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At least 199 records · Page 11

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗

Dynamic compensation of Venus's geoid: A comparison with Earth

Unlike Earth, on Venus long wavelength geoid anomalies correlate well with topography. Venus's admittance curve between harmonic degrees 3 and 18 is inconsistent with Airy isostasy but is consistent with dynamic support from convection being the dominant mechanism of compensation on Venus. We model dynamic compensation on Venus using simple flow models which assume a spherically symmetric Newtonian mantle viscosity profile. Preliminary models parameterize the viscosity variation with depth as a 2 layer model with a boundary at 720 km depth. A model in which viscosity in the lower mantle is a factor of 10 lower than in the upper mantle can explain Venus's observed admittance curve for degrees 3 through 18. Dynamic models which include a chemical boundary between the upper and lower mantle do not successfully explain the observed admittance curve, indicating that Venus does not have a chemically layered mantle.

Kiefer, W. S.↗

A Hybrid Multiscale Framework for Subsurface Flow and Transport Simulations

Extensive research efforts have been invested in reducing model errors to improve the predictive ability of biogeochemical earth and environmental system simulators, with applications ranging from contaminant transport and remediation to impacts of biogeochemical elemental cycling (e.g., carbon and nitrogen) on local ecosystems and regional to global climate. While the bulk of this research has focused on improving model parameterizations in the face of observational limitations, the more challenging type of model error/uncertainty to identify and quantify is model structural error which arises from incorrect mathematical representations of (or failure to consider) important physical, chemical, or biological processes, properties, or system states in model formulations. While improved process understanding can be achieved through scientific study, such understanding is usually developed at small scales. Process-based numerical models are typically designed for a particular characteristic length and time scale. For application-relevant scales, it is generally necessary to introduce approximations and empirical parameterizations to describe complex systems or processes. This single-scale approach has been the best available to date because of limited understanding of process coupling combined with practical limitations on system characterization and computation. While computational power is increasing significantly and our understanding of biological and environmental processes at fundamental scales is accelerating, using this information to advance our knowledge of the larger system behavior requires the development of multiscale simulators. Accordingly there has been much recent interest in novel multiscale methods in which microscale and macroscale models are explicitly coupled in a single hybrid multiscale simulation. A limited number of hybrid multiscale simulations have been developed for biogeochemical earth systems, but they mostly utilize application-specific and sometimes ad-hoc approaches for model coupling. We are developing a generalized approach to hierarchical model coupling designed for high-performance computational systems, based on the Swift computing workflow framework. In this presentation we will describe the generalized approach and provide two use cases: 1) simulation of a mixing-controlled biogeochemical reaction coupling pore- and continuum-scale models, and 2) simulation of biogeochemical impacts of groundwater – river water interactions coupling fine- and coarse-grid model representations. This generalized framework can be customized for use with any pair of linked models (microscale and macroscale) with minimal intrusiveness to the at-scale simulators. It combines a set of python scripts with the Swift workflow environment to execute a complex multiscale simulation utilizing an approach similar to the well-known Heterogeneous Multiscale Method. User customization is facilitated through user-provided input and output file templates and processing function scripts, and execution within a high-performance computing environment is handled by Swift, such that minimal to no user modification of at-scale codes is required.

54 ENVIRONMENTAL SCIENCES↗

Comparison of tropical pacific temperature and current simulations with two vertical mixing schemes embedded in an ocean general circulation model and reference to observations

The Pacanowski-Philander (PP) and Mellor-Yamada (MY) parameterization models of vertical mixing by turbulent processes were embedded in the Geophysical Fluid Dynamics Laboratory high-resolution ocean general circulation model of the tropical Pacific Ocean. All other facets of the numerical simulations were the same. Simulations were made for the 1987-1988 period. At the equator the MY simulation produced near-surface temperatures more uniform with depth, a deeper thermocline, a deeper core speed of the Equatorial Undercurrent, and a South Equatorial Current with greater vertical thickness compared with that computed with the PP method. Along 140 deg W, between 5 deg N and 10 deg N, both simulations were the same. Moored buoy current and temperature observations had been recorded by the Pacific Marine Environmental Laboratory at three sites (165 deg E, 140 deg W, 110 deg W) along the equator and at three sites (5 deg N, 7 deg N, 9 deg N) along 140 deg W. Simulated temperatures were lower than those observed in the near-surface layer and higher than those observed in the thermocline. Temperature simulations were in better agreement with observations compared to current simulations. At the equator, PP current and temperature simulations were more representative of the observations than MY simulations.

Halpern, David↗

Estimation of net primary productivity using a process-based model in Gansu Province, Northwest China

The ecological structure in the arid and semi-arid region of Northwest China with forest, grassland, agriculture, Gobi, and desert, is complex, vulnerable, and unstable. It is a challenging and sustaining job to keep the ecological structure and improve its ecological function. Net primary productivity (NPP) modeling can help to improve the understanding of the ecosystem, and therefore, improve ecological efficiency. The boreal ecosystem productivity simulator (BEPS) model provides the possibility of NPP modeling in terrestrial ecosystem, but it has some limitations for application in arid and semi-arid regions. In this paper we improve the BEPS model, in terms of its water cycle by adding the processes of infiltration and surface runoff, to be applicable in arid and semi-arid regions. We model the NPP of forest, grass, and crop in Gansu Province as an experimental area in Northwest China in 2003 using the improved BEPS model, parameterized with moderate resolution remote sensing imageries and meteorological data. The modeled NPP using improved BEPS agrees better with the ground measurements in Qilian Mountain than that with original BEPS, with a higher R2 of 0.746 and lower root mean square error (RMSE) of 46.53 gC/m2 compared to R2 of 0.662 and RMSE of 60.19 gC/m2 from original BEPS. The modeled NPP of three vegetation types using improved BEPS show evident differences compared to that using original BEPS, with the highest difference ratio of 9.21% in forest and the lowest value of 4.29% in crop. The difference ratios between different vegetation types lie on the dependence on natural water sources. The modeled NPP in five geographic zones using improved BEPS are higher than those with original BEPS, with higher difference ratio in dry zones and lower value in wet zones.

Wang, Peijuan↗

Mesoscopic modeling of DNA denaturation rates: Sequence dependence and experimental comparison

Using rare event simulation techniques, we calculated DNA denaturation rate constants for a range of sequences and temperatures for the Peyrard-Bishop-Dauxois (PBD) model with two different parameter sets. We studied a larger variety of sequences compared to previous studies that only consider DNA homopolymers and DNA sequences containing an equal amount of weak AT- and strong GC-base pairs. Our results show that, contrary to previous findings, an even distribution of the strong GC-base pairs does not always result in the fastest possible denaturation. In addition, we applied an adaptation of the PBD model to study hairpin denaturation for which experimental data are available. This is the first quantitative study in which dynamical results from the mesoscopic PBD model have been compared with experiments. Our results show that present parameterized models, although giving good results regarding thermodynamic properties, overestimate denaturation rates by orders of magnitude. We believe that our dynamical approach is, therefore, an important tool for verifying DNA models and for developing next generation models that have higher predictive power than present ones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Kinetic Approach to Modeling the Manufacture of High Density Polyurethane Structural Foam: Foaming and Polymerization

This report details the study of PMDI structural foam and the development of a computational model to represent it. This polyurethane has a fast catalyst, such that foaming and polymerization occur simultaneously. The foam is over-packed to twice or more of its free rise density to reach the density of interest. Our approach is to combine model development closely with experiments to discover underlying physical phenomena, to parameterize models, and to validate the models once they have been developed. The model must be able to represent the expansion, filling of molds, curing, and final foam properties. PMDI is a chemically blown foam, where carbon dioxide is produced via the reaction of water and isocyanate. The isocyanate also reacts with polyol in a competing reaction, which produces the polymer. A new kinetic model is developed and implemented, which follows a simplified mathematical formalism that decouples these two reactions. The model predicts the polymerization reaction via condensation chemistry, where vitrification and glass transition temperature evolution must be included to correctly predict the temperature dependence of the kinetics. The foam gas generation kinetics are determined by tracking the molar concentration of both water and carbon dioxide. Understanding the thermal history and loads on the foam due to exothermicity and oven curing is very important to the results, since the kinetics and material properties are all sensitive to temperature. The conservation equations, including the equations of motion, an energy balance, and three rate equations are solved via a stabilized finite element method. We assume generalized-Newtonian rheology that is dependent on the extent of cure, gas fraction, and temperature. The conservation equations are combined with a level set method to determine the location of the free surface over time. Results from the model are compared to experimental flow visualization data and post-test X-ray computed tomography (CT) data for the density. Several geometries are investigated including a mock encapsulation part, two configurations of a mock structural part, and a bar geometry to specifically test the density model. We have found that the model predicts both average density and filling profiles well. However, it under predicts density gradients, especially in the gravity direction. Further model improvements are also discussed.

36 MATERIALS SCIENCE↗

HARP targets pion production cross section and yield measurements. Implications for MiniBooNE neutrino flux

The prediction of the muon neutrino flux from a 71.0 cm long beryllium target for the MiniBooNE experiment is based on a measured pion production cross section which was taken from a short beryllium target (2.0 cm thick - 5% nuclear interaction length) in the Hadron Production (HARP) experiment at CERN. To verify the extrapolation to our longer target, HARP also measured the pion production from 20.0 cm and 40.0 cm beryllium targets. The measured production yields, d 2 N π± (p; θ )=dpd Ω, on targets of 50% and 100% nuclear interaction lengths in the kinematic rage of momentum from 0.75 GeV/c to 6.5 GeV/c and the range of angle from 30 mrad to 210 mrad are presented along with an update of the short target cross sections. The best fitted extended Sanford-Wang (SW) model parameterization for updated short beryllium target π + production cross section is presented. Yield measurements for all three targets are also compared with that from the Monte Carlo predictions in the MiniBooNE experiment for different SW parameterization. The comparisons of v μ flux predictions for updated SW model is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Effects of Rapid Permafrost Thaw on CO2 and CH4 Fluxes in a Warmer and Wetter Future (Final Technical Report)

When ice-rich permafrost thaws, the ground subsides, creating thermokarst landscapes with dramatically different soil conditions and carbon fluxes than the original ecosystem. Roughly 20% of the northern permafrost region is susceptible to thermokarst formation (Olefeldt et al., 2016). Thermokarst formation is often rapid; tens of meters of permafrost can thaw within a few years (Schuur et al., 2015). In topographically low areas, thermokarst thaw converts boreal forest or tundra dry shrub ecosystems into sedge or Sphagnum moss wetlands (Olefeldt et al., 2016). While this type of landscape transformation releases carbon stored in permafrost into the atmosphere, on longer time scales, it facilitates sequestration of atmospheric carbon in plant biomass because permafrost thaw releases plant-available nutrients and wetlands are highly productive (M. C. Jones et al., 2017). However, wetlands also generate methane, which is a potent greenhouse gas. Methane emissions from thermokarst wetlands can cause these carbon-sequestering systems to have a positive global warming potential (Johansson et al., 2006; Turetsky et al., 2007). Our project objective was to improve Earth System and environmental predictability by advancing understanding of how CO 2 and CH 4 flux in permafrost thaw-induced wetlands (thermokarst) will change in the future as temperatures and climate conditions shift. Northern latitudes are expected to get warmer and wetter (IPCC 2013), and initiation and expansion of thermokarst thaw is expected to increase (Jorgenson et al. 2006; Zhang et al. 2017). Given these expected changes, our work sought to address three broad questions: Q1) How will northern latitude CO2 and CH4 emissions respond to warming temperatures? Q2) What is the impact of precipitation on permafrost thaw and carbon emissions? Q3) How do CO2 and CH4 emissions change as wetlands age after permafrost thaw? To answer these questions, we took both a modeling and measurement approach. Modeling work was conducted with DOE’s Energy Exascale Earth System Model (E3SM) land model (ELM). Empirical work took place in two primary locations. The first was a thermokarst site near Fairbanks, AK. The site is part of the Bonanza Creek Long Term Ecological Research program and is well instrumented (Neumann et al., 2019). The second was an isolated thawing permafrost wetland located on Kenai Peninsula — Brown’s Lake bog (B. M. Jones et al., 2016) — where the current climate is representative of what is expected at higher latitudes in the future. This site provides an ideal opportunity to test our hypotheses about how thermokarst wetland dynamics will respond to future environmental conditions. In addition, the project collaborated with researchers asking similar questions who were collecting measurements in high latitude post-glacial lakes located in collaborators tackling similar questions in Sweden (Stordalen Mire) (Emerson et al., 2021). Project efforts directly aligned with the stated goal of the funding opportunity announcement (FOA), which was “to improve the understanding and representation of terrestrial ecosystems in ways that advance Earth system model parameterizations and capabilities... thereby improving the quality of Earth and environmental model projections and providing the scientific foundation needed to support DOE’s science and energy missions.” Specifically, the project improved sophistication and accuracy of the Energy Exascale Earth System Model (E3SM), which is being developed primarily at DOE National Laboratories to support scientific research and decision-making.

54 ENVIRONMENTAL SCIENCES↗

A TRACER METHOD FOR COMPUTING TYPE IA SUPERNOVA YIELDS: BURNING MODEL CALIBRATION, RECONSTRUCTION OF THICKENED FLAMES, AND VERIFICATION FOR PLANAR DETONATIONS

We refine our previously introduced parameterized model for explosive carbon–oxygen fusion during thermonuclear Type Ia supernovae (SNe Ia) by adding corrections to post-processing of recorded Lagrangian fluid-element histories to obtain more accurate isotopic yields. Deflagration and detonation products are verified for propagation in a medium of uniform density. A new method is introduced for reconstructing the temperature–density history within the artificially thick model deflagration front. We obtain better than 5% consistency between the electron capture computed by the burning model and yields from post-processing. For detonations, we compare to a benchmark calculation of the structure of driven steady-state planar detonations performed with a large nuclear reaction network and error-controlled integration. We verify that, for steady-state planar detonations down to a density of 5 × 10{sup 6} g cm{sup −3}, our post-processing matches the major abundances in the benchmark solution typically to better than 10% for times greater than 0.01 s after the passage of the shock front. As a test case to demonstrate the method, presented here with post-processing for the first time, we perform a two-dimensional simulation of a SN Ia in the scenario of a Chandrasekhar-mass deflagration–detonation transition (DDT). We find that reconstruction of deflagration tracks leads to slightly more complete silicon burning than without reconstruction. The resulting abundance structure of the ejecta is consistent with inferences from spectroscopic studies of observed SNe Ia. We confirm the absence of a central region of stable Fe-group material for the multi-dimensional DDT scenario. Detailed isotopic yields are tabulated and change only modestly when using deflagration reconstruction.

79 ASTRONOMY AND ASTROPHYSICS↗

A particle dry-deposition parameterization scheme for use in tracer transport models

A parameterization scheme for the dry deposition of aerosol particles for use in tracer transport models is presented. Expressions of the particle deposition velocities relative to the bottom transport model level are derived for smooth surfaces, surfaces with bluff roughness elements, ocean surfaces, and vegetative canopies. The scheme is applied to a general circulation model to calculate the deposition velocities of ambient tropospheric aerosols. The calculated deposition velocities show a strong dependence upon the wind field in the bottom model level, the particle size distribution, and the surface type.

Giorgi, F.↗

Evaluation of a GCM cirrus parameterization using satellite observations

This study applies a simple yet effective methodology to validate a general circulation model parameterization of cirrus ice water path. The methodology combines large-scale dynamic and thermodynamic fields from operational analyses with prescribed occurrence of cirrus clouds from satellite observations to simulate a global distribution of ice water path. The predicted cloud properties are then compared with the corresponding satellite measurements of visible optical depth and infrared cloud emissivity to evaluate the reliability of the parameterization. This methodology enables the validation to focus strictly on the water loading side of the parameterization by eliminating uncertainties involved in predicting the occurrence of cirrus internally within the parameterization. Overall the parameterization performs remarkably well in capturing the observed spatial patterns of cirrus optical properties. Spatial correlations between the observed and the predicted optical depths are typically greater than 0.7 for the tropics and northern hemisphere midlatitudes. The good spatial agreement largely stems from the strong dependence of the ice water path upon the temperature of the environment in which the clouds form. Poorer correlations (r approximately 0.3) are noted over the southern hemisphere midlatitudes, suggesting that additional processes not accounted for by the parameterization may be important there. Quantitative evaluation of the parameterization is hindered by the present uncertainty in the size distribution of cirrus ice particles. Consequently, it is difficult to determine if discrepancies between the observed and the predicted optical properties are attributable to errors in the parameterized ice water path or to geographic variations in effective radii.

Soden, B. J.↗

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

59 BASIC BIOLOGICAL SCIENCES↗

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗

Climate models show colorado drying sooner and with greater certainty east of the continental divide

Many studies have examined the aridity of the Colorado River Basin and the possible impacts of climate change which could further strain already over-allocated water resources in the region. Fewer studies have examined the multiple Colorado Rocky Mountain headwater regions specifically. This is especially true of areas East of the Continental Divide, despite water originating there being critical to cities and agriculture in Eastern Colorado and further downstream. This paper explores and compares drying trends in the Eastern and Western Colorado Rocky Mountains using single-model initial-condition large ensembles from ten global climate models. The use of multiple models allows us to identify signals that are consistent across different physics parameterizations, model grids, and other model intricacies. The large ensembles also allow us to quantify the time of emergence of these climate change signals--that is, when did (or when will) the long term change due to anthropogenic greenhouse gasses exceed the internal variability of the climate system. Consistent with previous studies, we find evidence of drying on both sides of the Continental Divide. That drying is more pronounced, occurs sooner, and is more consistent across global climate models in the East, however, highlighting the region’s importance despite generally receiving less attention than the West.

Rugg, Allyson↗

The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such “free” model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process—the genetic algorithm (GA)—has become especially popular. As applied to modeling, GAs represent a Darwinian process: an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as “parents” who pass their parameters onto a new generation. At the U.S. Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to “score” the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions. Significance Statement We wanted to develop a forecasting system by which a weather model is run over the Savannah River Site each day and repeatedly adjusted according to how well it performed the previous day. To run the model, a series of values (parameters) must be set to control how the model will calculate winds, temperatures, and other desired variables. Each day the model was run several times using different combinations of these parameters and later compared with observed meteorological conditions. Parameters that produced the most accurate forecasts were preferentially reused to create the forecasts for the next day. The process was tested for the summer of 2020 and exhibited lower errors than forecasts produced by the model using default values of the parameters.

54 ENVIRONMENTAL SCIENCES↗

A Macroalgal Cultivation Modeling System (MACMODS): Evaluating the Role of Physical-Biological Coupling on Nutrients and Farm Yield

Offshore aquaculture has the potential to expand the macroalgal industry. However, moving into deeper waters requires suspended structures that will present novel farm-environment interactions. Here, we present a computational modeling framework, the Macroalgal Cultivation Modeling System (MACMODS), to explore within-farm modifications to light, seawater flow, and nutrient fields across time and space scales relevant to macroalgae. A regional ocean model informs the site-specific setting, the Santa Barbara Channel in the Southern California Bight. A fine-scale hydrodynamic model predicts modified flows and turbulent mixing within the farm. A spatially resolved macroalgal growth model, parameterized for giant kelp, Macrocystis pyrifera , predicts kelp biomass. Key findings from model integration are that regional ocean conditions set overall farm performance, while fine-scale within-farm circulation and nutrient delivery are important to resolve variation in within-farm macroalgal performance. Therefore, we conclude that models resolving within-farm dynamics can provide benefit to farmers with insight on how farm design and regional ocean conditions interact to influence overall yield. Here, the presence of repeating longlines aligned with the mean current generate flow diversions around the farm as well as attached Langmuir circulations and increased turbulence intensity. These flow-induced phenomena lead to less biomass in the interior portion of the farm relative to the edges. We also find that there is an effluent “footprint” that extends as much as 20 km beyond the farm. In this regard, MACMODS can be used to not only evaluate farm design and cultivation practices that maximize yield but also explore interactions between the farm and ecosystem in order to minimize impacts.

54 ENVIRONMENTAL SCIENCES↗