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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Aircraft System Noise Prediction Uncertainty Quantification for a Hybrid Wing Body Subsonic Transport Concept

Aircraft system level noise prediction for advanced, unconventional concepts has undergone significant improvement over the past two decades. The prediction modeling uncertainty must be quantified so that potential benefits of unconventional configurations, which are outside of the range of empirical models, can be reliably assessed. This paper builds on previous work in an effort to improve estimates of element prediction uncertainties where the prediction methodology has been improved, or new experimental validation data are available, to provide an estimate of the system level uncertainty in the prediction process. In general, the uncertainty of the prediction will be strongly dependent on the aircraft configuration as well as which technologies are integrated. While the quantitative uncertainty values contained here are specific to the hybrid wing body design presented, the underlying process is the same regardless of configuration. A refined process for determining the uncertainty for each element of the noise prediction is detailed in this paper. The system level uncertainty in the prediction of the aircraft noise is determined at the three certification points, using a Monte Carlo method. Comparisons with previous work show a reduction of 1 EPNdB in the 95%coverage interval of the cumulative noise level. The largest impediment for continued reduction in uncertainty for the hybrid wing body concept is the need for improved modeling and validation experiments for fan noise, propulsion airframe aeroacoustic effects, and the Krueger flap, which comprise the bulk of the uncertainty in the cumulative certification noise level.

June, Jason C.↗
DOE OSTI2021

Machine learning to extend and understand the sources and limits of water cycle predictability on subseasonal-to-decadal timescales in the Earth system

This white paper provides initial insight on how artificial intelligence (AI) and machine learning (ML), including interpretability and explainable AI (XAI) methods, can be leveraged to glean insight from complex data for a paradigm-changing improvement in Earth system predictability on subseasonal-to-seasonal (S2S) and seasonal-to-decadal (S2D) timescales. The application of AI to extend and improve predictability, in combination with causal inference and uncertainty quantification, could lead to a transformative understanding of the integrative water cycle and associated extremes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Biogeochemical Effects of Rising Atmospheric CO2 on Terrestrial and Ocean Systems

Rising carbon dioxide (CO2) has decreased seawater pH at long-term observing stations around the world, including in the open ocean north of Oahu, Hawaii, near Alaska's Aleutian Islands, the Gulf of Maine shore, and on Gray's Reef in the southeastern United States. This ocean acidification process has already affected some marine species and altered fundamental ecosystem processes, and further effects are likely. While atmospheric CO rises at approximately the same rate all over the globe, its non-climate effects on land vary depending on climate and dominant species. In terrestrial ecosystems, rising atmospheric CO concentrations are expected to increase plant photosynthesis, growth, and water-use efficiency, though these effects are reduced when nutrients, drought or other factors limit plant growth. Rising CO would likely change carbon storage and influence terrestrial hydrology and biogeochemical cycling, but concomitant effects on vegetation composition and nutrient feedbacks are challenging to predict, making decadal forecasts uncertain. Consequences of rising atmospheric CO are expected to include difficult-to-predict changes in the ecosystem services that terrestrial and ocean systems provide to humans. For instance, ocean acidification resulting from rising CO has decreased the supply of larvae that sustains commercial shellfish production in the northwestern United States. In addition, CO fertilization (increases) plus warming (decreases) are changing terrestrial crop yields. Continued persistence of uptake of carbon by the land and ocean is uncertain. Climate and environmental change create complex feedbacks to the carbon cycle and it is not clear how feedbacks modulate future effects of rising CO on carbon sinks. These are several mechanisms that could reduce future sink capacity.

Biogeochemical↗
NASA NTRS2002

The Current Status of Unsteady CFD Approaches for Aerodynamic Flow Control

An overview of the current status of time dependent algorithms is presented. Special attention is given to algorithms used to predict fluid actuator flows, as well as other active and passive flow control devices. Capabilities for the next decade are predicted, and principal impediments to the progress of time-dependent algorithms are identified.

Carpenter, Mark H.↗
DOE OSTI2021

Data-Driven Exploration of Climate Attractor Manifolds For Long-Term Predictability

Focal Area: This white paper responds to Focal Area 3. We seek to gain insight into decadal-scale climate predictability by applying novel manifold-finding probabilistic AI techniques to the complex data produced by Earth System models (ESMs) such as E3SM. The associated portfolio of research activities leverages DOE’s asset mix of HPC platforms, climate expertise, climate simulation codes, and AI expertise. Science Challenge: Climate and climate models are dynamical systems exhibiting properties that are interpretable through chaos theory. The theory contains an important concept that is relevant to multi-decade-scale climate prediction: a chaotic attractor. While the space containing all the possible states of the Earth’s atmosphere and ocean, the possible weather, is large, the realized states tend to stay near the smaller-dimensioned attractor. This behavior is responsible for the “order behind the irregularity” [1] of climate phenomena. Climate change can be thought of as a change in the properties of the attractor, and predicting the climate over years to decades is equivalent to predicting how those properties will change. To date, the attractor has been a useful conceptual tool, but has not been amenable to direct characterization. A new development is the advent of efficient high-dimensional manifold-finding probabilistic AI techniques, which permit a data-driven characterization of the ESM attractor and its probability distribution over weather states. Such a characterization would result in a natural dimensional reduction — a “non-linear Principal Components Analysis (PCA) adapted to climate simulation data” — leading to important advances in scenario-based long-term climate prediction, long-term prediction of water cycle extremes, ESM verification, inter-model comparison, and process model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

Abstract Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill.

Geology↗
DOE OSTI2022

Long-range prediction and the stratosphere

Abstract. Over recent years there have been concomitant advances in the development of stratosphere-resolving numerical models, our understanding of stratosphere–troposphere interaction, and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing for new and improved capability in long-range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal, and annual-to-decadal climate predictions and multidecadal projections. We end with an outlook towards the future and identify areas of improvement that could further benefit these rapidly evolving predictions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

GISS Analysis of Surface Temperature Change

We describe the current GISS analysis of surface temperature change for the period 1880-1999 based primarily on meteorological station measurements. The global surface temperature in 1998 was the warmest in the period of instrumental data. The rate of temperature change was higher in the past 25 years than at any previous time in the period of instrumental data. The warmth of 1998 was too large and pervasive to be fully accounted for by the recent El Nino. Despite cooling in the first half of 1999, we suggest that the mean global temperature, averaged over 2-3 years, has moved to a higher level, analogous to the increase that occurred in the late 1970s. Warming in the United States over the past 50 years has been smaller than in most of the world, and over that period there was a slight cooling trend in the Eastern United States and the neighboring Atlantic Ocean. The spatial and temporal patterns of the temperature change suggest that more than one mechanism was involved in this regional cooling. The cooling trend in the United States, which began after the 1930s and is associated with ocean temperature change patterns, began to reverse after 1979. We suggest that further warming in the United States to a level rivaling the 1930s is likely in the next decade, but reliable prediction requires better understanding of decadal oscillations of ocean temperature.

Hansen, J.↗
DOE OSTI2021

Verification of Coupled Hydraulic Fracturing Simulators Using Laboratory-Scale Experiments

In this work, we aim to verify the predictions of the numerical simulators, which are used for designing field-scale hydraulic stimulation experiments. Although a strong theoretical understanding of this process has been gained over the past few decades, numerical predictions of fracture propagation in low-permeability rocks still remains a challenge. Against this background, we performed controlled laboratory-scale hydraulic fracturing experiments in granite samples, which not only provides high-quality experimental data but also a well-characterized experimental set-up. Using the experimental pressure responses and the final fracture sizes as benchmark, we compared the numerical predictions of two coupled hydraulic fracturing simulators—CSMP and GEOS. Both the simulators reproduced the experimental pressure behavior by implementing the physics of Linear Elastic Fracture Mechanics (LEFM) and lubrication theory within a reasonable degree of accuracy. The simulation results indicate that even in the very low-porosity (1–2 %) and low-permeability (10 -18 m 2 - 10 -19 m 2 ) crystalline rocks, which are usually the target of EGS, fluid-loss into the matrix and unsaturated flow impacts the formation breakdown pressure and the post-breakdown pressure trends. Therefore, underestimation of such parameters in numerical modeling can lead to significant underestimation of breakdown pressure. The simulation results also indicate the importance of implementing wellbore solvers for considering the effect of system compressibility and pressure drop due to friction in the injection line. The varying injection rate as a result of decompression at the instant of fracture initiation affects the fracture size, while the entry friction at the connection between the well and the initial notch may cause an increase in the measured breakdown pressure.

15 GEOTHERMAL ENERGY↗
NASA NTRS2010

Prediction Activities at NASA's Global Modeling and Assimilation Office

The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the climate community. An improved understanding of the nature of decadal variability and its predictability has important implications for efforts to assess the impacts of global change in the coming decades. In fact, the GMAO has taken on the challenge of carrying out experimental decadal predictions in support of the IPCC AR5 effort.

Schubert, Siegfried↗
DOE OSTI2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Role of Sea‐Surface Salinity in Simulating Historical Decadal Variations of Atlantic Meridional Overturning Circulation in a Coupled Climate Model

Abstract Extending climate prediction from seasonal to decadal timescales requires realistic initialization of not only upper ocean heat content but also the Atlantic meridional overturning circulation (AMOC). However, it remains a major challenge to realistically initialize AMOC in a coupled system while also maintaining a balanced atmosphere‐ocean initial state. This study demonstrates the feasibility of generating fully coupled historical states with realistic AMOC variability. Employing a forced ocean—sea‐ice (FOSI) model simulation as the “truth,” we show reproducibility of key features of historical AMOC decadal variability in a fully coupled model by restoring sea‐surface salinity, in addition to sea‐surface temperature restoring widely used in seasonal prediction. The atmospheric state of the restored coupled model solution is much closer to that of the free coupled simulation than to the observations used in FOSI, pointing to potential advantages of using this approach for initializing decadal predictions with reduced ocean initialization shock.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

An Overview of Energetic Particle Precipitation Effects on the Earth's Atmosphere and (Potentially) Climate

Energetic precipitating particles (EPPs) can cause significant constituent changes in the polar mesosphere and stratosphere (middle atmosphere) during certain periods. Both protons and electrons can influence the polar middle atmosphere through ionization and dissociation processes. EPPs can enhance HOx (H, OH, HO2) through the formation of positive ions followed by complex ion chemistry and NOx (N, NO, NO2) through the dissociation of molecular nitrogen. The solar EPP-created HOx increases can lead to ozone destruction in the mesosphere and upper stratosphere via several catalytic loss cycles. Such middle atmospheric HOx-caused ozone loss is rather short-lived due to the relatively short lifetime (hours) of the HOx constituents. The HOx-caused ozone depletion of greater than 30% has been observed during several large solar proton events (SPEs) in the past 50 years. HOx enhancements due to SPEs were confirmed by observations in solar cycle 23. A number of modeling studies have been undertaken over this time period that show predictions of enhanced HOx accompanied by decreased ozone due to energetic particles. The solar EPP-created NOx family has a longer lifetime than the HOx family and can also lead to catalytic ozone destruction. EPP-caused enhancements of the NOx family can affect ozone promptly, if produced in the stratosphere, or subsequently, if produced in the lower thermosphere or mesosphere and transported to the stratosphere. NOx enhancements due to auroral electrons, medium and high energy electrons, relativistic electron precipitation (REP) events, and SPEs have been measured and/or modeled for decades. Model predictions and measurements show that certain years have significant winter-time meteorological events, which result in the transport of EPP-caused NOx enhancements in the upper mesosphere and lower thermosphere to lower altitudes. The NOx-caused ozone depletion has also been observed during several solar proton events (SPEs) in the past 50 years. Model predictions indicate that the longer-lived SPE-caused polar stratospheric and mesospheric ozone decrease can be >10% for up to five months past the largest events and is statistically significant; however, total ozone measurements do not indicate any long-term SPE impact.

Jackman, Charles H.↗
DOE OSTI2023

Northern Hemisphere Snow Drought in Earth System Model Simulations and ERA5‐Land Data in 1980–2014

Abstract Low snow levels over the past few decades and predictions of a low‐to‐no snow future have spurred research into snow droughts, which pose a threat to water security and management. Systematic data‐model comparisons of snow drought have been lacking, hindering our understanding of the drivers of snow drought in the past. To address this gap, we analyzed snow drought events using standardized snow water equivalent index derived from monthly results of four numerical experiments using the E3SM Land Model (ELM) and ERA5‐Land data during the period of 1980–2014. Additionally, we compared snow drought duration calculated from models with those from the ERA5‐Land data during selected El Niño‐Southern Oscillation (ENSO) years. The numerical experiments were conducted with ELM driven by two prescribed atmospheric forcings, and with the coupled land‐atmosphere configuration of E3SM with and without plant hydraulics scheme feedback. Analysis reveals that 20%–30% of snow droughts occur due to factors other than above‐normal temperature and low snowfall, such as low soil moisture, warm soil temperature, and low relative humidity, etc., especially in high latitudes (50° North). Furthermore, our study highlights the exacerbating effect of ENSO events on snow drought conditions in various regions, despite some discrepancies between model and ERA5‐Land results. We also identified limitations of the coupled land‐atmosphere models in our current configuration in capturing the spatial patterns of snow droughts. This study underscores the challenge of predicting and mitigating snow drought and the need for a comprehensive understanding of the factors contributing to snow drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Experimental observation of open structures in elemental magnesium at terapascal pressures

Investigating how solid matter behaves at enormous pressures, such as those found in the deep interiors of giant planets, is a great experimental challenge. Over the past decade, computational predictions have revealed that compression to terapascal pressures may bring about counter-intuitive changes in the structure and bonding of solids as quantum mechanical forces grow in influence. Although this behavior has been observed at modest pressures in the highly compressible light alkali metals, it has not been established whether it is commonplace among high-pressure solids more broadly. We used shaped laser pulses at the National Ignition Facility to compress elemental Mg up to 1.3 TPa, which is approximately four times the pressure at the Earth’s core. By directly probing the crystal structure using nanosecond-duration X-ray diffraction, we found that Mg changes its crystal structure several times with non-close-packed phases emerging at the highest pressures. Our results demonstrate that phase transformations of extremely condensed matter, previously only accessible through theoretical calculations, can now be experimentally explored.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Comparison of Interface Growth Models Applied to Rayleigh–Taylor and Richtmyer–Meshkov Instabilities

Sophisticated numerical models that contain fluid interfaces rely upon interface evolution models to approximate the transition to turbulence near interfaces, in the presence of Rayleigh–Taylor (RTI) or Richtmyer–Meshkov (RMI) instability. Semi-analytical models have been developed in recent decades to predict the interface growth from an initial state into the nonlinear regime. Two of these models are considered in this study. They are the Goncharov and the z-models. Both of these interface models have strengths and weaknesses, which are examined here. Both of them have been implemented in the xRAGE compressible flow solver, which models fluid interfaces. The flow solver provides the fluids acceleration as a body force to the interface model. The purpose of such interface model is to evolve the early times of interface position as a subgrid model within a compressible flow simulation in order to then initialize a turbulence model. In this work, the interface models are assessed and compared for their evolution of RTI and RMI. The z-model performed better than the Goncharov model for all cases that were explored.

42 ENGINEERING↗
DOE OSTI2026

A practical guide to unbinned unfolding

Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data.

Canelli, Florencia [Univ. of Zurich (Switzerland)]↗
DOE OSTI2024

Decadal Thermal Variability of the Upper Southern Ocean: Zonal Asymmetry

Abstract As the major sink of anthropogenic heat, the Southern Ocean has shown quasi-symmetric, deep-reaching warming since the mid-twentieth century. In comparison, the shorter-term heat storage pattern of the Southern Ocean is more complex and has notable impacts on regional climate and marine ecosystems. By analyzing observational datasets and climate model simulations, this study reveals that the Southern Ocean exhibits prominent decadal (>8 years) variability extending to ∼700-m depth and is characterized by out-of-phase changes in the Pacific and Atlantic–Indian Ocean sectors. Changes in the Pacific sector are larger in magnitude than those in the Atlantic–Indian Ocean sectors and dominate the total heat storage of the Southern Ocean on decadal time scales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and preindustrial simulations of the Community Earth System Model version 1 (CESM1) suggest that these variations in Southern Ocean winds arise primarily from natural variability of the tropical Pacific, as represented by the interdecadal Pacific oscillation (IPO). Through atmospheric teleconnection, the positive phase of the IPO gives rise to higher-than-normal sea level pressure and anticyclonic wind anomalies in the 50°–70°S band of the Pacific sector. These winds lead to warming of 0–700 m by driving the convergence of warm water. The opposite processes, involving cyclonic winds and upper-layer divergence, occur in the Atlantic–Indian Ocean sector. These findings aid our understanding of the time-varying heat storage of the Southern Ocean and provide useful implications on initialized decadal climate prediction.

Meteorology & Atmospheric Sciences↗