to be extended to include these moving platforms to address data needs for ship-based field campaigns or to support LES modeling in a Lagrangian framework. With these considerations in mind, we develop ARM-type Lagrangian large-scale forcing data sets based on the lagtraj framework (Boeing et al. 2020) with notable enhancements in generating forcings that are more suitable for ARM field campaigns. The lagtraj is a novel tool that generates forcings for LES and SCM simulation in both Lagrangian and Eulerian perspective. This technical report focuses on the major changes we performed on the lagtraj algorithm and provides an overview of the ARM Lagrangian Large-Scale Forcing Data (ARMLAGTRAJ) value-added products.

54 ENVIRONMENTAL SCIENCES↗