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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
NASA NTRS1975

Analysis of cloud polarization measurements

The polarization of sunlight reflected by various cloud types was measured in the near-infrared with a polarimeter flown aboard the NASA Convair 990. The data reduction has been completed and detailed comparisons have been made between the observations and theoretical computations. The analysis presented shows that the polarization in the near-infrared can be used on a routine basis to reliably yield the cloud particle phase (water or ice) and the average particle size in the top part of water clouds (usually within + or - 25%). In some cases the polarization also yields the cloud optical thickness (or a lower limit) and a measure of the width of the particle size distribution in the top of the water clouds.

Hansen, J. E.↗
DOE OSTI2021

A Lagrangian perspective on tropical anvil cloud lifecycle in present and future climate

The evolution of tropical anvil clouds from their origin in deep convective cores to their slow decay determines the climatic effects of clouds in tropical convective regions. Despite the relevance of anvil clouds for climate and responses of clouds to global warming, processes dominating their evolution are not well understood. Currently available observational data reveal instantaneous snapshots of anvil cloud properties, but cannot provide a process-based perspective on anvil evolution. We therefore conduct simulations with the high resolution version of the Exascale Earth System Model in which we track mesoscale convective systems over the Tropical Western Pacific and compute trajectories that follow air parcels detrained from peaks of convective activity. With this approach we gain new insight into the anvil cloud evolution both in present day and future climate. Comparison with geostationary satellite data shows that the model is able to simulate maritime mesoscale convective systems reasonably well. Trajectory results indicate that anvil cloud lifetime is about 15 hours with no significant change in a warmer climate. The anvil ice mixing ratio is larger in a warmer climate due to a larger source of ice by detrainment and larger depositional growth leading to a more negative net cloud radiative effect along detrained trajectories. However, the increases in sources are counteracted by increases in sinks of ice, particularly snow formation and sedimentation. Furthermore, we find that the mean anvil cloud feedback along trajectories is positive and consistent with results from more traditional cloud feedback calculation methods.

climate, weather, mesocale cellular convection↗
NASA NTRS1971

Apollo contamination analysis

The Apollo 12 photographic observations of clouds produced by venting liquids into space were reduced and analyzed. The number of Baker-Nunn photographs required that film measurement and data processing be automated, and the microdensitometer was adapted to automatic digital operation. Computer programs were prepared to process the image-density measurements. A substantial selection of Apollo 12 data was processed; both hydrogen and oxygen clouds are represented. Some limited data were obtained from Apollo 13 and 14 missions. During the Apollo 12 data analysis, the theory and models of cloud phenomena were refined because the models were initially applied to inferior oxygen data from earlier missions. The reported observations of water dumps from Apollo 12 are also discussed, along with the use of the oxygen and hydrogen data from Apollo 12 photographs with the cloud models.

Source record↗
NASA NTRS2019

Impact of Clouds and Hazes on the Simulated JWST Transmission Spectra of Habitable Zone Planets in the TRAPPIST-1 System

The TRAPPIST-1 system, consisting of an ultracool host star having seven known Earth-sized planets, will be a prime target for atmospheric characterization with the James Webb Space Telescope (JWST). However, the detectability of atmospheric molecular species may be severely impacted by the presence of clouds and/or hazes. In this work, we perform 3D general circulation model (GCM) simulations with the LMD-G model supplemented by 1D photochemistry simulations at the terminator with the Atmos model to simulate several possible atmospheres for TRAPPIST-1e, 1f, and 1g: (1) modern Earth, (2) Archean Earth, and (3) CO2-rich atmospheres. The JWST synthetic transit spectra were computed using the GSFC Planetary Spectrum Generator. We find that the TRAPPIST-1e, 1f, and 1g atmospheres, with clouds and/or hazes, could be detected using JWST's NIRSpec Prism from the CO2 absorption line at 4.3 μm in less than 15 transits at 3σ or less than 35 transits at 5σ. However, our analysis suggests that other gases would require hundreds (or thousands) of transits to be detectable. We also find that H2O, mostly confined in the lower atmosphere, is very challenging to detect for these planets or similar systems if the planets' atmospheres are not in a moist greenhouse state. This result demonstrates that the use of GCMs, self-consistently taking into account the effect of clouds and subsaturation, is crucial to evaluate the detectability of atmospheric molecules of interest, as well as for interpreting future detections in a more global (and thus robust and relevant) approach.

Thomas J. Fauchez↗
NASA NTRS2019

Cellular Statistical Models of Broken Cloud Fields. Part IV: Effects of Pixel Size on Idealized Satellite Observations

In the fourth part of our “Cellular Statistical Models of Broken Cloud Fields” series we use the binary Markov processes framework for quantitative investigation of the effects of low resolution of idealized satellite observations on the statistics of the retrieved cloud masks. We assume that the cloud fields are Markovian and are characterized by the “actual” cloud fraction (CF) and scale length. We use two different models of observations: a simple discrete-point sampling and a more realistic “pixel” protocol. The latter is characterized by a state attribution function (SAF) which has the meaning of the probability that the pixel with a certain CF is declared cloudy in the observed cloud mask. The stochasticity of the SAF means that the cloud/clear attribution is not ideal and can be affected by external or unknown factors. We show that the observed cloud masks can be accurately described as Markov chains of pixels and use the master-matrix formalism (introduced in Part III of the series) for analytical computation of their parameters: the “observed” CF and scale length. This procedure allows us to establish a quantitative relationship (which is pixel-size dependent) between the actual and the observed cloud-field statistics. The feasibility of restoring the former from the latter is considered. The adequacy of our analytical approach to idealized observations is evaluated using numerical simulations. Comparison of the observed parameters of the simulated datasets with their theoretical expectations showed an agreement within 0.005 for the CF, while for the scale length it is within 1% in the sampling case and within 4% in the pixel case.

Mikhail D. Alexandrov↗
DOE OSTI2019

Quantum Associative Memory in Hep Track Pattern Recognition

We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Subcloud and Cloud-Base Latent Heat Fluxes during Shallow Cumulus Convection

Doppler and Raman lidar observations of vertical velocity and water vapor mixing ratio are used to probe the physics and statistics of subcloud and cloud-base latent heat fluxes during cumulus convection at the ARM Southern Great Plains (SGP) site in Oklahoma, United States. The statistical results show that latent heat fluxes increase with height from the surface up to ~0.8 Z i (where Z i is the convective boundary layer depth) and then decrease to ~0 at Z i . Peak fluxes aloft exceeding 500 W m −2 are associated with periods of increased cumulus cloud cover and stronger jumps in the mean humidity profile. These entrainment fluxes are much larger than the surface fluxes, indicating substantial drying over the 0–0.8 Z i layer accompanied by moistening aloft as the CBL deepens over the diurnal cycle. We also show that the boundary layer humidity budget is approximately closed by computing the flux divergence across the 0–0.8 Z i layer. Composite subcloud velocity and water vapor anomalies show that clouds are linked to coherent updraft and moisture plumes. The moisture anomaly is Gaussian, most pronounced above 0.8 Z i and systematically wider than the velocity anomaly, which has a narrow central updraft flanked by downdrafts. This size and shape disparity results in downdrafts characterized by a high water vapor mixing ratio and thus a broad joint probability density function (JPDF) of velocity and mixing ratio in the upper CBL. We also show that cloud-base latent heat fluxes can be both positive and negative and that the instantaneous positive fluxes can be very large (~10 000 W m −2 ). However, since cloud fraction tends to be small, the net impact of these fluxes remains modest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ArQTiC: A Full-stack Software Package for Simulating Materials on Quantum Computers

ArQTiC is an open-source, full-stack software package built for the simulations of materials on quantum computers. It currently can simulate materials that can be modeled by any Hamiltonian derived from a generic, one-dimensional, time-dependent Heisenberg Hamiltonian. ArQTiC includes modules for generating quantum programs for real- and imaginary-time evolution, quantum circuit optimization, connection to various quantum backends via the cloud, and post-processing of quantum results. By enabling users to seamlessly design, execute, and analyze materials simulations on quantum computers, ArQTiC opens this field to a broader community of scientists from a wider range of scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

FRAGMENTATION AND SPREADING OF A METEOR-LIKE OBJECT

The phenomenon of fragmentation and spreading of a high-speed flying object resembling a meteorite is studied experimentally and theoretically. Experimentally, a model made of graphite is launched in a ballistic range and is made to fragment and spread. The flow field produced by the cloud of the fragments is observed optically. The observed deceleration and spreading behavior is numerically reconstructed using computational-fluid-dynamic calculations, applying an improved meteoroid fragmentation theory. The existing meteoroid fragmentation theory is improved by introducing the hypothesis that the incubation process of the pressurized fluid permeating through the fragment precedes the splitting process. The incubation time is determined by the ratio of permeability of the fragment to the fluid's viscosity and is much longer than the time for splitting given by the existing theory. Agreement is obtained between the observed and calculated behavior of the fragment cloud by appropriately choosing this ratio.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1978

The structure and spectrum of a colliding-cloud system and its possible relationship to QSOs

A collision between two gas clouds with initial densities of approximately 10 million per cu cm, velocities of about 1000 km/s, and radii of approximately 1 pc is investigated quantitatively by coupling a calculation of the radiation spectrum resulting from the anticipated shock fronts with a computation for the conversion of this high-energy radiation into optical emission in adjacent photoionized regions. The detailed structure of the colliding clouds is discussed, and the effects of an ambient magnetic field are considered. The combined emission-line spectrum is presented along with continuum emission estimates for thermal, synchrotron, and very-high-energy bremsstrahlung mechanisms. It is shown that significant continua can be produced over the range from 300 microns to 3 keV, including a blackbody contribution from a high-density neutral region between the shock fronts, free-free and free-bound radiation from the cooling zones directly behind the shocks, and free-free, free-bound, and two-photon radiation from the photoionized regions immediately ahead of and behind the cooling zones. The theoretical spectrum of the structure resulting from the collision is found to be similar in general and in some details to those observed for typical quasars.

Daltabuit, E.↗
NASA NTRS1982

Study of sensor spectral responses and data processing algorithms and architectures for onboard feature identification

A computational model of the deterministic and stochastic processes involved in remote sensing is used to study spectral feature identification techniques for real-time onboard processing of data acquired with advanced earth-resources sensors. Preliminary results indicate that: Narrow spectral responses are advantageous; signal normalization improves mean-square distance (MSD) classification accuracy but tends to degrade maximum-likelihood (MLH) classification accuracy; and MSD classification of normalized signals performs better than the computationally more complex MLH classification when imaging conditions change appreciably from those conditions during which reference data were acquired. The results also indicate that autonomous categorization of TM signals into vegetation, bare land, water, snow and clouds can be accomplished with adequate reliability for many applications over a reasonably wide range of imaging conditions. However, further analysis is required to develop computationally efficient boundary approximation algorithms for such categorization.

Huck, F. O.↗
NASA NTRS2023

Ames Contrail Simulation Model: Modeling Aviation Induced Contrails and the Computation of Contrail Radiative Forcing Using Air Traffic Data

NASA is developing traffic flow management strategies to reduce the impact of aviation on climate and improve efficiency of aircraft routes in the presence of limited airport and airspace capacity constraints, which are partly due to convective weather and natural calamities. Development of Strategic Traffic Flow Management (STFM) requires models of aircraft emissions and contrails, and models of their effect on climate. There is extensive literature on the modeling of contrails and the Radiative Forcing (RF) associated with contrails. This report captures the latest NASA developments in this research area and describes the integrated modeling, analysis, and software development to support STFM. Aircraft contrails are long, thin and often linear clouds triggered by aircraft engine exhausts in the high-altitude ice-saturated atmosphere. Contrails, similar to that of natural cirrus clouds, can impact global climate by reflecting shortwave radiation and trapping longwave radiation. Recent studies from the Intergovernmental Panel on Climate Change (IPCC) have shown that aircraft contrails are estimated to have greater impact on global warming than aircraft CO 2 emissions. The Ames Contrail Simulation Model (ACSM) presented in this report simulates the full life cycle of aircraft contrails, including their formation, dynamic evolution, and dissipation, and calculates the associated RF with actual meteorological and air traffic data. ACSM combines models from previous studies that focus on contrail formation and persistence based on the Schmidt-Appleman theoretical criteria, and it incorporates elements of cloud dynamics, microphysics, and climate modeling found in other surveyed contrail models, while also making adjustment for improved computational efficiency. In addition, ACSM is integrated with NASA's state-of-the-art flight simulation software for rapid assessment of aircraft contrail impacts. Applications include assessments of long-term global climate impact resulting from aviation-induced contrails and the design of optimal contrail-mitigation aircraft operation strategies.

aviation contrails↗
DOE OSTI2010

Self-Consistant Numerical Modeling of E-Cloud Driven Instability of a Bunch Train in the CERN SPS

The simulation package WARP-POSINST was recently upgraded for handling multiple bunches and modeling concurrently the electron cloud buildup and its effect on the beam, allowing for direct self-consistent simulation of bunch trains generating, and interacting with, electron clouds. We have used the WARP-POSINST package on massively parallel supercomputers to study the growth rate and frequency patterns in space-time of the electron cloud driven transverse instability for a proton bunch train in the CERN SPS accelerator. Results suggest that a positive feedback mechanism exists between the electron buildup and the e-cloud driven transverse instability, leading to a net increase in predicted electron density. Comparisons to selected experimental data are also given. Electron clouds have been shown to trigger fast growing instabilities on proton beams circulating in the SPS and other accelerators. So far, simulations of electron cloud buildup and their effects on beam dynamics have been performed separately. This is a consequence of the large computational cost of the combined calculation due to large space and time scale disparities between the two processes. We have presented the latest improvements of the simulation package WARP-POSINST for the simulation of self-consistent ecloud effects, including mesh refinement, and generation of electrons from gas ionization and impact at the pipe walls. We also presented simulations of two consecutive bunches interacting with electrons clouds in the SPS, which included generation of secondary electrons. The distribution of electrons in front of the first beam was initialized from a dump taken from a preceding buildup calculation using the POSINST code. In this paper, we present an extension of this work where one full batch of 72 bunches is simulated in the SPS, including the entire buildup calculation and the self-consistent interaction between the bunches and the electrons. Comparisons to experimental data are also given.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

Direct Numerical Modeling of E-Cloud Driven Instability of a Bunch Train in the CERN SPS

The simulation package WARP-POSINST was recently upgraded for handling multiple bunches and modeling concurrently the electron cloud buildup and its effect on the beam, allowing for direct self-consistent simulation of bunch trains generating, and interacting with, electron clouds. We have used the WARP-POSINST package on massively parallel supercomputers to study the buildup and interaction of electron clouds with a proton bunch train in the CERN SPS accelerator. Results suggest that a positive feedback mechanism exists between the electron buildup and the e-cloud driven transverse instability, leading to a net increase in predicted electron density. Electron clouds have been shown to trigger fast growing instabilities on proton beams circulating in the SPS and other accelerators. So far, simulations of electron cloud buildup and their effects on beam dynamics have been performed separately. This is a consequence of the large computational cost of the combined calculation due to large space and time scale disparities between the two processes. We have presented the latest improvements of the simulation package WARP-POSINST for the simulation of self-consistent ecloud effects, including mesh refinement, and generation of electrons from gas ionization and impact at the pipe walls. We also presented simulations of two consecutive bunches interacting with electrons clouds in the SPS, which included generation of secondary electrons. The distribution of electrons in front of the first beam was initialized from a dump taken from a preceding buildup calculation using the POSINST code. In this paper, we present an extension of this work where one full batch of 72 bunches is simulated in the SPS, including the entire buildup calculation and the self-consistent interaction between the bunches and the electrons.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Development of a Consistent MODIS and VIIRS Cloud Detection Approach for CERES

A consistent cloud fraction record across various satellite platforms is essential for maintaining a long-term and stable climate data record of Earth's energy budget. With the Aqua satellite nearing the end of its operational lifetime, the continuation of this record relies on utilizing VIIRS observations from NOAA20 for cloud detection in NASA’s Clouds and Earth’s Radiant Energy System (CERES) project. However, integrating data from VIIRS and MODIS instruments poses challenges due to their distinct characteristics, such as varying spatial resolutions and different spectral channels. As a result, deriving consistent cloud properties from these two sensors without introducing artificial discontinuities in the time series remains a complex and challenging task. This paper will present progress toward developing a unified MODIS and VIIRS cloud mask using common channels to produce consistent cloud properties for CERES next edition (Ed5) Earth radiation budget data products. The fundamental approach taken in the CERES cloud mask is to compare the observed radiances to the expected background clear sky radiances. Therefore, one vital step is to compute clear sky radiances with a radiative transfer model that accurately accounts for satellite-specific, spectrally dependent surface reflectance, surface emission, and atmospheric absorption. Refined radiative transfer models and updated ancillary data inputs including surface emissivity maps, IGBP, snow and ice maps are incorporated into the processing framework to improve cloud detection consistency and accuracy. Pixel level cloud mask results and monthly global cloud fraction comparisons between MODIS and VIIRS will be presented to evaluate their consistency. Remaining challenges will be discussed. It is expected that this work will contribute consistent cloud properties for CERES that adequately bridges the MODIS and VIIRS imager data records.

CERES↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2018

Explicit Cloud Representation in the Atmos 1D Climate Model for Earth and Rocky Planet Applications

1D climate models are less sophisticated than 3D global circulation models (GCMs), however their computational time is much less expensive, allowing a large number of runs in a short period of time to explore a wide parameter space. Exploring parameter space is particularly important for predicting the observable properties of exoplanets, for which few parameters are known with certainty. Therefore, 1D climate models are still very useful tools for planetary studies. In most of these 1D models, clouds are not physically represented in the atmosphere, despite having a well-known, significant impact on a planetary radiative budget. This impact is simulated by artificially raising surface albedo, in order to reproduce the observed-averaged surface temperature (i.e. 288 K for modern Earth) and a radiative balance at the top of the atmosphere. This non-physical representation of clouds, causes atmospheric long wave and shortwaves fluxes to not match observational data. Additionally, this technique represents a parameter that is highly-tuned to modern Earth’s climate, and may not be appropriate for planets that deviate from modern Earth’s climate conditions. In this paper, we present an update to the climate model within the Atmos 1­D atmospheric modeling package with a physical representation of clouds. We show that this physical representation of clouds in the atmosphere allows both long wave and shortwave fluxes to match observational data. This improvement will allow us to study the energy fluxes for a variety of cloudy rocky planets, and increase our confidence in future simulations of temperature profile and net energy balance.

Thomas Fauchez↗