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At least 631 records · Page 35

Translucent molecular clouds: Theory and observations

Few suitable stars behind molecular clouds have been identified. A limited survey was performed of interstellar lines toward highly reddened stars in the southern sky, using the ESO 1.4 m CAT telescope with a Reticon detector, and the Cerro Tololo 4 m telescope equipped with a GEC charge coupled device (CCD) detector. Because of the reduced extinction at longer wavelengths, molecules were searched for with transitions in the red part of the spectrum such as C2 and CN. For some lines-of-sight for which C2 was detected, the 4300 A line of CH was also observed. Absorption lines of interstellar C2 around 8750 A were detected in the spectra of about 1/4 of the 36 observed stars. The inferred C2 column densities range between 10 to the 13th power and 10 to the 14th power sq. cm., and are up to an order of magnitude larger than those found for diffuse clouds. The observed column densities of CH correlate very well with those of C2 over this range. In contrast, the measured column densities of CN vary by orders of magnitude between the various regions, and they do not correlate with those of C2 and CH. The observed rotational population distribution of C2 also provides information about the physical conditions in the clouds. Models of translucent molecular clouds have been constructed along the lines described by van Dishoeck and Black (1986) for diffuse clouds. The models compute accurately the fractions of atomic and molecular hydrogen as functions of depth into the clouds, as well as the excitation of H2 by ultraviolet pumping. They also incorporate a detailed treatment of the photodissociation processes of the molecules (cf. van Dishoeck 1986), which play an important role in the chemistry up to depths of about 3 mag.

Vandishoeck, E. F.↗

Big Data in the Earth Observing System Data and Information System

Approaches that are being pursued for the Earth Observing System Data and Information System (EOSDIS) data system to address the challenges of Big Data were presented to the NASA Big Data Task Force. Cloud prototypes are underway to tackle the volume challenge of Big Data. However, advances in computer hardware or cloud won't help (much) with variety. Rather, interoperability standards, conventions, and community engagement are the key to addressing variety.

data systems↗

Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR

Vegetation canopy structure is a critically important habit characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have enabled remote sensing-based studies of vegetation canopies by capturing three-dimensional structures, yielding information not available in two-dimensional images of the landscape pro- vided by traditional multi-spectral remote sensing platforms. However, the large volume data sets produced by airborne LiDAR instruments pose a significant computational challenge, requiring algorithms to identify and analyze patterns of interest buried within LiDAR point clouds in a computationally efficient manner, utilizing state-of-art computing infrastructure. We developed and applied a computationally efficient approach to analyze a large volume of LiDAR data and to characterize and map the vegetation canopy structures for 139,859 hectares (540 sq. miles) in the Great Smoky Mountains National Park. This study helps improve our understanding of the distribution of vegetation and animal habitats in this extremely diverse ecosystem.

Clustering↗

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗

Unprecedented cloud resolution in a GPU-enabled full-physics atmospheric climate simulation on OLCF’s summit supercomputer

Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation.

58 GEOSCIENCES↗

A New Approach to Using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols

Recently Grabowski and Khairoutdinov and Randall have proposed and demonstrated, the feasibility of using 2D CRMs (these did not include detailed land processes and used periodic lateral boundary conditions) as 'super-parameterizations or a multi-scale modeling framework MMF' for cloud processes within atmospheric generation circulation models (GCMs). In the MMF, a fine-resolution 2D CRM takes the place of the single-column parameterization that is used in conventional GCMs. An overview of this approach is given by Randall et al.. Since a CRM can explicitly simulate cloud processes at the natural space and time scales of clouddynamical processes, it's possible to compute statistics of cloud fields, including radiances and radar backscatters, directly simulated/produced by the CRM. A seed fund is available at NASA Goddard to build a MMF based on a 2D GCE model and the Goddard fvGCM. A prototype MMF will be developed at end of 2004. The major objectives are to test and evaluate its performance using the GCE model as the super parameterization and compare its results with other super parameterizations, conventional GCMs and observations. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (WE) model. The GCE model is a CRM and it has been improved and applied to study the precipitation processes in the past two decades. Atmospheric aerosols are also described using number density size-distribution functions. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and summertime convection over a mid-latitude continent with different concentrations of CCN: a low 'clean' concentration and a high 'dirty' concentration. The impact of atmospheric aerosol concentration on cloud and precipitation will be investigated. In addition, the similarities and differences between bulk- and spectral-bin microphysics will be presented,

Tao, Wei-Kuo↗

X-ray secondary heating and ionization in quasar emission-line clouds

Accurate Monte Carlo computations of the X-ray secondary electron heating, ionization, and excitation of H and He gas in interstellar space and in quasar emission-line clouds, are presented. The fraction of energy deposited in each form is sensitive to the background ionization fraction, x = n(H+)/n(Htot), and can affect the temperature, ionization state, and line emissivities at large depths in X-ray photoionized clouds. Analytic fits are provided for these energy fractions over the range 0.0001-1 for primary electron energies up to many keV. In both broad-line and narrow-line clouds, emission lines sensitive to the energy budget and electron density may be strongly affected.

Shull, J. M.↗

RACORO Extended-Term Aircraft Observations of Boundary-Layer Clouds

Small boundary-layer clouds are ubiquitous over many parts of the globe and strongly influence the Earths radiative energy balance. However, our understanding of these clouds is insufficient to solve pressing scientific problems. For example, cloud feedback represents the largest uncertainty amongst all climate feedbacks in general circulation models (GCM). Several issues complicate understanding boundary-layer clouds and simulating them in GCMs. The high spatial variability of boundary-layer clouds poses an enormous computational challenge, since their horizontal dimensions and internal variability occur at spatial scales much finer than the computational grids used in GCMs. Aerosol-cloud interactions further complicate boundary-layer cloud measurement and simulation. Additionally, aerosols influence processes such as precipitation and cloud lifetime. An added complication is that at small scales (order meters to 10s of meters) distinguishing cloud from aerosol is increasingly difficult, due to the effects of aerosol humidification, cloud fragments and photon scattering between clouds.

boundary layer↗

Longwave radiation budget parameters computed from ISCCP and HIRS2/MSU products

The International Satellite Cloud Climatology Project (ISCCP) retrieved cloud fields and the high-resolution radiation sounder 2 (HIRS2) retrieved cloud fields yield similar outgoing longwave radiation (OLR) due to the cancelation effect of cloud-top altitudes and cloud amount on OLR. For the large regional discrepancies of the order of 30 W/sq m found over northern Africa are largely due to surface temperature differences, and extensive discrepancies over the ocean are due to humidity differences. Harmonic analysis is applied to OLR, clear-sky OLR, and cloud-radiative forcing at the top of the atmosphere using the ISCCP products. The diurnal variations of these parameters from 60 deg S to 60 deg N are larger over land than over the ocean. The clear-sky OLR peaks around 1500 LST, usually over land areas, and is associated with the surface/air temperature maximum. The OLR over cloudy regions shows a similar diurnal phase as the cloud top pressure. The cloud radiative forcing at the top of the atmosphere has maximum value near 2100 LST mountain areas and near 0000 and 0300 LST over equatorial regions. The ISCCP-computed longwave cloud radiative forcing has smaller diurnal variations over the ocean than the HIRS2-computed longwave cloud radiative forcing. The global mean bias of OLR is around 0.74 W/sq m, and locally, the bias can be as large as 5 to 10 W/sq m. This appears to indicate that the twice-a-day measurements of the HIRS2/MSU (around 0315 and 1515 LST, which are the equatorial cross times at nadir track) can be used to derive monthly mean OLR without significant bias.

Wu, Man L. C.↗

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.

Yoginath, Srikanth↗

Stratocumulus cloud field reflected fluxes - The effect of cloud shape

The reflected fluxes of stratocumulus cloud fields are calculated as a function of sky cover aspect ratio and cloud shape. In order to obtain a better fit with general circulation models (GCMs), cloud liquid water volume values were kept invariant relative to cloud shape. On the basis of the required accuracy of the Earth Radiation Budget Experiment (ERBE), an order of magnitude value of 10 W per sq m was used to estimate the difference between plane-parallel and broken cloudiness reflected fluxes. An empirical relationship for effective cloud cover at a solar zenith angle of 60 deg is derived. The relationship allows for the accurate computation of broken cloud field reflected fluxes using plane-parallel calculations. It is predicted that more accurate estimates of broken cloud field radiative properties may be possible in the future.

Welch, R. M.↗

Earth Radiation Budget Climate Record Composed of Multiple Satellite Observations

Effects of cloud diurnal cycle on top-of-atmosphere (TOA) and surface regional monthly mean irradiances, climatological mean, and anomalies are analyzed using CERES derived TOA irradiances and surface irradiances computed with MODIS derived cloud properties. Cloud properties derived from Terra and Aqua MODIS are sufficient to capture cloud diurnal cycle to compute regional monthly mean surface irradiances. While missing cloud diurnal cycle leads to a biased TOA and surface regional irradiances for regions with a strong cloud diurnal cycle, monthly regional TOA and surface anomalies derived from one sun-synchronous orbit agrees well with those derived from two sun-synchronous orbits. Based on these results, the algorithm to produce Edition 4.2 CERES EBAF product is developed. Regional TOA and surface climatological means derived from one sun-synchronous orbit are adjusted to match corresponding climatological means derived from Terra+Aqua observations. This climatological adjustment approach is used to merge the Terra only period to the Terra+Aqua period and to extend the Terra and Aqua record by merging NOAA20 observations. Two additional differences of Edition 4.2 EBAF algorithm to compute surface irradiances compared to the earlier version are: 1) no geostationary satellite derived cloud properties are used and 2) temperature and humidity from MERRA-2 instead of GEOIS-5.4.1 are used. Once surface monthly regional mean irradiances are compared with surface observations, the agreement is equivalent to the agreement with the earlier version. However, because surface irradiances are not affected by geostationary satellite artifacts, regional surface irradiance anomaly time series is significantly improved, especially for longwave irradiances.

Seiji Kato↗

Idealized Simulations of the Tropical Climate and Variability in the Single Column Atmosphere Model (SCAM): Radiative-Convective Equilibrium

To explore the interactions among column processes in the Community Atmosphere Model (CAM), the single-column version of CAM (SCAM) is integrated for 1000 days in radiative-convective equilibrium (RCE) with tropical values of boundary conditions, spanning a parameter or configuration space of model physics versions (v5 vs. v6), vertical resolution (standard and 60 levels), sea surface temperature (SST), and some interpretation-driven experiments. The simulated time-mean climate is reasonable, near observations and RCE of a cyclic cloud-resolving model. Updraft detrainment in the deep convection scheme produces distinctive grid-scale structures in humidity and cloud, which also interact with radiative transfer processes. These grid artifacts average out in multi-column RCE results reported elsewhere, illustrating the nuts-and-bolts interpretability that SCAM adds to the hierarchy of model configurations. Multi-day oscillations of precipitation arise from descent of warm convection-capping layers starting near the tropopause, eventually reset by a burst of convective deepening. Experiments reveal how these oscillations depend critically on an internal parameter that controls the number of neutral buoyancy levels allowed for determining cloud top and computing dilute convective available potential energy in the deep convection scheme, and merely modified a little by disabling cloud-base radiation (heating of cloud base). This strong dependence of transient behavior in 1D on this parameter will be tested in the second part of this work, in which SCAM is coupled to a parameterized dynamics of two-dimensional, linearized gravity wave, and in the 3D simulations in future study.

Meteorology & Atmospheric Sciences↗

Electrical Charging of the Clouds of Titan

We have used recent data on graphitic cloud particles in the atmosphere or Titan to compute the electrical charging of the particles (radii ranging from 0.01 microns to 0.26 microns). The charging on the nightside was rather similar to that obtained earlier (Borucki et al, Icarus, 72, 604-622, 1987) except that charge distributions on the particles are now computed and recently obtained cloud particle sizes and density distributions were employed. The negative charge on particles of 0.26 microns peaked at 9 at 150 km altitude. The computations were repeated for the dayside with the addition of photoelectron emission by the particles as a result of the absorption of solar UV radiation. Particles (except the very smallest) now became positively charged with particles of radius 0.26 microns being charged up to +47. Next, very small particles (radii approximately 3 x 10^-4 microns) of polycyclic aromatic hydrocarbons (PAHs) were introduced and treated as sources of negative ions since they could be either neutral or carry one negative charge. Moreover, they are mobile so that they had to be treated like molecular size negative ions although much more massive. They had the effect of substantially reducing the electron densities in the altitude range 190 to 310 km to values less than the negative PAH densities and increasing the peak electron charge on the larger particles. Particles of radius 0.26 microns bore peak charges of plus or minus 47 at altitudes of approximately 250 km. The simulated effect of PAHs on the nightside proved to be much less pronounced; at the peak negative PAH density, it was less than the electron density. The physics governing these results will be discussed.

Borucki, William J.↗

Electrical Charging of Aerosols and Conductivity of Titan's Atmosphere

We have used recent data on graphitic cloud particles in the atmosphere of Titan to compute the electrical charging of the particles (radii ranging from 0.01 microns to 0.26 microns). The charging on the nightside was rather similar to that obtained earlier except that charge distributions on the particles are now computed and recently obtained cloud particle sizes and density distributions were employed. The negative charge on particles of 0.26 microns peaked at 9 at 150 km altitude. The computations were repeated for the dayside with the addition of photoelectron emission by the particles as a result of the absorption of solar UV radiation. Particles (except the very smallest) now became positively charged with particles of radius 0.26 microns being charged up to +47. Next, very small particles (radii approx. 3 x 10 (sup -4) microns) of polycyclic aromatic hydrocarbons (PAHs) were introduced and treated as sources of negative ions since they could be either neutral or carry one negative charge. Moreover, they are mobile so that they had to be treated like molecular size negative ions although much more massive. They had the effect of substantially reducing the electron densities in the altitude range 190 to 310 km to values less than the negative PAH densities and increasing the peak electron charge on the larger particles. Particles of radius 0.26 microns bore peak charges of approx. +47 at altitudes of approx. 250 km. The simulated effect of PAHs on the nightside proved to be much less pronounced; at the peak negative PAH density, it was less than the electron density. The physics governing these results will be discussed.

Borucki, W. J.↗

Comparative accuracy of the Albedo, transmission and absorption for selected radiative transfer approximations

Illustrations of both the relative and absolute accuracy of eight different radiative transfer approximations as a function of optical thickness, solar zenith angle and single scattering albedo are given. Computational results for the plane albedo, total transmission and fractional absorption were obtained for plane-parallel atmospheres composed of cloud particles. These computations, which were obtained using the doubling method, are compared with comparable results obtained using selected radiative transfer approximations. Comparisons were made between asymptotic theory for thick layers and the following widely used two stream approximations: Coakley-Chylek's models 1 and 2, Meador-Weaver, Eddington, delta-Eddington, PIFM and delta-discrete ordinates.

King, M. D.↗

NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗

28 NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning: Preprint

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗