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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Macquarie Island Cloud and Radiation Experiment (MICRE) Ice Nucleating Particle Measurements Field Campaign Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Macquarie Island Cloud and Radiation Experiment (MICRE) Ice Nucleating Particle Measurements campaign was aligned as a supplemental activity with the second year of the overall MICRE campaign. The purpose of the supplemental campaign was to provide a first-ever annual cycle of ice nucleating particle (INP) measurements at a Southern Ocean (SO) site, toward the investigation/characterization of sources of INPs in the marine boundary layer as being from the production or primary sea spray aerosol (SSA) emissions versus other long-range transport influences. Furthermore, measuring the variability in INPs seasonally in this region, as this may relate to meteorology and ocean biological processes, is expected to help constrain (via parameterizations) numerical model simulations of cloud phase and regional climate. In concert with other SO studies during the time frame of the campaign (March 2017 to March 2018), MICRE sought to evaluate the hypotheses that SSA INPs represent the major INP source in the SO and, due to their generally less efficient INP activation properties compared to terrestrial sources, that they may directly influence the persistence of supercooled clouds in a unique manner for this climatic region. These measurements supplemented the original deployment of primarily radiation measurements provided in the ARM MICRE request and the associated Australian Antarctic Division (AAD) Antarctic Clouds and Radiation Experiment (ACRE). Assistance and collaboration was obtained from the AAD, the Commonwealth Scientific and Industrial Research Organization (CSIRO) and the Australian Bureau of Meteorology (BOM). Follow-up plans will include investigations with their additional aerosol, cloud, and radiative transfer measurements. AAD also saw to the transport to Australia and storage of filters at the end of the campaign. The added effort involved an average of twice-weekly collections (a 48-hour and a 72-hour collection) of pre-sterilized, open-faced, polycarbonate filter samples for post-processing of INP number concentrations and tests to analyze the nature of INP sources. Collections were made by AAD technician Emry Thaggard Crocker after online training from our team. Images of the filter sampler and site are shown in Figure 1. Sampling occurred from an approximate elevation of 4m MSL. Filters were returned frozen to Colorado State University (CSU) at the end of the campaign. Initial processing to obtain INP temperature spectra (0 to -28°C) of the concentrations of INPs active via the immersion freezing mechanism were conducted using the CSU ice spectrometer (IS) instrument systems (Hiranuma et al. 2015; McCluskey et al. 2018). In this method, particles are first removed from filters using ultrapure water while under mechanical agitation. Arrays of the particle suspensions from a portion of filter rinses are then cooled to obtain immersion freezing data per volume of water, and finally are related to sampling volumes to obtain numbers per volume of air following the analysis methods of Vali (1971). A total of 103 filters were collected, including blanks at selected intervals throughout the project. For archival and completion of tasks under this ARM proposal, nearly half of the filter collections (46) were processed for basic temperature spectra, and half of these (23) were also tested for microbial/proteinaceous contributions toward INPs via 95°C pre-treatment of portions of the original suspensions.

Southern Ocean, aerosols, marine clouds, MARCUS ca↗
DOE OSTI2018

DEVELOPMENT OF ENSEMBLE-BASED PROBABILISTIC FLOOD MAPS FOR PROBABLE MAXIMUM FLOOD

The magnitude and frequency of hydro-meteorological extremes are expected to increase in a changing environment that threaten the security of US energy-water assets. This includes probable maximum precipitation (PMP) and probable maximum flood (PMF) which are used as hydraulic design standards for critical infrastructures such as major hydropower reservoirs and nuclear power plants. To assess the potential flood hazards due to PMP/PMF, an integrated high resolution process-based hydro-meteorologic modeling framework was utilized to develop ensemble-based probabilistic flood maps based on best-available historic observations and future climate projections. A graphical process unit accelerated 2-dimensional hydrodynamic model was used to simulate the surface inundation areas corresponding to a total of PMF hydrographs. These ensemble-based PMF maps were compared with flood maps obtained from the conventional deterministic PMP/PMF approach, revealing added information of conditional probability of flooding. Further, a relative sensitivity test was conducted to explore the effects of various factors in the framework such as meteorological forcings, antecedent hydrologic conditions, reservoir storage, and flood model input resolution and parameters. The proposed framework better illustrates the uncertainties associated with model inputs, parameterization and hydro-meteorological factors allowing more informed decision-making for future emergency preparation.

13 HYDRO ENERGY↗
DOE OSTI2024

Effective parameterization of phase-field models of fission gas bubble growth

Fission gas bubbles are one of the most important microstructural features of ceramic nuclear fuels. As gas bubbles grow and interconnect, they allow release of gases, with important consequences for fuel performance. Phase-field modeling has been increasingly used to simulate the evolution of fission gas bubble microstructural because of its capability to capture complex microstructural features. However, computational performance limitations have made it difficult to simulate all the defects present in fuels during operation. For this reason, phase-field models have often simulated only vacancies and used multiple approaches to include the effect of vacancy-interstitial recombination and sinks in a simplified way. Here, we compare some of the most prevalent approaches, including source-only and source/sink. The kinetics of bubble growth using these approaches are analyzed analytically, and simulations with these approaches are compared to a full vacancy-interstitial model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Stochastic Parameterization: Toward a New View of Weather and Climate Models

The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Moist Process Biases in Simulations of the Madden–Julian Oscillation Episodes Observed during the AMIE/DYNAMO Field Campaign

Two Madden–Julian oscillation (MJO) episodes observed during the 2011 Atmospheric Radiation Measurement Program MJO Investigation Experiment (AMIE)/DYNAMO field campaign are simulated using a regional model with various cumulus parameterizations, a regional cloud-permitting model, and a global variable-resolution model with a high-resolution region centered over the tropical Indian Ocean. Model biases in relationships relevant to existing instability theories of MJO are examined and their relative contributions to the overall model errors are quantified using a linear statistical model. The model simulations capture the observed approximately log-linear relationship between moisture saturation fraction and precipitation, but precipitation associated with the given saturation fraction is overestimated especially at low saturation fraction values. This bias is a major contributor to the excessive precipitation during the suppressed phase of MJO. After accounting for this bias using a linear statistical model, the spatial and temporal structures of the model-simulated MJO episodes are much improved, and what remains of the biases is strongly correlated with biases in saturation fraction. The excess precipitation bias during the suppressed phase of the MJO episodes is accompanied by excessive column-integrated radiative forcing and surface evaporation. A large portion of the bias in evaporation is related to biases in wind speed, which are correlated with those of precipitation. Furthermore, these findings suggest that the precipitation bias sustains itself at least partly by cloud radiative feedbacks and convection–surface wind interactions.

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

Parameterizing Perennial Bioenergy Crops in Version 5 of the Community Land Model Based on Site‐Level Observations in the Central Midwestern United States

Abstract With projected expansion of biofuel production at a global scale, there is a pressing need to develop adequate representation of bioenergy crops in land surface models to help effectively quantify the biogeophysical and biogeochemical effects of its associated land use changes. This study implements two new perennial bioenergy crops, Miscanthus and switchgrass, into the Community Land Model Version 5 based on site‐level observations from the midwestern United States by modifying parameters associated with photosynthesis, phenology, allocation, decomposition, and carbon cost of nitrogen uptake and integrating concomitantly land management practices. Sensitivity analyses indicate that carbon and energy fluxes of the perennial crops are most sensitive to photosynthesis and phenology parameters. Validation of simulated fluxes against site‐level measurements demonstrates that the model is capable of capturing the overall patterns of energy and carbon fluxes, as well as physiological transitions from leaf emergence to senescence. Compared to annual crops, perennial crops feature longer growing season, greater leaf areas, and higher productivity, leading to increased transpiration, lower annual runoff, and larger carbon uptake. The model simulations suggest that with higher CO 2 assimilation rates and lower demands for nutrients and water, high‐yielding perennial crops are promising alternatives of bioenergy feedstocks compared to traditional annual crops not only for mitigating climate change but also for environmental conservation purposes by reducing fertilizer application and therefore alleviating surface‐ and ground‐water contaminations. Although the local‐scale simulations shed light on potential benefits of using perennial grasses as bioenergy feedstocks, quantifying consequences of their plantations at larger scales warrants additional investigation.

Cheng, Yanyan↗
DOE OSTI2023

Quantifying model prediction sensitivity to model-form uncertainty

Computational and mathematical models are essential to understanding complex systems and phenomena. However, when developing such models, limited knowledge and/or resources necessitates the use of simplifying assumptions. It is therefore crucial to quantify the impact of such simplifying assumptions on the reliability and accuracy of resulting model predictions. This work develops a first-of-its-kind approach to quantify the impact of physics modeling assumptions on predictions. Here, we leverage the emerging field of model-form uncertainty (MFU) representations, which are parameterized modifications to modeling assumptions, in combination with grouped Sobol’ indices to quantitatively measure an assumption’s importance. Specifically, we compute the grouped Sobol’ index for the MFU representation’s parameters as a single importance measure of the assumption for which the MFU representation characterizes uncertainty. To ensure this approach is robust to the subjective choice of how to parameterize a MFU representation, we establish bounds for the difference between sensitivity results for two different MFU representations based on differences in model prediction statistics. The capabilities associated with this approach are demonstrated on three exemplar problems: an upscaled subsurface contaminant transport problem, ablation modeling for hypersonic flight, and nuclear waste repository modeling. We found that our grouped approach is able to assess the impact of modeling assumptions on predictions and offers computational advantages over classical Sobol’ index computation while providing more interpretable results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Overview of the HI-SCALE Field Campaign: A New Perspective on Shallow Convective Clouds

Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land-atmosphere interactions and aerosol-radiation-cloud interactions can influence the formation of shallow clouds and their properties. However, these processes exhibit large spatial and temporal variability and occur at the sub-grid scale for all climate and operational forecast models and must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface layer, boundary layer, and aerosol properties. Predictions in the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data is a key factor needed to achieve a more complete understanding of the lifecycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation and Measurement (ARM) Southern Great Plains site in north-central Oklahoma during 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, the overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss the remaining scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data including: the effects of heterogeneous land-use, vegetation, and soil moisture conditions on boundary layer mixing and consequently cloud formation; factors that contribute to the transition of shallow to deep convection; the role of cloud population on

Fast, Jerome D.↗
DOE OSTI2014

Integrating Cloud Processes in the Community Atmosphere Model, Version 5.

This paper provides a description on the parameterizations of global cloud system in CAM5. Compared to the previous versions, CAM5 cloud parameterization has the following unique characteristics: (1) a transparent cloud macrophysical structure that has horizontally non-overlapped deep cumulus, shallow cumulus and stratus in each grid layer, each of which has own cloud fraction, mass and number concentrations of cloud liquid droplets and ice crystals, (2) stratus-radiation-turbulence interaction that allows CAM5 to simulate marine stratocumulus solely from grid-mean RH without relying on the stability-based empirical empty stratus, (3) prognostic treatment of the number concentrations of stratus liquid droplets and ice crystals with activated aerosols and detrained in-cumulus condensates as the main sources and evaporation-sedimentation-precipitation of stratus condensate as the main sinks, and (4) radiatively active cumulus. By imposing consistency between diagnosed stratus fraction and prognosed stratus condensate, CAM5 is free from empty or highly-dense stratus at the end of stratus macrophysics. CAM5 also prognoses mass and number concentrations of various aerosol species. Thanks to the aerosol activation and the parameterizations of the radiation and stratiform precipitation production as a function of the droplet size, CAM5 simulates various aerosol indirect effects associated with stratus as well as direct effects, i.e., aerosol controls both the radiative and hydrological budgets. Detailed analysis of various simulations revealed that CAM5 is much better than CAM3/4 in the global performance as well as the physical formulation. However, several problems were also identifed, which can be attributed to inappropriate regional tuning, inconsistency between various physics parameterizations, and incomplete model physics. Continuous efforts are going on to further improve CAM5.

CAM5↗
DOE OSTI2018

Advanced Tool for Computer Aided Battery Engineering (SNL)

This project aims to provide experimental support, including support for validation and parameterization, for mechanical failure modeling work as part of DOE's Computer Aided Engineering for Electric-Drive Vehicle Batteries (CAEBAT) program. This work involves mechanical deformation testing on both charged and discharged cells, including abusive mechanical testing leading to battery failure. The mechanical data generated is provided to the modeling teams to provide empirical parameterization as well as validation for newly developed models. Testing fully charged cells and packs (abusive battery testing) also allows for a better understanding of what conditions are most likely to lead to a potentially hazardous thermal runaway event.

25 ENERGY STORAGE↗
DOE OSTI2020

Characterization of Spall in Hard Rock from Observations and Simulations of the Source Physics Experiment Phase I

In this work, spall signals from the Source Physics Experiments are presented, analyzed, and modeled for insight to the explosion source. The observed signal is similar in nature to nearby historical nuclear explosions, and the surface force-time history or velocity can be interpreted with the same model. We use the models for peak spall velocity, spalled mass, and spall depth and radius derived from historical nuclear explosions to parameterize the physical force-time history model from Stump (1985) and show that this parameterized model can be used for spall prediction. The spall signal is also investigated with a numerical continuum model that incorporates gravity. Peak velocity and dwell time are well predicted, and the multiple slap-down phases are captured if one includes a weak near-surface layer similar to the geologic observation.

58 GEOSCIENCES↗
DOE OSTI2014

A novel approach for introducing cloud spatial structure into cloud radiative transfer parameterizations

Subgrid-scale variability is one of the main reasons why parameterizations are needed in large-scale models. Although some parameterizations started to address the issue of subgrid variability by introducing a subgrid probability distribution function for relevant quantities, the spatial structure has been typically ignored and thus the subgrid-scale interactions cannot be accounted for physically. Here we present a new statistical-physics-like approach whereby the spatial autocorrelation function can be used to physically capture the net effects of subgrid cloud interaction with radiation. The new approach is able to faithfully reproduce the Monte Carlo 3D simulation results with several orders less computational cost, allowing for more realistic representation of cloud radiation interactions in large-scale models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Persistent, extensive channelized drainage modeled beneath Thwaites Glacier, West Antarctica

Abstract. Subglacial hydrology is a leading control on basal friction and the dynamics of glaciers and ice sheets. At low discharge, subglacial water flows through high-pressure, sheet-like systems that lead to low effective pressures. However, at high discharge, subglacial water melts the overlying ice into localized channels that efficiently remove water from the bed, thereby increasing effective pressure and basal friction. Recent observations suggest channelized subglacial flow exists beneath Thwaites Glacier, yet it remains unclear if stable channelization is feasible in West Antarctica, where surface melting is nonexistent and water at the bed is limited. Here, we use the MPAS-Albany Land Ice model to run a suite of over 130 subglacial hydrology simulations of Thwaites Glacier across a wide range of physical parameter choices to assess the likelihood of channelization. We then narrow our range of viable simulations by comparing modeled water thicknesses to previously observed radar specularity content, which indicates flat, spatially extensive water bodies at the bed. In all of our data-compatible simulations, stable channels reliably form within 100–200 km of the grounding line and reach individual discharge rates of 35–110 m3 s−1 at the ice–ocean boundary. While only one to two channels typically form across the 200 km width of the glacier in our simulations, their high efficiency drains water across the entire lateral extent of the glacier. We posit the large catchment size of Thwaites Glacier, its funnel-like geometry, and high basal melt rates together accumulate enough water to form stable channels. No simulations resembled observed specularity content when channelization was disabled. Our results suggest channelized subglacial hydrology has two consequences for Thwaites Glacier dynamics: (i) amplifying submarine melting of the terminus and ice shelf while (ii) simultaneously raising effective pressure within 100 km of the grounding line and increasing basal friction. The distribution of effective pressure implied from our modeling differs from parameterizations typically used in large-scale ice sheet models, suggesting the development of more process-based parameterizations may be necessary.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Functional-type modeling approach and data-driven parameterization of methane emissions in wetlands (Final Technical Science Report)

Our goals are to improve understanding and quantitative representation of the multiple processes that affect methane emissions at a high (patch level, vertically detailed) spatial resolution, and translate this understanding to improved modeling capability of coastal wetland fluxes using the E3SM Land Model (ELM v1) wetland CH4 biogeochemistry module. We propose an experimental approach to identify and parameterize uncertainties in ELM. Understanding of methane emissions can be improved along three conceptual axes: (i) horizontal (ecohydrological patch resolution), (ii) vertical (through the depth of the soil column), and (iii) process level (e.g., resolving microbial pathways, vegetation specific transport pathways). Along each of the three axes, we will characterize, quantify, and model, the key ecological, hydrological, and meteorological controls of methane (CH4) flux heterogeneity in four model coastal wetlands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization

To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Improving parameterization of ice microphysical processes in Arctic clouds using a synergistic modeling and observational approach (Final Report)

The primary goals of the project were to: 1) Improve the fundamental quantitative models of ice growth processes using ARM radar observations. 2) Better understand the growth of ice particle populations through vapor deposition, riming, and aggregation in Arctic clouds using a new particle-property microphysical model. 3) Robustly estimate and constrain key unknown model parameters using ARM radar observations combined with the particle-property model to develop an improved parameterization of ice growth processes for traditional bulk parameterizations used in cloud and climate models.

58 GEOSCIENCES↗