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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Engineering the Novel Extremophile Alga Chlamydomonas pacifica for High Lipid and High Starch Production as a Path to Developing Commercially Relevant Strains

Microalgae offer a compelling platform for the production of commodity products, due to their superior photosynthetic efficiency, adaptability to nonarable lands and nonpotable water, and their capacity to produce a versatile array of bioproducts, including biofuels and biomaterials. However, the scalability of microalgae as a bioresource has been hindered by challenges such as costly biomass production related to vulnerability to pond crashes during large-scale cultivation. This study presents a pipeline for the genetic engineering and pilot-scale production of biodiesel and thermoplastic polyurethane precursors in the extremophile species Chlamydomonas pacifica. This extremophile microalga exhibits exceptional resilience to high pH (>11.5), high salinity (up to 2% NaCl), and elevated temperatures (up to 42 °C). Initially, we evolved this strain to also have a high tolerance to high light intensity (>2000 μE/m 2 /s) through mutagenesis, breeding, and selection. We subsequently genetically engineered C. pacifica to significantly enhance lipid production by 28% and starch accumulation by 27%, all without affecting its growth rate. We demonstrated the scalability of these engineered strains by cultivating them in pilot-scale raceway ponds and converting the resulting biomass into biodiesel and thermoplastic polyurethanes. This study showcases the complete cycle of transforming a newly discovered species into a commercially relevant commodity production strain. This research underscores the potential of extremophile algae, including C. pacifica, as a key species for the burgeoning sustainable bioeconomy, offering a viable path forward in mitigating environmental challenges and supporting global bioproduct demands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evaluation of Potential DAS Array Geometries for the Source Physics Experiment, Phase III

Distributed Acoustic Sensing (DAS) is a rapidly developing technology that can record acoustic wavefields at high sampling rates and with dense spatial spacing difficult to achieve with seismometers. However, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. A better quantitative understanding of DAS array behavior prior to SPE Phase III acquisition can help improve the quality of the data recorded by guiding the DAS array design. Here we use array response functions as well as beamforming and backprojection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A seven-sided polygon DAS array demonstrated improved capabilities for recording regional sources over segmented linear arrays, with potential improvements in recording and locating local sources. These results help reveal DAS array performance as a function of geometry.

58 GEOSCIENCES↗
DOE OSTI2021

ISMIP6-based projections of ocean-forced Antarctic Ice Sheet evolution using the Community Ice Sheet Model

Abstract. The future retreat rate for marine-based regions of the Antarctic Ice Sheet is one of the largest uncertainties in sea-level projections. The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) aims to improve projections and quantify uncertainties by running an ensemble of ice sheet models with atmosphere and ocean forcing derived from global climate models. Here, the Community Ice Sheet Model (CISM) is used to run ISMIP6-based projections of ocean-forced Antarctic Ice Sheet evolution. Using multiple combinations of sub-ice-shelf melt parameterizations and calibrations, CISM is spun up to steady state over many millennia. During the spin-up, basal friction parameters and basin-scale thermal forcing corrections are adjusted to optimize agreement with the observed ice thickness. The model is then run forward for 550 years, from 1950–2500, applying ocean thermal forcing anomalies from six climate models. In all simulations, the ocean forcing triggers long-term retreat of the West Antarctic Ice Sheet, especially in the Filchner–Ronne and Ross sectors. Mass loss accelerates late in the 21st century and then rises steadily for several centuries without leveling off. The resulting ocean-forced sea-level rise at year 2500 varies from about 150 to 1300 mm, depending on the melt scheme and ocean forcing. Further experiments show relatively high sensitivity to the basal friction law, moderate sensitivity to grid resolution and the prescribed collapse of small ice shelves, and low sensitivity to the stress-balance approximation. The Amundsen sector exhibits threshold behavior, with modest retreat under many parameter settings but complete collapse under some combinations of low basal friction and high thermal forcing anomalies. Large uncertainties remain, as a result of parameterized sub-shelf melt rates, simplified treatments of calving and basal friction, and the lack of ice–ocean coupling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Fabrication and Characterization of Composite Membranes for the Concentration of Lithium Containing Solutions Using Forward Osmosis

In this study, an energy and highly time efficient, cost effective, and scalable forward osmosis (FO) composite polyvinylidene fluoride (PVDF) membrane is developed to concentrate lithium chloride recovered from naturally occurring geothermal brine resources. Dense thin robust defect-free Kynar-PVDF membranes are fabricated on porous PVDF hollow fiber supports. Various membrane fabrication parameters such as membrane thickness, coating solution concentration, cross-linker concentration, and curing temperature are studied to develop a defect-free dense membrane. Advanced characterization tools such as FIB-SEM, Raman, FTIR spectroscopy, and XRD are used to study the surface and cross-sectional morphology, chemistry of crosslinking, crystallinity and chemical structure of the dense PVDF membrane. FO is utilized to concentrate LiCl in the solution from 36 to 175 g L -1 with an average water transfer rate of 0.450 L m -2 h -1 (LMH) at 85 °C. Furthermore, the membrane showed stable performance for more than 550 h while maintaining the purity of the concentrated lithium chloride solution of >99.99 wt%. Overall, the results suggest that the FO-based technology is an innovative, time and energy efficient approach compared to the conventional technologies for concentration of battery grade LiCl for application in lithium ion batteries and other clean energy technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Using a 3D Porous Flow-Through Electrode Geometry for High-Rate Electrochemical Reduction of CO 2 to CO in Ionic Liquid

Three-dimensional porous flow-through electrodes promise the realization of higher current densities in the electrochemical carbon dioxide reduction reaction (CO 2 RR) by overcoming mass-transport limitations associated with the diffusion of dissolved CO 2 in the bulk electrolyte. Using an ionic-liquid-based electrolyte, [EMIM]BF 4 , offers the additional benefit of higher CO 2 solubility compared to water-based electrolytes. Here, we quantitatively evaluated the effects of flow-driven reactant transport on electrode activity and selectivity for the electrochemical CO 2 RR in [EMIM]BF4. High-surface-area porous flow-through electrodes were fabricated by wet-chemical deposition of Ag nanoflowers on commercial macroporous Al foams. At the highest electrolyte flow-through rate of 100 mL min –1 , we observed a 70-fold increase in partial CO current density, and a 7-fold increase in the Faradaic efficiency toward CO, from 10% without flow to 75% with flow. At the same time, the selectivity changed from oxalate as the main product without flow to CO in the electrolyte flow-through configuration. These results demonstrate that the flow-through approach provides a promising path forward to control selectivity and to overcome mass-transport limitations of the electrochemical CO 2 RR.

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

Application of machine learning techniques for fast MeV x-ray spectra unfolding from filter stack spectrometer data

Recovery of MeV x-ray spectra from detector signals is difficult because the response matrix inversion is ill-conditioned and current methods are too slow for high-repetition-rate experiments. In this work, we make use of neural networks to unfold MeV x-ray spectra from measurements obtained with a filter stack spectrometer at rates of near 40 Hz. The neural network was trained on synthetic data and tested on both synthetic and experimental data, the latter obtained in two separate experiments performed at the Omega EP laser facility. We show here that this unfolding method has good performance on synthetic data and that it is a promising option for experimental data of up to 40 MeV. The accuracy on experimental data is verified by using a simple forward model to compare against measured values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Physics-Informed Neural Network Modeling of Li-Ion Batteries: Preprint

Li-ion batteries (LIB) are a promising solution to enable storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (external temperature, load). To plan the installation of batteries, appropriate understanding and prediction capabilities of their lifecycle is needed. High-fidelity numerical models of LIBs such as the pseudo-two-dimensional (P2D) model have been shown to accurately represent the charge-discharge-cycle of an LIB given ac- curate choice of the physical parameters. Given the large number of P2D parameters, adjusting them using forward runs is intractable. This work describes the development of a physics-informed neural network (PINN) as a surrogate substitute of the P2D model that captures parameter dependence. The PINN is advantageous as it needs little to no data, and can naturally encode the dependence of every model parameter. Here, a specific training procedure is adopted to efficiently cover parameter space, handle model stiffness and enforce boundary conditions. The trained PINN is validated against numerical solutions of the P2D model, and its applicability to battery degradation modeling is discussed.

battery degradation↗
DOE OSTI2024

Discovering neutrino tridents at the Large Hadron Collider

Neutrino trident production of di-lepton pairs is well recognized as a sensitive probe of both electroweak physics and physics beyond the Standard Model. Although a rare process, it could be significantly boosted by such new physics, and it also allows the electroweak theory to be tested in a new regime. We demonstrate that the forward neutrino physics program at the Large Hadron Collider offers a promising opportunity to measure for the first time, dimuon neutrino tridents with a statistical significance exceeding $5\sigma$. We present predictions for various proposed experiments and outline a specific experimental strategy to identify the signal and mitigate backgrounds, based on "reverse tracking" dimuon pairs in the FASER$\nu$2 detector. We also discuss prospects for constraining beyond Standard Model contributions to neutrino trident rates at high energies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Hawaii Solar Desalination Project

This project built the world’s largest Forward Osmosis (FO) solar thermal desalination plant at the NELHA Hawaii Ocean Science and Technology Park for a high visibility “on sun” demonstration of seawater desalination for agricultural applications. The project included re-commissioning an existing 2MW micro-dish solar thermal array and coupling it to a next-generation 500m3/day FO system. The purpose of the project was to validate FO at commercial scale; to verify that the water produced is safe for potential downstream agricultural users; to gather data on capital cost, energy consumption and operating expense for the technology; and to demonstrate that FO can be successfully coupled to low-grade thermal resources. The plant implemented advancements in FO component technology, including hot nano-filtration, polymeric heat exchangers, draw solutions, and improved FO membranes, and operated from June 2022 to September 2023 - producing 30gpm of permeate water. Thermal energy was below 30kWh/m 3 during operation, establishing a new benchmark in thermal desalination technology, with recovery rates at times exceeding 65%, well above what commercial RO systems can achieve. Combining the plant capital cost (CAPEX) and the operating expenses (OPEX) yields a Levelized Cost of Water (LCOW) that is competitive with existing carbon-intensive technologies such as RO. The ability of the plant to operate from a renewable resource paves the way toward a low-carbon desalination future using low-grade thermal resources.

14 SOLAR ENERGY↗
DOE OSTI2021

Adjoint Klein-Nishina Sampling Methods: Efficiency, Speed and Applications

Three new rejection sampling methods for generating samples from the adjoint Klein-Nishina cross section are discussed: the two-branch rejection sampling procedure, the three-branch linear rejection sampling procedure and the three-branch inverse rejection sampling procedure. These methods have all been implemented in the Framework for REsearch in Nuclear ScIence and Engineering (FRENSIE). The efficiency and sample generation rate of each of these methods are evaluated to characterize the methods and to make recommendations regarding their use. The use of these methods in realistic transport simulations is also evaluated by incorporating a scattering function into the sampling process. Furthermore, the results of an infinite medium problem are presented to verify that the sampling procedure can be used in an adjoint Monte Carlo simulation to generate results that are in agreement with an equivalent forward simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Intrinsic nonlocality of spin- and polarization-resolved probabilities in strong-field quantum electrodynamics

Spin and polarization are central to precision tests of fundamental physics and for interpreting radiation from astrophysical sources and ultraintense laser-matter experiments. Here, focusing on the fundamental process of nonlinear Compton scattering, we demonstrate that a key assumption underlying current strong-field quantum electrodynamics models, i.e., that emission can be treated as an instantaneous random event sampled from a local differential rate, is inconsistent once emission angles, electron spin, and/or photon polarization are resolved. Namely, even in strictly constant and uniform fields , the resulting fully differential distribution is sign indefinite, yielding negative inferred probabilities. The physical reason is that the photon emission probability builds up over a finite length of the electron trajectory, the formation region, during which the electron direction changes by roughly the same small angle that defines the radiation cone. Therefore, we put forward a new method where we integrate over this formation region analytically to obtain a physically consistent electron spin and photon polarization model. We show that the implementation of our model is compatible with existing Monte Carlo and particle-in-cell workflows. Simulations of a GeV-class electron-laser collision accessible at current petawatt facilities and of emission in a pulsarlike magnetic field are shown to reveal spin and polarization patterns that differ even qualitatively from state-of-the-art local models. In particular, our new model predicts substantial angle-dependent circular photon polarization where the well-known collinear-emission approach yields none, and a pronounced helicity bias in the recoiling electrons absent from current predictions. These findings have direct implications for upcoming strong-field QED experiments and for interpreting polarized radiation from extreme astrophysical environments.

astrophysical electromagnetic fields↗
DOE OSTI2025

An integrated integral projection model ( IPM 2 ) to disentangle size‐structured harvest and natural mortality

Abstract Body size is one of the most important traits governing individual‐level demographic rates and modulating population‐level processes. Multiple size‐dependent demographic rates can simultaneously change population structure, so distinguishing their individual contributions to overall population dynamics remains a challenge. Disentangling size‐dependent harvest rates from other demographic rates is critical for assessing the impact of removal on populations of invasive species. Inference about invasive populations can be difficult, however, as observations are often collected opportunistically as part of removal programs, rather than experimentally designed. Yet accurate inference is essential for understanding the feasibility of population suppression and optimising management decisions. We develop an integrated integral projection model (IPM 2 ) that leverages the strengths of the integrated population model and integral projection model to enable inference about complex, size‐structured demographic rates from imperfect observations. We apply the IPM 2 in the context of invasive European green crab ( Carcinus maenas ), a species for which individual body size strongly regulates both the observation‐generating process and latent, population dynamics. The IPM 2 facilitates the distinct estimation of green crab size‐structured harvest and natural mortality rates, parameters for which no explicit data is collected and that are unidentifiable in component datasets of the integrated population model. The model represents how the green crab population changes over time, providing the first estimates of size‐structured abundance of this high‐priority species. By forecasting the stable size distribution and equilibrium population size under varying removal efforts, we demonstrate that extremely high levels of removal effort can reduce the equilibrium green crab population size. Yet these high mortality rates also shift the stable size distribution and increase the equilibrium abundance of smaller crabs, since size‐selective removal alters intraspecific interactions. The ecological outcome of this shift in size structure will be variable, as green crab size modulates only some of its interactions with other species. These results highlight the value of the IPM 2 framework for inferring complex population dynamics with information needs that outpace information in individual observational datasets, providing a path forward for accurate assessment of conservation programs.

Keller, Abigail G. [Department of Environment Scie↗
DOE OSTI2022

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their economic viability and environmental reduction potential compared to already-deployed technologies. These comparisons can be misleading, as emerging technologies typically experience a drastic increase in performance and decrease in cost and greenhouse gas emissions as they develop from research to mass-market deployment due to various forms of learning. These changes complicate the interpretation of early techno-economic assessments (TEAs) and life cycle assessments (LCAs) of emerging CCU technologies. The effects of learning over time or cumulative production themselves can be quantitatively described using technology learning curves (TLCs). While learning curve approaches have been developed for various technologies, a harmonized methodology for using TLCs in TEA and LCA for CCU in particular is required. To address this, we describe a methodology that incorporates TLCs into TEA and LCA to forecast the environmental and economic performance of emerging CCU technologies. This methodology is based on both an evaluation of the state of the art of learning curve assessment and a literature review of TLC approaches developed in various manufacturing and energy generation sectors. Additionally, we demonstrate how to implement this methodology using a case study on a CO 2 mineralization pathway. Finally, commentary is provided on how researchers, technology developers, and LCA and TEA practitioners can advance the use of TLCs to allow for consistent, high-resolution modeling of technological learning for CCU going forward and enable holistic assessments and fairer comparisons with other climate technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

The Upgrade of the SNS Extraction Kicker Power Supplies for the Proton Power Upgrade

The Proton Power Upgrade (PPU) project at the Spallation Neutron Source (SNS) aims to enhance the linear accelerator's power output from 1.4 to 2.8 megawatts. A critical focus lies on upgrading the Extraction Kicker Power Supplies to handle the amplified beam power. The existing capacitor charging power supplies lacked the capability to charge the Blumlein-type Pulse Forming Network (PFN) to the required 40 kV at a repetition rate of 60 Hz. To address this limitation, an initial solution involving the use of a resonant charging circuit was proposed. However, a challenge arose with a self-firing issue in the thyristor switch, which proved unsolvable within the given timeframe. Facing a critical decision point in the project, an alternative approach was explored: integrating a supplementary power supply to enhance the overall power capacity. This solution was tested and ultimately chosen as the path forward. To accommodate the additional power supply, the power distribution system had to undergo upgrades. Throughout this endeavor, a comprehensive examination of the system took place, with meticulous identification of key components. This paper delves into the technical challenges encountered during the process and the corresponding solutions that were implemented.

Tan, Yugang [ORNL] (ORCID:0000000155968252)↗
DOE OSTI2023

Microbial Models for Simulating Soil Carbon Dynamics: A Review

Abstract Soils store the largest amount of carbon (C) in the biosphere, and the C pool in soil is critical to the global C balance. Numerous microbial models have been developed over the last few decades to represent microbial processes that regulate the responses of soil organic carbon (SOC) to climate change. However, the representation of microbial processes varies, and how microbial processes are incorporated into SOC models has not been well explored. Here, we reviewed 71 microbial models to characterize the microbial processes incorporated into SOC models and analyzed variations in mechanistic complexity. We revealed that (a) four processes (microbial‐mediated decomposition, mineral interaction, microbial necromass recycling, and active and dormant microbial dynamics) are commonly incorporated in microbial models, (b) ∼48% of models simulate only one microbial process (i.e., microbial‐mediated decomposition) and 35% of models simulate two microbial processes: for example, microbial‐mediated decomposition and mineral interaction, (c) more than 80% microbial models use nonlinear equations, such as forward Michaelis‐Menten kinetics, to represent SOC decomposition, (d) the concept of persistence of SOC due to its intrinsic properties has been replaced by organo‐mineral interaction (∼39% of microbial models) that protects SOC from decomposition, and (e) various temperature and moisture modifiers and pH effects have been used to explain the environmental effect on microbial processes. In the future, to realistically incorporate microbial processes into Earth System Models, it is imperative to identify experimental evidence on rate limitation processes and firmly ground model structure on the field and laboratory data.

Environmental Sciences & Ecology↗
DOE OSTI2024

Modest set of simulations of local-type primordial non-Gaussianity in the DESI era

A measurement of a primordial non-Gaussianity (PNG) signal through late- or early-Universe probes has the potential to transform our understanding of the physics of the primordial Universe. While large-scale structure observables in principle contain vital information, interpreting these measurements is challenging due to poorly understood astrophysical effects. Luckily, N-body simulations, such as the abacuspng set presented in this study, consisting of 9 boxes, each of size L box = 2 Gpc/h and particle mass of 1.01 × 10 10 M ⊙ /h, provide a viable path forward. As validation, we find good agreement between the simulations and our expectations from one-loop perturbation theory (PT) and the "separate universe" method for the matter bispectrum, matter power spectrum and the halo bias parameter associated with PNG, b φ . As a science application, here we investigate the link between halo assembly bias and b φ for halo properties known to play a vital role in accurately predicting galaxy clustering: concentration, shear (environment), and accretion rate. We find a strong response for all three parameters, suggesting that the connection between b φ and the assembly history of halos needs to be taken into account by future PNG analyses. We further perform the first study of the b φ parameter from fits to early DESI data of the luminous red galaxy (LRG) and quasistellar object (QSO) samples and comment on the effect on f NL constraints for the allowed galaxy-halo models (note that σ[f NL ]∝ $\frac{σ[b_φ]}{b_φ}$). We find that the error on f NL is 15, 8, 7 for the LRGs at z = 0.5 and z = 0.8 and QSOs at z = 1.4, respectively, suggesting that a thorough understanding of galaxy assembly bias is warranted so as to perform robust high-precision analysis of local-type PNG with future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

An Observational Comparison of Level of Neutral Buoyancy and Level of Maximum Detrainment in Tropical Deep Convective Clouds

Tropical deep convective clouds are important drivers of large-scale atmospheric circulation representing the main vertical transport pathway through the depth of the troposphere for heat, momentum, water, and chemical species. The strength and depth of this transport are impacted by the convective updraft size and intensity that are driven by buoyancy, dynamical forcing, and mixing of environmental air, i.e., entrainment. In this paper, we identify tropical deep convective systems with well-defined forward anvils using Atmospheric Radiation Measurement (ARM) ground-based profiling radars, at three ARM fixed-sites in the Tropical Western Pacific (TWP; i.e., Manus, Nauru, Darwin) and three ARM Mobile Facility deployments in Niamey, Niger; Gan Island, Maldives; and Manacapuru, Brazil. We use the difference between the level of neutral buoyancy (LNB) and the level of maximum detrainment (LMD) as a proxy for the effective bulk convective entrainment (εproxy). The LNB, the theoretical height that a parcel raised above the level of free convection would reach with no mixing, is calculated based on pre-convection radiosonde measurements using parcel theory. The LMD is the height of the maximum reflectivity observed in forward anvil clouds by profiling radars. Deep convective systems over the TWP show higher LNBs that extend to 16.3 km on average and larger εproxy (median LNB minus LMD up to 6.5 km) compared to their continental counterparts in the Amazon and West Africa. Oceanic conditions show larger convective available potential energy (CAPE) coupled with higher moisture at low levels which favors larger εproxy. In contrast, continental cases initiate and develop under high convective inhibition, steeper environmental lapse rate, and high wind shear conditions, which show smaller offset between LNB and LMD. Deep convective cases that promote significant cold pools at the surface experience less εproxy. Using a Random Forest regression algorithm, CAPE is associated with the highest feature importance score for predicting convective εproxy, followed by low-level relative humidity. For continental cases, the low-level wind shear also indicates higher importance.

54 ENVIRONMENTAL SCIENCES↗