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At least 1,207 records · Page 67

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NASA NTRS1999

Middle Atmosphere Transport Properties of Assimilated Datasets

One of the most compelling reasons for performing data assimilation in the middle atmosphere is to obtain global, balanced datasets for studies of trace gas transport and chemistry. This is a major motivation behind the Goddard Earth observation System-Data Assimilation System (GEOS-DAS). Previous studies have shown that while this and other data assimilation systems can generally obtain good estimates of the extratropical rotational velocity field, the divergent part of the dynamical field is deficient; this impacts the "residual circulation" and leads to spurious trace gas transport on seasonal and interannual timescales. These problems are impacted by the quality and the method of use of the observational data and by deficiencies in the atmospheric general circulation model. Whichever the cause at any place and time, the "solution" is to introduce non-physical forcing terms into the system (the so-called incremental analysis updates); these can directly (thermal) or indirectly (mechanical) affect the residual circulation. This paper will illustrate how the divergent circulation is affected by deficiencies in both observations and models. Theoretical considerations will be illustrated with examples from the GEOS-DAS and from simplified numerical experiments. These are designed to isolate known problems, such as the inability of models to sustain a quasi-biennial oscillation and sparse observational constraints on tropical dynamics, or radiative inconsistencies in the presence of volcanic aerosols.

Pawson, Steven↗

NASA NTRS1999

Middle Atmospheric Transport Properties of Assimilated Datasets

Pawson, Steven↗

DOE OSTI2022

KilonovaNet : Surrogate models of kilonova spectra with conditional variational autoencoders

ABSTRACT Detailed radiative transfer simulations of kilonova spectra play an essential role in multimessenger astrophysics. Using the simulation results in parameter inference studies requires building a surrogate model from the simulation outputs to use in algorithms requiring sampling. In this work, we present kilonovanet, an implementation of conditional variational autoencoders (cVAEs) for the construction of surrogate models of kilonova spectra. This method can be trained on spectra directly, removing overhead time of pre-processing spectra, and greatly speeds up parameter inference time. We build surrogate models of three state-of-the-art kilonova simulation data sets and present in-depth surrogate error evaluation methods, which can in general be applied to any surrogate construction method. By creating synthetic photometric observations from the spectral surrogate, we perform parameter inference for the observed light-curve data of GW170817 and compare the results with previous analyses. Given the speed with which kilonovanet performs during parameter inference, it will serve as a useful tool in future gravitational wave observing runs to quickly analyse potential kilonova candidates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2002

Tropospheric Ozone during the TRACE-P Mission: Comparison between TOMS Satellite Retrievals and Aircraft Lidar Data, March 2001

Over the past several years, we have developed two new tropospheric ozone retrievals from the TOMS (Total Ozone Mapping Spectrometer) satellite instrument that are of sufficient resolution to follow pollution episodes. The modified-residual technique uses v. 7 TOMS total ozone and is applicable to tropical regimes in which the wave-one pattern in total ozone is observed. The TOMS-direct method ('TDOT' = TOMS Direct Ozone in the Troposphere) represents a new algorithm that uses TOMS radiances directly to extract tropospheric ozone in regions of constant stratospheric ozone. It is not geographically restricted, using meteorological regimes as the basis for classifying TOMS radiances and for selecting appropriate comparison data. TDOT is useful where tropospheric ozone displays high mixing ratios and variability characteristic of pollution. Some of these episodes were observed downwind of Asian biomass burning during the TRACE-P (Transport and Atmospheric Chemical Evolution-Pacific) field experiment in March 2001. This paper features comparisons among TDOT tropospheric ozone column depth, integrated uv-DIAL measurements made from NASA's DC-8, and ozonesonde data.

Frolov, A. D.↗

DOE OSTI2020

Characterization of partially observed epidemics through Bayesian inference: application to COVID-19

We demonstrate a Bayesian method for the "real-time'" characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients.The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted.The method also detected different disease dynamics when applied to specific region of New Mexico

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Comparison of active interrogation methods for source location in a scattering and absorbing medium, consisting of PGNAA, and an AmBe quasi-forward biased directional source

Source location of Special Nuclear Material (SNM) encompassing 95% 235 U and 239 Pu is identified by utilizing a directional source from Patent No: US20190013109A1 using Prompt Gamma Neutron Activation Analysis (PGNAA) and neutron spectroscopy simulated with Monte-Carlo N-Particle transport 6.2 (MCNP). BC-408, HPGe, LaBr 3 detector arrays were used to identify the location of the SNM using total counts incident on each detector, and PGNAA photopeaks from HPGe and LaBr 3 detector arrays in a polyethylene shield. The conducted simulations varied the volume and location of the SNM in the MCNP input files to observe how the source location method behaved. PGNAA photopeaks used for source identification include 61 keV from fission, 2.223 MeV prompt gamma from hydrogen, 511 keV annihilation, and a single and double escape peaks from the prompt gamma interaction from hydrogen. The capabilities of each detector systems to acquire well resolved photopeaks with a 1% relative error or less, and total relative error for F4 and F8 tallies were less than 0.015% relative error. Source predictions of the SNM with uneven amounts of polyethylene shielding between the source and detectors was observed to overpredict and give invalid source location predictions. Source locations of the SNM with even amounts of polyethylene material between the source and each detector were found to be valid. With a 1 Ci 241 Am source activity, it was determined that 1630 s were needed to obtain the results for each detector system with the quasi-forward directional AmBe source. Coupling source and material identification together would increase acquisition time but would only require one system to determine.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Unifying and benchmarking state-of-the-art quantum error mitigation techniques

Error mitigation is an essential component of achieving a practical quantum advantage in the near term, and a number of different approaches have been proposed. In this work, we recognize that many state-of-the-art error mitigation methods share a common feature: they are data-driven, employing classical data obtained from runs of different quantum circuits. For example, Zero-noise extrapolation (ZNE) uses variable noise data and Clifford-data regression (CDR) uses data from near-Clifford circuits. We show that Virtual Distillation (VD) can be viewed in a similar manner by considering classical data produced from different numbers of state preparations. Observing this fact allows us to unify these three methods under a general data-driven error mitigation framework that we call UNIfied Technique for Error mitigation with Data (UNITED). In certain situations, we find that our UNITED method can outperform the individual methods (i.e., the whole is better than the individual parts). Specifically, we employ a realistic noise model obtained from a trapped ion quantum computer to benchmark UNITED, as well as other state-of-the-art methods, in mitigating observables produced from random quantum circuits and the Quantum Alternating Operator Ansatz (QAOA) applied to Max-Cut problems with various numbers of qubits, circuit depths and total numbers of shots. We find that the performance of different techniques depends strongly on shot budgets, with more powerful methods requiring more shots for optimal performance. For our largest considered shot budget (10 10 ), we find that UNITED gives the most accurate mitigation. Hence, our work represents a benchmarking of current error mitigation methods and provides a guide for the regimes when certain methods are most useful.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Observing Arctic Sea Ice

Our understanding of Arctic sea ice and its wide-ranging influence is deeply rooted in observation. Advancing technologies have profoundly improved our ability to observe Arctic sea ice, document its processes and properties, and describe atmosphere-ice-ocean interactions with unprecedented detail. Yet, our progress toward better understanding the Arctic sea ice system is mired by the stark disparities between observations that tend to be siloed by method, scientific discipline, and application. This article presents a review and philosophical design for observing sea ice and accelerating our understanding of the Arctic sea ice system. We give a brief history of Arctic sea ice observations and showcase the 2018 melt season within the context of five observational themes: spatial heterogeneity, temporal variability, cross-disciplinary science, scalability, and retrieval uncertainty. We synthesize buoy data, optical imagery, satellite retrievals, and airborne measurements to demonstrate how disparate data sets can be woven together to transcend observational-scale issues. The results show that there are limitations to interpreting any single data set alone. However, many of these limitations can be surmounted by combining observations that cross spatial and temporal scales. We conclude the article with pathways toward coordinating across observational platforms in order to: (1) optimize the scientific, operational, and community return on observational investments, and (2) facilitate a richer understanding of Arctic sea ice and its role in the climate system.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

A Variational Method to Retrieve the Extinction Profile in Liquid Clouds Using Multiple Field-of-View Lidar

While liquid clouds playa very important role in the global radiation budget, it's been very difficult to remotely determine their internal cloud structure. Ordinary lidar instruments (similar to radars but using visible light pulses) receive strong signals from such clouds, but the information is limited to a thin layer near the cloud boundary. Multiple field-of-view (FOV) lidars offer some new hope as they are able to isolate photons that were scattered many times by cloud droplets and penetrated deep into a cloud before returning to the instrument. Their data contains new information on cloud structure, although the lack of fast simulation methods made it challenging to interpret the observations. This paper describes a fast new technique that can simulate multiple-FOV lidar signals and can even estimate the way the signals would change in response to changes in cloud properties-an ability that allows quick refinements in our initial guesses of cloud structure. Results for a hypothetical airborne three-FOV lidar suggest that this approach can help determine cloud structure for a deeper layer in clouds, and can reliably determine the optical thickness of even fairly thick liquid clouds. The algorithm is also applied to stratocumulus observations by the 8-FOV airborne "THOR" lidar. These tests demonstrate that the new method can determine the depth to which a lidar provides useful information on vertical cloud structure. This work opens the way to exploit data from spaceborne lidar and radar more rigorously than has been possible up to now.

Pounder, Nicola L.↗

NASA NTRS2001

Porosity and Variations in Microgravity Aerogel Nano-Structures: New Laser Speckle Characterization Methods - 2

The transition from sol to gel is a process that is critical to the properties of engineered nanomaterials, but one with few available techniques for observing the dynamic processes occurring during the evolution of the gel network. Specifically, the observation of various cluster aggregation models, such as diffusion-limited and reaction-limited cluster growth can be quite difficult. This can be rather important as the actual aggregation model can dramatically influence the mechanical properties of gels, and is significantly affected by the presence of convective flows, or their absence in microgravity. We have developed two new non-intrusive optical methods for observing the aggregation processes within gels in real time. These make use of the dynamic behavior of laser speckle patterns produced when an intense laser source is passed through a gelling sol. The first method is a simplified time-correlation measurement, where the speckle pattern is observed using a CCD camera and information on the movement of the scattering objects is readily apparent. This approach is extremely sensitive to minute variations in the flow field as the observed speckle pattern is a diffraction-based image, and is therefore sensitive to motions within the sol on the order of the wavelength of the probing light. Additionally, this method has proven useful in determining a precise time for the gel-point, an event often difficult to measure. Monitoring the evolution of contrast within the speckle field is another method that has proven useful for studying aeration. In this case, speckle contrast is dependent upon the size (correlation length) and number of scattering centers, increasing with increasing size, and decreasing with increasing numbers. The dynamic behavior of cluster growth in gels causes both of these to change simultaneously with time, the exact rate of which is determined by the specific aggregation model involved. Actual growth processes can now be observed, and the effects of varying gravity fields on the growth processes qualitatively described. Results on preliminary ground-based measurements have been obtained.

Hunt, A. J.↗

NASA NTRS2019

New York City Panel on Climate Change 2019 Report Chapter 1: Introduction

While urban areas like New York City and its surrounding metropolitan region are key drivers of climate change through emissions of greenhouse gases, cities are also significantly impacted by climate shifts, both chronic changes and extreme events. These are already affecting the New York metropolitan region, including the five boroughs of New York City through higher temperatures, more intense precipitation, and higher sea levels, and will increasingly do so in the coming decades. The City of New York has embarked on a flexible adaptation pathway (i.e., strategies that can evolve through time as climate risk assessment, evaluation of adaptation strategies, and monitoring continues) to respond to climate change challenges. This entails significant programs to develop resilience in communities and critical infrastructure to observed and projected changes in temperature, precipitation, and sea level. The first NPCC Report laid out the risk management framing for the city and region via flexible adaptation pathways. The second New York City Panel on Climate Change Report (NPCC2) developed the “climate projections of record” that are currently being used by the City of New York in its resilience programs . The NPCC3 2019 Report co-generates new tools and methods for the next generation of climate risk assessments and implementation of region-wide resilience. Co-generation is an interactive process by which stakeholders and scientists work together to produce climate change information that is targeted to decision-making needs. These tools and methods can be used to observe, project, and map climate extremes; monitor risks and responses; and engage with communities to develop effective programs. They are especially important at “transformation points” in the adaptation process when large changes in the structure and function of physical, ecological, and social systems of the city and region are undertaken.

Rosenzweig, Cynthia↗

NASA NTRSDate not supplied

A Machine Learning Approach for the Remote Sensing Retrieval of Oil Spills from Polarimetric Measurements

The Fresnel laws of specular reflection directly connect remote sensing observations of light polarization within the sunglint region to the ocean surface refractive index. This parameter is instrumental to identify areas affected by oil, or any other floating substance capable of modifying the refractive index of pure seawater. In preparation for the NASA Plankton, Aerosol, ocean Ecosystem (PACE) mission, we are developing an advanced retrieval scheme that exploits such measurements to deliver the refractive index as an operational product. The original method was developed based on observations of the airborne Research Scanning Polarimeter. Here we present several aspects related to the extension of this technique to the PACE HARP-2 sensor (especially regarding the projected accuracy), and discuss preliminary results obtained by applying neural-network trainings to look-up tables produced with the forward radiative transfer code, with the goal of improving the computational performance. Success in the final implementation will guarantee a version of the retrieval algorithm suitable to fast-response needs and disaster management.

machine learning↗

DOE OSTI2022

Iron corrosion in the “inert” supercritical CO 2 , ab initio dynamics insights: How impurities matter

Metal corrosion from exposure to supercritical fluids is a long-standing challenge lacking atomistic understanding for effective corrosion prevention in diverse important applications. Here, in this work, reactions relevant to corrosion in supercritical CO 2 (sCO 2 ), with H 2 O and NO 2 as impurities, are revealed using ab initio molecular dynamics and enhanced sampling methods. While little reactivity is observed in bulk sCO 2 , the iron surface is found to play a crucial almost-catalytic role in its own corrosion, activating the inert CO2 and hence facilitating $\ast$HCO 3 – and $\ast$CO 3 2– formation at the interface. Moreover, the hydrogen bond network of $\ast$H 2 O assists the H shuttling to the activated CO 2 and impurities, forming reactive species observed in experiments. Such atomistic insight elucidates the reaction mechanisms of metal corrosion in aqueous and supercritical fluids and can point to possible remedies.

36 MATERIALS SCIENCE↗

NASA NTRS1986

On crossover adjustment in satellite altimetry and its oceanographic implications

It is argued that, to remove the satellite orbital errors, the crossover adjustment method that represents orbital errors in terms of Fourier series is preferable to the conventional bias-and-tilt crossover adjustment method. However, no matter what technique is used in the crossover adjustment, orbital errors will introduce a geographically dependent error (mostly a large-scale slope, which, in terms of spherical harmonics, is represented by the degree 1 terms) into the derived sea surface topography. This is because the responsible orbital errors have left no traces in crossover differences and therefore can never be removed. However, it is possible to reduce this fundamental uncertainty substantially if the record length is long enough; its implications on observing the basin-scale ocean circulation by satellite altimetry are discussed. New methods for observing the large-scale oceanic variability are also proposed (the conventional approach being limited to observing the mesoscale variability).

Tai, C.-K.↗

DOE OSTI2026

A score-based diffusion model approach for adaptive learning of stochastic partial differential equation solutions

In this paper, we propose a novel framework for adaptively learning the time-evolving solutions of stochastic partial differential equations (SPDEs) using score-based diffusion models within a recursive Bayesian inference setting. SPDEs play a central role in modeling complex physical systems under uncertainty, but their numerical solutions often suffer from model errors and reduced accuracy due to incomplete physical knowledge and environmental variability. To address these challenges, we encode the governing physics into the score function of a diffusion model using simulation data and incorporate observational information via a likelihood-based correction in a reverse-time stochastic differential equation. This enables adaptive learning through iterative refinement of the solution as new data becomes available. To improve computational efficiency in high-dimensional settings, we introduce the ensemble score filter, a training-free approximation of the score function designed for real-time inference. Numerical experiments on benchmark SPDEs demonstrate the accuracy and robustness of the proposed method under sparse and noisy observations.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1988

Investigations of turbulent and directed motions in solar flares

A method is presented for fitting asymmetric line profiles from spectrally resolved observations of solar flares obtained during the past solar maximum. The method is applied to more than 40 flares observed with the Bent Crystal Spectrometer on the Solar Maximum Mission. Correlations between turbulent and directed motions are discussed. From ratios of Ca XIX and Fe XXV line intensities, the temperature of the upflowing component is inferred separately from the stationary component.

Lemen, J. R.↗

DOE OSTI2021

Estimating basis functions in massive fields under the spatial mixed effects model

Abstract Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology.

Pazdernik, Karl↗

DOE OSTI2023

Constructing a Simulation Surrogate with Partially Observed Output

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. In conclusion, the proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.

42 ENGINEERING↗