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At least 1,297 records · Page 72

Accounting for hydrological and microbial processes on greenhouse gas budgets from river systems (Final Report)

River systems have a disproportionately large impact on carbon and nitrogen cycling on this planet. For instance, despite their much smaller size in area, it is estimated that global carbon dioxide emission from river systems exceed the emission from lakes and reservoirs, and are on par with the estimated oceanic and land carbon sinks. Closing carbon and nitrogen budgets in river systems requires knowledge of end member greenhouse gas (GHG) products (e.g. carbon dioxide, nitrous oxide, methane), which typically have not been measured in river systems. Moreover, while the production of these gasses is thought to be microbially derived, little is known about microbial activities in sediments of large rivers, despite their potentially high influence on biogeochemical budgets. Typically, when we think of rivers we visualize the channels that carve through the earth, but this only represents a small part of the hydrologic exchange that a river participates in. Rivers are more than conduits; they are dynamically exchanging water and materials with surrounding terrestrial and subsurface environments. In fact, 96% of the carbon dioxide produced within river systems occurs in hyporheic zones, or sediment regions beneath and alongside a river where groundwater and river water mix. In fact, it is widely recognized that microbial processes in hyporheic zones, also known as the river’s liver, strongly influence the fate of carbon, nutrients, and contaminants transported through watersheds. Despite this general knowledge, observational studies linking nitrogen and carbon gas production to microbial activity, and studies of overall gas emissions, are limited across river systems, but especially absent from larger rivers. To identify microbial processes responsible for GHG emissions from large rivers, and to quantify their environmental drivers and dependencies, we selected the Columbia River as a representative field site. This is the largest river west of the North American Continental Divide, and produces a drainage and discharge roughly double of the Nile in Egypt. This river is also an important source for agricultural irrigation and generates more hydroelectric power than any other North American river. Dams manage the release of water in the river, with these changes in river discharge patterns strongly impacting microbial activities in hyporheic zones. Specifically, we target a region down-river from the Priest Rapids Dam (WA) associated with the PNNL SFA, where water discharge from the dam is highly controlled, resulting in 1.5 m fluctuations in river stage daily. This dynamic river stage creates an ideal research opportunity for investigating hydrological perturbations in GHG production, uptake, and emission from large river systems. In this exploratory 1-year project, we conducted data-driven field research to provide observations needed to support predictions about GHG flux from river ecosystems. Using porewater dialysis and surface flux measurements, we observed where and when GHGs were produced and is the emission impacted by river stage. Using meta-omics tools, we determined what microbial processes control production and consumption of GHG in these systems. Finally, we provided key observations to parameterize models that could predict the effect of environmental conditions on GHG emission in the Columbia River near shore environment, at the scale that the modeled processes operate. Closing knowledge gaps in the linkages between river stage, microbial processes, and GHG production and emission, as well as the spatial distribution and ecosystem dependencies of these processes, is necessary to better parameterize land surface models. We provide some of the first, detailed GHG measurements paired microbial activity from large river systems. Our findings have direct relevance to many other large rivers with flows altered by hydroelectric energy, reservoirs, snowmelt, tides, or agriculture.

54 ENVIRONMENTAL SCIENCES↗

CABS Milestone C1.1 Report: Collect constitutive Models for NMC Materials and Report on Use of Mesoscale Data to Project Lead

Mesoscale (100s of particles) electrochemical-thermal-mechanical models and simulations of NMC cathodes are a critical outcome of the CABS project. While the mathematical model formulation for these mesoscale simulations is well established, these simulations also require (1) calibrated parameterization of the mathematical model and (2) mesostructure geometries on which to perform the simulations. In this milestone report, we present a parameterized mathematical model, primarily based on parameter values available in the open literature, that will form the basis of future simulations. We also discuss options for obtaining and using representative mesostructure data in these simulations.

42 ENGINEERING↗

Coupling Mechanistically the Convective Motions and Cloud Macrophysics in a Climate Model (Final Technical Report for CM4 Project)

Global climate models fail to adequately represent shallow cumulus conditions. The activities at the University of Texas at Austin focus on deploying advanced statistical approaches to identify linkages between numerical models and observations using the E3SM-Single Column Model (SCM) and different ARM campaigns focused on shallow cumulus conditions at SGP. The current version of the SCM uses CLUBB to represent shallow cumulus. The parameterization represents a significant evolution from older generation parameterizations insofar as it contains a number of prognostic equations that keep track of the turbulent nature of the environment that are carried forward in time. The parameters associated with this scheme address the rate at which different prognostic quantities that determine vertical turbulent fluxes of heat and moisture decay with time. The large number of parameters gives the CLUBB scheme a lot of flexibility. The expectation of its developers is that it should represent any shallowly convective environment. The concern among developers and other theorists was not whether it could represent any one environment, but whether a single judicious choice of parameters would allow it to predict the turbulent characteristics of a range of environments. Through several exhaustive studies that made use of Bayesian statistical calibration, tens of thousands of SCM experiments, and ensembles of observations of shallow cumulus at SGP that were also modeled using Large Eddy Simulations (LES), we have only achieved partial success in using CLUBB within SCM to capture important aspects of what has been observed and modeled with LES at SGP. Even as we worked to improve observational tests and make a more scientifically focused attempt at modifying subsets of CLUBB parameters, model performance continues to be lacking in some important respects. In one calibration, we were able to achieve a near perfect match to observed properties of liquid water path, cloud radiative effects, and cloud fraction but with clouds that were trapped within a shallow layer and without the highly turbulent fluxes and vertical extent observed with ARM data and modeled with LES (Figure 1). The conclusion we are reaching is that shallow cumulus conditions are much more challenging to represent than was previously thought. This result is also prompting us to consider some aspects of the experiment design that may be limiting the performance of SCM in this case. The work has garnered the attention of Vincent Larson (CLUBB developer at Univ. Wisconsin – Milwaukee) and model developers Steven Klein and Yunyan Zhang (LLNL) with whom this work and will continue.

58 GEOSCIENCES↗

Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Field Campaign Report

General circulation models and downscaled regional models exhibit persistent biases in deep convective initiation location and timing, cloud top height, stratiform area and precipitation fraction, and anvil coverage (e.g., Del Genio 2012, Del Genio et al. 2012, Hohenegger and Stevens 2013, Song et al. 2013). Despite important impacts on the distribution of atmospheric heating, moistening, momentum, and precipitation (e.g., Hartmann et al. 1984, Fritsch et al. 1986, Houze 1989, 2004, Donner et al. 2001, Del Genio and Kovari 2002, Schumacher et al. 2004, Nesbitt et al. 2006, Storelvmo 2012), nearly all climate models fail to represent mesoscale convective organization (Del Genio 2012), while system evolution is not represented at all (Ovchinnikov et al. 2006). Recent advances in cumulus parameterization coupled with increasing model resolution have improved predictions, but even relatively higher-resolution models without parameterized deep convection have some persistent kinematic and microphysical biases (e.g., Blossey et al. 2007, Matsui et al. 2009, Luo et al. 2010, Lang et al. 2011, Varble et al. 2011, Fridlind et al. 2012, Hagos et al. 2014, Varble et al. 2014a-b, Fan et al. 2017, Stanford et al. 2017, Han et al. 2019). To improve representation of convective systems in models requires adequate characterization of their predictability as a function of environmental conditions. Because of the significant sensitivities of deep convective initiation, intensity, lifetime, propagation, and mesoscale convective organization to many factors including multi-scale atmospheric circulations, ambient environmental stability, humidity, wind distributions, and cloud microphysical processes, this characterization relies on comprehensively observing many cases of convective initiation, non-initiation, organization, and non-organization.

54 ENVIRONMENTAL SCIENCES↗

Improving Predictability of Mixed-Phase Clouds and Aerosol Interactions in the Community Earth System Model (CESM) with ARM Measurements

The objective of this project is to improve the simulation and predictability of mixed-phase clouds and aerosol interactions in the Community Earth System Model (CESM) through comparisons with the DOE ARM observations. There are four major goals of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Develop a long-term multi-sensor mixed-phase cloud dataset; (3) Test the performance of improved ice microphysics in CESM-CAM5 with the ARM data; and (4) Examine mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds that include ice nucleation, ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process, feedbacks between cloud microphysics, dynamics and radiation, and provides links to aerosol properties in the Community Earth System Model (CESM)-Community Atmosphere Model version 5 (CAM5); (2) Developed a long-term multi-sensor mixed-phase cloud dataset. The mixed-phase cloud retrieval algorithms are improved by refining the phase determinations and ice number concentration retrievals. A multi-year mixed-phase cloud observation dataset (1999-2004; October 2013-February 2017) is regenerated at the ARM Barrow site with the improved algorithms. The dataset has been used in the global model evaluation to improve the model parameterizations; (3) Tested the performance of improved ice microphysics in CESM-CAM5 with the ARM data. CESM-CAM5 is run in the DOE Cloud-Associated Parameterizations Testbed (CAPT) and in the single-column model (SCM) mode to facilitate comparison with ARM observations. Multi-year CAPT simulations of seasonal variations of cloud microphysical properties, cloud liquid water path (LWP) and ice water path (IWP), cloud longwave and shortwave forcings, cloud occurrence frequency are evaluated against the ARM multi-sensor mixed-phase cloud retrievals; and (4) Examined mixed-phase cloud microphysics-aerosol-dynamics-radiation interactions in CESM-CAM5 that include the impacts of different ice nucleation mechanisms, sensitivity to dust IN concentration, treatment of the WBF process on mixed-phase cloud properties. Aerosol effect on mixed-phase clouds through the liquid phase (droplet activation) and ice phase processes (e.g., the glaciation indirect effect) is investigated.

54 ENVIRONMENTAL SCIENCES↗

Evaporation Sub-model Development for Volume of Fluid (eVOF) Method Applicable to Spray-Wall Interaction Including Film Characteristics with Validation at High Pressure and Temperature Conditions (Final Report)

Internal combustion engines have seen a great evolution over the last several decades through application of high pressure direct injection, multiple injections, and other technologies to reduced fuel consumption, NOx, and PM. Although combustion systems with advanced injection strategies have been studied extensively, there exists a significant fundamental knowledge gap on the fuel-spray interactions with the piston surface and chamber walls. Advanced computational codes validated with experimental techniques have to be developed for accurate representation of the drop impingement, fuel film formation, and vaporization. Current engine CFD spray models utilize a Lagrangian framework for modeling which lacks critical considerations of the physics pertaining to these interactions and thus requiring extensive parameterization, tuning and validation. The team from Michigan Technological University, University of Massachusetts Dartmouth, and Argonne National Laboratory is composed of experts in sprays, combustion, engines and CFD with a wide spectrum of knowledge including specific expertise in the area under consideration. In the proposed work, a VOF modeling approach has been adopted for the spray-wall interaction, film formation and spreading, and vaporization. With the inclusion of a vaporization submodel, a more predictive and accurate simulation of the spray-film was performed without extensive need of parameterization and tuning. Extensive experimentation of the spray-wall interaction under the range of conditions matching the thermodynamic and surface temperatures that occur in diesel and gasoline engines were conducted to validate the SWI submodels and for development of the evaporation sub-model, which has been implemented in the Converge software.

42 ENGINEERING↗

Evaporation Submodel Development for Volume of Fluid (eVOF) Method Applicable to Spray-Wall Interaction Including Film Characteristics with Validation at High Pressure and Temperature Conditions

Internal combustion engines have seen a great evolution over the last several decades through application of high pressure direct injection, multiple injections, and other technologies to reduced fuel consumption, NOx, and PM. Although combustion systems with advanced injection strategies have been studied extensively, there exists a significant fundamental knowledge gap on the fuel-spray interactions with the piston surface and chamber walls. Advanced computational codes validated with experimental techniques have to be developed for accurate representation of the drop impingement, fuel film formation, and vaporization. Current engine CFD (Computational Fluid Dynamics) spray models utilize a Lagrangian framework for modeling which lacks critical considerations of the physics pertaining to these interactions and thus requiring extensive parameterization, tuning and validation. The team from Michigan Technological University, University of Massachusetts Dartmouth, and Argonne National Laboratory is composed of experts in sprays, combustion, engines and CFD with a wide spectrum of knowledge including specific expertise in the area under consideration. In the proposed work, a VOF (Volume of Fluid) modeling approach has been adopted for the spray-wall interaction, film formation and spreading, and vaporization. With the inclusion of a vaporization submodel, a more predictive and accurate simulation of the spray-film was performed without extensive need of parameterization and tuning. Extensive experimentation of the spray-wall interaction under the range of conditions matching the thermodynamic and surface temperatures that occur in diesel and gasoline engines were conducted to validate the SWI submodels and for development of the evaporation submodel, which has been implemented in the flow solver.

42 ENGINEERING↗

Joint Bayesian Inference for Near-surface Explosion Yield

Geophysical observations of an explosion scale with the yield of the explosion. Guided by the principal of yield-scaling and observed relationships in the data, we defined and parameterized seismoacoustic forward models in Ford et al. [2014]. This work extends the model with additional data features of maximum velocity and positive phase duration for the seismic and acoustic data types, respectively, and a new optic data type. Parameters for the seismoacoustic models are estimated via Bayesian inference. The parameterized models can then be used to infer the yield of a new explosion.

58 GEOSCIENCES↗

PBX9502 PAGOSA/SURF Calibration for 100 µm Grid Size

There are two methods to calibrate the SURF reactive burn explosives model.The first is to calibrate using Pop plot data. Because there are only two main parameters in SURF, called A and B, there is just enough flexibility in SURF to determine the Pop plot line, i.e., equivalent information to a slope and a single point on the line. However, due to the linearity of the Pop plot being based on logarithmic scales, a slight deviation off the Pop plot (though the fit to the Pop plot might be very good overall) can make considerable difference when comparing with the velocity profile data. The second calibration method thusly focuses strongly on matching entire velocity profile data. This is to say that individual velocity profiles extracted from embedded gages (in the case of PBX 9502, we refer to R. Gustavsen’s experimental data) may fit poorly, while the overall fit to the Pop plot may seem reasonable. In addition, there are other factors that could produce slightly different calibrations for SURF, while leading to an overall acceptable fit to data. One of those may be the physics code used and here we use Pagosa. Another is mesh size dependence. A third one stems from fitting a reactive burn model being done in conjunction with an equation of state (EOS) of the reactants and one for the products – different EOS can result in slightly different parameterizations. Finally, there are several secondary material parameters in SURF, and the overall fit will depend on the entire parameterization. The goal of this report is to clarify some of our previously made statements about SURF calibration, as well as to redo a systematic calibration using 100 µm grid resolution.

42 ENGINEERING↗

Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations (Final Report)

Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.

54 ENVIRONMENTAL SCIENCES↗

Implementation of a Quasi-3D Multiscale Modeling Framework to ACME

Representation of unresolved cloud processes has been one of the most challenging problems in numerical modeling of the atmosphere. During the last decades, considerable efforts have been spent to better represent the collective effects of those processes in terms of resolved scales (see Arakawa 2004 for a review). Still, the progress on this problem has been unacceptably slow as pointed out by Randall et al. (2003). The large part of the difficulty is inherent in the artificial separation of atmospheric processes into the explicitly simulated resolved scales and the highly parameterized subgrid scales. Arakawa (2004) and Arakawa et al. (2011) suggested two plausible approaches to eliminate the problem of artificial separation ofatmospheric processes: “parameterize everything” and “resolve everything”.

58 GEOSCIENCES↗

Use of Remote Sensing and In-situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the Accelerated Climate Model for Energy. Final report

Representation of unresolved cloud processes has been one of the most challenging problems in numerical modeling of the atmosphere. During the last decades, considerable efforts have been spent to better represent the collective effects of those processes in terms of resolved scales (see Arakawa 2004 for a review). Still, the progress on this problem has been unacceptably slow as pointed out by Randall et al. (2003). The large part of the difficulty is inherent in the artificial separation of atmospheric processes into the explicitly simulated resolved scales and the highly parameterized subgrid scales. Arakawa (2004) and Arakawa et al. (2011) suggested two plausible approaches to eliminate the problem of artificial separation of atmospheric processes: “parameterize everything” and “resolve everything”.

58 GEOSCIENCES↗

Ice Nucleating Particles, Aerosols and Clouds over the Higher Latitude Southern Ocean

Improved understanding of aerosol-cloud-precipitation interactions is a key goal of the DOE-ASR program. Ice Nucleating Particles (INPs) are rare aerosol particles that trigger ice formation in clouds. By doing so they may initiate precipitation in clouds, and this impacts cloud lifetime. Nowhere may the role of INPs be manifested so dramatically as in the Southern Ocean (SO). This vast region was the focus of the DOE-ARM MARCUS (Measurement of Aerosols, Radiation and CloUds over the Southern Oceans) and MICRE (Macquarie Island Cloud and Radiation Experiment) campaigns. In the SO, a deficit of INPs has been a leading hypothesis for why liquid clouds persist, which underlays the bias of global climate models in predicting excess shortwave radiation reaching the ocean surface between 55°S and Antarctica. In this study, we used the 6-month acquisition of INP data on four MARCUS cruises from Hobart, Tasmania, to Antarctica, and the single-location full annual INP cycle observed in MICRE, to gain a holistic, quantitative picture of the INP number concentrations as a function of temperature and their variability over SO latitudes from 43°S to Antarctica. Augmenting initial processing of filter collections of aerosol particles during MARCUS and MICRE, additional freezing studies following thermal treatment (to removes INPs associated with microbes) and peroxide treatment (to remove all organics), as well as ionic chemistry and total organic carbon analysis, were applied to samples to improve time and space resolution, and differentiate marine from terrestrial contributions. We also performed Next Generation Sequencing to determine that bacterial composition can tag periods of enhanced and degraded marine organic INPs and the relative lack of occurrence of land-sourced bio-particles. Through these and other analyses, INP data were categorized into representative source types (e.g., marine versus terrestrially-influenced). The products of our analyses are being used to parameterize ice formation for use in numerical modeling studies to determine if, and when, ocean-derived INPs control the microphysical composition and radiative balance of SO clouds in a manner not presently captured by global models. The specific objectives and approaches proposed were largely accomplished, including: 1) Additional and value-added chemical and biological analyses of archived samples of aerosols were completed to categorize and quantify INP types and concentrations over the SO. 2) Analyses were completed to place INP data in meteorological and aerosol context. 3) Sea spray aerosol INPs were demonstrated to dominate the SO region in all but episodic events. 4) New parameterizations for INP sources relevant to the SO were constructed. 5) Collaborative modeling studies demonstrating the crucial role of INPs in determining cloud radiative and precipitation properties were conducted with MARCUS and MICRE partners. Collaborative publication submissions in this regard ensued within the two year study. This work will advance the science of interactions of aerosols, clouds and precipitation, to improve their representation in regional and global climate models. Results have informed representation of primary ice crystal nucleation, give inference to the role of secondary processes, and improve investigations of aerosol influences on clouds via ice nucleation over SO high latitudes and similar vast ocean regions. Application will improve representation of such interactions for clouds in both regional and global climate models. The data base and analyses methods applied will continue to serve research studies in the future.

58 GEOSCIENCES↗

Unitary Bethe-Salpeter Methods in Two- and Three-Body Systems

The study of resonances, unstable particles formed in particle scattering, is motivated by the questions about the strong interaction and the composition of hadrons. In order to advance this study in the intermediate energy region, a variety of techniques are used. In this thesis, we present work done on two different analyses that use the principle of unitarity and the Bethe-Salpeter equation to study the resonances Lambda(1405), Sigma(1385), and a1(1260). Firstly, we perform a simultaneous analysis of Sand P-waves of the Strangeness = -1 two-body meson-baryon scattering amplitude using all low-energy data. For the first time, differential cross section data are included for chiral unitary coupled-channel models. From this model, S- and P-wave amplitudes are extracted and we observe both well-known I(JP)=0(1/2^-) S-wave states as well as a new I(JP)=1(1/2^+) state absent in quark models and lattice QCD results. Multiple statistical and phenomenological tests suggest that, while the data clearly require an I =1 P-wave resonance, the new state just accounts for the absence of the decuplet Sigma(1385)3/2+ in the model which is subsequently included in the parameterization. Secondly, we formulate the final state interaction of the a1(1260) resonance decay in a manifestly three-body-unitary parameterization and fit it to the a1(1260) lineshape measured by the ALEPH experiment. Dalitz plots calculated from this fit are presented. The work demonstrates the feasibility to numerically solve a previously derived amplitude and its generalization to isobars with spin and coupled channels. The model is a good test case because it can also be applied to other meson decays including exotic states and modified for the finite-volume problem as it arises in lattice QCD due to its manifest unitarity. The understanding of the resonances being analyzed in both the two-body work and the three-body work gives a better understanding of intermediate energy QCD.

Sadasivan, Daniel↗

Iodine Waste Form Corrosion: PNNL Summary Testing Report

A test method to correlate the laboratory corrosion behavior of iodine-containing waste forms (IWFs) to a conceptual long-term corrosion models is required. Several candidate methods have been evaluated previously, and each method presents unique challenges and limitations. Dynamic leach tests have emerged as the preferred candidate test method to evaluate IWFs and parameterize an eventual long-term corrosion model. This report was prepared in support of this effort to define the test suite to evaluate the corrosion of IWFs, with the goal of defining and parameterizing a conceptual long-term corrosion model. A review was presented highlighting the high degree of variability between the IWF corrosion data that is available in the literature and the need for a standardized test suite. The targeted test suite is composed of a single interval screening test, a dynamic leach test, and post-test solid characterization of the monolithic IWF samples. The data presented serves two purposes: to expand the range of IWFs evaluated with the test suite to assess method variability and to further understand the corrosion of IWFs to best inform conceptual long-term corrosion model development. A series of IWF types were included in the testing, including hot isostatically pressed Ag-mordenite, silver functionalized silica-based aerogels, iodosodalites (hot isostatic pressing, sintered, glass bonded ceramic), iodoapatite, tellurite glass and glass composite materials.

42 ENGINEERING↗

Facilitating better and faster simulations of aerosol-cloud interactions in Earth system models

Focal Area(s): 1. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising a hierarchy of models. 2. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: One major challenge that Earth system models (ESMs) face in providing credible prediction of the Earth system and its water cycle characteristics (e.g., mean state, variability, and extreme events) is to accurately simulate aerosol-cloud interactions (ACI). The physical, chemical, and dynamical processes affecting ACI are extremely complex and they range from nanoscale to planetary scale. In each model development cycle, scientists spend significant efforts investigating model deficiencies and uncertainties associated with aerosols (e.g., emissions, chemical processes, aerosol microphysics, and transport) and clouds (e.g., macrophysics, microphysics, turbulence, and large-scale circulation) in order to develop improved treatments. However, despite decades of active research, ACI is still a major source of uncertainty in climate projections, even though great progress has been made. Specific scientific challenges include: (i) Parameterizations are developed based on limited data; (ii) The complexity of a parameterization required for accurate predictions is not understood; (iii) Incomplete and unknown physics leads to errors in the fully coupled Earth system; and (iv) Complex physics is computationally too expensive to employ in ESMs.

54 ENVIRONMENTAL SCIENCES↗

Structurally flexible cloud microphysics, observationally constrained at all scales via ML-accelerated Bayesian inference

We discuss the challenge of developing observationally informed parameterizations of microphysics for use at a hierarchy of modeling scales. Our proposed approach is applicable to any domain that suffers from a two-fold parameterization problem, where physical processes are not resolved at the model scale (the first problem), and those processes are uncertain at any scale (the second problem). For such problems, a physical approach facilitates modeling across scales, as well as systematic observational inference accelerated by machine learning (ML) surrogate models, and quantification of physical uncertainties.

54 ENVIRONMENTAL SCIENCES↗

Wind Energy Forecasting with the Weather Research and Forecasting Model

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Siemens Energy, Inc. (Siemens) to develop a wind resource forecasting tool. LLNL was to develop an independent high-resolution mesoscale modeling capability forecasting tool that could be implemented in conjunction with existing wind farm control and monitoring software to provide forecasting of wind resources using local observations of winds and temperature. Research with LLNL’s state-of-the-art large-eddy simulation meteorological prediction model, based on the community WRF model and innovative turbulence parameterizations, would improve that model’s applicability to large wind farms offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.

17 WIND ENERGY↗