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NAS (NASA Advanced Supercomputing) Division: Advanced Computing: High-End Computing Capability (HECC) Project; Quantum Artificial Intelligence Lab (QuAIL); NASA Earth Exchange (NEX); Data Analytics Infrastructure for NASA; Cloud Services for Science & Engineering Projects.
By using the results of highly accurate T-matrix computations for randomly oriented oblate and prolate spheroids and Chebyshev particles with varying degrees of asphericity, we analyze the effects of a deviation of water-droplet shapes from that of a perfect sphere on the behavior of Lorenz-Mie morphology-dependent resonances of various widths. We demonstrate that the positions and profiles of the resonances can change significantly with increasing asphericity. The absolute degree of asphericity required to suppress a Lorenz-Mie resonance is approximately proportional to the resonance width. Our results imply that numerical averaging of scattering characteristics of real cloud droplets over sizes may rely on a significantly coarser size-parameter resolution than that required for ideal, perfectly spherical particles.
The encounter of the spacecraft Voyager 2 with Neptune and its large satellite Triton in August 1989 will provide a crucial test of ideas regarding the origin and chemical composition of the outer solar system. In this pre-encounter publication, the possibility is quantified that Titron is a captured moon which, like Pluto and Charon, originally condensed as a major planetesimal within the gas ring that was shed by the contracting protosolar cloud at Neptune's orbit. Ideas of supersonic convective turbulence are used to compute the gas pressure, temperature and rat of catalytic synthesis of CH4, CO2, and C(s) within the protosolar cloud, assuming that all C is initially present as CO. The calculations lead to a unique composition for Triton, Pluto, Charon: each body consists of, by mass, 18 1/2 percent solid CO2 ice, 4 percent graphite, 1/2 percent CH4 ice, 29 percent methanated water ice and 48 percent of anhydrous rock. This mix has a density consistent with that of the Pluto-Charon system and yields a predicted mean density for Triton of 2.20 + or - 0.5 g/cu cm, for satellite radius equal to 1,750 km.
With the advent of virtual machine (VM)-based platforms for parallel computing, it is now possible to execute parallel discrete event simulations (PDES) over multiple virtual machines, in contrast to executing in native mode directly over hardware as is traditionally done over the past decades. While mature VM-based parallel systems now offer new, compelling benefits such as serviceability, dynamic reconfigurability and overall cost effectiveness, the runtime performance of parallel applications can be significantly affected. In particular, most VM-based platforms are optimized for general workloads, but PDES execution exhibits unique dynamics significantly different from other workloads. Here we first present results from experiments that highlight the gross deterioration of the runtime performance of VM-based PDES simulations when executed using traditional VM schedulers, quantitatively showing the bad scaling properties of the scheduler as the number of VMs is increased. The mismatch is fundamental in nature in the sense that any fairness-based VM scheduler implementation would exhibit this mismatch with PDES runs. We also present a new scheduler optimized specifically for PDES applications, and describe its design and implementation. Experimental results obtained from running PDES benchmarks (PHOLD and vehicular traffic simulations) over VMs show over an order of magnitude improvement in the run time of the PDES-optimized scheduler relative to the regular VM scheduler, with over 20 reduction in run time of simulations using up to 64 VMs. The observations and results are timely in the context of emerging systems such as cloud platforms and VM-based high performance computing installations, highlighting to the community the need for PDES-specific support, and the feasibility of significantly reducing the runtime overhead for scalable PDES on VM platforms.
Analyzing high-dimensional point clouds is a classical challenge in visual analytics. Traditional techniques, such as projections or axis-based techniques, suffer from projection artifacts, occlusion, and visual complexity.We propose to split data analysis into two parts to address these shortcomings. First, a structural overview phase abstracts data by its density distribution. This phase performs topological analysis to support accurate and non-overlapping presentation of the high-dimensional cluster structure as a topological landscape profile. Utilizing a landscape metaphor, it presents clusters and their nesting as hills whose height, width, and shape reflect cluster coherence, size, and stability, respectively. A second local analysis phase utilizes this global structural knowledge to select individual clusters or point sets for further, localized data analysis. Focusing on structural entities significantly reduces visual clutter in established geometric visualizations and permits a clearer, more thorough data analysis. In conclusion, this analysis complements the global topological perspective and enables the user to study subspaces or geometric properties, such as shape.
Clouds remain the largest source of uncertainty in our understanding of global climate change. Different aspects of the planetary cloud field can provide positive and negative feedback to the Earth’s energy balance, and clouds of course are directly implicated in changes to the planetary distribution of precipitation. A fundamental problem in our current understanding of the role of clouds in the dynamics of climate are that current resolutions do not resolve the fundamental length scales associated with clouds. We expect our understanding of the role of clouds in climate to undergo a qualitative change as the resolutions of global models begin to encompass clouds. At these resolutions (which roughly scale with the tropopause height of 10km) non-hydrostatic dynamics become significant and deep convective processes are resolved. We are poised at the threshold of being able to run global scale simulations that include direct, nonparameterized, simulations of deep convective clouds. The goal of this research is to use the Argonne Leadership Computing Facility to explore the frontier of weather prediction and climate modeling with the newly developed Geophysical Fluid Dynamics (GFDL) global cloud-resolving model. A single unified atmospheric modeling system with a cubed-sphere dynamical core and bulk cloud micro-physics running at 3.5km resolutions was run with the goal of capturing the climatology of clouds and severe storms in a warming world. The ability to reproduce historical tropical storm statistics will be used as a test of this ground-breaking model.
A sequence of ATS 3 pictures including the development history of large anvil clouds near Salina, Kansas was enlarged by NASA into 8X negatives which were used to obtain the best quality prints by mixing scan lines in 8 steps to minimize checker-board patterns. These images resulted in the best possible resolution, permitting use to compute the heights of overshooting tops above environmental anvil levels based on cloud shadow relationships along with the techniques of lunar topographic mapping. Of 39 heights computed, 6 were within 15 miles of reported positions of 3 tornadoes. It was found that the tornado proximity tops were mostly less than 5000 ft, with one exception of 7000 ft, suggesting that tornadoes are most likely to occur when overshooting height decreases. In order to simulate surface vortices induced by cloud-scale rotation and updraft fields, a laboratory model was constructed. The model experiment has shown that the rotation or updraft field induces a surface vortex but their combination does prevent the formation of the surface vortex. This research leads to a conclusion that the determination of the cloud-top topography and its time variation is of extreme importance in predicting severe local storms for a period of 0 to 6 hours.
The obscuring circumnuclear torus of dusty molecular gas is one of the major components of active galactic nuclei (AGN). The torus can be studied by analyzing the time response of its infrared (IR) dust emission to variations in the AGN continuum luminosity, a technique known as reverberation mapping. The IR response is the convolution of the AGN ultraviolet/optical light curve with a transfer function that contains information about the size, geometry, and structure of the torus. Here, we describe a new computer model that simulates the reverberation response of a clumpy torus. Given an input optical light curve, the code computes the emission of a 3D ensemble of dust clouds as a function of time at selected IR wavelengths, taking into account light travel delays. We present simulated dust emission responses at 3.6, 4.5, and 30 μ m that explore the effects of various geometrical and structural properties, dust cloud orientation, and anisotropy of the illuminating radiation field. We also briefly explore the effects of cloud shadowing (clouds are shielded from the AGN continuum source). Example synthetic light curves have also been generated, using the observed optical light curve of the Seyfert 1 galaxy NGC 6418 as input. The torus response is strongly wavelength-dependent, due to the gradient in cloud surface temperature within the torus, and because the cloud emission is strongly anisotropic at shorter wavelengths. Anisotropic illumination of the torus also significantly modifies the torus response, reducing the lag between the IR and optical variations.
In a collaborating scientific research arena it is important to have an environment where analysts have access to a shared repository of information, documents, and software tools, and be able to accurately maintain and track historical changes in models. The new Safety Portal cloud-based environment will be accessible remotely from anywhere regardless of computing platforms given that the platform has available Internet access and proper browser capabilities. Information stored at this environment would be restricted based on user assigned credentials. This report discusses current development of a cloud-based web portal for PRA tools.
The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.
In global atmospheric models, resolving stratocumulus (Sc) in the vertical is computationally expensive. However, Sc appear only under special meteorological conditions. Therefore, there is motivation to refine the vertical grid levels adaptively. In order to facilitate the possibility of parallelization on graphical processing units, our grid adaptation method prescribes the number of vertical levels a priori. Then grid levels are relocated toward altitude ranges in need of refinement. Because the method relocates existing grid levels, rather than adding extra levels, there is a risk of creating regions with overly coarse grid spacing, that is, voids in the grid mesh. To prevent such voids from forming, a simple method is developed to impose a maximum grid spacing. To decide where to place enhanced resolution, the authors develop an empirical mesh refinement criterion. It refines grid spacing near the ground, near strong temperature gradients, and within clouds. Our grid adaptation method is implemented in a single-column model and evaluated on four test cases: decaying stratocumulus, developing shallow cumulus, a quasi-stationary stratocumulus deck, and the diurnal cycle of a dry boundary layer. In the stratocumulus cases, mesh refinement leads to improvements in both the time evolution of fields and their time averages. The other two cases show smaller differences.
A workshop sponsored by the U.S. Department of Energy (DOE) Atmospheric System Research (ASR) Program was held at the Pacific Northwest National Laboratory (PNNL) on 3-5 February 2016. This workshop was a response to the rapid approach of climate models with cloud-permitting (1- to 10-km grid spacing) resolutions. The expansion of computational resources and advances in numerical methods are making such models a reality. The workshop participants considered specifically the new set of challenges for representing convective cloud processes in this generation of models. The challenges arise from the fact that some aspects of convection are partly resolved. Short- and long-term strategies that would lead to accurately representing convection in these models were discussed, and opportunities for the ASR program to effectively leverage DOE’s unique capabilities, high-performance computing resources, and Atmospheric Radiation Measurement (ARM) Climate Research Facility observational facilities to this end were identified. The workshop was organized around the premise that a strategy to accurately treat convection in the next generation of climate models comprises addressing challenges on three core themes and their integration.
We present a new analysis of the motion of pressure-confined, broad-line region (BLR) clouds in active galactic nuclei (AGNs) taking into account the combined influence of gravity and radiation pressure. We calculate cloud orbits under a large range of conditions and include the effect of column density variation as a function of location. The dependence of radiation pressure force on the level of ionization and the column density are accurately computed. The main results are as follows. (1) The mean cloud locations (r{sub BLR}) and line widths (FWHMs) are combined in such a way that the simple virial mass estimate, r{sub BLR}, FWHM{sup 2}/G, gives a reasonable approximation to M{sub BH} even when radiation pressure force is important. The reason is that L/M rather than L is the main parameter affecting the planar cloud motion. (2) Reproducing the mean observed r{sub BLR}, FWHM, and line intensity of H{beta} and C IV {lambda}1549 requires at least two different populations of clouds. (3) The cloud location is a function of both L {sup 1/2} and L/M. Given this, we suggest a new approximation for r{sub BLR} which, when inserted into the BH mass equation, results in a new approximation for M{sub BH}. The new expression involves L {sup 1/2}, FWHM, and two constants that are obtained from a comparison with available M-{sigma}* mass estimates. It deviates only slightly from the old mass estimate at all luminosities. (4) The quality of the present black hole mass estimators depends, critically, on the way the present M-{sigma}* AGN sample (29 objects) represents the overall population, in particular the distribution of L/L{sub Edd}.
We explored a novel framework for simulating the small-scale processes that drive the evolution of aerosol, cloud, and precipitation particles, which are a critical gap in the predictive understanding of weather and climate. Particle-based methods have emerged as an effective tool for modeling aerosol-cloud-precipitation interactions, but existing particle-based models are computationally too expensive to simulate the large domains relevant for the atmosphere or to represent the full suite of relevant processes. The lack of a comprehensive and efficient reference model is a critical bottleneck in our understanding of cloud and precipitation processes and our ability to parameterize these processes for regional- and global-scale simulations. To address this need, we explored an approach to accelerate and expand particle-based models using a new machine learning approach, graph network-based simulators (GNS). Rather than modeling the evolution of the system by numerically integrating continuity equations, the GNS represents dynamics through learned message passing. Our aim was to develop fast and accurate surrogate models for particle-based simulations. We explored applying GNS to simulate cloud droplet transport, growth, and evaporation under turbulent conditions, but we found the GNS over-smoothed the simulations. We then applied the GNS to simulate aerosol dynamics through gas condensation and found the GNS was able to reproduce the benchmark, physics-based simulation with high accuracy.
The Atlantic Ocean covers approximately 25% of Earth’s surface and the atmosphere above it is home to a complex array of clouds and aerosols that have important influences on regional and global weather and climate. These influences must be accurately depicted in short range, medium range, seasonal, and climate forecast models. Conditions over the tropical Atlantic are particularly complex due to continental scale plumes of dust from the Sahara Desert and smoke from agricultural burning in Africa that drift across the Atlantic Ocean basin toward the Americas. These plumes are often found meandering in the lower atmosphere above shallow tropical clouds that form above the ocean surface, presumably mingling with these clouds on occasion due to convective mixing processes. Elevated dust and smoke particles absorb incoming sunlight and substantially warm the marine atmosphere in the layer in which they are present. This warming may alter the thermal stability of the marine atmosphere and may throttle or enhance the development of clouds, change their internal structure and the rate at which they precipitate. Alternatively, it may isolate the lower atmosphere from drier layers above enabling water vapor to accumulate near the ocean surface potentially leading to the development of deeper convection. Our study was organized around the principal concept of determining the impact of African smoke and dust plumes upon cloud development at ASI and understanding how a popular shallow convection parameterization used in models responds to the presence of this aerosol. We analyzed observations collected during the US Department of Energy (DOE) Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign using the Atmospheric Radiation Measurement Program’s Mobile Facility #1 (AMF-1), which was deployed to Ascension Island (ASI) in Southeastern Atlantic for a one-year period. Ascension Island is immediately downwind from an African source of these plumes, but far enough removed to enable the lower atmosphere to have reacted to their presence. A main goal of our study was to compute radiative heating rate profiles over Ascension Island and along the trajectory from the biomass burning regions along coastal Africa to Ascension Island. To compute the radiative heating rate due to aerosols and clouds, we employed observed profiles of temperature, humidity, and clouds from LASIC alongside aerosol optical properties from the Modern-era Retrospective analysis for Research and Applications Version 2 (MERRA-2), as input for the Rapid Radiation Transfer Model (RRTM). Radiative heating was also assessed across the southeast Atlantic Ocean using an ensemble of back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We were successful in this effort and the resulting publication is already being cited despite its relatively short lifetime in the literature. The second part of our study involved the process-level representation of the clouds observed over the Southeastern Atlantic in models. Our initial task, upon which the balance of this portion of the study depended, was to evaluate the representativeness of the clouds observed by AMF-1. What orographic influence did Ascension Island have on the measured cloud structure? We set out to answer this question by trying to separate observations that were clearly indicative of orographic forcing from those that were representative of open ocean. The siting of AMF-1, which was 340-m above sea-level on the slope of a steep escarpment, proved demonstrably problematic for cloud process measurements. Despite considerable effort using multiple approaches including artificial intelligence (AI), we were unable to successfully compensate for the orographic forcing present at the AMF-1 site on ASI and, hence, produce process-level summaries of the convective mass flux representative of open ocean over the Southeastern Atlantic. Even though the project has officially ended, we are making a final attempt using two new types of clustering algorithm (i.e., AI) on the recommendation of a former student, who is an expert in this area. Since we have only recently begun to test these new AI schemes, the recommendations from the second part of our project outlined below are based on our experience at the time of this report.
We describe the current status of ongoing research and development of the electrostatic plasma lens for focusing and manipulating intense negatively charged particle beams, electrons, and negative ions. The physical principle of this kind of plasma lens is based on magnetic isolation electrons providing creation of a dynamical positive space charge cloud in shortly restricted volume propagating beam. Here, the new results of experimental investigations and computer simulations of wide-aperture, intense electron beam focusing by plasma lens with positive space charge cloud produced due to the cylindrical anode layer accelerator creating a positive ion stream towards an axis system is presented.
A key to predicting climate change is to observe an understand the global distribution of clouds and their physical properties such as optical thickness and droplet size. Since clouds change rapidly over short time and space intervals, they are difficult to simulate in computer models. But it is essential that global climate models predict realistic spatial and temporal distribution of cloud optical depth. The best way to verify these distributions is to infer optical depth from global coverage satellite data. However, satellite methods have many sources of uncertainty; thus, independent and reliable ground-based estimates are essential for validation. For aerosol, there is the AERONET - a ground based monitoring network that consists of identical multi-channel radiometers for assessing aerosol optical properties and validating their satellite retrievals. In addition to AEROSOL, we want the network monitoring CLOUD optical properties. It will use AERONET "time" (inappropriate for aerosol studies) to make basic new measurements related to cloud physics. In the presentation we will report on a new technique that retrieves cloud optical thickness for even broken clouds above green vegetation from surface measurements of zenith radiance in the visible (VIS) and near-IR (NIR) spectral regions. The idea of the method is simple: since green vegetation reflects 40-50% of incoming radiation in the NIR and only 5-10% in the VIS region, ground measurements under thin clouds have little spectral contrast between VIS and NIR, while thick clouds reflect much more of the surface-reflected radiation in the NIR than in VIS. Based on this idea, we use a combination of measurements (spectral indices) in VIS and NIR to estimate cloud optical thickness. By analogy with NDVI, the simplest index that can be defined is the Normalized Difference Cloud Index (NDCI) which is a ratio between the difference and the sum of two radiances measured for two narrow spectral bands in VIS and NIR.
This AMS Meteorological Monographs is dedicated to Dr. Joanne Simpson for her many pioneering research efforts in tropical meteorology during her fifty-year career. Dr. Simpson's major areas of scientific research involved the "hot tower" hypothesis and its role in hurricanes, structure and maintenance of trade winds, air-sea interaction, and observations and the mechanism for hurricanes and waterspouts. She was also a pioneer in cloud modeling with the first one-dimensional model and had the first cumulus model on a computer. She also played a major role in planning and leading observational experiments on convective cloud systems. The launch of the Tropical Rainfall Measuring Mission (TRMM) satellite, a joint U.S.-Japan project, in November of 1997 made it possible for quantitative measurements of tropical rainfall to be obtained on a continuous basis over the entire global tropics. Dr. Simpson was the TRAM Project Scientist from 1986 until its launch in 1997. Her efforts during this crucial period ensured that the mission was both well planned scientifically and well engineered as well as within budget. In this paper, Dr. J. Simpson's nine specific accomplishments during her fifty-year career: (1) hot tower hypothesis, (2) hurricanes, (3) airflow and clouds over heated islands, (4) cloud models, (5) trade winds and their role in cumulus development, (6) air-sea interaction, (7) cloud-cloud interactions and mergers, (8) waterspouts, and (9) TRMM science, will be described and discussed.