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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Construction and applications of the manifestly gauge invariant expressions of the solutions of the cosmological perturbation theory

After giving how to construct the gauge invariant perturbation variable in an arbitrarily high order and in the arbitrary background spacetime, we consider the manifestly gauge invariant theory of the cosmological perturbations in the long wavelength limit with the spatially flat homogeneous isotropic universe being the background spacetime. In the previous paper, the physical laws, such as the evolution equations, the constraint equations and the junction conditions become manifestly gauge invariant, by writing them in terms of only the single time background/scalar like objects defined by our previous paper. In the present paper, by extending our definition of the background/scalar like objects from the single time case to the many time case and by writing the solution of the physical law in the form where many time background/scalar like objects are vanishing, the solutions become the manifestly gauge invariant. We derive the formula changing the bases of the many time background/scalar like objects by which we can change the time slices for many times appearing in the solution including the initial time and the final time. We use this formula to our treatment of the evolution of the several slow rolling scalar fields using the τ function introduced by our previous paper. In the manifestly gauge invariant manner, we discuss the solution of the many step reheating, that is, the reheating with the arbitrarily many energy transfer steps. Using the energy density ρ as the evolution parameter, we discuss how well the junction model in which the energy transfers from the oscillatory scalar field fluid to the radiation fluid are described by the metric junctions approximates the reheating process described by the differential equations with the decay terms. By using the useful parametrization of the many step reheating, we manifestly prove that in the many step reheating system where any initial perturbation of each component does not become extremely large compared with the initial perturbations of the other components, compared with the effects of the prominent fluctuation generation processes the nearest to the present time the effects of all the fluctuations generated by the previous fluctuation generation processes become negligibly small. Therefore it can be concluded that in order to know the first/second order Bardeen parameters at the present time it is sufficient to calculate only the effects of the fluctuation of the scalar field fluid which becomes energetically dominant lastly (the curvaton mechanism) and the fluctuation of the decay parameter by which this scalar field fluid decays into the radiation (the modulated reheating mechanism).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A phase-field fracture formulation for generalized standard materials: The interplay between thermomechanics and damage

Accurately modeling fracture of ductile materials poses open challenges in the field of computational mechanics due to the multiphysics nature of their failure processes. Integrating the interplay between thermodynamics and damage into ductile fracture models is vital for predicting critical failure modes. Here, in this paper, we develop a versatile phase-field (PF) framework for modeling ductile fracture, taking into account finite-strain elasto-plasticity. The framework stems from a variational formulation of constitutive relations for generalized standard materials (GSMs), whose response is described by a Helmholtz free energy and a dissipation pseudo-potential. Its variational structure is based on a minimum principle for a functional that expresses the sum of power densities for reversible and irreversible processes. By minimizing this functional with a constraint on a von Mises yield function, we derive the evolution equation for the equivalent plastic strain and an associative flow rule. This constrained optimization problem is analytically solved for a wide class of thermo-viscoplasticity models. The key innovations of the current work include (i) a cubic plastic degradation function that accounts for a non-vanishing damage-dependent yield stress, (ii) closed-form expressions of the Helmholtz free energy and dissipation pseudo-potential for three thermo-viscoplasticity models, (iii) an extended Johnson–Cook plasticity model with a nonlinear hardening law, and (iv) a plastic work heat source that depends on the plastic degradation function and a variable Taylor–Quinney (TQ) coefficient. The capabilities of the proposed framework are tested with the aid of four ductile fracture problems, including the Sandia Fracture Challenge. In each of these problems, we examine the evolution of relevant field variables such as the PF order parameter, the equivalent plastic strain, the temperature, and the internal power dissipation density, in addition to the overall structural response quantified by the force–displacement curve. These numerical studies demonstrate that the proposed framework effectively represents ductile fracture, yielding computational results that exhibit good agreement with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model

We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1-ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and Δ 14 C values around the world. We analyzed observed and simulated SOC stocks and Δ 14 C values using machine learning methods at the Earth System Model grid cell scale (~200 km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and Δ 14 C values near the surface but underestimated Δ 14 C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC Δ 14 C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (≤30 cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and Δ 14 C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical-chemical protection of SOC via root activities. Allowing for grid cell-specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted Δ 14 C values and SOC content. Furthermore, our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.

58 GEOSCIENCES↗
DOE OSTI2024

4-Clique network minor embedding for quantum annealers

Quantum annealing is a quantum algorithm for computing solutions to combinatorial optimization problems. This study proposes a method for minor embedding optimization problems onto sparse quantum annealing hardware graphs called 4-clique network minor embedding. This method is in contrast to the standard minor embedding technique of using a path of linearly connected qubits in order to represent a logical variable state. The 4-clique minor embedding is possible on Pegasus graph connectivity, which is the native hardware graph for some of the current D-Wave quantum annealers. The Pegasus hardware graph contains many cliques of size 4, making it possible to form a graph composed entirely of paths of connected 4-cliques on which a problem can be minor-embedded. The 4-clique chains come at the cost of additional qubit usage on the hardware graph, but they allow for stronger coupling within each chain, thereby increasing chain integrity, reducing chain breaks, and allow for greater usage of the available energy scale for programming logical problem coefficients on current quantum annealers. The 4-clique minor embedding technique is compared with the standard linear path minor embedding with experiments on two D-Wave quantum annealing processors with Pegasus hardware graphs. We show proof-of-concept experiments where the 4-clique minor embeddings can use weak chain strengths while successfully carrying out the computation of minimizing random all-to-all spin glass problem instances. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Strangeness-neutral equation of state for QCD with a critical point

We present a strangeness-neutral equation of state for QCD that exhibits critical behavior and matches lattice QCD results for the Taylor-expanded thermodynamic variables up to fourth-order in µ B /T. It is compatible with the SMASH hadronic transport approach and has a range of temperatures and baryonic chemical potentials relevant for phase II of the Beam Energy Scan at RHIC. We provide an updated version of the software BES-EoS, which produces an equation of state for QCD that includes a critical point in the 3D Ising model universality class. This new version also includes isentropic trejectories and the critical contribution to the correlation length. Since heavy-ion collisions have zero global net-strangeness density and a fixed ratio of electric charge to baryon number, the BES-EoS is more suitable to describe this system. Comparison with the previous version of the EoS is thoroughly discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Toward Improving the Generation Quality of Autoregressive Slot VAEs

Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations (“slots”) from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multiobject relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multiobject environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.

MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O ( N P 2 ) in training and O ( P ) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a ‘Susceptible, Infected, Recovered’ (SIR) toy problem, along with the experimental ‘Cascaded Tanks’ benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

Hayes, Kyle↗
DOE OSTI2019

Compensatory Mitigation and Comprehensive Mitigation Planning for Solar Energy Development

This report provides an overview of compensatory mitigation (CM) used to compensate for unavoidable adverse impacts at solar energy facilities. The report discusses the types of CM actions taken, as well as the methods used to satisfy CM requirements. It also addresses recent changes in CM requirements from the Department of the Interior. The goal of the report is to provide a comprehensive background on how CM has been applied to existing solar energy projects and how it may be applied in the future based on federal, state, and local requirements and policies. Using best available data, we present CM requirements and costs for solar energy facilities that vary in size, geographical location, and resources affected in order to better understand the variability in CM costs. In general, higher CM costs have been associated with: CM requirements under the Clean Water Act (CWA) and Endangered Species Act (ESA); land acquisition, preservation, and/or restoration methods; large solar energy facility nameplate capacity (MW); solar energy facilities located in states that have additional state-specific CM requirements.

14 SOLAR ENERGY↗
DOE OSTI2023

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

Potyrailo, Radislav A.↗
DOE OSTI2018

A reproducing kernel enhanced approach for peridynamic solutions

The most common discretization method for peridynamic models used in engineering problems is the node-based meshfree approach. This method discretizes peridynamic domains by a set of nodes, each associated with a nodal cell with a characteristic volume, leading to a particle-based description of continuum systems. The behavior of each particle is then considered representative of its cell. This limits the convergence rate to the first order. In this paper, we introduce a reproducing kernel (RK) approximation to the field variables in the peridynamic equations to increase the order of convergence of peridynamic numerical solutions. Furthermore, the numerical results demonstrate improved convergence rates in static peridynamic problems using the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On the fast kinetics of B2–L2 1 ordering in Ni-Co-Mn-In metamagnetic shape memory alloys

B2–L2 1 configurational order resulting from thermal processing of Ni-Co-Mn-In Heusler alloys is one of the main factors defining the characteristics of the martensitic transformation experienced by these alloys. While the effects of the quenched-in disorder on the martensitic transformation have been extensively investigated, the kinetics of the ordering process is not well known and mostly inferred from the indirect results such as the dependence of the martensitic transformation or magnetic transition temperatures to thermal processing. However, recent results suggested that the kinetics of B2– L 2 1 ordering could have a more significant role than expected in the final state of the material and its functional properties. In the current work, an attempt has been made to investigate the kinetics of B2–L2 1 ordering in order to evaluate the dependence of the quenched-in disorder on the different variables of the thermal procedures. Results showed that ordering rate has a strong dependence on annealing temperature. Below 600 K, slow kinetics is forcing the crystal to always contain a certain amount of quenched-in disorder at room temperature. Between 600 K and 750 K, kinetics is fast enough to reach thermal equilibrium state within tens of minutes during the annealing process. Above 750 K, kinetics is very fast and thermal equilibrium can be reached during heating, so no isothermal holding is required. Furthermore, these predictions have been confirmed using differential scanning calorimetry and in-situ high-energy X-Ray diffraction measurements for non-direct estimations of the degree of order.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Large Blooms of Bacillales ( Firmicutes ) Underlie the Response to Wetting of Cyanobacterial Biocrusts at Various Stages of Maturity

Biological soil crusts (biocrusts) account for a substantial portion of primary production in dryland ecosystems. They successionally mature to deliver a suite of ecosystem services, such as carbon sequestration, water retention and nutrient cycling, and climate regulation. Biocrust assemblages are extremely well adapted to survive desiccation and to rapidly take advantage of the periodic precipitation events typical of arid ecosystems. Here we focus on the wetting response of incipient cyanobacterial crusts as they mature from “light” to “dark.” We sampled a cyanobacterial biocrust chronosequence before (dry) and temporally following a controlled wetting event and used high-throughput 16S rRNA and rRNA gene sequencing to monitor the dynamics of microbial response. Overall, shorter-term changes in phylogenetic beta diversity attributable to periodic wetting were as large as those attributable to biocrust successional stage. Notably, more mature crusts showed significantly higher resistance to precipitation disturbance. A large bloom of a few taxa within the Firmicutes , primarily in the order Bacillales , emerged 18 h after wetting, while filamentous crust-forming cyanobacteria showed variable responses to wet-up across the successional gradient, with populations collapsing in less-developed light crusts but increasing in later-successional-stage dark crusts. Overall, the consistent Bacillales bloom accompanied by the variable collapse of pioneer cyanobacteria of the Oscillatoriales order across the successional gradient suggests that the strong response of few organisms to a hydration pulse with the mortality of the autotroph might have important implications for carbon (C) balance in semiarid ecosystems. IMPORTANCE Desert biological soil crusts are terrestrial topsoil microbial communities common to arid regions that comprise 40% of Earth’s terrestrial surface. They successionally develop over years to decades to deliver a suite of ecosystem services of local and global significance. Ecosystem succession toward maturity has been associated with both resistance and resilience to disturbance. Recent work has shown that the impacts of both climate change and physical disturbance on biocrusts increase the potential for successional resetting. A larger proportion of biocrusts are expected to be at an early developmental stage, hence increasing susceptibility to changes in precipitation frequencies. Therefore, it is essential to characterize how biocrusts respond to wetting across early developmental stages. In this study, we document the wetting response of microbial communities from a biocrust chronosequence. Overall, our results suggest that the cumulative effects of altered precipitation frequencies on the stability of biocrusts will depend on biocrust maturity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A second-order-in-time, explicit approach addressing the redundancy in the low-Mach, variable-density Navier-Stokes equations

A novel algorithm for explicit temporal discretization of the variable-density, low-Mach Navier-Stokes equations is presented here in this study. Recognizing there is a redundancy between the mass conservation equation, the equation of state, and the transport equation(s) for the scalar(s) which characterize the thermochemical state, and that it destabilizes explicit methods, we demonstrate how to analytically eliminate the redundancy and propose an iterative scheme to solve the resulting transformed scalar equations. The method obtains second-order accuracy in time regardless of the number of iterations, so one can terminate this subproblem once stability is achieved. Hence, flows with larger density ratios can be simulated while still retaining the efficiency, low cost, and parallelizability of an explicit scheme. The temporal discretization algorithm is used within a pseudospectral direct numerical simulation which extends the method of Kim, Moin, and Moser for incompressible flow to the variable-density, low-Mach setting, where we demonstrate stability for density ratios up to ~25.7.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Blast Effects on Buildings (Final Report)

Lawrence Livermore National Laboratory (LLNL) has conservatively reduced the explosive safety standards suggested by the Small Quantities in Research Laboratories (SQRL) program testing by 60%. For this reason, further research into the detailed effects of small amounts of explosives in typically constructed rooms is needed in order to improve their factor of safety. The team was tasked with designing an experiment to investigate the effect of different variables on drywall under explosive blasts. In order to meet this objective, the team conducted a comprehensive literature review to gain an understanding of industry-standard construction practices and review previous tests conducted by the Army corp of engineers. Since the team used PBXN-5 rather than C4, as in SQRL, an initial shot was conducted to compare damage levels. From those results and the physical constraints of the testing chamber, the team redesigned multiple single-panel drywall frames to capture the entirety of the incurred damage. Proposed designs were narrowed down using a decision matrix. From the study of previous tests and literature review, variables were chosen that the team hypothesized to have an impact on drywall strength. The variables that were tested were chosen from the results of that work and specific variables the sponsor was interested in, and they were paint, humidity/moisture content, and explosive positioning relative to the studs. Detailed plans were made for each variable according to what conditions the team wanted to investigate. For humidity, this involved testing low, ambient, and high conditions by treating the panels in a chamber. Preliminary shots were performed to test the structural integrity of the frame and streamline the test diagnostics which involved a high-speed camera placed behind the drywall, outside of the chamber, and a pressure probe placed behind the drywall, inside the chamber. Once the instrumentation, diagnostics, and frame design were finalized, a quantitative damage criteria matrix was created to categorize the results of the main shot series. In conjunction with evidence from the high-speed video, the achieved damage levels indicate that high moisture content drywall is better able to withstand explosive blasts. Larger stud damage and lower drywall damage occurred when the explosive was located directly in front of a stud. Paint had no noticeable impact on strength. Ultimately, the team conducted a total of 17 tests, leaving the door open for future in-depth research into the impact of humidity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations

The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamflow permanence in headwater systems. However, few process-based models consider the continuum of hillslope-stream network connectivity as a control on streamflow permanence in headwater systems. The objective of this study was to expand a process-based, catchment-scale hydrologic model to better understand the spatiotemporal dynamics of headwater streamflow permanence and to identify controls of streamflow expansion and contraction in a headwater network. Further, we aimed to develop an approach that enhanced the fidelity of model simulations, yet required little additional data, with the intent that the model might be later transferred to catchments with limited long-term and spatially explicit measurements. This approach facilitated network-scale estimates of the controls of streamflow expansion and contraction, albeit with higher degrees of uncertainty in individual reaches due to data constraints. Our model simulated that streamflow permanence was highly dynamic in first-order reaches with steep slopes and variable contributing areas. The simulated stream network length ranged from nearly 98±2% of the geomorphic channel extent during wet periods to nearly 50±10% during dry periods. The model identified a discharge threshold of approximately 1 mm d –1 , above which the rate of streamflow expansion decreases by nearly an order of magnitude, indicating a lack of sensitivity of streamflow expansion to hydrologic forcing during high-flow periods. Overall, we demonstrate that process-based, catchment-scale models offer important insights on the controls of streamflow permanence, despite uncertainties and limitations of the model. We encourage researchers to increase data collection efforts and develop benchmarks to better evaluate such models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Adaptive immersed isogeometric level-set topology optimization

Here, this paper presents for the first time an adaptive immersed approach for level-set topology optimization using higher-order truncated hierarchical B-spline discretizations for design and state variable fields. Boundaries and interfaces are represented implicitly by the iso-contour of one or multiple level-set functions. An immersed finite element method, the eXtended IsoGeometric Analysis, is used to predict the physical response. The proposed optimization framework affords different adaptively refined higher-order B-spline discretizations for individual design and state variable fields. The increased continuity of higher-order B-spline discretizations together with local refinement enables direct control over the accuracy of the representation of each field while simultaneously reducing computational cost compared to uniformly refined discretizations. A flexible mesh adaptation strategy enables local refinement based on geometric measures or physics-based error indicators. These adaptive discretization and analysis approaches are integrated into gradient-based optimization schemes, evaluating the design sensitivities using the adjoint method. Numerical studies illustrate the features of the proposed framework with static, linear elastic, multi-material, two- and three-dimensional problems. The examples provide insight into the effect of refining the design variable field on the optimization result and the convergence rate of the optimization process. Using coarse higher-order B-spline discretizations for level-set fields promotes the development of smooth designs and suppresses the emergence of small features. Moreover, adaptive mesh refinement for state variable fields results in a reduction of overall computational cost. Higher-order B-spline discretizations are especially interesting when evaluating gradients of state variable fields due to their higher inter-element continuity.

36 MATERIALS SCIENCE↗
DOE OSTI2024

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Understanding the interaction between humans and the Earth system is a computationally daunting task, with many possible approaches depending on resources available and questions of interest. For example, state-of-the-art impact models require decade-long time series of relatively high frequency, spatially resolved and often multiple variables representing climatic impact-drivers (Ruane et al., 2022). Most commonly these are derived from the outputs of detailed, computationally expensive Earth System Models (ESMs) run according to a standard, limited set of future scenarios, the latest being the SSP-RCPs run under CMIP6/ScenarioMIP (Eyring et al., 2016; O’Neill et al., 2016). At the time of writing, O’Neill et al. (2016) has been cited more than 1750 times and Eyring et al. (2016) more than 5000 times, highlighting the broad, general applications of this data. Often, however, impact modeling seeks to explore new scenarios that were not part of the ScenarioMIP protocol, and/or needs a larger set of initial condition ensemble members than are typically available to quantify the effects of ESM internal variability. In addition, the recognition that the human and Earth systems are fundamentally intertwined, and may feature potentially significant feedback loops, is making integrated, simultaneous modeling of the coupled human-Earth system increasingly necessary, if computationally challenging with most existing tools (Thornton et al., 2017). For impact modelers, climate model emulators can be the answer to meet both the needs of: 1) creating realizations for novel scenarios and 2) achieving a simplified, computationally tractable representation of ESM behavior in a coupled human-Earth system modeling framework. We proposed a new, comprehensive approach to such emulation of gridded, multivariate ESM outputs for novel scenarios without the computational cost of a full ESM, STITCHES (Tebaldi et al., 2022). The approach outlined in Tebaldi et al. (2022) should be extensible to future CMIP eras, although the STITCHES software at present is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/). The corresponding STITCHES Python package uses existing archives of ESMs’ scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate realizations of new scenarios provided by reduced complexity climate models (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018), or to enrich existing initial condition ensembles. Its output provides the same characteristics as the emulated ESM output: multivariate (spanning potentially all variables that the ESM has saved), spatially resolved (down to the native grid of the ESM), and preserving the same high frequency as the original data. A new realization of multiple variables can be generated on the order of minutes with STITCHES, rather than the hours or sometimes days that ESMs require.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of “truth” and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes.

54 ENVIRONMENTAL SCIENCES↗