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DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Modeling and Simulation of Advanced Manufacturing Techniques using MOOSE and MALAMUTE

Advanced manufacturing techniques offer increased geometry complexity, energy and material usage efficiency improvements, and an expanded palette of materials as compared to conventional manufacturing approaches. Advanced-manufacturing-produced parts can experience wide variations in the final microstructure, and these microstructure variations significantly impact the parts’ performance. In this chapter, we present recent code developments within Multiphysics Object-Oriented Simulation Environment (MOOSE) and in the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE). Here we demonstrate applying these modeling and simulation codes to two advanced manufacturing process types: advanced sintering techniques and laser-based additive manufacturing techniques. The multiphysics and multiscale capabilities of these codes enable prediction of the microstructure evolution resulting from variations in the Advanced manufacturing process parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2011

New Developments in MadGraph/MadEvent

We here present some recent developments of MadGraph/MadEvent since the latest published version, 4.0. These developments include: Jet matching with Pythia parton showers for both Standard Model and Beyond the Standard Model processes, decay chain functionality, decay width calculation and decay simulation, process generation for the Grid, a package for calculation of quarkonium amplitudes, calculation of Matrix Element weights for experimental events, automatic dipole subtraction for next-to-leading order calculations, and an interface to FeynRules, a package for automatic calculation of Feynman rules and model files from the Lagrangian of any New Physics model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

Light Water Reactor Sustainability Program: Survey of Models for Concrete Degradation

Concrete is widely used in the construction of nuclear facilities because of its structural strength and its ability to shield radiation. The use of concrete in nuclear facilities for containment and shielding of radiation and radioactive materials has made its performance crucial for the safe operation of the facility. As such, when life extension is considered for nuclear power plants, it is critical to have predictive tools to address concerns related to aging processes of concrete structures and the capacity of structures subjected to age-related degradation. The goal of this report is to review and document the main aging mechanisms of concern for concrete structures in nuclear power plants (NPPs) and the models used in simulations of concrete aging and structural response of degraded concrete structures. This is in preparation for future work to develop and apply models for aging processes and response of aged NPP concrete structures in the Grizzly code. To that end, this report also provides recommendations for developing more robust predictive models for aging effects of performance of concrete.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

An overview of process systems engineering approaches for process intensification: State of the art

Process intensification offers the potential to drastically reduce the energy consumption and cost of producing chemicals from both bulk and distributed feedstocks. This review article aims to offer an extensive survey on state-of-the-art process systems engineering (PSE) approaches for process intensification. From both academic and industrial perspectives, this paper provides an overview of the development of various process intensification technologies, specifically those under the categories of separation, reaction, hybrid reaction/separation, and alternative energy sources. A current status analysis in the areas of modeling and simulation is then provided. An indicative list of PSE publications specialized on process intensification is presented to illustrate the progresses made so far towards the deployment of novel process intensification technologies. We also highlight some recent advances for the modeling, design, and synthesis of intensified systems, as well as for the assessment of their controllability/operability/safety performance. As a result, key open questions in these areas include: (i) how to systematically derive intensified designs, and (ii) how to ensure the operability and optimality of the derived intensified structures while delivering their expected functionality.

42 ENGINEERING↗

DOE OSTI2017

Analysis of Radiation Transport Due to Activated Coolant in the ITER Neutral Beam Injection Cell

Detailed spatial distributions of the biological dose rate due to a variety of sources are required for the design of the ITER tokamak facility to ensure that all radiological zoning limits are met. During operation, water in the Integrated loop of Blanket, Edge-localized mode and vertical stabilization coils, and Divertor (IBED) cooling system will be activated by plasma neutrons and will flow out of the bioshield through a complex system of pipes and heat exchangers. This paper discusses the methods used to characterize the biological dose rate outside the tokamak complex due to 16 N gamma radiation emitted by the activated coolant in the Neutral Beam Injection (NBI) cell of the tokamak building. Activated coolant will enter the NBI cell through the IBED Primary Heat Transfer System (PHTS), and the NBI PHTS will also become activated due to radiation streaming through the NBI system. To properly characterize these gamma sources, the production of 16 N, the decay of 16 N, and the flow of activated water through the coolant loops were modeled. The impact of conservative approximations on the solution was also examined. Once the source due to activated coolant was calculated, the resulting biological dose rate outside the north wall of the NBI cell was determined through the use of sophisticated variance reduction techniques. The AutomateD VAriaNce reducTion Generator (ADVANTG) software implements methods developed specifically to provide highly effective variance reduction for complex radiation transport simulations such as those encountered with ITER. Using ADVANTG with the Monte Carlo N-particle (MCNP) radiation transport code, radiation responses were calculated on a fine spatial mesh with a high degree of statistical accuracy. In conclusion, advanced visualization tools were also developed and used to determine pipe cell connectivity, to facilitate model checking, and to post-process the transport simulation results.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Toward connecting core-collapse supernova theory with observations. I. Shock revival in a 15 M {sub ☉} blue supergiant progenitor with SN 1987A energetics

We study the evolution of the collapsing core of a 15 M {sub ☉} blue supergiant supernova progenitor from the core bounce until 1.5 s later. We present a sample of hydrodynamic models parameterized to match the explosion energetics of SN 1987A. We find the spatial model dimensionality to be an important contributing factor in the explosion process. Compared to two-dimensional (2D) simulations, our three-dimensional (3D) models require lower neutrino luminosities to produce equally energetic explosions. We estimate that the convective engine in our models is 4% more efficient in 3D than in 2D. We propose that the greater efficiency of the convective engine found in 3D simulations might be due to the larger surface-to-volume ratio of convective plumes, which aids in distributing energy deposited by neutrinos. We do not find evidence of the standing accretion shock instability or turbulence being a key factor in powering the explosion in our models. Instead, the analysis of the energy transport in the post-shock region reveals characteristics of penetrative convection. The explosion energy decreases dramatically once the resolution is inadequate to capture the morphology of convection on large scales. This shows that the role of dimensionality is secondary to correctly accounting for the basic physics of the explosion. We also analyze information provided by particle tracers embedded in the flow and find that the unbound material has relatively long residency times in 2D models, while in 3D a significant fraction of the explosion energy is carried by particles with relatively short residency times.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Calculation of collisionless pitch-angle scattering of runaway electrons with synchrotron radiation via high-order guiding-centre equation

Recently, the collisionless pitch-angle scattering for relativistic runaway electrons (REs) in toroidal geometries such as tokamaks was discovered through a full orbit simulation approach (Liu et al., Nucl. Fusion, vol. 56, 2016, p. 064002), and it was then theoretically investigated that a new expression for the magnetic moment, including the second-order corrections, could essentially reproduce the so-called collisionless pitch-angle scattering process (Liu et al., Nucl. Fusion, vol. 58, 2018, p. 106018). In this paper, with synchrotron radiation, extensive numerical verification of the validity of the high-order guiding-centre theory is given for simulations involving REs by incorporating such an expression for the magnetic moment into our particle tracing code. A high-order guiding-centre simulation approach with synchrotron radiation (HGSA) is applied. Synchrotron radiation plays an essential role in the life cycle of REs. The energy of REs first increases and then becomes saturated until the electric field acceleration is balanced by the radiation dissipation. Unfortunately, the process cannot be simulated accurately with the standard guiding-centre model, i.e. the first-order guiding-centre model. Remarkably, it is found that the HGSA can effectively produce the fundamental process of REs. Since the time scale of the energy saturation of REs is close to seconds, the computational cost becomes significant. In order to save costs, it is necessary to estimate the time of energy saturation. An analytical estimate is derived for the time it takes for synchrotron drag to balance an accelerating electric field and the provided formula has been numerically verified. Finally, test calculations reveal that HGSA is favourable for exploiting the dynamics of REs in tokamak plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Hybrid-PIC modeling of laser-plasma interactions and hot electron generation in gold hohlraum walls

The walls of the hohlraum used in experiments at the national ignition facility are heated by laser beams with intensities ∼1015 W/cm2, a wavelength of ∼1/3 μm, and pulse lengths on the order of a ns, with collisional absorption believed to be the primary heating mechanism. X-rays generated by the hot ablated plasma at the gold walls are then used to implode a target in the hohlraum interior. In addition to the collisional absorption of laser energy at the walls, non-linear laser-plasma interactions (LPI), such as stimulated Raman scattering and two plasmon decay, are believed to generate a population of supra-thermal electrons which, if present in the hohlraum, can have a deleterious effect on target implosion. We describe results of hohlraum modeling using a hybrid particle-in-cell code. To enable this work, new particle-based algorithms for a multiple-ion magneto-hydrodynamic (MHD) treatment, and a particle-based ray-tracing model were developed. The use of such hybrid methods relaxes the requirement to resolve the laser wavelength, and allows for relatively large-scale hohlraum simulations with a reasonable number of cells. But the non-linear effects which are believed to be the cause of hot electron generation can only be captured by fully kinetic simulations with good resolution of the laser wavelength. For this reason, we employ a two-tiered approach to hohlraum modeling. Large-scale simulations of the collisional absorption process can be conducted using the fast quasi-neutral MHD algorithm with fluid particle species. From these simulations, we can observe the time evolution of the hohlraum walls and characterize the density and temperature profiles. From these results, we can transition to smaller-scale highly resolved simulations using traditional kinetic particle-in-cell methods, from which we can fully model all of the non-linear laser-plasma interactions, as well as assess the details of the electron distribution function. We find that vacuum hohlraums should be stable to both two plasmon decay and stimulated Raman scattering instabilities for intensities ≤1015 W/cm2. In gas-filled hohlraums, shocks may be induced in the blowoff gold plasma, which leads to more complex density and temperatures profiles. The resulting effect on LPI stability depends strongly on the details of the profile, and it is possible for the gas-filled hohlraum to become unstable to two plasmon decay at 1015 W/cm2 if the quarter-critical surface reaches temperatures exceeding 1 keV.

Thoma, C.↗

DOE OSTI2021

Crystal plasticity model of residual stress in additive manufacturing using the element elimination and reactivation method

Selective laser melting is receiving increasing interest as an additive manufacturing technique. Residual stresses induced by the large temperature gradients and inhomogeneous cooling process can favour the generation of cracks. In this work, a crystal plasticity finite element model is developed to simulate the formation of residual stresses and to understand the correlation between plastic deformation, grain orientation and residual stresses in the additive manufacturing process. The temperature profile and grain structure from thermal-fluid flow and grain growth simulations are implemented into the crystal plasticity model. An element elimination and reactivation method is proposed to model the melting and solidification and to reinitialize state variables, such as the plastic deformation, in the reactivated elements. The accuracy of this method is judged against previous method based on the stiffness degradation of liquid regions by comparing the plastic deformation as a function of time induced by thermal stresses. The method is used to investigate residual stresses parallel and perpendicular to the laser scan direction, and the correlation with the maximum Schmid factor of the grains along those directions. The magnitude of the residual stress can be predicted as a function of the depth, grain orientation and position with respect to the molten pool. The simulation results are directly comparable to X-ray diffraction experiments and stress–strain curves.

316 stainless steel↗

DOE OSTI2015

Low-energy electro- and photo-emission spectroscopy of GaN materials and devices

In hot-electron semiconductor devices, carrier transport extends over a wide range of conduction states, which often includes multiple satellite valleys. Electrical measurements can hardly give access to the transport processes over such a wide range without resorting to models and simulations. An alternative experimental approach however exists which is based on low-energy electron spectroscopy and provides, in a number of cases, very direct and selective information on hot-electron transport mechanisms. Recent results obtained in GaN crystals and devices by electron emission spectroscopy are discussed. Using near-band-gap photoemission, the energy position of the first satellite valley in wurtzite GaN is directly determined. By electro-emission spectroscopy, we show that the measurement of the electron spectrum emitted from a GaN p-n junction and InGaN/GaN light-emitting diodes (LEDs) under electrical injection of carriers provides a direct observation of transport processes in these devices. In particular, at high injected current density, high-energy features appear in the electro-emission spectrum of the LEDs showing that Auger electrons are being generated in the active region. These measurements allow us identifying the microscopic mechanism responsible for droop which represents a major hurdle for widespread adoption of solid-state lighting.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

A Collaborative Extensible User Environment for Simulation and Knowledge Management

In scientific simulation, scientists use measured data to create numerical models, execute simulations and analyze results from advanced simulators executing on high performance computing platforms. This process usually requires a team of scientists collaborating on data collection, model creation and analysis, and on authorship of publications and data. This paper shows that scientific teams can benefit from a user environment called Akuna that permits subsurface scientists in disparate locations to collaborate on numerical modeling and analysis projects. The Akuna user environment is built on the Velo framework that provides both a rich client environment for conducting and analyzing simulations and a Web environment for data sharing and annotation. Akuna is an extensible toolset that integrates with Velo, and is designed to support any type of simulator. This is achieved through data-driven user interface generation, use of a customizable knowledge management platform, and an extensible framework for simulation execution, monitoring and analysis. This paper describes how the customized Velo content management system and the Akuna toolset are used to integrate and enhance an effective collaborative research and application environment. The extensible architecture of Akuna is also described and demonstrates its usage for creation and execution of a 3D subsurface simulation.

collaborative science tools↗

DOE OSTI2023

Simulating the Excited-State Dynamics of Polaritons with Ab Initio Multiple Spawning

Over the past decade, there has been a growth of interest in polaritonic chemistry, where the formation of hybrid light–matter states (polaritons) can alter the course of photochemical reactions. These hybrid states are created by strong coupling between molecules and photons in resonant optical cavities and can even occur in the absence of light when the molecule is strongly coupled with the electromagnetic fluctuations of the vacuum field. Here, we present a first-principles model to simulate nonadiabatic dynamics of such polaritonic states inside optical cavities by leveraging graphical processing units (GPUs). Our first implementation of this model is specialized for a single molecule coupled to a single-photon mode confined inside the optical cavity but with any number of excited states computed using complete active space configuration interaction (CASCI) and a Jaynes–Cummings-type Hamiltonian. Using this model, we have simulated the excited-state dynamics of a single salicylideneaniline (SA) molecule strongly coupled to a cavity photon with the ab initio multiple spawning (AIMS) method. We demonstrate how the branching ratios of the photodeactivation pathways for this molecule can be manipulated by coupling to the cavity. We also show how one can stop the photoreaction from happening inside of an optical cavity. Finally, we also investigate cavity-based control of the ordering of two excited states (one optically bright and the other optically dark) inside a cavity for a set of molecules, where the dark and bright states are close in energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser’s Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. Finally, the present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Joint Geophysical and Numerical Insights of the Coupled Thermal‐Hydro‐Mechanical Processes During Heating in Salt

Abstract Salt offers an optimal medium for the permanent isolation of heat‐producing radioactive waste due to its impermeability, high thermal conductivity, and ability to close fractures through creep. A thorough understanding of the thermal‐hydrological‐mechanical (THM) processes, encompassing brine migration, is fundamental for secure radioactive waste disposal within salt formations. At the Waste Isolation Pilot Plant (WIPP), we conducted joint in situ geophysical monitoring experiments during active heating to investigate brine migration near excavations. This experiment incorporated electrical resistivity tomography (ERT) alongside high‐resolution fiber‐optic‐based distributed temperature sensing within a controlled heating experiment. Additionally, discrete element model (DEM) based numerical simulations were conducted to simulate THM processes during heating, providing a more mechanistic understanding of the coupled processes leading to the observed changes in geophysical measurements. During heating, resistivity shifts near the heater were reasonably explained by temperature effects. However, in more distant, cooler regions, the resistivity decrease exceeded predictions based solely on temperature. DEM simulations highlighted brine migration, propelled by pore pressure gradients, as the likely primary factor contributing to the additional resistivity decline beyond temperature effects. The comparison between the predicted ERT responses and observations was much improved when considering the effects of brine migration based on the DEM simulations. These geophysical and simulation findings shed light on brine migration in response to salt heating, enhancing our understanding of the coupled THM processes in salt for safe radioactive waste disposal.

58 GEOSCIENCES↗

DOE OSTI2013

Optimization of energy window for {sup 90}Y bremsstrahlung SPECT imaging for detection tasks using the ideal observer with model-mismatch

Purpose: In yttrium-90 ({sup 90}Y) microsphere brachytherapy (radioembolization) of unresectable liver cancer, posttherapy {sup 90}Y bremsstrahlung single photon emission computed tomography (SPECT) has been used to document the distribution of microspheres in the patient and to help predict potential side effects. The energy window used during projection acquisition can have a significant effect on image quality. Thus, using an optimal energy window is desirable. However, there has been great variability in the choice of energy window due to the continuous and broad energy distribution of {sup 90}Y bremsstrahlung photons. The area under the receiver operating characteristic curve (AUC) for the ideal observer (IO) is a widely used figure of merit (FOM) for optimizing the imaging system for detection tasks. The IO implicitly assumes a perfect model of the image formation process. However, for {sup 90}Y bremsstrahlung SPECT there can be substantial model-mismatch (i.e., difference between the actual image formation process and the model of it assumed in reconstruction), and the amount of the model-mismatch depends on the energy window. It is thus important to account for the degradation of the observer performance due to model-mismatch in the optimization of the energy window. The purpose of this paper is to optimize the energy window for {sup 90}Y bremsstrahlung SPECT for a detection task while taking into account the effects of the model-mismatch. Methods: An observer, termed the ideal observer with model-mismatch (IO-MM), has been proposed previously to account for the effects of the model-mismatch on IO performance. In this work, the AUC for the IO-MM was used as the FOM for the optimization. To provide a clinically realistic object model and imaging simulation, the authors used a background-known-statistically and signal-known-statistically task. The background was modeled as multiple compartments in the liver with activity parameters independently following a Gaussian distribution; the signal was modeled as a tumor with a Gaussian-distributed activity parameter located randomly with equal probability at one of three positions. The IO test statistics (i.e., likelihood ratios) were estimated using Markov-chain Monte Carlo methods. The authors realistically modeled human anatomy using a digital phantom code, and realistically simulated {sup 90}Y bremsstrahlung SPECT imaging with a clinical SPECT system and typical imaging parameters using a previously validated Monte Carlo bremsstrahlung simulation method. Model-mismatch was included by modeling image formation process in the calculation of IO test statistics using an analytic modeling method previously developed for quantitative {sup 90}Y bremsstrahlung SPECT. To demonstrate the effects of the model-mismatch on the detection task, the authors optimized the energy window both with and without model-mismatch included in the IO. Results: For all the energy windows, the AUC values for the IO-MM were smaller than that for the IO. The optimal windows for the IO-MM and the IO were 80-180 and 60-400 keV, respectively. Conclusions: The authors have demonstrated the degradation of the ideal performance due to model-mismatch and optimized the energy window for {sup 90}Y bremsstrahlung SPECT for detection tasks by accounting for the effects of the model-mismatch. The obtained optimal window was much narrower when taking into account the model-mismatch and similar to that obtained previously for estimation tasks.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Sea Ice and Cloud Processes Mediating Compensation between Atmospheric and Oceanic Meridional Heat Transports across the CMIP6 Preindustrial Control Experiment

Abstract Bjerknes compensation (BJC) refers to the anticorrelation observed between atmospheric and oceanic heat transport (AHT/OHT) variability, particularly on decadal to longer time scales that may be important to the predictability of the climate system. This study investigates the spread in BJC across fully coupled simulations of phase 6 of the Coupled Model Intercomparison Project (CMIP6) and critical processes (particularly related to sea ice and clouds) that may contribute to that spread. BJC on decadal to longer time scales is confirmed across all the simulations evaluated, and it is strongest in the Northern Hemisphere (NH) between 60° and 70°N. At these latitudes, BJC appears to be primarily driven by the exchange of turbulent fluxes (sensible and latent) in the Greenland, Iceland, and Barents Seas. Metrics to break down how sea ice and clouds uniquely modify the radiative balance of the polar atmosphere during anomalous OHT events are presented. These metrics quantify the impacts of sea ice and clouds on surface and top of atmosphere (latent, sensible, longwave, and shortwave radiative) energy fluxes. Cloud responses tend to counter the clear sky impacts over the Marginal Ice Zone (MIZ). It is further shown that the degree of BJC present in a simulation at high latitudes is heavily influenced by the sensitivity of the sea ice to OHT, which is most influential over the MIZ. These results are qualitatively robust across models and explain the intermodel spread in NH BJC in the preindustrial control experiment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Tracking Aerosol Convection Interactions Experiment (TRACER) Field Campaign Report

Convective clouds serve a critical role in the Earth’s energy and water cycles through their transport of heat, moisture, momentum, and chemical species through the troposphere driving the global circulation (e.g., Hartmann et al. 1984, Del Genio et al. 2012, Su et al. 2014). On more local scales, convective clouds impact the atmospheric heating profile through diabatic heating effects, removal of water from the atmospheric column through precipitation, and conditioning of the local environment impacting further development of clouds (e.g., Sullivan and Voigt 2021). These critical roles underscore the importance of realistic representation of convective processes across scales of models from large-eddy simulation (LES), to convection-permitting models (CPM; e.g., Kendon et al. 2020, Marinescu et al. 2021), to numerical weather prediction (NWP) models used for operational weather forecasting, to Earth system models used to predict climate sensitivity (Sanderson et al. 2011, Sherwood et al. 2014, Tomassini et al. 2014, Zhao et al. 2016, Cronin et al. 2017). A key component of improving model representation of convective clouds is better quantification and parameterization of updraft microphysics and dynamics, including their interactions with the surrounding environment and storm organization (Bony et al. 2015, Hagos and Houze 2016, Donner et al. 2016, Morrison et al. 2020). Aerosol is an important environmental factor that could affect convective clouds and precipitation since cloud droplet and ice formation processes are initiated by it. Andrae et al. (2004) hypothesized that aerosols associated with increased biomass burning particles acting as cloud condensation nuclei (CCN) result in smaller and more monodisperse cloud droplets leading to suppression of warm rain formation, ultimately leading to more cloud water being lofted above the freezing level based on observations in the Amazon region. The subsequent increase in latent heat release increases the buoyancy of rising convective parcels invigorating the deep convection. This work was followed by a description of the theoretical basis for this “cold-phase invigoration” by Rosenfeld et al. (2008), who argued that it could have a significant effect for deep convective clouds with warm cloud-bases. Several modeling studies (e.g., Khain et al. 2005, 2009, van den Heever et al. 2006, Fan et al. 2007, 2009, 2012, Lee et al. 2008, Storer et al. 2010, Lebo et al. 2012, Storer and van den Heever 2013, Chen et al. 2020, Dagan et al. 2022) have investigated these aerosol-convection interactions and the environmental factors that influence their relative importance and magnitude. More recently, several studies have indicated that “warm-phase invigoration”, the enhancement of convection through condensational heating, also appears to play a role in enhancing both shallow cumuli (Seiki and Nakajima 2014, Saleeby et al 2015) and deeper tropical convection (Lebo and Seinfeld 2011, Khain et al. 2012, Sheffield et al 2015, Fan et al. 2018, Igel and van den Heever 2021), as well as Houston thunderstorms (Fan et al. 2007, 2020). However, still other studies have provided additional evidence of systematic biases in simulated convective outflow ice size distribution properties, which are consistent with a lack of poorly understood secondary ice production within convective updrafts (e.g., Fridlind et al. 2017). To help address these critical gaps in our understanding of cloud processes, aerosol processes and aerosol-cloud interactions, the Tracking Aerosol Convection Interactions Experiment was designed building upon efforts by the Aerosol, Cloud, Precipitation and Climate (ACPC) Initiative (http://acpcintiative.org/), a joint effort of the International Geosphere-Biosphere Programme (IGBP) and the World Climate Research Program (WCRP) that focused on resolving uncertainties in the interactions between aerosol and clouds towards better understanding the role that these interactions play in the climate system. The TRACER campaign was motivated by recommendations from a number of pilot studies undertaken by ACPC (van den Heever et al. 2017, Fridlind et al. 2019, Hu et al. 2019, Fan et al. 2020, Marinescu et al. 2021, Hernandez-Deckers et al. 2022) that pointed towards the southeastern Texas region as a locale where aerosol-convection interactions could be studied owing to the copious occurrence of isolated convection during the summer months accompanied by diverse and significant sources of aerosols from both anthropogenic and natural sources. The TRACER campaign began on 01 October 2021 and extended through 30 September 2022 with an intensive operational period (IOP) during June-September 2022. Three main sites (Table 1) were managed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility.

54 ENVIRONMENTAL SCIENCES↗