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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Dehydration of the Upper Troposphere and Lower Stratosphere by Subvisible Cirrus Clouds Near the Tropical Tropopause

The extreme dryness of the lower stratosphere is believed to be caused by freeze-drying of air as it enters the stratosphere through the cold tropical tropopause. Previous investigations have been focused on dehydration occurring at the tops of deep convective cloud systems, However, recent observations of a ubiquitous stratiform cirrus cloud layer near the tropical tropopause suggest the possibility of dehydration as air is slowly lifted by large-scale motions, In this study, we have evaluated this possibility using a detailed ice cloud model. Simulations of ice cloud formation in the temperature minima of gravity waves (wave periods of 1 - 2 hours) indicate that large numbers of ice crystals will likely form due to the low temperatures and rapid cooling. As a result, the crystals do not grow larger than about 10 microns, fallspeeds are no greater than a few cm/s, and little or no precipitation or dehydration occurs. However, ice cloud's formed by large-scale vertical motions (with lifetimes of a day or more) should have,fever crystals and more time for crystal sedimentation to occur, resulting in water vapor depletions as large as 1 ppmv near the tropopause. We suggest that gradual lifting near the tropical tropopause, accompanied by formation of thin cirrus, may account for the dehydration.

Jensen, Eric J.↗
NASA NTRSDate not supplied

EPIC L2 Cloud Products Update

In this presentation, we will update the status of the EPIC L2 cloud products. During this research period, we have conducted investigation on cloud detection over snow and ice using the Oxygen A- and B-band (paper published), as well as cloud detection over ocean sunglint regions. The cloud system algorithm has been upgraded. The new version of cloud product will be tested and released soon.

DSCOVR↗
DOE OSTI2021

Execute BEE workflows on private cloud infrastructure-2.3.6.01 - LANL ATDM ST / STNS01-22 Milestone Completion Documentation (BEE-FY21 P6-2) [Slides]

This work involves the creation of the Cloud Launcher, a new subcomponent of BEE, and the extension of the BEETaskManager to run on Cloud systems. BEE will be able to interact with the Google Compute Engine and OpenStack cloud APIs to set up simple Cloud clusters for launching HPC job scripts. BEE will use existing functionality to launch jobs that previously could only be launched on HPC systems. The BEETaskManager will handle launching tasks on the Cloud cluster.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis

Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain expertise and domain scientists without system fault tolerance knowledge identify those critical variables accounted for correct application execution restoration in a failure for C/R. To address this problem, we propose an analytical model and a tool (AutoCheck) that can automatically identify critical variables to checkpoint for C/R. AutoCheck relies on first, analytically tracking and optimizing data dependency between variables and other application execution state, and second, a set of heuristics that identify critical variables for checkpointing from the refined data dependency graph (DDG). AutoCheck allows programmers to pinpoint critical variables to checkpoint quickly within a few minutes. We evaluate AutoCheck on 13 representative HPC benchmarks, demonstrating that AutoCheck can efficiently identify correct critical variables to checkpoint.

HPC↗
DOE OSTI2026

Drop clustering and drop size correlations from holographic imagery suggest cloud droplet spectral broadening via entrainment-mixing

The question of how droplets rapidly grow large enough to initiate collision-coalescence has persisted for decades. Many theories explaining the production of sufficiently large drops (i.e., those in the “bottleneck” size range; ∼ 25–50 µm diameters) involve drop clustering on millimeter scales. A novel method is introduced to evaluate drop clustering trends particle-by-particle (i.e., the number/proximity of neighboring drops for given droplets; defined as drop clustering fields) which are diagnosed relative to drops within their shared drop environments – in contrast to previous studies which diagnose drop clustering of defined sample volumes, or in terms of absolute length scales. Specifically, this study evaluates the statistical likelihood that drops of a given size are associated with either a significant number of neighboring drops, or are significantly isolated from neighboring drops. Observations are acquired from the HOLODEC during the Cloud System Evolution in the Trades campaign, which sampled subtropical marine clouds. The HOLODEC measures drop size distributions and the 3D spatial coordinates of droplets. Results show drops within the bottleneck size range (diameters of ∼ 25–50 µm) are most likely to be significantly isolated from neighboring drops. This “isolated large drop trend” is primarily observed at subsaturated conditions, suggesting entrainment is the contributing factor. Holograms associated with this trend are more likely to have broader drop size distributions, larger maximum drop sizes and overly regions where precipitation reaches the lowest altitudes from the sampled cloud, suggesting entrainment-mixing drop size distribution broadening is a relevant precipitation-initiation mechanism.

D'Alessandro, John J. [Univ. of Washington, Seattl↗
NASA NTRS1986

T-28 data acquisition during COHMEX 1986

As part of the 1986 Cooperative Huntsville Meteorological Experiment (COHMEX) a cloud physics instrumented T-28 aircraft was used in conjunction with multiple ground based Doppler radars to characterize hydrometeors and updraft structure within developing summertime cumulus and cumulonimbus cloud systems near Huntsville, Alabama. Instrumentation aboard the aircraft included a Particle Measuring Systems (PMS) Forward Scattering Spectrometer Probe (FSSP), a PMS 2D Cloud Probe and a PMS 2D Precipitation Probe, as well as a hail spectrometer and a foil impactor. Hydrometeor spectra were obtained in the interior of mature thunderstorms over the size range from cloud droplets through hailstones. In addition, vertical wind speed, temperature, Johnson-Williams (JW) liquid water content and electric field measurements were made. Significant microphysical differences exist between these clouds and summertime cumulonimbus clouds which develop over the Central Plains. One notable difference in clouds displaying similar radar reflectivities is that COHMEX hydrometeors are typically smaller and more numerous than those observed in the Central Plains. The COHMEX cloud microphysical measurements represent ground truth values for the remote sensing instrumentation which was flown over the cloud tops at altitudes between 60,000 and 70,000 ft aboard NASA U-2 and ER-2 aircraft. They are also being used jointly with a numerical cloud model to assist in understanding the development of summertime subtropical clouds.

Musil, Dennis J.↗
NASA NTRS1987

Cloud fluid models of gas dynamics and star formation in galaxies

The large dynamic range of star formation in galaxies, and the apparently complex environmental influences involved in triggering or suppressing star formation, challenges the understanding. The key to this understanding may be the detailed study of simple physical models for the dominant nonlinear interactions in interstellar cloud systems. One such model is described, a generalized Oort model cloud fluid, and two simple applications of it are explored. The first of these is the relaxation of an isolated volume of cloud fluid following a disturbance. Though very idealized, this closed box study suggests a physical mechanism for starbursts, which is based on the approximate commensurability of massive cloud lifetimes and cloud collisional growth times. The second application is to the modeling of colliding ring galaxies. In this case, the driving processes operating on a dynamical timescale interact with the local cloud processes operating on the above timescale. The results is a variety of interesting nonequilibrium behaviors, including spatial variations of star formation that do not depend monotonically on gas density.

Struck-Marcell, Curtis↗
NASA NTRS1987

A dynamic threshold method for obtaining cloud cover from satellite imagery data

Errors in cloud cover derived by using a fixed threshold applied to imagery data depend not only on the fractional cover but also on cloud size. As a result, a fixed threshold applied to two scenes having the same cloud cover will produce different estimates of the cover when the clouds in the two scenes have different sizes. To allow for this influence due to cloud size, a dynamic threshold method is presented. In this method an infrared threshold is adjusted to achieve the highest correlation between the threshold-derived cloud cover and the mean emitted radiance for mesoscale-sized subregions within the scene. For single-layered cloud systems this threshold achieves a cancellation of errors in the cloud cover for the subregions so that the resulting cloud cover for the region and the associated estimates of cloud properties are in fair agreement with estimates obtained using the spatial coherence method. The agreement illustrates the validity of the layered cloud model used in different ways by the two methods. The performance of the dynamic threshold method is contrasted with that of a fixed threshold applied to the same data in order to illustrate the merits of applying a scene-dependent threshold.

Coakley, James A., Jr.↗
NASA NTRS1991

Cloud albedo control by cloud-top entrainment

Marine stratus and stratocumulus clouds exert a considerable influence on the earth's heat budget, mainly due to their high albedos relative to the ocean surface. It is therefore important to understand the processes that control the radiative properties of these extensive cloud systems, particularly during daylight hours. Aircraft measurements of a stratocumulus cloud deck taken around local noon during the 1987 field phase of the First International Satellite Cloud Climatology Project Regional Experiment are the topic of this paper. A mixing line analysis of data from a series of flight tracks across a strong gradient in cloud albedo provides evidence that variations in the water vapor content of the air above the marine inversion can be responsible for the albedo change. The implications of this unexpected result for climate modeling are discussed.

Hanson, Howard P.↗
NASA NTRS1991

Prebiotic chemistry in clouds

The chemical evolution hypothesis of Woese (1979), according to which prebiotic reactions occurred rapidly in droplets in giant atmospheric reflux columns was criticized by Scherer (1985). This paper proposes a mechanism for prebiotic chemistry in clouds that answers Scherer's concerns and supports Woese's hypothesis. According to this mechanism, rapid prebiotic chemical evolution was facilitated on the primordial earth by cycles of condensation and evaporation of cloud drops containing clay condensation nuclei and nonvolatile monomers. For example, amino acids supplied by, or synthesized during entry of meteorites, comets, and interplanetary dust, would have been scavenged by cloud drops containing clay condensation nuclei and would be polymerized within cloud systems during cycles of condensation, freezing, melting, and evaporation of cloud drops.

Oberbeck, Verne R.↗
NASA NTRS2012

A FIRE-ACE/SHEBA Case Study of Mixed-Phase Arctic Boundary Layer Clouds: Entrainment Rate Limitations on Rapid Primary Ice Nucleation Processes

Observations of long-lived mixed-phase Arctic boundary layer clouds on 7 May 1998 during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)Arctic Cloud Experiment (ACE)Surface Heat Budget of the Arctic Ocean (SHEBA) campaign provide a unique opportunity to test understanding of cloud ice formation. Under the microphysically simple conditions observed (apparently negligible ice aggregation, sublimation, and multiplication), the only expected source of new ice crystals is activation of heterogeneous ice nuclei (IN) and the only sink is sedimentation. Large-eddy simulations with size-resolved microphysics are initialized with IN number concentration N(sub IN) measured above cloud top, but details of IN activation behavior are unknown. If activated rapidly (in deposition, condensation, or immersion modes), as commonly assumed, IN are depleted from the well-mixed boundary layer within minutes. Quasi-equilibrium ice number concentration N(sub i) is then limited to a small fraction of overlying N(sub IN) that is determined by the cloud-top entrainment rate w(sub e) divided by the number-weighted ice fall speed at the surface v(sub f). Because w(sub c)< 1 cm/s and v(sub f)> 10 cm/s, N(sub i)/N(sub IN)<< 1. Such conditions may be common for this cloud type, which has implications for modeling IN diagnostically, interpreting measurements, and quantifying sensitivity to increasing N(sub IN) (when w(sub e)/v(sub f)< 1, entrainment rate limitations serve to buffer cloud system response). To reproduce observed ice crystal size distributions and cloud radar reflectivities with rapidly consumed IN in this case, the measured above-cloud N(sub IN) must be multiplied by approximately 30. However, results are sensitive to assumed ice crystal properties not constrained by measurements. In addition, simulations do not reproduce the pronounced mesoscale heterogeneity in radar reflectivity that is observed.

Fridlin, Ann↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗
NASA NTRS2002

Microphysics, Radiation and Surface Processes in the Goddard Cumulus Ensemble (GCE) Model

One of the most promising methods to test the representation of cloud processes used in climate models is to use observations together with Cloud Resolving Models (CRMs). The CRMs use more sophisticated and realistic representations of cloud microphysical processes, and they can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems (size about 2-200 km). The CRMs also allow explicit interaction between out-going longwave (cooling) and in-coming solar (heating) radiation with clouds. Observations can provide the initial conditions and validation for CRM results. The Goddard Cumulus Ensemble (GCE) Model, a CRM, has been developed and improved at NASA/Goddard Space Flight Center over the past two decades. The GCE model has been used to understand the following: 1) water and energy cycles and their roles in the tropical climate system; 2) the vertical redistribution of ozone and trace constituents by individual clouds and well organized convective systems over various spatial scales; 3) the relationship between the vertical distribution of latent heating (phase change of water) and the large-scale (pre-storm) environment; 4) the validity of assumptions used in the representation of cloud processes in climate and global circulation models; and 5) the representation of cloud microphysical processes and their interaction with radiative forcing over tropical and midlatitude regions. Four-dimensional cloud and latent heating fields simulated from the GCE model have been provided to the TRMM Science Data and Information System (TSDIS) to develop and improve algorithms for retrieving rainfall and latent heating rates for TRMM and the NASA Earth Observing System (EOS). More than 90 referred papers using the GCE model have been published in the last two decades. Also, more than 10 national and international universities are currently using the GCE model for research and teaching. In this talk, five specific major GCE improvements: (1) ice microphysics, (2) longwave and shortwave radiative transfer processes, (3) land surface processes, (4) ocean surface fluxes and (5) ocean mixed layer processes are presented. The performance of these new GCE improvements will be examined. Observations are used for model validation.

Tao, Wei-Kuo↗
DOE OSTI2020

Formulation of autoconversion and drop spectra shape in shallow cumulus clouds

Two-moment autoconversion parameterizations as compared to accretion parameterizations exhibit significant errors suggesting that additional moments are needed to increase their accuracy. We develop a three-moment autoconversion parameterization using output from an LES model with size-resolved microphysics. Adding the third moment decreases the errors of parameterization and improves precipitation prediction. However, the errors are still significantly larger than errors of accretion rate. An analysis of the cloud drop size distributions (DSDs) in the simulated tropical convective cloud system reveals that most of DSDs have a significant fraction of cloud liquid water content (qc) in the mid-size droplet range (radii from 20 to 40 microns). Our data indicates that more than 30% of DSDs have over half of qc contained in the mid-size range and about 60% of spectra have, at least, one third of qc in this range. Even when the rain/drizzle mode is small (radar reflectivity Z < -10 dBZ), there is a significant number of spectra in which fraction of qc in the mid-size range is as large as 60%. These DSDs are more complex than the frequently used single-mode Gamma or Log-normal distributions, which usually do not have a considerable tail extending to the mid-size range and can be defined by three microphysical moments. The need to define DSDs by more than three moments explains the large errors in the three-moment autoconversion parameterization. The limitation of three-parameter Gamma or Log-normal distributions should be kept in mind when applying them in precipitating shallow Cu clouds.

Kogan, Yefim↗
DOE OSTI2021

Climatology of Linear Mesoscale Convective System Morphology in the United States based on Random Forests Method

This study uses machine learning methods, specifically the random forest (RF), on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004-2016. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, and reanalysis environmental information from 5-26yr manually identified nonlinear and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, in which half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (through March to October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus have higher potential in producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.

Cui, Wenjun↗
NASA NTRS1998

Sensitivity of Cirrus Simulations in Idealized Situations: The WG2 Test Cases

GCSS Cirrus Cloud Systems Working Group (WG2) is presently conducting a comparison of cirrus cloud models for idealized initial conditions. The experiments involve binary (off/on) tests of model sensitivity to infrared radiative processes, and thermal stratification, and vertical wind shear for situations of weakly forced (3 cm/s uplift) cold (-60 to -70 C) and warm (-35 to -50 C) cirrus clouds. A range of model types are involved including parcel, SCM, 2-D CRM, 3-D CRM and LES models. The test cases will be described and results from 2-dimensional cirrus cloud models with bulk microphysics (implicit second moment scheme) and explicit bin microphysics will be compared. Vertical ice mass flux (particle fall speed) is a critical model component leading to significant intermodel differences. Efforts are ongoing to better quantify this aspect. Future plans of WG2 will also be briefly described and include model comparisons for a well-observed case of cold (ARM IOP) cirrus and of warm (EUCREX) cirrus, as well as, a joint activity with WG4 to consider the treatment of anvil cirrus in a variety of models.

Starr, David OC.↗
NASA NTRS2000

GCSS Idealized Cirrus Model Comparison Project

The GCSS Working Group on Cirrus Cloud Systems (WG2) is conducting a systematic comparison and evaluation of cirrus cloud models. This fundamental activity seeks to support the improvement of models used for climate simulation and numerical weather prediction through assessment and improvement of the "process" models underlying parametric treatments of cirrus cloud processes in large-scale models. The WG2 Idealized Cirrus Model Comparison Project is an initial comparison of cirrus cloud simulations by a variety of cloud models for a series of idealized situations with relatively simple initial conditions and forcing. The models (16) represent the state-of-the-art and include 3-dimensional large eddy simulation (LES) models, two-dimensional cloud resolving models (CRMs), and single column model (SCM) versions of GCMs. The model microphysical components are similarly varied, ranging from single-moment bulk (relative humidity) schemes to fully size-resolved (bin) treatments where ice crystal growth is explicitly calculated. Radiative processes are included in the physics package of each model. The baseline simulations include "warm" and "cold" cirrus cases where cloud top initially occurs at about -47C and -66C, respectively. All simulations are for nighttime conditions (no solar radiation) where the cloud is generated in an ice supersaturated layer, about 1 km in depth, with an ice pseudoadiabatic thermal stratification (neutral). Continuing cloud formation is forced via an imposed diabatic cooling representing a 3 cm/s uplift over a 4-hour time span followed by a 2-hour dissipation stage with no cooling. Variations of these baseline cases include no-radiation and stable-thermal-stratification cases. Preliminary results indicated the great importance of ice crystal fallout in determining even the gross cloud characteristics, such as average vertically-integrated ice water path (IWP). Significant inter-model differences were found. Ice water fall speed is directly related to the shape of the particle size distribution and the habits of the ice crystal population, whether assumed or explicitly calculated. In order to isolate the fall speed effect from that of the associated ice crystal population, simulations were also performed where ice water fall speed was set to the same constant value everywhere in each model. Values of 20 and 60 cm/s were assumed. Current results of the project will be described and implications will be drawn. In particular, this exercise is found to strongly focus the definition of issues resulting in observed inter-model differences and to suggest possible strategies for observational validation of the models. The next step in this project is to perform similar comparisons for well observed case studies with sufficient high quality data to adequately define model initiation and forcing specifications and to support quantitative validation of the results.

Starr, David OC.↗
NASA NTRS2008

Role of Gravity Waves in Determining Cirrus Cloud Properties

Cirrus clouds are important in the Earth's radiation budget. They typically exhibit variable physical properties within a given cloud system and from system to system. Ambient vertical motion is a key factor in determining the cloud properties in most cases. The obvious exception is convectively generated cirrus (anvils), but even in this case, the subsequent cloud evolution is strongly influenced by the ambient vertical motion field. It is well know that gravity waves are ubiquitous in the atmosphere and occur over a wide range of scales and amplitudes. Moreover, researchers have found that inclusion of statistical account of gravity wave effects can markedly improve the realism of simulations of persisting large-scale cirrus cloud features. Here, we use a 1 -dimensional (z) cirrus cloud model, to systematically examine the effects of gravity waves on cirrus cloud properties. The model includes a detailed representation of cloud microphysical processes (bin microphysics and aerosols) and is run at relatively fine vertical resolution so as to adequately resolve nucleation events, and over an extended time span so as to incorporate the passage of multiple gravity waves. The prescribed gravity waves "propagate" at 15 m s (sup -1), with wavelengths from 5 to 100 km, amplitudes range up to 1 m s (sup -1)'. Despite the fact that the net gravity wave vertical motion forcing is zero, it will be shown that the bulk cloud properties, e.g., vertically-integrated ice water path, can differ quite significantly from simulations without gravity waves and that the effects do depend on the wave characteristics. We conclude that account of gravity wave effects is important if large-scale models are to generate realistic cirrus cloud property climatology (statistics).

OCStarr, David↗