Cloud computing for comparative genomics

PubMed Central

2010-01-01

Background Large comparative genomics studies and tools are becoming increasingly more compute-expensive as the number of available genome sequences continues to rise. The capacity and cost of local computing infrastructures are likely to become prohibitive with the increase, especially as the breadth of questions continues to rise. Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies going forward. To test this, we redesigned a typical comparative genomics algorithm, the reciprocal smallest distance algorithm (RSD), to run within Amazon's Elastic Computing Cloud (EC2). We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes. Results We ran more than 300,000 RSD-cloud processes within the EC2. These jobs were farmed simultaneously to 100 high capacity compute nodes using the Amazon Web Service Elastic Map Reduce and included a wide mix of large and small genomes. The total computation time took just under 70 hours and cost a total of $6,302 USD. Conclusions The effort to transform existing comparative genomics algorithms from local compute infrastructures is not trivial. However, the speed and flexibility of cloud computing environments provides a substantial boost with manageable cost. The procedure designed to transform the RSD algorithm into a cloud-ready application is readily adaptable to similar comparative genomics problems. PMID:20482786

Cloud computing for comparative genomics.

PubMed

Wall, Dennis P; Kudtarkar, Parul; Fusaro, Vincent A; Pivovarov, Rimma; Patil, Prasad; Tonellato, Peter J

2010-05-18

Large comparative genomics studies and tools are becoming increasingly more compute-expensive as the number of available genome sequences continues to rise. The capacity and cost of local computing infrastructures are likely to become prohibitive with the increase, especially as the breadth of questions continues to rise. Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies going forward. To test this, we redesigned a typical comparative genomics algorithm, the reciprocal smallest distance algorithm (RSD), to run within Amazon's Elastic Computing Cloud (EC2). We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes. We ran more than 300,000 RSD-cloud processes within the EC2. These jobs were farmed simultaneously to 100 high capacity compute nodes using the Amazon Web Service Elastic Map Reduce and included a wide mix of large and small genomes. The total computation time took just under 70 hours and cost a total of $6,302 USD. The effort to transform existing comparative genomics algorithms from local compute infrastructures is not trivial. However, the speed and flexibility of cloud computing environments provides a substantial boost with manageable cost. The procedure designed to transform the RSD algorithm into a cloud-ready application is readily adaptable to similar comparative genomics problems.

Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

PubMed

Angiuoli, Samuel V; White, James R; Matalka, Malcolm; White, Owen; Fricke, W Florian

2011-01-01

The widespread popularity of genomic applications is threatened by the "bioinformatics bottleneck" resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.

Resources and Costs for Microbial Sequence Analysis Evaluated Using Virtual Machines and Cloud Computing

PubMed Central

Angiuoli, Samuel V.; White, James R.; Matalka, Malcolm; White, Owen; Fricke, W. Florian

2011-01-01

Background The widespread popularity of genomic applications is threatened by the “bioinformatics bottleneck” resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. Results We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Conclusions Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers. PMID:22028928

Galaxy CloudMan: delivering cloud compute clusters.

PubMed

Afgan, Enis; Baker, Dannon; Coraor, Nate; Chapman, Brad; Nekrutenko, Anton; Taylor, James

2010-12-21

Widespread adoption of high-throughput sequencing has greatly increased the scale and sophistication of computational infrastructure needed to perform genomic research. An alternative to building and maintaining local infrastructure is "cloud computing", which, in principle, offers on demand access to flexible computational infrastructure. However, cloud computing resources are not yet suitable for immediate "as is" use by experimental biologists. We present a cloud resource management system that makes it possible for individual researchers to compose and control an arbitrarily sized compute cluster on Amazon's EC2 cloud infrastructure without any informatics requirements. Within this system, an entire suite of biological tools packaged by the NERC Bio-Linux team (http://nebc.nerc.ac.uk/tools/bio-linux) is available for immediate consumption. The provided solution makes it possible, using only a web browser, to create a completely configured compute cluster ready to perform analysis in less than five minutes. Moreover, we provide an automated method for building custom deployments of cloud resources. This approach promotes reproducibility of results and, if desired, allows individuals and labs to add or customize an otherwise available cloud system to better meet their needs. The expected knowledge and associated effort with deploying a compute cluster in the Amazon EC2 cloud is not trivial. The solution presented in this paper eliminates these barriers, making it possible for researchers to deploy exactly the amount of computing power they need, combined with a wealth of existing analysis software, to handle the ongoing data deluge.

Menu-driven cloud computing and resource sharing for R and Bioconductor

PubMed Central

Bolouri, Hamid; Angerman, Michael

2011-01-01

Summary: We report CRdata.org, a cloud-based, free, open-source web server for running analyses and sharing data and R scripts with others. In addition to using the free, public service, CRdata users can launch their own private Amazon Elastic Computing Cloud (EC2) nodes and store private data and scripts on Amazon's Simple Storage Service (S3) with user-controlled access rights. All CRdata services are provided via point-and-click menus. Availability and Implementation: CRdata is open-source and free under the permissive MIT License (opensource.org/licenses/mit-license.php). The source code is in Ruby (ruby-lang.org/en/) and available at: github.com/seerdata/crdata. Contact: hbolouri@fhcrc.org PMID:21685055

Deploying Crowd-Sourced Formal Verification Systems in a DoD Network

DTIC Science & Technology

2013-09-01

INTENTIONALLY LEFT BLANK 1 I. INTRODUCTION A. INTRODUCTION In 2014 cyber attacks on critical infrastructure are expected to increase...CSFV systems on the Internet‒‒possibly using cloud infrastructure (Dean, 2013). By using Amazon Compute Cloud (EC2) systems, DARPA will use ordinary...through standard access methods. Those clients could be mobile phones, laptops, netbooks, tablet computers or personal digital assistants (PDAs) (Smoot

Exploiting Redundancy and Application Scalability for Cost-Effective, Time-Constrained Execution of HPC Applications on Amazon EC2

DOE PAGES

Marathe, Aniruddha P.; Harris, Rachel A.; Lowenthal, David K.; ...

2015-12-17

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost bymore » exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.« less

A Cloud-Based Simulation Architecture for Pandemic Influenza Simulation

PubMed Central

Eriksson, Henrik; Raciti, Massimiliano; Basile, Maurizio; Cunsolo, Alessandro; Fröberg, Anders; Leifler, Ola; Ekberg, Joakim; Timpka, Toomas

2011-01-01

High-fidelity simulations of pandemic outbreaks are resource consuming. Cluster-based solutions have been suggested for executing such complex computations. We present a cloud-based simulation architecture that utilizes computing resources both locally available and dynamically rented online. The approach uses the Condor framework for job distribution and management of the Amazon Elastic Computing Cloud (EC2) as well as local resources. The architecture has a web-based user interface that allows users to monitor and control simulation execution. In a benchmark test, the best cost-adjusted performance was recorded for the EC2 H-CPU Medium instance, while a field trial showed that the job configuration had significant influence on the execution time and that the network capacity of the master node could become a bottleneck. We conclude that it is possible to develop a scalable simulation environment that uses cloud-based solutions, while providing an easy-to-use graphical user interface. PMID:22195089

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

PubMed

Yazar, Seyhan; Gooden, George E C; Mackey, David A; Hewitt, Alex W

2014-01-01

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5-78.2) for E.coli and 53.5% (95% CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5-303.1) and 173.9% (95% CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Benchmarking Undedicated Cloud Computing Providers for Analysis of Genomic Datasets

PubMed Central

Yazar, Seyhan; Gooden, George E. C.; Mackey, David A.; Hewitt, Alex W.

2014-01-01

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5–78.2) for E.coli and 53.5% (95% CI: 34.4–72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5–303.1) and 173.9% (95% CI: 134.6–213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE. PMID:25247298

CE-ACCE: The Cloud Enabled Advanced sCience Compute Environment

NASA Astrophysics Data System (ADS)

Cinquini, L.; Freeborn, D. J.; Hardman, S. H.; Wong, C.

2017-12-01

Traditionally, Earth Science data from NASA remote sensing instruments has been processed by building custom data processing pipelines (often based on a common workflow engine or framework) which are typically deployed and run on an internal cluster of computing resources. This approach has some intrinsic limitations: it requires each mission to develop and deploy a custom software package on top of the adopted framework; it makes use of dedicated hardware, network and storage resources, which must be specifically purchased, maintained and re-purposed at mission completion; and computing services cannot be scaled on demand beyond the capability of the available servers.More recently, the rise of Cloud computing, coupled with other advances in containerization technology (most prominently, Docker) and micro-services architecture, has enabled a new paradigm, whereby space mission data can be processed through standard system architectures, which can be seamlessly deployed and scaled on demand on either on-premise clusters, or commercial Cloud providers. In this talk, we will present one such architecture named CE-ACCE ("Cloud Enabled Advanced sCience Compute Environment"), which we have been developing at the NASA Jet Propulsion Laboratory over the past year. CE-ACCE is based on the Apache OODT ("Object Oriented Data Technology") suite of services for full data lifecycle management, which are turned into a composable array of Docker images, and complemented by a plug-in model for mission-specific customization. We have applied this infrastructure to both flying and upcoming NASA missions, such as ECOSTRESS and SMAP, and demonstrated deployment on the Amazon Cloud, either using simple EC2 instances, or advanced AWS services such as Amazon Lambda and ECS (EC2 Container Services).

Operating Dedicated Data Centers - Is It Cost-Effective?

NASA Astrophysics Data System (ADS)

Ernst, M.; Hogue, R.; Hollowell, C.; Strecker-Kellog, W.; Wong, A.; Zaytsev, A.

2014-06-01

The advent of cloud computing centres such as Amazon's EC2 and Google's Computing Engine has elicited comparisons with dedicated computing clusters. Discussions on appropriate usage of cloud resources (both academic and commercial) and costs have ensued. This presentation discusses a detailed analysis of the costs of operating and maintaining the RACF (RHIC and ATLAS Computing Facility) compute cluster at Brookhaven National Lab and compares them with the cost of cloud computing resources under various usage scenarios. An extrapolation of likely future cost effectiveness of dedicated computing resources is also presented.

Cloud Computing: An Overview

NASA Astrophysics Data System (ADS)

Qian, Ling; Luo, Zhiguo; Du, Yujian; Guo, Leitao

In order to support the maximum number of user and elastic service with the minimum resource, the Internet service provider invented the cloud computing. within a few years, emerging cloud computing has became the hottest technology. From the publication of core papers by Google since 2003 to the commercialization of Amazon EC2 in 2006, and to the service offering of AT&T Synaptic Hosting, the cloud computing has been evolved from internal IT system to public service, from cost-saving tools to revenue generator, and from ISP to telecom. This paper introduces the concept, history, pros and cons of cloud computing as well as the value chain and standardization effort.

Cross-VM Side Channels and Their Use to Extract Private Keys

DTIC Science & Technology

2012-10-16

clouds such as Amazon EC2 and Rackspace, but also by other Xen use cases. For ex- 4 ample, many virtual desktop infrastructure ( VDI ) solutions (e.g...whose bit length is, for example, 337, 403, or 457 when κ is 2048 , 3072, or 4096, respectively. We note that this deviates from standard ElGamal, in

CLUSTOM-CLOUD: In-Memory Data Grid-Based Software for Clustering 16S rRNA Sequence Data in the Cloud Environment.

PubMed

Oh, Jeongsu; Choi, Chi-Hwan; Park, Min-Kyu; Kim, Byung Kwon; Hwang, Kyuin; Lee, Sang-Heon; Hong, Soon Gyu; Nasir, Arshan; Cho, Wan-Sup; Kim, Kyung Mo

2016-01-01

High-throughput sequencing can produce hundreds of thousands of 16S rRNA sequence reads corresponding to different organisms present in the environmental samples. Typically, analysis of microbial diversity in bioinformatics starts from pre-processing followed by clustering 16S rRNA reads into relatively fewer operational taxonomic units (OTUs). The OTUs are reliable indicators of microbial diversity and greatly accelerate the downstream analysis time. However, existing hierarchical clustering algorithms that are generally more accurate than greedy heuristic algorithms struggle with large sequence datasets. To keep pace with the rapid rise in sequencing data, we present CLUSTOM-CLOUD, which is the first distributed sequence clustering program based on In-Memory Data Grid (IMDG) technology-a distributed data structure to store all data in the main memory of multiple computing nodes. The IMDG technology helps CLUSTOM-CLOUD to enhance both its capability of handling larger datasets and its computational scalability better than its ancestor, CLUSTOM, while maintaining high accuracy. Clustering speed of CLUSTOM-CLOUD was evaluated on published 16S rRNA human microbiome sequence datasets using the small laboratory cluster (10 nodes) and under the Amazon EC2 cloud-computing environments. Under the laboratory environment, it required only ~3 hours to process dataset of size 200 K reads regardless of the complexity of the human microbiome data. In turn, one million reads were processed in approximately 20, 14, and 11 hours when utilizing 20, 30, and 40 nodes on the Amazon EC2 cloud-computing environment. The running time evaluation indicates that CLUSTOM-CLOUD can handle much larger sequence datasets than CLUSTOM and is also a scalable distributed processing system. The comparative accuracy test using 16S rRNA pyrosequences of a mock community shows that CLUSTOM-CLOUD achieves higher accuracy than DOTUR, mothur, ESPRIT-Tree, UCLUST and Swarm. CLUSTOM-CLOUD is written in JAVA and is freely available at http://clustomcloud.kopri.re.kr.

CLUSTOM-CLOUD: In-Memory Data Grid-Based Software for Clustering 16S rRNA Sequence Data in the Cloud Environment

PubMed Central

Park, Min-Kyu; Kim, Byung Kwon; Hwang, Kyuin; Lee, Sang-Heon; Hong, Soon Gyu; Nasir, Arshan; Cho, Wan-Sup; Kim, Kyung Mo

2016-01-01

High-throughput sequencing can produce hundreds of thousands of 16S rRNA sequence reads corresponding to different organisms present in the environmental samples. Typically, analysis of microbial diversity in bioinformatics starts from pre-processing followed by clustering 16S rRNA reads into relatively fewer operational taxonomic units (OTUs). The OTUs are reliable indicators of microbial diversity and greatly accelerate the downstream analysis time. However, existing hierarchical clustering algorithms that are generally more accurate than greedy heuristic algorithms struggle with large sequence datasets. To keep pace with the rapid rise in sequencing data, we present CLUSTOM-CLOUD, which is the first distributed sequence clustering program based on In-Memory Data Grid (IMDG) technology–a distributed data structure to store all data in the main memory of multiple computing nodes. The IMDG technology helps CLUSTOM-CLOUD to enhance both its capability of handling larger datasets and its computational scalability better than its ancestor, CLUSTOM, while maintaining high accuracy. Clustering speed of CLUSTOM-CLOUD was evaluated on published 16S rRNA human microbiome sequence datasets using the small laboratory cluster (10 nodes) and under the Amazon EC2 cloud-computing environments. Under the laboratory environment, it required only ~3 hours to process dataset of size 200 K reads regardless of the complexity of the human microbiome data. In turn, one million reads were processed in approximately 20, 14, and 11 hours when utilizing 20, 30, and 40 nodes on the Amazon EC2 cloud-computing environment. The running time evaluation indicates that CLUSTOM-CLOUD can handle much larger sequence datasets than CLUSTOM and is also a scalable distributed processing system. The comparative accuracy test using 16S rRNA pyrosequences of a mock community shows that CLUSTOM-CLOUD achieves higher accuracy than DOTUR, mothur, ESPRIT-Tree, UCLUST and Swarm. CLUSTOM-CLOUD is written in JAVA and is freely available at http://clustomcloud.kopri.re.kr. PMID:26954507

Exploration of cloud computing late start LDRD #149630 : Raincoat. v. 2.1.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Echeverria, Victor T.; Metral, Michael David; Leger, Michelle A.

This report contains documentation from an interoperability study conducted under the Late Start LDRD 149630, Exploration of Cloud Computing. A small late-start LDRD from last year resulted in a study (Raincoat) on using Virtual Private Networks (VPNs) to enhance security in a hybrid cloud environment. Raincoat initially explored the use of OpenVPN on IPv4 and demonstrates that it is possible to secure the communication channel between two small 'test' clouds (a few nodes each) at New Mexico Tech and Sandia. We extended the Raincoat study to add IPSec support via Vyatta routers, to interface with a public cloud (Amazon Elasticmore » Compute Cloud (EC2)), and to be significantly more scalable than the previous iteration. The study contributed to our understanding of interoperability in a hybrid cloud.« less

Cloud computing and validation of expandable in silico livers.

PubMed

Ropella, Glen E P; Hunt, C Anthony

2010-12-03

In Silico Livers (ISLs) are works in progress. They are used to challenge multilevel, multi-attribute, mechanistic hypotheses about the hepatic disposition of xenobiotics coupled with hepatic responses. To enhance ISL-to-liver mappings, we added discrete time metabolism, biliary elimination, and bolus dosing features to a previously validated ISL and initiated re-validated experiments that required scaling experiments to use more simulated lobules than previously, more than could be achieved using the local cluster technology. Rather than dramatically increasing the size of our local cluster we undertook the re-validation experiments using the Amazon EC2 cloud platform. So doing required demonstrating the efficacy of scaling a simulation to use more cluster nodes and assessing the scientific equivalence of local cluster validation experiments with those executed using the cloud platform. The local cluster technology was duplicated in the Amazon EC2 cloud platform. Synthetic modeling protocols were followed to identify a successful parameterization. Experiment sample sizes (number of simulated lobules) on both platforms were 49, 70, 84, and 152 (cloud only). Experimental indistinguishability was demonstrated for ISL outflow profiles of diltiazem using both platforms for experiments consisting of 84 or more samples. The process was analogous to demonstration of results equivalency from two different wet-labs. The results provide additional evidence that disposition simulations using ISLs can cover the behavior space of liver experiments in distinct experimental contexts (there is in silico-to-wet-lab phenotype similarity). The scientific value of experimenting with multiscale biomedical models has been limited to research groups with access to computer clusters. The availability of cloud technology coupled with the evidence of scientific equivalency has lowered the barrier and will greatly facilitate model sharing as well as provide straightforward tools for scaling simulations to encompass greater detail with no extra investment in hardware.

Hybrid Pluggable Processing Pipeline (HyP3): A cloud-based infrastructure for generic processing of SAR data

NASA Astrophysics Data System (ADS)

Hogenson, K.; Arko, S. A.; Buechler, B.; Hogenson, R.; Herrmann, J.; Geiger, A.

2016-12-01

A problem often faced by Earth science researchers is how to scale algorithms that were developed against few datasets and take them to regional or global scales. One significant hurdle can be the processing and storage resources available for such a task, not to mention the administration of those resources. As a processing environment, the cloud offers nearly unlimited potential for compute and storage, with limited administration required. The goal of the Hybrid Pluggable Processing Pipeline (HyP3) project was to demonstrate the utility of the Amazon cloud to process large amounts of data quickly and cost effectively, while remaining generic enough to incorporate new algorithms with limited administration time or expense. Principally built by three undergraduate students at the ASF DAAC, the HyP3 system relies on core Amazon services such as Lambda, the Simple Notification Service (SNS), Relational Database Service (RDS), Elastic Compute Cloud (EC2), Simple Storage Service (S3), and Elastic Beanstalk. The HyP3 user interface was written using elastic beanstalk, and the system uses SNS and Lamdba to handle creating, instantiating, executing, and terminating EC2 instances automatically. Data are sent to S3 for delivery to customers and removed using standard data lifecycle management rules. In HyP3 all data processing is ephemeral; there are no persistent processes taking compute and storage resources or generating added cost. When complete, HyP3 will leverage the automatic scaling up and down of EC2 compute power to respond to event-driven demand surges correlated with natural disaster or reprocessing efforts. Massive simultaneous processing within EC2 will be able match the demand spike in ways conventional physical computing power never could, and then tail off incurring no costs when not needed. This presentation will focus on the development techniques and technologies that were used in developing the HyP3 system. Data and process flow will be shown, highlighting the benefits of the cloud for each step. Finally, the steps for integrating a new processing algorithm will be demonstrated. This is the true power of HyP3; allowing people to upload their own algorithms and execute them at archive level scales.

Highly parameterized model calibration with cloud computing: an example of regional flow model calibration in northeast Alberta, Canada

NASA Astrophysics Data System (ADS)

Hayley, Kevin; Schumacher, J.; MacMillan, G. J.; Boutin, L. C.

2014-05-01

Expanding groundwater datasets collected by automated sensors, and improved groundwater databases, have caused a rapid increase in calibration data available for groundwater modeling projects. Improved methods of subsurface characterization have increased the need for model complexity to represent geological and hydrogeological interpretations. The larger calibration datasets and the need for meaningful predictive uncertainty analysis have both increased the degree of parameterization necessary during model calibration. Due to these competing demands, modern groundwater modeling efforts require a massive degree of parallelization in order to remain computationally tractable. A methodology for the calibration of highly parameterized, computationally expensive models using the Amazon EC2 cloud computing service is presented. The calibration of a regional-scale model of groundwater flow in Alberta, Canada, is provided as an example. The model covers a 30,865-km2 domain and includes 28 hydrostratigraphic units. Aquifer properties were calibrated to more than 1,500 static hydraulic head measurements and 10 years of measurements during industrial groundwater use. Three regionally extensive aquifers were parameterized (with spatially variable hydraulic conductivity fields), as was the aerial recharge boundary condition, leading to 450 adjustable parameters in total. The PEST-based model calibration was parallelized on up to 250 computing nodes located on Amazon's EC2 servers.

Reducing and Analyzing the PHAT Survey with the Cloud

NASA Astrophysics Data System (ADS)

Williams, Benjamin F.; Olsen, Knut; Khan, Rubab; Pirone, Daniel; Rosema, Keith

2018-05-01

We discuss the technical challenges we faced and the techniques we used to overcome them when reducing the Panchromatic Hubble Andromeda Treasury (PHAT) photometric data set on the Amazon Elastic Compute Cloud (EC2). We first describe the architecture of our photometry pipeline, which we found particularly efficient for reducing the data in multiple ways for different purposes. We then describe the features of EC2 that make this architecture both efficient to use and challenging to implement. We describe the techniques we adopted to process our data, and suggest ways these techniques may be improved for those interested in trying such reductions in the future. Finally, we summarize the output photometry data products, which are now hosted publicly in two places in two formats. They are in simple fits tables in the high-level science products on MAST, and on a queryable database available through the NOAO Data Lab.

Sentinel-1 Archive and Processing in the Cloud using the Hybrid Pluggable Processing Pipeline (HyP3) at the ASF DAAC

NASA Astrophysics Data System (ADS)

Arko, S. A.; Hogenson, R.; Geiger, A.; Herrmann, J.; Buechler, B.; Hogenson, K.

2016-12-01

In the coming years there will be an unprecedented amount of SAR data available on a free and open basis to research and operational users around the globe. The Alaska Satellite Facility (ASF) DAAC hosts, through an international agreement, data from the Sentinel-1 spacecraft and will be hosting data from the upcoming NASA ISRO SAR (NISAR) mission. To more effectively manage and exploit these vast datasets, ASF DAAC has begun moving portions of the archive to the cloud and utilizing cloud services to provide higher-level processing on the data. The Hybrid Pluggable Processing Pipeline (HyP3) project is designed to support higher-level data processing in the cloud and extend the capabilities of researchers to larger scales. Built upon a set of core Amazon cloud services, the HyP3 system allows users to request data processing using a number of canned algorithms or their own algorithms once they have been uploaded to the cloud. The HyP3 system automatically accesses the ASF cloud-based archive through the DAAC RESTful application programming interface and processes the data on Amazon's elastic compute cluster (EC2). Final products are distributed through Amazon's simple storage service (S3) and are available for user download. This presentation will provide an overview of ASF DAAC's activities moving the Sentinel-1 archive into the cloud and developing the integrated HyP3 system, covering both the benefits and difficulties of working in the cloud. Additionally, we will focus on the utilization of HyP3 for higher-level processing of SAR data. Two example algorithms, for sea-ice tracking and change detection, will be discussed as well as the mechanism for integrating new algorithms into the pipeline for community use.

Are Cloud Environments Ready for Scientific Applications?

NASA Astrophysics Data System (ADS)

Mehrotra, P.; Shackleford, K.

2011-12-01

Cloud computing environments are becoming widely available both in the commercial and government sectors. They provide flexibility to rapidly provision resources in order to meet dynamic and changing computational needs without the customers incurring capital expenses and/or requiring technical expertise. Clouds also provide reliable access to resources even though the end-user may not have in-house expertise for acquiring or operating such resources. Consolidation and pooling in a cloud environment allow organizations to achieve economies of scale in provisioning or procuring computing resources and services. Because of these and other benefits, many businesses and organizations are migrating their business applications (e.g., websites, social media, and business processes) to cloud environments-evidenced by the commercial success of offerings such as the Amazon EC2. In this paper, we focus on the feasibility of utilizing cloud environments for scientific workloads and workflows particularly of interest to NASA scientists and engineers. There is a wide spectrum of such technical computations. These applications range from small workstation-level computations to mid-range computing requiring small clusters to high-performance simulations requiring supercomputing systems with high bandwidth/low latency interconnects. Data-centric applications manage and manipulate large data sets such as satellite observational data and/or data previously produced by high-fidelity modeling and simulation computations. Most of the applications are run in batch mode with static resource requirements. However, there do exist situations that have dynamic demands, particularly ones with public-facing interfaces providing information to the general public, collaborators and partners, as well as to internal NASA users. In the last few months we have been studying the suitability of cloud environments for NASA's technical and scientific workloads. We have ported several applications to multiple cloud environments including NASA's Nebula environment, Amazon's EC2, Magellan at NERSC, and SGI's Cyclone system. We critically examined the performance of the applications on these systems. We also collected information on the usability of these cloud environments. In this talk we will present the results of our study focusing on the efficacy of using clouds for NASA's scientific applications.

BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2.

PubMed

Lee, Hyungro; Yang, Youngik; Chae, Heejoon; Nam, Seungyoon; Choi, Donghoon; Tangchaisin, Patanachai; Herath, Chathura; Marru, Suresh; Nephew, Kenneth P; Kim, Sun

2012-09-01

MicroRNAs, by regulating the expression of hundreds of target genes, play critical roles in developmental biology and the etiology of numerous diseases, including cancer. As a vast amount of microRNA expression profile data are now publicly available, the integration of microRNA expression data sets with gene expression profiles is a key research problem in life science research. However, the ability to conduct genome-wide microRNA-mRNA (gene) integration currently requires sophisticated, high-end informatics tools, significant expertise in bioinformatics and computer science to carry out the complex integration analysis. In addition, increased computing infrastructure capabilities are essential in order to accommodate large data sets. In this study, we have extended the BioVLAB cloud workbench to develop an environment for the integrated analysis of microRNA and mRNA expression data, named BioVLAB-MMIA. The workbench facilitates computations on the Amazon EC2 and S3 resources orchestrated by the XBaya Workflow Suite. The advantages of BioVLAB-MMIA over the web-based MMIA system include: 1) readily expanded as new computational tools become available; 2) easily modifiable by re-configuring graphic icons in the workflow; 3) on-demand cloud computing resources can be used on an "as needed" basis; 4) distributed orchestration supports complex and long running workflows asynchronously. We believe that BioVLAB-MMIA will be an easy-to-use computing environment for researchers who plan to perform genome-wide microRNA-mRNA (gene) integrated analysis tasks.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community.

PubMed

Krampis, Konstantinos; Booth, Tim; Chapman, Brad; Tiwari, Bela; Bicak, Mesude; Field, Dawn; Nelson, Karen E

2012-03-19

A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community

PubMed Central

2012-01-01

Background A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Results Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Conclusions Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them. PMID:22429538

Cloud computing and validation of expandable in silico livers

PubMed Central

2010-01-01

Background In Silico Livers (ISLs) are works in progress. They are used to challenge multilevel, multi-attribute, mechanistic hypotheses about the hepatic disposition of xenobiotics coupled with hepatic responses. To enhance ISL-to-liver mappings, we added discrete time metabolism, biliary elimination, and bolus dosing features to a previously validated ISL and initiated re-validated experiments that required scaling experiments to use more simulated lobules than previously, more than could be achieved using the local cluster technology. Rather than dramatically increasing the size of our local cluster we undertook the re-validation experiments using the Amazon EC2 cloud platform. So doing required demonstrating the efficacy of scaling a simulation to use more cluster nodes and assessing the scientific equivalence of local cluster validation experiments with those executed using the cloud platform. Results The local cluster technology was duplicated in the Amazon EC2 cloud platform. Synthetic modeling protocols were followed to identify a successful parameterization. Experiment sample sizes (number of simulated lobules) on both platforms were 49, 70, 84, and 152 (cloud only). Experimental indistinguishability was demonstrated for ISL outflow profiles of diltiazem using both platforms for experiments consisting of 84 or more samples. The process was analogous to demonstration of results equivalency from two different wet-labs. Conclusions The results provide additional evidence that disposition simulations using ISLs can cover the behavior space of liver experiments in distinct experimental contexts (there is in silico-to-wet-lab phenotype similarity). The scientific value of experimenting with multiscale biomedical models has been limited to research groups with access to computer clusters. The availability of cloud technology coupled with the evidence of scientific equivalency has lowered the barrier and will greatly facilitate model sharing as well as provide straightforward tools for scaling simulations to encompass greater detail with no extra investment in hardware. PMID:21129207

Cloud Computing Value Chains: Understanding Businesses and Value Creation in the Cloud

NASA Astrophysics Data System (ADS)

Mohammed, Ashraf Bany; Altmann, Jörn; Hwang, Junseok

Based on the promising developments in Cloud Computing technologies in recent years, commercial computing resource services (e.g. Amazon EC2) or software-as-a-service offerings (e.g. Salesforce. com) came into existence. However, the relatively weak business exploitation, participation, and adoption of other Cloud Computing services remain the main challenges. The vague value structures seem to be hindering business adoption and the creation of sustainable business models around its technology. Using an extensive analyze of existing Cloud business models, Cloud services, stakeholder relations, market configurations and value structures, this Chapter develops a reference model for value chains in the Cloud. Although this model is theoretically based on porter's value chain theory, the proposed Cloud value chain model is upgraded to fit the diversity of business service scenarios in the Cloud computing markets. Using this model, different service scenarios are explained. Our findings suggest new services, business opportunities, and policy practices for realizing more adoption and value creation paths in the Cloud.

Galaxy CloudMan: delivering cloud compute clusters

PubMed Central

2010-01-01

Background Widespread adoption of high-throughput sequencing has greatly increased the scale and sophistication of computational infrastructure needed to perform genomic research. An alternative to building and maintaining local infrastructure is “cloud computing”, which, in principle, offers on demand access to flexible computational infrastructure. However, cloud computing resources are not yet suitable for immediate “as is” use by experimental biologists. Results We present a cloud resource management system that makes it possible for individual researchers to compose and control an arbitrarily sized compute cluster on Amazon’s EC2 cloud infrastructure without any informatics requirements. Within this system, an entire suite of biological tools packaged by the NERC Bio-Linux team (http://nebc.nerc.ac.uk/tools/bio-linux) is available for immediate consumption. The provided solution makes it possible, using only a web browser, to create a completely configured compute cluster ready to perform analysis in less than five minutes. Moreover, we provide an automated method for building custom deployments of cloud resources. This approach promotes reproducibility of results and, if desired, allows individuals and labs to add or customize an otherwise available cloud system to better meet their needs. Conclusions The expected knowledge and associated effort with deploying a compute cluster in the Amazon EC2 cloud is not trivial. The solution presented in this paper eliminates these barriers, making it possible for researchers to deploy exactly the amount of computing power they need, combined with a wealth of existing analysis software, to handle the ongoing data deluge. PMID:21210983

Using Amazon Web Services (AWS) to enable real-time, remote sensing of biophysical and anthropogenic conditions in green infrastructure systems in Philadelphia, an ultra-urban application of the Internet of Things (IoT)

NASA Astrophysics Data System (ADS)

Montalto, F. A.; Yu, Z.; Soldner, K.; Israel, A.; Fritch, M.; Kim, Y.; White, S.

2017-12-01

Urban stormwater utilities are increasingly using decentralized "green" infrastructure (GI) systems to capture stormwater and achieve compliance with regulations. Because environmental conditions, and design varies by GSI facility, monitoring of GSI systems under a range of conditions is essential. Conventional monitoring efforts can be costly because in-field data logging requires intense data transmission rates. The Internet of Things (IoT) can be used to more cost-effectively collect, store, and publish GSI monitoring data. Using 3G mobile networks, a cloud-based database was built on an Amazon Web Services (AWS) EC2 virtual machine to store and publish data collected with environmental sensors deployed in the field. This database can store multi-dimensional time series data, as well as photos and other observations logged by citizen scientists through a public engagement mobile app through a new Application Programming Interface (API). Also on the AWS EC2 virtual machine, a real-time QAQC flagging algorithm was developed to validate the sensor data streams.

A high performance scientific cloud computing environment for materials simulations

NASA Astrophysics Data System (ADS)

Jorissen, K.; Vila, F. D.; Rehr, J. J.

2012-09-01

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.

Geometric Data Perturbation-Based Personal Health Record Transactions in Cloud Computing

PubMed Central

Balasubramaniam, S.; Kavitha, V.

2015-01-01

Cloud computing is a new delivery model for information technology services and it typically involves the provision of dynamically scalable and often virtualized resources over the Internet. However, cloud computing raises concerns on how cloud service providers, user organizations, and governments should handle such information and interactions. Personal health records represent an emerging patient-centric model for health information exchange, and they are outsourced for storage by third parties, such as cloud providers. With these records, it is necessary for each patient to encrypt their own personal health data before uploading them to cloud servers. Current techniques for encryption primarily rely on conventional cryptographic approaches. However, key management issues remain largely unsolved with these cryptographic-based encryption techniques. We propose that personal health record transactions be managed using geometric data perturbation in cloud computing. In our proposed scheme, the personal health record database is perturbed using geometric data perturbation and outsourced to the Amazon EC2 cloud. PMID:25767826

Geometric data perturbation-based personal health record transactions in cloud computing.

PubMed

Balasubramaniam, S; Kavitha, V

2015-01-01

Cloud computing is a new delivery model for information technology services and it typically involves the provision of dynamically scalable and often virtualized resources over the Internet. However, cloud computing raises concerns on how cloud service providers, user organizations, and governments should handle such information and interactions. Personal health records represent an emerging patient-centric model for health information exchange, and they are outsourced for storage by third parties, such as cloud providers. With these records, it is necessary for each patient to encrypt their own personal health data before uploading them to cloud servers. Current techniques for encryption primarily rely on conventional cryptographic approaches. However, key management issues remain largely unsolved with these cryptographic-based encryption techniques. We propose that personal health record transactions be managed using geometric data perturbation in cloud computing. In our proposed scheme, the personal health record database is perturbed using geometric data perturbation and outsourced to the Amazon EC2 cloud.

Cloud characteristics, thermodynamic controls and radiative impacts during the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giangrande, Scott E.; Feng, Zhe; Jensen, Michael P.

Routine cloud, precipitation and thermodynamic observations collected by the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) and Aerial Facility (AAF) during the 2-year US Department of Energy (DOE) ARM Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign are summarized. These observations quantify the diurnal to large-scale thermodynamic regime controls on the clouds and precipitation over the undersampled, climatically important Amazon basin region. The extended ground deployment of cloud-profiling instrumentation enabled a unique look at multiple cloud regimes at high temporal and vertical resolution. This longer-term ground deployment, coupled with two short-term aircraft intensive observing periods, allowed new opportunitiesmore » to better characterize cloud and thermodynamic observational constraints as well as cloud radiative impacts for modeling efforts within typical Amazon wet and dry seasons.« less

Cloud characteristics, thermodynamic controls and radiative impacts during the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment

DOE PAGES

Giangrande, Scott E.; Feng, Zhe; Jensen, Michael P.; ...

2017-12-06

Routine cloud, precipitation and thermodynamic observations collected by the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) and Aerial Facility (AAF) during the 2-year US Department of Energy (DOE) ARM Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign are summarized. These observations quantify the diurnal to large-scale thermodynamic regime controls on the clouds and precipitation over the undersampled, climatically important Amazon basin region. The extended ground deployment of cloud-profiling instrumentation enabled a unique look at multiple cloud regimes at high temporal and vertical resolution. This longer-term ground deployment, coupled with two short-term aircraft intensive observing periods, allowed new opportunitiesmore » to better characterize cloud and thermodynamic observational constraints as well as cloud radiative impacts for modeling efforts within typical Amazon wet and dry seasons.« less

An Architecture for Cross-Cloud System Management

NASA Astrophysics Data System (ADS)

Dodda, Ravi Teja; Smith, Chris; van Moorsel, Aad

The emergence of the cloud computing paradigm promises flexibility and adaptability through on-demand provisioning of compute resources. As the utilization of cloud resources extends beyond a single provider, for business as well as technical reasons, the issue of effectively managing such resources comes to the fore. Different providers expose different interfaces to their compute resources utilizing varied architectures and implementation technologies. This heterogeneity poses a significant system management problem, and can limit the extent to which the benefits of cross-cloud resource utilization can be realized. We address this problem through the definition of an architecture to facilitate the management of compute resources from different cloud providers in an homogenous manner. This preserves the flexibility and adaptability promised by the cloud computing paradigm, whilst enabling the benefits of cross-cloud resource utilization to be realized. The practical efficacy of the architecture is demonstrated through an implementation utilizing compute resources managed through different interfaces on the Amazon Elastic Compute Cloud (EC2) service. Additionally, we provide empirical results highlighting the performance differential of these different interfaces, and discuss the impact of this performance differential on efficiency and profitability.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marathe, Aniruddha P.; Harris, Rachel A.; Lowenthal, David K.

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost bymore » exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.« less

Boverhof's App Earns Honorable Mention in Amazon's Web Services

Science.gov Websites

» Boverhof's App Earns Honorable Mention in Amazon's Web Services Competition News & Publications News Publications Facebook Google+ Twitter Boverhof's App Earns Honorable Mention in Amazon's Web Services by Amazon Web Services (AWS). Amazon officially announced the winners of its EC2 Spotathon on Monday

GATE Monte Carlo simulation of dose distribution using MapReduce in a cloud computing environment.

PubMed

Liu, Yangchuan; Tang, Yuguo; Gao, Xin

2017-12-01

The GATE Monte Carlo simulation platform has good application prospects of treatment planning and quality assurance. However, accurate dose calculation using GATE is time consuming. The purpose of this study is to implement a novel cloud computing method for accurate GATE Monte Carlo simulation of dose distribution using MapReduce. An Amazon Machine Image installed with Hadoop and GATE is created to set up Hadoop clusters on Amazon Elastic Compute Cloud (EC2). Macros, the input files for GATE, are split into a number of self-contained sub-macros. Through Hadoop Streaming, the sub-macros are executed by GATE in Map tasks and the sub-results are aggregated into final outputs in Reduce tasks. As an evaluation, GATE simulations were performed in a cubical water phantom for X-ray photons of 6 and 18 MeV. The parallel simulation on the cloud computing platform is as accurate as the single-threaded simulation on a local server and the simulation correctness is not affected by the failure of some worker nodes. The cloud-based simulation time is approximately inversely proportional to the number of worker nodes. For the simulation of 10 million photons on a cluster with 64 worker nodes, time decreases of 41× and 32× were achieved compared to the single worker node case and the single-threaded case, respectively. The test of Hadoop's fault tolerance showed that the simulation correctness was not affected by the failure of some worker nodes. The results verify that the proposed method provides a feasible cloud computing solution for GATE.

STORMSeq: an open-source, user-friendly pipeline for processing personal genomics data in the cloud.

PubMed

Karczewski, Konrad J; Fernald, Guy Haskin; Martin, Alicia R; Snyder, Michael; Tatonetti, Nicholas P; Dudley, Joel T

2014-01-01

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5-10 hours to process a full exome sequence and $30 and 3-8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2.

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

DOE PAGES

Sadooghi, Iman; Martin, Jesus Hernandez; Li, Tonglin; ...

2015-02-19

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context tomore » price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.« less

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sadooghi, Iman; Martin, Jesus Hernandez; Li, Tonglin

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context tomore » price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.« less

MarDRe: efficient MapReduce-based removal of duplicate DNA reads in the cloud.

PubMed

Expósito, Roberto R; Veiga, Jorge; González-Domínguez, Jorge; Touriño, Juan

2017-09-01

This article presents MarDRe, a de novo cloud-ready duplicate and near-duplicate removal tool that can process single- and paired-end reads from FASTQ/FASTA datasets. MarDRe takes advantage of the widely adopted MapReduce programming model to fully exploit Big Data technologies on cloud-based infrastructures. Written in Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for scalable Big Data processing. On a 16-node cluster deployed on the Amazon EC2 cloud platform, MarDRe is up to 8.52 times faster than a representative state-of-the-art tool. Source code in Java and Hadoop as well as a user's guide are freely available under the GNU GPLv3 license at http://mardre.des.udc.es . rreye@udc.es. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Hybrid Pluggable Processing Pipeline (HyP3): Programmatic Access to Cloud-Based Processing of SAR Data

NASA Astrophysics Data System (ADS)

Weeden, R.; Horn, W. B.; Dimarchi, H.; Arko, S. A.; Hogenson, K.

2017-12-01

A problem often faced by Earth science researchers is the question of how to scale algorithms that were developed against few datasets and take them to regional or global scales. This problem only gets worse as we look to a future with larger and larger datasets becoming available. One significant hurdle can be having the processing and storage resources available for such a task, not to mention the administration of those resources. As a processing environment, the cloud offers nearly unlimited potential for compute and storage, with limited administration required. The goal of the Hybrid Pluggable Processing Pipeline (HyP3) project was to demonstrate the utility of the Amazon cloud to process large amounts of data quickly and cost effectively. Principally built by three undergraduate students at the ASF DAAC, the HyP3 system relies on core Amazon cloud services such as Lambda, Relational Database Service (RDS), Elastic Compute Cloud (EC2), Simple Storage Service (S3), and Elastic Beanstalk. HyP3 provides an Application Programming Interface (API) through which users can programmatically interface with the HyP3 system; allowing them to monitor and control processing jobs running in HyP3, and retrieve the generated HyP3 products when completed. This presentation will focus on the development techniques and enabling technologies that were used in developing the HyP3 system. Data and process flow, from new subscription through to order completion will be shown, highlighting the benefits of the cloud for each step. Because the HyP3 system can be accessed directly from a user's Python scripts, powerful applications leveraging SAR products can be put together fairly easily. This is the true power of HyP3; allowing people to programmatically leverage the power of the cloud.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giangrande, Scott E.; Feng, Zhe; Jensen, Michael P.

Routine cloud, precipitation and thermodynamic observations collected by the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) and Aerial Facility (AAF) during the 2-year US Department of Energy (DOE) ARM Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign are summarized. These observations quantify the diurnal to large-scale thermodynamic regime controls on the clouds and precipitation over the undersampled, climatically important Amazon basin region. The extended ground deployment of cloud-profiling instrumentation enabled a unique look at multiple cloud regimes at high temporal and vertical resolution. This longer-term ground deployment, coupled with two short-term aircraft intensive observing periods, allowed new opportunitiesmore » to better characterize cloud and thermodynamic observational constraints as well as cloud radiative impacts for modeling efforts within typical Amazon wet and dry seasons.« less

Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration

PubMed Central

Kim, Hyunjoo; Parashar, Manish; Foran, David J.; Yang, Lin

2010-01-01

This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues. A policy-driven scheduling agent uses the QoS constraints along with performance history and the state of the resources to determine the appropriate size and mix of the public and private cloud resource that should be allocated to a specific request. The virtual computational cloud and the medical image registration service have been developed using the CometCloud engine and have been deployed on a combination of private clouds at Rutgers University and the Cancer Institute of New Jersey and Amazon EC2. An experimental evaluation is presented and demonstrates the effectiveness of autonomic cloudbursts and policy-based autonomic scheduling for this application. PMID:20640235

The Dynamo package for tomography and subtomogram averaging: components for MATLAB, GPU computing and EC2 Amazon Web Services

PubMed Central

Castaño-Díez, Daniel

2017-01-01

Dynamo is a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance. Dynamo is built upon mbtools (middle layer toolbox), a general-purpose MATLAB library for object-oriented scientific programming specifically developed to underpin Dynamo but usable as an independent tool. Its structure intertwines a flexible MATLAB codebase with precompiled C++ functions that carry the burden of numerically intensive operations. The package can be delivered as a precompiled standalone ready for execution without a MATLAB license. Multicore parallelization on a single node is directly inherited from the high-level parallelization engine provided for MATLAB, automatically imparting a balanced workload among the threads in computationally intense tasks such as alignment and classification, but also in logistic-oriented tasks such as tomogram binning and particle extraction. Dynamo supports the use of graphical processing units (GPUs), yielding considerable speedup factors both for native Dynamo procedures (such as the numerically intensive subtomogram alignment) and procedures defined by the user through its MATLAB-based GPU library for three-dimensional operations. Cloud-based virtual computing environments supplied with a pre-installed version of Dynamo can be publicly accessed through the Amazon Elastic Compute Cloud (EC2), enabling users to rent GPU computing time on a pay-as-you-go basis, thus avoiding upfront investments in hardware and longterm software maintenance. PMID:28580909

The Dynamo package for tomography and subtomogram averaging: components for MATLAB, GPU computing and EC2 Amazon Web Services.

PubMed

Castaño-Díez, Daniel

2017-06-01

Dynamo is a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance. Dynamo is built upon mbtools (middle layer toolbox), a general-purpose MATLAB library for object-oriented scientific programming specifically developed to underpin Dynamo but usable as an independent tool. Its structure intertwines a flexible MATLAB codebase with precompiled C++ functions that carry the burden of numerically intensive operations. The package can be delivered as a precompiled standalone ready for execution without a MATLAB license. Multicore parallelization on a single node is directly inherited from the high-level parallelization engine provided for MATLAB, automatically imparting a balanced workload among the threads in computationally intense tasks such as alignment and classification, but also in logistic-oriented tasks such as tomogram binning and particle extraction. Dynamo supports the use of graphical processing units (GPUs), yielding considerable speedup factors both for native Dynamo procedures (such as the numerically intensive subtomogram alignment) and procedures defined by the user through its MATLAB-based GPU library for three-dimensional operations. Cloud-based virtual computing environments supplied with a pre-installed version of Dynamo can be publicly accessed through the Amazon Elastic Compute Cloud (EC2), enabling users to rent GPU computing time on a pay-as-you-go basis, thus avoiding upfront investments in hardware and longterm software maintenance.

CloVR-Comparative: automated, cloud-enabled comparative microbial genome sequence analysis pipeline.

PubMed

Agrawal, Sonia; Arze, Cesar; Adkins, Ricky S; Crabtree, Jonathan; Riley, David; Vangala, Mahesh; Galens, Kevin; Fraser, Claire M; Tettelin, Hervé; White, Owen; Angiuoli, Samuel V; Mahurkar, Anup; Fricke, W Florian

2017-04-27

The benefit of increasing genomic sequence data to the scientific community depends on easy-to-use, scalable bioinformatics support. CloVR-Comparative combines commonly used bioinformatics tools into an intuitive, automated, and cloud-enabled analysis pipeline for comparative microbial genomics. CloVR-Comparative runs on annotated complete or draft genome sequences that are uploaded by the user or selected via a taxonomic tree-based user interface and downloaded from NCBI. CloVR-Comparative runs reference-free multiple whole-genome alignments to determine unique, shared and core coding sequences (CDSs) and single nucleotide polymorphisms (SNPs). Output includes short summary reports and detailed text-based results files, graphical visualizations (phylogenetic trees, circular figures), and a database file linked to the Sybil comparative genome browser. Data up- and download, pipeline configuration and monitoring, and access to Sybil are managed through CloVR-Comparative web interface. CloVR-Comparative and Sybil are distributed as part of the CloVR virtual appliance, which runs on local computers or the Amazon EC2 cloud. Representative datasets (e.g. 40 draft and complete Escherichia coli genomes) are processed in <36 h on a local desktop or at a cost of <$20 on EC2. CloVR-Comparative allows anybody with Internet access to run comparative genomics projects, while eliminating the need for on-site computational resources and expertise.

STORMSeq: An Open-Source, User-Friendly Pipeline for Processing Personal Genomics Data in the Cloud

PubMed Central

Karczewski, Konrad J.; Fernald, Guy Haskin; Martin, Alicia R.; Snyder, Michael; Tatonetti, Nicholas P.; Dudley, Joel T.

2014-01-01

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5–10 hours to process a full exome sequence and $30 and 3–8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2. PMID:24454756

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing.

PubMed

Zhao, Shanrong; Prenger, Kurt; Smith, Lance; Messina, Thomas; Fan, Hongtao; Jaeger, Edward; Stephens, Susan

2013-06-27

Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html.

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing

PubMed Central

2013-01-01

Background Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Results Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Conclusions Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html. PMID:23802613

Toward a web-based real-time radiation treatment planning system in a cloud computing environment.

PubMed

Na, Yong Hum; Suh, Tae-Suk; Kapp, Daniel S; Xing, Lei

2013-09-21

To exploit the potential dosimetric advantages of intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), an in-depth approach is required to provide efficient computing methods. This needs to incorporate clinically related organ specific constraints, Monte Carlo (MC) dose calculations, and large-scale plan optimization. This paper describes our first steps toward a web-based real-time radiation treatment planning system in a cloud computing environment (CCE). The Amazon Elastic Compute Cloud (EC2) with a master node (named m2.xlarge containing 17.1 GB of memory, two virtual cores with 3.25 EC2 Compute Units each, 420 GB of instance storage, 64-bit platform) is used as the backbone of cloud computing for dose calculation and plan optimization. The master node is able to scale the workers on an 'on-demand' basis. MC dose calculation is employed to generate accurate beamlet dose kernels by parallel tasks. The intensity modulation optimization uses total-variation regularization (TVR) and generates piecewise constant fluence maps for each initial beam direction in a distributed manner over the CCE. The optimized fluence maps are segmented into deliverable apertures. The shape of each aperture is iteratively rectified to be a sequence of arcs using the manufacture's constraints. The output plan file from the EC2 is sent to the simple storage service. Three de-identified clinical cancer treatment plans have been studied for evaluating the performance of the new planning platform with 6 MV flattening filter free beams (40 × 40 cm(2)) from the Varian TrueBeam(TM) STx linear accelerator. A CCE leads to speed-ups of up to 14-fold for both dose kernel calculations and plan optimizations in the head and neck, lung, and prostate cancer cases considered in this study. The proposed system relies on a CCE that is able to provide an infrastructure for parallel and distributed computing. The resultant plans from the cloud computing are identical to PC-based IMRT and VMAT plans, confirming the reliability of the cloud computing platform. This cloud computing infrastructure has been established for a radiation treatment planning. It substantially improves the speed of inverse planning and makes future on-treatment adaptive re-planning possible.

Toward a web-based real-time radiation treatment planning system in a cloud computing environment

NASA Astrophysics Data System (ADS)

Hum Na, Yong; Suh, Tae-Suk; Kapp, Daniel S.; Xing, Lei

2013-09-01

To exploit the potential dosimetric advantages of intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), an in-depth approach is required to provide efficient computing methods. This needs to incorporate clinically related organ specific constraints, Monte Carlo (MC) dose calculations, and large-scale plan optimization. This paper describes our first steps toward a web-based real-time radiation treatment planning system in a cloud computing environment (CCE). The Amazon Elastic Compute Cloud (EC2) with a master node (named m2.xlarge containing 17.1 GB of memory, two virtual cores with 3.25 EC2 Compute Units each, 420 GB of instance storage, 64-bit platform) is used as the backbone of cloud computing for dose calculation and plan optimization. The master node is able to scale the workers on an ‘on-demand’ basis. MC dose calculation is employed to generate accurate beamlet dose kernels by parallel tasks. The intensity modulation optimization uses total-variation regularization (TVR) and generates piecewise constant fluence maps for each initial beam direction in a distributed manner over the CCE. The optimized fluence maps are segmented into deliverable apertures. The shape of each aperture is iteratively rectified to be a sequence of arcs using the manufacture’s constraints. The output plan file from the EC2 is sent to the simple storage service. Three de-identified clinical cancer treatment plans have been studied for evaluating the performance of the new planning platform with 6 MV flattening filter free beams (40 × 40 cm2) from the Varian TrueBeamTM STx linear accelerator. A CCE leads to speed-ups of up to 14-fold for both dose kernel calculations and plan optimizations in the head and neck, lung, and prostate cancer cases considered in this study. The proposed system relies on a CCE that is able to provide an infrastructure for parallel and distributed computing. The resultant plans from the cloud computing are identical to PC-based IMRT and VMAT plans, confirming the reliability of the cloud computing platform. This cloud computing infrastructure has been established for a radiation treatment planning. It substantially improves the speed of inverse planning and makes future on-treatment adaptive re-planning possible.

The role of dedicated data computing centers in the age of cloud computing

NASA Astrophysics Data System (ADS)

Caramarcu, Costin; Hollowell, Christopher; Strecker-Kellogg, William; Wong, Antonio; Zaytsev, Alexandr

2017-10-01

Brookhaven National Laboratory (BNL) anticipates significant growth in scientific programs with large computing and data storage needs in the near future and has recently reorganized support for scientific computing to meet these needs. A key component is the enhanced role of the RHIC-ATLAS Computing Facility (RACF) in support of high-throughput and high-performance computing (HTC and HPC) at BNL. This presentation discusses the evolving role of the RACF at BNL, in light of its growing portfolio of responsibilities and its increasing integration with cloud (academic and for-profit) computing activities. We also discuss BNL’s plan to build a new computing center to support the new responsibilities of the RACF and present a summary of the cost benefit analysis done, including the types of computing activities that benefit most from a local data center vs. cloud computing. This analysis is partly based on an updated cost comparison of Amazon EC2 computing services and the RACF, which was originally conducted in 2012.

An interactive web-based system using cloud for large-scale visual analytics

NASA Astrophysics Data System (ADS)

Kaseb, Ahmed S.; Berry, Everett; Rozolis, Erik; McNulty, Kyle; Bontrager, Seth; Koh, Youngsol; Lu, Yung-Hsiang; Delp, Edward J.

2015-03-01

Network cameras have been growing rapidly in recent years. Thousands of public network cameras provide tremendous amount of visual information about the environment. There is a need to analyze this valuable information for a better understanding of the world around us. This paper presents an interactive web-based system that enables users to execute image analysis and computer vision techniques on a large scale to analyze the data from more than 65,000 worldwide cameras. This paper focuses on how to use both the system's website and Application Programming Interface (API). Given a computer program that analyzes a single frame, the user needs to make only slight changes to the existing program and choose the cameras to analyze. The system handles the heterogeneity of the geographically distributed cameras, e.g. different brands, resolutions. The system allocates and manages Amazon EC2 and Windows Azure cloud resources to meet the analysis requirements.

SU-E-T-314: The Application of Cloud Computing in Pencil Beam Scanning Proton Therapy Monte Carlo Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Z; Gao, M

Purpose: Monte Carlo simulation plays an important role for proton Pencil Beam Scanning (PBS) technique. However, MC simulation demands high computing power and is limited to few large proton centers that can afford a computer cluster. We study the feasibility of utilizing cloud computing in the MC simulation of PBS beams. Methods: A GATE/GEANT4 based MC simulation software was installed on a commercial cloud computing virtual machine (Linux 64-bits, Amazon EC2). Single spot Integral Depth Dose (IDD) curves and in-air transverse profiles were used to tune the source parameters to simulate an IBA machine. With the use of StarCluster softwaremore » developed at MIT, a Linux cluster with 2–100 nodes can be conveniently launched in the cloud. A proton PBS plan was then exported to the cloud where the MC simulation was run. Results: The simulated PBS plan has a field size of 10×10cm{sup 2}, 20cm range, 10cm modulation, and contains over 10,000 beam spots. EC2 instance type m1.medium was selected considering the CPU/memory requirement and 40 instances were used to form a Linux cluster. To minimize cost, master node was created with on-demand instance and worker nodes were created with spot-instance. The hourly cost for the 40-node cluster was $0.63 and the projected cost for a 100-node cluster was $1.41. Ten million events were simulated to plot PDD and profile, with each job containing 500k events. The simulation completed within 1 hour and an overall statistical uncertainty of < 2% was achieved. Good agreement between MC simulation and measurement was observed. Conclusion: Cloud computing is a cost-effective and easy to maintain platform to run proton PBS MC simulation. When proton MC packages such as GATE and TOPAS are combined with cloud computing, it will greatly facilitate the pursuing of PBS MC studies, especially for newly established proton centers or individual researchers.« less

NASA Cloud-Based Climate Data Services

NASA Astrophysics Data System (ADS)

McInerney, M. A.; Schnase, J. L.; Duffy, D. Q.; Tamkin, G. S.; Strong, S.; Ripley, W. D., III; Thompson, J. H.; Gill, R.; Jasen, J. E.; Samowich, B.; Pobre, Z.; Salmon, E. M.; Rumney, G.; Schardt, T. D.

2012-12-01

Cloud-based scientific data services are becoming an important part of NASA's mission. Our technological response is built around the concept of specialized virtual climate data servers, repetitive cloud provisioning, image-based deployment and distribution, and virtualization-as-a-service (VaaS). A virtual climate data server (vCDS) is an Open Archive Information System (OAIS) compliant, iRODS-based data server designed to support a particular type of scientific data collection. iRODS is data grid middleware that provides policy-based control over collection-building, managing, querying, accessing, and preserving large scientific data sets. We have deployed vCDS Version 1.0 in the Amazon EC2 cloud using S3 object storage and are using the system to deliver a subset of NASA's Intergovernmental Panel on Climate Change (IPCC) data products to the latest CentOS federated version of Earth System Grid Federation (ESGF), which is also running in the Amazon cloud. vCDS-managed objects are exposed to ESGF through FUSE (Filesystem in User Space), which presents a POSIX-compliant filesystem abstraction to applications such as the ESGF server that require such an interface. A vCDS manages data as a distinguished collection for a person, project, lab, or other logical unit. A vCDS can manage a collection across multiple storage resources using rules and microservices to enforce collection policies. And a vCDS can federate with other vCDSs to manage multiple collections over multiple resources, thereby creating what can be thought of as an ecosystem of managed collections. With the vCDS approach, we are trying to enable the full information lifecycle management of scientific data collections and make tractable the task of providing diverse climate data services. In this presentation, we describe our approach, experiences, lessons learned, and plans for the future.; (A) vCDS/ESG system stack. (B) Conceptual architecture for NASA cloud-based data services.

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

NASA Astrophysics Data System (ADS)

Sareen, Sanjay; Gupta, Sunil Kumar; Sood, Sandeep K.

2017-10-01

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.

Cloud-based Predictive Modeling System and its Application to Asthma Readmission Prediction

PubMed Central

Chen, Robert; Su, Hang; Khalilia, Mohammed; Lin, Sizhe; Peng, Yue; Davis, Tod; Hirsh, Daniel A; Searles, Elizabeth; Tejedor-Sojo, Javier; Thompson, Michael; Sun, Jimeng

2015-01-01

The predictive modeling process is time consuming and requires clinical researchers to handle complex electronic health record (EHR) data in restricted computational environments. To address this problem, we implemented a cloud-based predictive modeling system via a hybrid setup combining a secure private server with the Amazon Web Services (AWS) Elastic MapReduce platform. EHR data is preprocessed on a private server and the resulting de-identified event sequences are hosted on AWS. Based on user-specified modeling configurations, an on-demand web service launches a cluster of Elastic Compute 2 (EC2) instances on AWS to perform feature selection and classification algorithms in a distributed fashion. Afterwards, the secure private server aggregates results and displays them via interactive visualization. We tested the system on a pediatric asthma readmission task on a de-identified EHR dataset of 2,967 patients. We conduct a larger scale experiment on the CMS Linkable 2008–2010 Medicare Data Entrepreneurs’ Synthetic Public Use File dataset of 2 million patients, which achieves over 25-fold speedup compared to sequential execution. PMID:26958172

Accelerating statistical image reconstruction algorithms for fan-beam x-ray CT using cloud computing

NASA Astrophysics Data System (ADS)

Srivastava, Somesh; Rao, A. Ravishankar; Sheinin, Vadim

2011-03-01

Statistical image reconstruction algorithms potentially offer many advantages to x-ray computed tomography (CT), e.g. lower radiation dose. But, their adoption in practical CT scanners requires extra computation power, which is traditionally provided by incorporating additional computing hardware (e.g. CPU-clusters, GPUs, FPGAs etc.) into a scanner. An alternative solution is to access the required computation power over the internet from a cloud computing service, which is orders-of-magnitude more cost-effective. This is because users only pay a small pay-as-you-go fee for the computation resources used (i.e. CPU time, storage etc.), and completely avoid purchase, maintenance and upgrade costs. In this paper, we investigate the benefits and shortcomings of using cloud computing for statistical image reconstruction. We parallelized the most time-consuming parts of our application, the forward and back projectors, using MapReduce, the standard parallelization library on clouds. From preliminary investigations, we found that a large speedup is possible at a very low cost. But, communication overheads inside MapReduce can limit the maximum speedup, and a better MapReduce implementation might become necessary in the future. All the experiments for this paper, including development and testing, were completed on the Amazon Elastic Compute Cloud (EC2) for less than $20.

Scaling up ATLAS Event Service to production levels on opportunistic computing platforms

NASA Astrophysics Data System (ADS)

Benjamin, D.; Caballero, J.; Ernst, M.; Guan, W.; Hover, J.; Lesny, D.; Maeno, T.; Nilsson, P.; Tsulaia, V.; van Gemmeren, P.; Vaniachine, A.; Wang, F.; Wenaus, T.; ATLAS Collaboration

2016-10-01

Continued growth in public cloud and HPC resources is on track to exceed the dedicated resources available for ATLAS on the WLCG. Examples of such platforms are Amazon AWS EC2 Spot Instances, Edison Cray XC30 supercomputer, backfill at Tier 2 and Tier 3 sites, opportunistic resources at the Open Science Grid (OSG), and ATLAS High Level Trigger farm between the data taking periods. Because of specific aspects of opportunistic resources such as preemptive job scheduling and data I/O, their efficient usage requires workflow innovations provided by the ATLAS Event Service. Thanks to the finer granularity of the Event Service data processing workflow, the opportunistic resources are used more efficiently. We report on our progress in scaling opportunistic resource usage to double-digit levels in ATLAS production.

A price and performance comparison of three different storage architectures for data in cloud-based systems

NASA Astrophysics Data System (ADS)

Gallagher, J. H. R.; Jelenak, A.; Potter, N.; Fulker, D. W.; Habermann, T.

2017-12-01

Providing data services based on cloud computing technology that is equivalent to those developed for traditional computing and storage systems is critical for successful migration to cloud-based architectures for data production, scientific analysis and storage. OPeNDAP Web-service capabilities (comprising the Data Access Protocol (DAP) specification plus open-source software for realizing DAP in servers and clients) are among the most widely deployed means for achieving data-as-service functionality in the Earth sciences. OPeNDAP services are especially common in traditional data center environments where servers offer access to datasets stored in (very large) file systems, and a preponderance of the source data for these services is being stored in the Hierarchical Data Format Version 5 (HDF5). Three candidate architectures for serving NASA satellite Earth Science HDF5 data via Hyrax running on Amazon Web Services (AWS) were developed and their performance examined for a set of representative use cases. The performance was based both on runtime and incurred cost. The three architectures differ in how HDF5 files are stored in the Amazon Simple Storage Service (S3) and how the Hyrax server (as an EC2 instance) retrieves their data. The results for both the serial and parallel access to HDF5 data in the S3 will be presented. While the study focused on HDF5 data, OPeNDAP and the Hyrax data server, the architectures are generic and the analysis can be extrapolated to many different data formats, web APIs, and data servers.

The Seasonal Cycle of the Radiation Budget and Cloud Radiative Effect in the Amazon Rainforest of Brazil

NASA Astrophysics Data System (ADS)

Collow, A.; Miller, M. A.

2015-12-01

The Amazon Rainforest of Brazil is a region with potential climate sensitivities, especially with ongoing land surface changes and biomass burning aerosols due to deforestation. Ubiquitous moisture in the area make clouds a common feature over the Amazon Rainforest and along with the influences from deforestation have a significant impact on the radiation budget. This region experiences a seasonal contrast in clouds, precipitation, and aerosols making it an ideal location to study the relationship between these variables and the radiation budget. An internationally sponsored campaign entitled GOAmazon2014/15 included a deployment of an Atmospheric Radiation Measurement (ARM) Mobile Facility, which collected comprehensive measurements using in situ and remote sensors. Observations of clouds, aerosols, and radiative fluxes from the first year of the deployment are analyzed in conjunction with top of the atmosphere (TOA) observations from the Clouds and the Earth's Radiant Energy System (CERES) and analyses from the newly released Modern Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2). The combination of surface and TOA observations allows for the calculation of radiative flux divergence and cloud radiative effect (CRE) within the column, while the comparison to MERRA-2 enables the verification of a new reanalysis product and a view of the spatial variation of the radiation budget. Clouds are very reflective in the area, creating a cooling effect in the shortwave (SW) at the surface, with some seasonality present due to the reduction of optically thick clouds in the dry season. Clouds have little effect on the column itself in the SW due to the balance between the reflective and absorbing properties of the clouds with the majority of the impact on the atmosphere from clouds warming in the longwave. Influences of aerosols are seen in the dry season, and an increase in moisture above the Amazon River and its tributaries enhance the CRE.

Integration of Cloud resources in the LHCb Distributed Computing

NASA Astrophysics Data System (ADS)

Úbeda García, Mario; Méndez Muñoz, Víctor; Stagni, Federico; Cabarrou, Baptiste; Rauschmayr, Nathalie; Charpentier, Philippe; Closier, Joel

2014-06-01

This contribution describes how Cloud resources have been integrated in the LHCb Distributed Computing. LHCb is using its specific Dirac extension (LHCbDirac) as an interware for its Distributed Computing. So far, it was seamlessly integrating Grid resources and Computer clusters. The cloud extension of DIRAC (VMDIRAC) allows the integration of Cloud computing infrastructures. It is able to interact with multiple types of infrastructures in commercial and institutional clouds, supported by multiple interfaces (Amazon EC2, OpenNebula, OpenStack and CloudStack) - instantiates, monitors and manages Virtual Machines running on this aggregation of Cloud resources. Moreover, specifications for institutional Cloud resources proposed by Worldwide LHC Computing Grid (WLCG), mainly by the High Energy Physics Unix Information Exchange (HEPiX) group, have been taken into account. Several initiatives and computing resource providers in the eScience environment have already deployed IaaS in production during 2013. Keeping this on mind, pros and cons of a cloud based infrasctructure have been studied in contrast with the current setup. As a result, this work addresses four different use cases which represent a major improvement on several levels of our infrastructure. We describe the solution implemented by LHCb for the contextualisation of the VMs based on the idea of Cloud Site. We report on operational experience of using in production several institutional Cloud resources that are thus becoming integral part of the LHCb Distributed Computing resources. Furthermore, we describe as well the gradual migration of our Service Infrastructure towards a fully distributed architecture following the Service as a Service (SaaS) model.

Leveraging the Cloud for Robust and Efficient Lunar Image Processing

NASA Technical Reports Server (NTRS)

Chang, George; Malhotra, Shan; Wolgast, Paul

2011-01-01

The Lunar Mapping and Modeling Project (LMMP) is tasked to aggregate lunar data, from the Apollo era to the latest instruments on the LRO spacecraft, into a central repository accessible by scientists and the general public. A critical function of this task is to provide users with the best solution for browsing the vast amounts of imagery available. The image files LMMP manages range from a few gigabytes to hundreds of gigabytes in size with new data arriving every day. Despite this ever-increasing amount of data, LMMP must make the data readily available in a timely manner for users to view and analyze. This is accomplished by tiling large images into smaller images using Hadoop, a distributed computing software platform implementation of the MapReduce framework, running on a small cluster of machines locally. Additionally, the software is implemented to use Amazon's Elastic Compute Cloud (EC2) facility. We also developed a hybrid solution to serve images to users by leveraging cloud storage using Amazon's Simple Storage Service (S3) for public data while keeping private information on our own data servers. By using Cloud Computing, we improve upon our local solution by reducing the need to manage our own hardware and computing infrastructure, thereby reducing costs. Further, by using a hybrid of local and cloud storage, we are able to provide data to our users more efficiently and securely. 12 This paper examines the use of a distributed approach with Hadoop to tile images, an approach that provides significant improvements in image processing time, from hours to minutes. This paper describes the constraints imposed on the solution and the resulting techniques developed for the hybrid solution of a customized Hadoop infrastructure over local and cloud resources in managing this ever-growing data set. It examines the performance trade-offs of using the more plentiful resources of the cloud, such as those provided by S3, against the bandwidth limitations such use encounters with remote resources. As part of this discussion this paper will outline some of the technologies employed, the reasons for their selection, the resulting performance metrics and the direction the project is headed based upon the demonstrated capabilities thus far.

Observational constraints on mixed-phase clouds imply higher climate sensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Ivy; Storelvmo, Trude; Zelinka, Mark D.

Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO 2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here, in this paper, we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. Finally, wemore » point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback.« less

Observational constraints on mixed-phase clouds imply higher climate sensitivity

DOE PAGES

Tan, Ivy; Storelvmo, Trude; Zelinka, Mark D.

2016-04-08

Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO 2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here, in this paper, we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. Finally, wemore » point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback.« less

Observational constraints on mixed-phase clouds imply higher climate sensitivity.

PubMed

Tan, Ivy; Storelvmo, Trude; Zelinka, Mark D

2016-04-08

Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. We point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback. Copyright © 2016, American Association for the Advancement of Science.

SIMPLEX: Cloud-Enabled Pipeline for the Comprehensive Analysis of Exome Sequencing Data

PubMed Central

Fischer, Maria; Snajder, Rene; Pabinger, Stephan; Dander, Andreas; Schossig, Anna; Zschocke, Johannes; Trajanoski, Zlatko; Stocker, Gernot

2012-01-01

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome. PMID:22870267

Status and Roadmap of CernVM

NASA Astrophysics Data System (ADS)

Berzano, D.; Blomer, J.; Buncic, P.; Charalampidis, I.; Ganis, G.; Meusel, R.

2015-12-01

Cloud resources nowadays contribute an essential share of resources for computing in high-energy physics. Such resources can be either provided by private or public IaaS clouds (e.g. OpenStack, Amazon EC2, Google Compute Engine) or by volunteers computers (e.g. LHC@Home 2.0). In any case, experiments need to prepare a virtual machine image that provides the execution environment for the physics application at hand. The CernVM virtual machine since version 3 is a minimal and versatile virtual machine image capable of booting different operating systems. The virtual machine image is less than 20 megabyte in size. The actual operating system is delivered on demand by the CernVM File System. CernVM 3 has matured from a prototype to a production environment. It is used, for instance, to run LHC applications in the cloud, to tune event generators using a network of volunteer computers, and as a container for the historic Scientific Linux 5 and Scientific Linux 4 based software environments in the course of long-term data preservation efforts of the ALICE, CMS, and ALEPH experiments. We present experience and lessons learned from the use of CernVM at scale. We also provide an outlook on the upcoming developments. These developments include adding support for Scientific Linux 7, the use of container virtualization, such as provided by Docker, and the streamlining of virtual machine contextualization towards the cloud-init industry standard.

High Performance Molecular Visualization: In-Situ and Parallel Rendering with EGL.

PubMed

Stone, John E; Messmer, Peter; Sisneros, Robert; Schulten, Klaus

2016-05-01

Large scale molecular dynamics simulations produce terabytes of data that is impractical to transfer to remote facilities. It is therefore necessary to perform visualization tasks in-situ as the data are generated, or by running interactive remote visualization sessions and batch analyses co-located with direct access to high performance storage systems. A significant challenge for deploying visualization software within clouds, clusters, and supercomputers involves the operating system software required to initialize and manage graphics acceleration hardware. Recently, it has become possible for applications to use the Embedded-system Graphics Library (EGL) to eliminate the requirement for windowing system software on compute nodes, thereby eliminating a significant obstacle to broader use of high performance visualization applications. We outline the potential benefits of this approach in the context of visualization applications used in the cloud, on commodity clusters, and supercomputers. We discuss the implementation of EGL support in VMD, a widely used molecular visualization application, and we outline benefits of the approach for molecular visualization tasks on petascale computers, clouds, and remote visualization servers. We then provide a brief evaluation of the use of EGL in VMD, with tests using developmental graphics drivers on conventional workstations and on Amazon EC2 G2 GPU-accelerated cloud instance types. We expect that the techniques described here will be of broad benefit to many other visualization applications.

High Performance Molecular Visualization: In-Situ and Parallel Rendering with EGL

PubMed Central

Stone, John E.; Messmer, Peter; Sisneros, Robert; Schulten, Klaus

2016-01-01

Large scale molecular dynamics simulations produce terabytes of data that is impractical to transfer to remote facilities. It is therefore necessary to perform visualization tasks in-situ as the data are generated, or by running interactive remote visualization sessions and batch analyses co-located with direct access to high performance storage systems. A significant challenge for deploying visualization software within clouds, clusters, and supercomputers involves the operating system software required to initialize and manage graphics acceleration hardware. Recently, it has become possible for applications to use the Embedded-system Graphics Library (EGL) to eliminate the requirement for windowing system software on compute nodes, thereby eliminating a significant obstacle to broader use of high performance visualization applications. We outline the potential benefits of this approach in the context of visualization applications used in the cloud, on commodity clusters, and supercomputers. We discuss the implementation of EGL support in VMD, a widely used molecular visualization application, and we outline benefits of the approach for molecular visualization tasks on petascale computers, clouds, and remote visualization servers. We then provide a brief evaluation of the use of EGL in VMD, with tests using developmental graphics drivers on conventional workstations and on Amazon EC2 G2 GPU-accelerated cloud instance types. We expect that the techniques described here will be of broad benefit to many other visualization applications. PMID:27747137

An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks.

PubMed

Sareen, Sanjay; Sood, Sandeep K; Gupta, Sunil Kumar

2016-11-01

Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that automatically predict seizures using the information contained in electroencephalography (EEG) signals. The wireless sensor technology is used to capture the EEG signals of patients. The cloud-based services are used to collect and analyze the EEG data from the patient's mobile phone. The features from the EEG signal are extracted using the fast Walsh-Hadamard transform (FWHT). The Higher Order Spectral Analysis (HOSA) is applied to FWHT coefficients in order to select the features set relevant to normal, preictal and ictal states of seizure. We subsequently exploit the selected features as input to a k-means classifier to detect epileptic seizure states in a reasonable time. The performance of the proposed model is tested on Amazon EC2 cloud and compared in terms of execution time and accuracy. The findings show that with selected HOS based features, we were able to achieve a classification accuracy of 94.6 %.

Atlas2 Cloud: a framework for personal genome analysis in the cloud

PubMed Central

2012-01-01

Background Until recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues. Results We successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set. Conclusions We find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms. PMID:23134663

Atlas2 Cloud: a framework for personal genome analysis in the cloud.

PubMed

Evani, Uday S; Challis, Danny; Yu, Jin; Jackson, Andrew R; Paithankar, Sameer; Bainbridge, Matthew N; Jakkamsetti, Adinarayana; Pham, Peter; Coarfa, Cristian; Milosavljevic, Aleksandar; Yu, Fuli

2012-01-01

Until recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues. We successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set. We find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms.

Lessons learned in deploying a cloud-based knowledge platform for the Earth Science Information Partners Federation (ESIP)

NASA Astrophysics Data System (ADS)

Pouchard, L. C.; Depriest, A.; Huhns, M.

2012-12-01

Ontologies and semantic technologies are an essential infrastructure component of systems supporting knowledge integration in the Earth Sciences. Numerous earth science ontologies exist, but are hard to discover because they tend to be hosted with the projects that develop them. There are often few quality measures and sparse metadata associated with these ontologies, such as modification dates, versioning, purpose, number of classes, and properties. Projects often develop ontologies for their own needs without considering existing ontology entities or derivations from formal and more basic ontologies. The result is mostly orthogonal ontologies, and ontologies that are not modular enough to reuse in part or adapt for new purposes, in spite of existing, standards for ontology representation. Additional obstacles to sharing and reuse include a lack of maintenance once a project is completed. The obstacles prevent the full exploitation of semantic technologies in a context where they could become needed enablers for service discovery and for matching data with services. To start addressing this gap, we have deployed BioPortal, a mature, domain-independent ontology and semantic service system developed by the National Center for Biomedical Ontologies (NCBO), on the ESIP Testbed under the governance of the ESIP Semantic Web cluster. ESIP provides a forum for a broad-based, distributed community of data and information technology practitioners and stakeholders to coordinate their efforts and develop new ideas for interoperability solutions. The Testbed provides an environment where innovations and best practices can be explored and evaluated. One objective of this deployment is to provide a community platform that would harness the organizational and cyber infrastructure provided by ESIP at minimal costs. Another objective is to host ontology services on a scalable, public cloud and investigate the business case for crowd sourcing of ontology maintenance. We deployed the system on Amazon 's Elastic Compute Cloud (EC2) where ESIP maintains an account. Our approach had three phases: 1) set up a private cloud environment at the University of South Carolina to become familiar with the complex architecture of the system and enable some basic customization, 2) coordinate the production of a Virtual Appliance for the system with NCBO and deploy it on the Amazon cloud, and 3) outreach to the ESIP community to solicit participation, populate the repository, and develop new use cases. Phase 2 is nearing completion and Phase 3 is underway. Ontologies were gathered during updates to the ESIP cluster. Discussion points included the criteria for a shareable ontology and how to determine the best size for an ontology to be reusable. Outreach highlighted that the system can start addressing an integration of discovery frameworks via linking data and services in a pull model (data and service casting), a key issue of the Discovery cluster. This work thus presents several contributions: 1) technology injection from another domain into the earth sciences, 2) the deployment of a mature knowledge platform on the EC2 cloud, and 3) the successful engagement of the community through the ESIP clusters and Testbed model.

A Tale Of 160 Scientists, Three Applications, a Workshop and a Cloud

NASA Astrophysics Data System (ADS)

Berriman, G. B.; Brinkworth, C.; Gelino, D.; Wittman, D. K.; Deelman, E.; Juve, G.; Rynge, M.; Kinney, J.

2013-10-01

The NASA Exoplanet Science Institute (NExScI) hosts the annual Sagan Workshops, thematic meetings aimed at introducing researchers to the latest tools and methodologies in exoplanet research. The theme of the Summer 2012 workshop, held from July 23 to July 27 at Caltech, was to explore the use of exoplanet light curves to study planetary system architectures and atmospheres. A major part of the workshop was to use hands-on sessions to instruct attendees in the use of three open source tools for the analysis of light curves, especially from the Kepler mission. Each hands-on session involved the 160 attendees using their laptops to follow step-by-step tutorials given by experts. One of the applications, PyKE, is a suite of Python tools designed to reduce and analyze Kepler light curves; these tools can be invoked from the Unix command line or a GUI in PyRAF. The Transit Analysis Package (TAP) uses Markov Chain Monte Carlo (MCMC) techniques to fit light curves under the Interactive Data Language (IDL) environment, and Transit Timing Variations (TTV) uses IDL tools and Java-based GUIs to confirm and detect exoplanets from timing variations in light curve fitting. Rather than attempt to run these diverse applications on the inevitable wide range of environments on attendees laptops, they were run instead on the Amazon Elastic Cloud 2 (EC2). The cloud offers features ideal for this type of short term need: computing and storage services are made available on demand for as long as needed, and a processing environment can be customized and replicated as needed. The cloud environment included an NFS file server virtual machine (VM), 20 client VMs for use by attendees, and a VM to enable ftp downloads of the attendees' results. The file server was configured with a 1 TB Elastic Block Storage (EBS) volume (network-attached storage mounted as a device) containing the application software and attendees home directories. The clients were configured to mount the applications and home directories from the server via NFS. All VMs were built with CentOS version 5.8. Attendees connected their laptops to one of the client VMs using the Virtual Network Computing (VNC) protocol, which enabled them to interact with a remote desktop GUI during the hands-on sessions. We will describe the mechanisms for handling security, failovers, and licensing of commercial software. In particular, IDL licenses were managed through a server at Caltech, connected to the IDL instances running on Amazon EC2 via a Secure Shell (ssh) tunnel. The system operated flawlessly during the workshop.

Cloud Condensation Nuclei Activity of Aerosols during GoAmazon 2014/15 Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, J.; Martin, S. T.; Kleinman, L.

2016-03-01

Aerosol indirect effects, which represent the impact of aerosols on climate through influencing the properties of clouds, remain one of the main uncertainties in climate predictions (Stocker et al. 2013). Reducing this large uncertainty requires both improved understanding and representation of aerosol properties and processes in climate models, including the cloud activation properties of aerosols. The Atmospheric System Research (ASR) science program plan of January 2010 states that: “A key requirement for simulating aerosol-cloud interactions is the ability to calculate cloud condensation nuclei and ice nuclei (CCN and IN, respectively) concentrations as a function of supersaturation from the chemical andmore » microphysical properties of the aerosol.” The Observations and Modeling of the Green Ocean Amazon (GoAmazon 2014/15) study seeks to understand how aerosol and cloud life cycles are influenced by pollutant outflow from a tropical megacity (Manaus)—in particular, the differences in cloud-aerosol-precipitation interactions between polluted and pristine conditions. One key question of GoAmazon2014/5 is: “What is the influence of the Manaus pollution plume on the cloud condensation nuclei (CCN) activities of the aerosol particles and the secondary organic material in the particles?” To address this question, we measured size-resolved CCN spectra, a critical measurement for GoAmazon2014/5.« less

Examination about the Impact of Thin Clouds in IFOV of GOSAT TANSO-FTS on Estimates of XCO2 in the Amazon

NASA Astrophysics Data System (ADS)

Oishi, Y.; Kamei, A.; Murakami, K.; Dupuy, E.; Yokota, Y.; Hiraki, K.; Ninomiya, K.; Saito, M.; Yoshida, Y.; Morino, I.; Nakajima, T. Y.; Yokota, T.; Matsunaga, T.

2013-12-01

Greenhouse gases Observing SATellite (GOSAT) was launched in 2009 to measure the global atmospheric CO2 and CH4 concentrations. GOSAT is equipped with two sensors: the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (TANSO-CAI). The presence of clouds in the instantaneous field-of-view (IFOV) of the FTS leads to incorrect estimates of the CO2 or CH4 concentration. To deal with this problem, the FTS data which are suspected to be cloud-contaminated must be identified and rejected. As a result, there are very few remaining FTS data in the region of tropical rainforest such as the Amazon. In the meanwhile the feasibility studies of GOSAT-2 started for more precise monitoring of atmospheric greenhouse gases than GOSAT in 2011. To improve the accuracy of estimates of the column-averaged dry air mole fraction of atmospheric CO2 (XCO2), we need to understand the present situation about cloud screening in the rain forest regions and to examine the cloud-contaminated data whose processing might be possible with improvement of instruments or algorithms. In this study we evaluated the impact of thin clouds on estimates of the XCO2 using an atmospheric radiative transfer code, which can simulate the spectrum at the top of the atmosphere under thin cloud conditions. First, we decided the input parameters, among which relative position of the sun and satellite to observation point, surface reflectance using cloud-free GOSAT data products in the Amazon, FTS L1B data products (radiance spectral data), FTS L2 data products (CO2 column abundance data), and CAI L3 data products (clear-sky reflectance data). The evaluation was performed by comparing depths of the CO2 absorption lines in output radiation spectra with varying CO2 concentrations and cloud conditions, cloud type, cloud optical depth, and cloud top altitude. We will present our latest results.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schumacher, Courtney

One of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Parsivel2 disdrometers was deployed at the first ARM Mobile Facility (AMF1) T3 site in Manacapuru, Brazil at the beginning of the second Green Ocean Amazon (GoAmazon)2014/15 intensive operational period (IOP2) in September 2014 through the end of the field campaign in December 2015. The Parsivel2 provided one-minute drop-size distribution (DSD) observations that have already been used for a number of applications related to GoAmazon2014/15 science objectives. The first use was the creation of a reflectivity-rain rate (Z-R) relation enabling the calculation of rain rates frommore » the Brazilian Sistema de Protecao da Amazonia (SIPAM) S-band operational radar in Manaus. The radar-derived rainfall is an important constraint for the variational analysis of a large-scale forcing data set, which was recently released for the two IOPs that took place in the 2014 wet and transition seasons, respectively. The SIPAM radar rainfall is also being used to validate a number of cloud-resolving model simulations being run for the campaign. A second use of the Parsivel2 DSDs has been to provide a necessary reference point to calibrate the vertical velocity retrievals from the AMF1 W Band ARM Cloud Radar (WACR) cloud-profiling and ultra-high-frequency (UHF) wind-profiling instruments. Accurate retrievals of in-cloud vertical velocities are important to understand the microphysical and kinematic properties of Amazonian convective clouds and their interaction with the land surface and atmospheric aerosols. Further use of the Parsivel2 DSD observations can be made to better understand precipitation characteristics and their variability during GoAmazon2014/15.« less

Amazon boundary layer aerosol concentration sustained by vertical transport during rainfall

NASA Astrophysics Data System (ADS)

Wang, Jian; Krejci, Radovan; Giangrande, Scott; Kuang, Chongai; Barbosa, Henrique M. J.; Brito, Joel; Carbone, Samara; Chi, Xuguang; Comstock, Jennifer; Ditas, Florian; Lavric, Jost; Manninen, Hanna E.; Mei, Fan; Moran-Zuloaga, Daniel; Pöhlker, Christopher; Pöhlker, Mira L.; Saturno, Jorge; Schmid, Beat; Souza, Rodrigo A. F.; Springston, Stephen R.; Tomlinson, Jason M.; Toto, Tami; Walter, David; Wimmer, Daniela; Smith, James N.; Kulmala, Markku; Machado, Luiz A. T.; Artaxo, Paulo; Andreae, Meinrat O.; Petäjä, Tuukka; Martin, Scot T.

2016-11-01

The nucleation of atmospheric vapours is an important source of new aerosol particles that can subsequently grow to form cloud condensation nuclei in the atmosphere. Most field studies of atmospheric aerosols over continents are influenced by atmospheric vapours of anthropogenic origin (for example, ref. 2) and, in consequence, aerosol processes in pristine, terrestrial environments remain poorly understood. The Amazon rainforest is one of the few continental regions where aerosol particles and their precursors can be studied under near-natural conditions, but the origin of small aerosol particles that grow into cloud condensation nuclei in the Amazon boundary layer remains unclear. Here we present aircraft- and ground-based measurements under clean conditions during the wet season in the central Amazon basin. We find that high concentrations of small aerosol particles (with diameters of less than 50 nanometres) in the lower free troposphere are transported from the free troposphere into the boundary layer during precipitation events by strong convective downdrafts and weaker downward motions in the trailing stratiform region. This rapid vertical transport can help to maintain the population of particles in the pristine Amazon boundary layer, and may therefore influence cloud properties and climate under natural conditions.

Amazon boundary layer aerosol concentration sustained by vertical transport during rainfall.

PubMed

Wang, Jian; Krejci, Radovan; Giangrande, Scott; Kuang, Chongai; Barbosa, Henrique M J; Brito, Joel; Carbone, Samara; Chi, Xuguang; Comstock, Jennifer; Ditas, Florian; Lavric, Jost; Manninen, Hanna E; Mei, Fan; Moran-Zuloaga, Daniel; Pöhlker, Christopher; Pöhlker, Mira L; Saturno, Jorge; Schmid, Beat; Souza, Rodrigo A F; Springston, Stephen R; Tomlinson, Jason M; Toto, Tami; Walter, David; Wimmer, Daniela; Smith, James N; Kulmala, Markku; Machado, Luiz A T; Artaxo, Paulo; Andreae, Meinrat O; Petäjä, Tuukka; Martin, Scot T

2016-11-17

The nucleation of atmospheric vapours is an important source of new aerosol particles that can subsequently grow to form cloud condensation nuclei in the atmosphere. Most field studies of atmospheric aerosols over continents are influenced by atmospheric vapours of anthropogenic origin (for example, ref. 2) and, in consequence, aerosol processes in pristine, terrestrial environments remain poorly understood. The Amazon rainforest is one of the few continental regions where aerosol particles and their precursors can be studied under near-natural conditions, but the origin of small aerosol particles that grow into cloud condensation nuclei in the Amazon boundary layer remains unclear. Here we present aircraft- and ground-based measurements under clean conditions during the wet season in the central Amazon basin. We find that high concentrations of small aerosol particles (with diameters of less than 50 nanometres) in the lower free troposphere are transported from the free troposphere into the boundary layer during precipitation events by strong convective downdrafts and weaker downward motions in the trailing stratiform region. This rapid vertical transport can help to maintain the population of particles in the pristine Amazon boundary layer, and may therefore influence cloud properties and climate under natural conditions.

Processing Shotgun Proteomics Data on the Amazon Cloud with the Trans-Proteomic Pipeline*

PubMed Central

Slagel, Joseph; Mendoza, Luis; Shteynberg, David; Deutsch, Eric W.; Moritz, Robert L.

2015-01-01

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost. PMID:25418363

Processing shotgun proteomics data on the Amazon cloud with the trans-proteomic pipeline.

PubMed

Slagel, Joseph; Mendoza, Luis; Shteynberg, David; Deutsch, Eric W; Moritz, Robert L

2015-02-01

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Adopting Cloud Computing in the Pakistan Navy

DTIC Science & Technology

2015-06-01

administrative aspect is required to operate optimally, provide synchronized delivery of cloud services, and integrate multi-provider cloud environment...AND ABBREVIATIONS ANSI American National Standards Institute AWS Amazon web services CIA Confidentiality Integrity Availability CIO Chief...also adopted cloud computing as an integral component of military operations conducted either locally or remotely. With the use of 2 cloud services

Calibration of radio-astronomical data on the cloud. LOFAR, the pathway to SKA

NASA Astrophysics Data System (ADS)

Sabater, J.; Sánchez-Expósito, S.; Garrido, J.; Ruiz, J. E.; Best, P. N.; Verdes-Montenegro, L.

2015-05-01

The radio interferometer LOFAR (LOw Frequency ARray) is fully operational now. This Square Kilometre Array (SKA) pathfinder allows the observation of the sky at frequencies between 10 and 240 MHz, a relatively unexplored region of the spectrum. LOFAR is a software defined telescope: the data is mainly processed using specialized software running in common computing facilities. That means that the capabilities of the telescope are virtually defined by software and mainly limited by the available computing power. However, the quantity of data produced can quickly reach huge volumes (several Petabytes per day). After the correlation and pre-processing of the data in a dedicated cluster, the final dataset is handled to the user (typically several Terabytes). The calibration of these data requires a powerful computing facility in which the specific state of the art software under heavy continuous development can be easily installed and updated. That makes this case a perfect candidate for a cloud infrastructure which adds the advantages of an on demand, flexible solution. We present our approach to the calibration of LOFAR data using Ibercloud, the cloud infrastructure provided by Ibergrid. With the calibration work-flow adapted to the cloud, we can explore calibration strategies for the SKA and show how private or commercial cloud infrastructures (Ibercloud, Amazon EC2, Google Compute Engine, etc.) can help to solve the problems with big datasets that will be prevalent in the future of astronomy.

Thermodynamics and Cloud Radiative Effect from the First Year of GoAmazon

NASA Technical Reports Server (NTRS)

Collow, Allie Marquardt; Miller, Mark; Trabachino, Lynne

2015-01-01

Deforestation is an ongoing concern for the Amazon Rainforest of Brazil and associated changes to the land surface have been hypothesized to alter the climate in the region. A comprehensive set of meteorological observations at the surface and within the lower troposphere above Manacapuru, Brazil and data from the Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) are used to evaluate the seasonal cycle of cloudiness, thermodynamics, and the radiation budget. While ample moisture is present in the Amazon Rainforest year round, the northward progression of the Hadley circulation during the dry season contributes to a drying of the middle troposphere and inhibits the formation of deep convection. This results in a reduction in cloudiness and precipitation as well as an increase in the height of the lifting condensation level, which is shown to have a negative correlation to the fraction of low clouds. Frequent cloudiness prevents solar radiation from reaching the surface and clouds are often reflective with high values of shortwave cloud radiative effect at the surface and top of the atmosphere. Cloud radiative effect is reduced during the dry season however the dry season surface shortwave cloud radiative effect is still double what is observed during the wet season in other tropical locations. Within the column, the impact of clouds on the radiation budget is more prevalent in the longwave part of the spectrum, with a net warming in the wet season.

Software architecture and design of the web services facilitating climate model diagnostic analysis

NASA Astrophysics Data System (ADS)

Pan, L.; Lee, S.; Zhang, J.; Tang, B.; Zhai, C.; Jiang, J. H.; Wang, W.; Bao, Q.; Qi, M.; Kubar, T. L.; Teixeira, J.

2015-12-01

Climate model diagnostic analysis is a computationally- and data-intensive task because it involves multiple numerical model outputs and satellite observation data that can both be high resolution. We have built an online tool that facilitates this process. The tool is called Climate Model Diagnostic Analyzer (CMDA). It employs the web service technology and provides a web-based user interface. The benefits of these choices include: (1) No installation of any software other than a browser, hence it is platform compatable; (2) Co-location of computation and big data on the server side, and small results and plots to be downloaded on the client side, hence high data efficiency; (3) multi-threaded implementation to achieve parallel performance on multi-core servers; and (4) cloud deployment so each user has a dedicated virtual machine. In this presentation, we will focus on the computer science aspects of this tool, namely the architectural design, the infrastructure of the web services, the implementation of the web-based user interface, the mechanism of provenance collection, the approach to virtualization, and the Amazon Cloud deployment. As an example, We will describe our methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). Another example is the use of Docker, a light-weight virtualization container, to distribute and deploy CMDA onto an Amazon EC2 instance. Our tool of CMDA has been successfully used in the 2014 Summer School hosted by the JPL Center for Climate Science. Students had positive feedbacks in general and we will report their comments. An enhanced version of CMDA with several new features, some requested by the 2014 students, will be used in the 2015 Summer School soon.

The Green Ocean Amazon Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols, Clouds, and Rainfall over the Rain Forest

DOE PAGES

Martin, S. T.; Artaxo, P.; Machado, L.; ...

2017-05-15

The Observations and Modeling of the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment took place around the urban region of Manaus in central Amazonia across 2 years. The urban pollution plume was used to study the susceptibility of gases, aerosols, clouds, and rainfall to human activities in a tropical environment. Many aspects of air quality, weather, terrestrial ecosystems, and climate work differently in the tropics than in the more thoroughly studied temperate regions of Earth. GoAmazon2014/5, a cooperative project of Brazil, Germany, and the United States, employed an unparalleled suite of measurements at nine ground sites and on board two aircraftmore » to investigate the flow of background air into Manaus, the emissions into the air over the city, and the advection of the pollution downwind of the city. Here in this paper, to visualize this train of processes and its effects, observations aboard a low-flying aircraft are presented. Comparative measurements within and adjacent to the plume followed the emissions of biogenic volatile organic carbon compounds (BVOCs) from the tropical forest, their transformations by the atmospheric oxidant cycle, alterations of this cycle by the influence of the pollutants, transformations of the chemical products into aerosol particles, the relationship of these particles to cloud condensation nuclei (CCN) activity, and the differences in cloud properties and rainfall for background compared to polluted conditions. The observations of the GoAmazon2014/5 experiment illustrate how the hydrologic cycle, radiation balance, and carbon recycling may be affected by present-day as well as future economic development and pollution over the Amazonian tropical forest.« less

The Green Ocean Amazon Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols, Clouds, and Rainfall over the Rain Forest

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, S. T.; Artaxo, P.; Machado, L.

The Observations and Modeling of the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment took place around the urban region of Manaus in central Amazonia across 2 years. The urban pollution plume was used to study the susceptibility of gases, aerosols, clouds, and rainfall to human activities in a tropical environment. Many aspects of air quality, weather, terrestrial ecosystems, and climate work differently in the tropics than in the more thoroughly studied temperate regions of Earth. GoAmazon2014/5, a cooperative project of Brazil, Germany, and the United States, employed an unparalleled suite of measurements at nine ground sites and on board two aircraftmore » to investigate the flow of background air into Manaus, the emissions into the air over the city, and the advection of the pollution downwind of the city. Here in this paper, to visualize this train of processes and its effects, observations aboard a low-flying aircraft are presented. Comparative measurements within and adjacent to the plume followed the emissions of biogenic volatile organic carbon compounds (BVOCs) from the tropical forest, their transformations by the atmospheric oxidant cycle, alterations of this cycle by the influence of the pollutants, transformations of the chemical products into aerosol particles, the relationship of these particles to cloud condensation nuclei (CCN) activity, and the differences in cloud properties and rainfall for background compared to polluted conditions. The observations of the GoAmazon2014/5 experiment illustrate how the hydrologic cycle, radiation balance, and carbon recycling may be affected by present-day as well as future economic development and pollution over the Amazonian tropical forest.« less

Cloud Bursting with GlideinWMS: Means to satisfy ever increasing computing needs for Scientific Workflows

NASA Astrophysics Data System (ADS)

Mhashilkar, Parag; Tiradani, Anthony; Holzman, Burt; Larson, Krista; Sfiligoi, Igor; Rynge, Mats

2014-06-01

Scientific communities have been in the forefront of adopting new technologies and methodologies in the computing. Scientific computing has influenced how science is done today, achieving breakthroughs that were impossible to achieve several decades ago. For the past decade several such communities in the Open Science Grid (OSG) and the European Grid Infrastructure (EGI) have been using GlideinWMS to run complex application workflows to effectively share computational resources over the grid. GlideinWMS is a pilot-based workload management system (WMS) that creates on demand, a dynamically sized overlay HTCondor batch system on grid resources. At present, the computational resources shared over the grid are just adequate to sustain the computing needs. We envision that the complexity of the science driven by "Big Data" will further push the need for computational resources. To fulfill their increasing demands and/or to run specialized workflows, some of the big communities like CMS are investigating the use of cloud computing as Infrastructure-As-A-Service (IAAS) with GlideinWMS as a potential alternative to fill the void. Similarly, communities with no previous access to computing resources can use GlideinWMS to setup up a batch system on the cloud infrastructure. To enable this, the architecture of GlideinWMS has been extended to enable support for interfacing GlideinWMS with different Scientific and commercial cloud providers like HLT, FutureGrid, FermiCloud and Amazon EC2. In this paper, we describe a solution for cloud bursting with GlideinWMS. The paper describes the approach, architectural changes and lessons learned while enabling support for cloud infrastructures in GlideinWMS.

Cloud Bursting with GlideinWMS: Means to satisfy ever increasing computing needs for Scientific Workflows

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mhashilkar, Parag; Tiradani, Anthony; Holzman, Burt

Scientific communities have been in the forefront of adopting new technologies and methodologies in the computing. Scientific computing has influenced how science is done today, achieving breakthroughs that were impossible to achieve several decades ago. For the past decade several such communities in the Open Science Grid (OSG) and the European Grid Infrastructure (EGI) have been using GlideinWMS to run complex application workflows to effectively share computational resources over the grid. GlideinWMS is a pilot-based workload management system (WMS) that creates on demand, a dynamically sized overlay HTCondor batch system on grid resources. At present, the computational resources shared overmore » the grid are just adequate to sustain the computing needs. We envision that the complexity of the science driven by 'Big Data' will further push the need for computational resources. To fulfill their increasing demands and/or to run specialized workflows, some of the big communities like CMS are investigating the use of cloud computing as Infrastructure-As-A-Service (IAAS) with GlideinWMS as a potential alternative to fill the void. Similarly, communities with no previous access to computing resources can use GlideinWMS to setup up a batch system on the cloud infrastructure. To enable this, the architecture of GlideinWMS has been extended to enable support for interfacing GlideinWMS with different Scientific and commercial cloud providers like HLT, FutureGrid, FermiCloud and Amazon EC2. In this paper, we describe a solution for cloud bursting with GlideinWMS. The paper describes the approach, architectural changes and lessons learned while enabling support for cloud infrastructures in GlideinWMS.« less

Study of the thermodynamic phase of hydrometeors in convective clouds in the Amazon Basin

NASA Astrophysics Data System (ADS)

Ferreira, W. C.; Correia, A. L.; Martins, J.

2012-12-01

Aerosol-cloud interactions are responsible for large uncertainties in climatic models. One key fator when studying clouds perturbed by aerosols is determining the thermodynamic phase of hydrometeors as a function of temperature or height in the cloud. Conventional remote sensing can provide information on the thermodynamic phase of clouds over large areas, but it lacks the precision needed to understand how a single, real cloud evolves. Here we present mappings of the thermodynamic phase of droplets and ice particles in individual convective clouds in the Amazon Basin, by analyzing the emerging infrared radiance on cloud sides (Martins et al., 2011). In flights over the Amazon Basin with a research aircraft Martins et al. (2011) used imaging radiometers with spectral filters to record the emerging radiance on cloud sides at the wavelengths of 2.10 and 2.25 μm. Due to differential absorption and scattering of these wavelengths by hydrometeors in liquid or solid phases, the intensity ratio between images recorded at the two wavelengths can be used as proxy to the thermodynamic phase of these hydrometeors. In order to analyze the acquired dataset we used the MATLAB tools package, developing scripts to handle data files and derive the thermodynamic phase. In some cases parallax effects due to aircraft movement required additional data processing before calculating ratios. Only well illuminated scenes were considered, i.e. images acquired as close as possible to the backscatter vector from the incident solar radiation. It's important to notice that the intensity ratio values corresponding to a given thermodynamic phase can vary from cloud to cloud (Martins et al., 2011), however inside the same cloud the distinction between ice, water and mixed-phase is clear. Analyzing histograms of reflectance ratios 2.10/2.25 μm in selected cases, we found averages typically between 0.3 and 0.4 for ice phase hydrometeors, and between 0.5 and 0.7 for water phase droplets, consistent with the findings in Martins et al., (2011). Figure 1 shows an example of thermodynamic phase classification obtained with this technique. These experimental results can potentially be used in fast derivations of thermodynamic phase mappings in deep convective clouds, providing useful information for studies regarding aerosol-cloud interactions. Image of the ratio of reflectances at 2.10/2.25μm

Towards a Multi-Mission, Airborne Science Data System Environment

NASA Astrophysics Data System (ADS)

Crichton, D. J.; Hardman, S.; Law, E.; Freeborn, D.; Kay-Im, E.; Lau, G.; Oswald, J.

2011-12-01

NASA earth science instruments are increasingly relying on airborne missions. However, traditionally, there has been limited common infrastructure support available to principal investigators in the area of science data systems. As a result, each investigator has been required to develop their own computing infrastructures for the science data system. Typically there is little software reuse and many projects lack sufficient resources to provide a robust infrastructure to capture, process, distribute and archive the observations acquired from airborne flights. At NASA's Jet Propulsion Laboratory (JPL), we have been developing a multi-mission data system infrastructure for airborne instruments called the Airborne Cloud Computing Environment (ACCE). ACCE encompasses the end-to-end lifecycle covering planning, provisioning of data system capabilities, and support for scientific analysis in order to improve the quality, cost effectiveness, and capabilities to enable new scientific discovery and research in earth observation. This includes improving data system interoperability across each instrument. A principal characteristic is being able to provide an agile infrastructure that is architected to allow for a variety of configurations of the infrastructure from locally installed compute and storage services to provisioning those services via the "cloud" from cloud computer vendors such as Amazon.com. Investigators often have different needs that require a flexible configuration. The data system infrastructure is built on the Apache's Object Oriented Data Technology (OODT) suite of components which has been used for a number of spaceborne missions and provides a rich set of open source software components and services for constructing science processing and data management systems. In 2010, a partnership was formed between the ACCE team and the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) mission to support the data processing and data management needs. A principal goal is to provide support for the Fourier Transform Spectrometer (FTS) instrument which will produce over 700,000 soundings over the life of their three-year mission. The cost to purchase and operate a cluster-based system in order to generate Level 2 Full Physics products from this data was prohibitive. Through an evaluation of cloud computing solutions, Amazon's Elastic Compute Cloud (EC2) was selected for the CARVE deployment. As the ACCE infrastructure is developed and extended to form an infrastructure for airborne missions, the experience of working with CARVE has provided a number of lessons learned and has proven to be important in reinforcing the unique aspects of airborne missions and the importance of the ACCE infrastructure in developing a cost effective, flexible multi-mission capability that leverages emerging capabilities in cloud computing, workflow management, and distributed computing.

Rainforest aerosols as biogenic nuclei of clouds and precipitation in the Amazon.

PubMed

Pöschl, U; Martin, S T; Sinha, B; Chen, Q; Gunthe, S S; Huffman, J A; Borrmann, S; Farmer, D K; Garland, R M; Helas, G; Jimenez, J L; King, S M; Manzi, A; Mikhailov, E; Pauliquevis, T; Petters, M D; Prenni, A J; Roldin, P; Rose, D; Schneider, J; Su, H; Zorn, S R; Artaxo, P; Andreae, M O

2010-09-17

The Amazon is one of the few continental regions where atmospheric aerosol particles and their effects on climate are not dominated by anthropogenic sources. During the wet season, the ambient conditions approach those of the pristine pre-industrial era. We show that the fine submicrometer particles accounting for most cloud condensation nuclei are predominantly composed of secondary organic material formed by oxidation of gaseous biogenic precursors. Supermicrometer particles, which are relevant as ice nuclei, consist mostly of primary biological material directly released from rainforest biota. The Amazon Basin appears to be a biogeochemical reactor, in which the biosphere and atmospheric photochemistry produce nuclei for clouds and precipitation sustaining the hydrological cycle. The prevailing regime of aerosol-cloud interactions in this natural environment is distinctly different from polluted regions.

Rainforest Aerosols as Biogenic Nuclei of Clouds and Precipitation in the Amazon

NASA Astrophysics Data System (ADS)

Pöschl, U.; Martin, S. T.; Sinha, B.; Chen, Q.; Gunthe, S. S.; Huffman, J. A.; Borrmann, S.; Farmer, D. K.; Garland, R. M.; Helas, G.; Jimenez, J. L.; King, S. M.; Manzi, A.; Mikhailov, E.; Pauliquevis, T.; Petters, M. D.; Prenni, A. J.; Roldin, P.; Rose, D.; Schneider, J.; Su, H.; Zorn, S. R.; Artaxo, P.; Andreae, M. O.

2010-09-01

The Amazon is one of the few continental regions where atmospheric aerosol particles and their effects on climate are not dominated by anthropogenic sources. During the wet season, the ambient conditions approach those of the pristine pre-industrial era. We show that the fine submicrometer particles accounting for most cloud condensation nuclei are predominantly composed of secondary organic material formed by oxidation of gaseous biogenic precursors. Supermicrometer particles, which are relevant as ice nuclei, consist mostly of primary biological material directly released from rainforest biota. The Amazon Basin appears to be a biogeochemical reactor, in which the biosphere and atmospheric photochemistry produce nuclei for clouds and precipitation sustaining the hydrological cycle. The prevailing regime of aerosol-cloud interactions in this natural environment is distinctly different from polluted regions.

Unidata's Vision for Transforming Geoscience by Moving Data Services and Software to the Cloud

NASA Astrophysics Data System (ADS)

Ramamurthy, M. K.; Fisher, W.; Yoksas, T.

2014-12-01

Universities are facing many challenges: shrinking budgets, rapidly evolving information technologies, exploding data volumes, multidisciplinary science requirements, and high student expectations. These changes are upending traditional approaches to accessing and using data and software. It is clear that Unidata's products and services must evolve to support new approaches to research and education. After years of hype and ambiguity, cloud computing is maturing in usability in many areas of science and education, bringing the benefits of virtualized and elastic remote services to infrastructure, software, computation, and data. Cloud environments reduce the amount of time and money spent to procure, install, and maintain new hardware and software, and reduce costs through resource pooling and shared infrastructure. Cloud services aimed at providing any resource, at any time, from any place, using any device are increasingly being embraced by all types of organizations. Given this trend and the enormous potential of cloud-based services, Unidata is taking moving to augment its products, services, data delivery mechanisms and applications to align with the cloud-computing paradigm. Specifically, Unidata is working toward establishing a community-based development environment that supports the creation and use of software services to build end-to-end data workflows. The design encourages the creation of services that can be broken into small, independent chunks that provide simple capabilities. Chunks could be used individually to perform a task, or chained into simple or elaborate workflows. The services will also be portable, allowing their use in researchers' own cloud-based computing environments. In this talk, we present a vision for Unidata's future in a cloud-enabled data services and discuss our initial efforts to deploy a subset of Unidata data services and tools in the Amazon EC2 and Microsoft Azure cloud environments, including the transfer of real-time meteorological data into its cloud instances, product generation using those data, and the deployment of TDS, McIDAS ADDE and AWIPS II data servers and the Integrated Data Server visualization tool.

On the Emergent Constraints of Climate Sensitivity [On proposed emergent constraints of climate sensitivity

DOE PAGES

Qu, Xin; Hall, Alex; DeAngelis, Anthony M.; ...

2018-01-11

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable tomore » a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. Additionally, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.« less

On the Emergent Constraints of Climate Sensitivity [On proposed emergent constraints of climate sensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qu, Xin; Hall, Alex; DeAngelis, Anthony M.

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable tomore » a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. Additionally, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.« less

Smoke Invigoration Versus Inhibition of Clouds over the Amazon

NASA Technical Reports Server (NTRS)

Koren, Ilan; Martins, J. Vanderlei; Lorraine, A. Remer; Afargan, Hila

2008-01-01

The effect of anthropogenic aerosols on clouds is one of the most important and least understood aspects of human-induced climate change. Small changes in the amount of cloud coverage can produce a climate forcing equivalent in magnitude and opposite in sign to that caused by anthropogenic greenhouse gases, and changes in cloud height can shift the effect of clouds from cooling to warming. Focusing on the Amazon, we show a smooth transition between two opposing effects of aerosols on clouds: the microphysical and the radiative. We show how a feedback between the optical properties of aerosols and the cloud fraction can modify the aerosol forcing, changing the total radiative energy and redistributing it over the atmospheric column.

wFReDoW: A Cloud-Based Web Environment to Handle Molecular Docking Simulations of a Fully Flexible Receptor Model

PubMed Central

De Paris, Renata; Frantz, Fábio A.; Norberto de Souza, Osmar; Ruiz, Duncan D. A.

2013-01-01

Molecular docking simulations of fully flexible protein receptor (FFR) models are coming of age. In our studies, an FFR model is represented by a series of different conformations derived from a molecular dynamic simulation trajectory of the receptor. For each conformation in the FFR model, a docking simulation is executed and analyzed. An important challenge is to perform virtual screening of millions of ligands using an FFR model in a sequential mode since it can become computationally very demanding. In this paper, we propose a cloud-based web environment, called web Flexible Receptor Docking Workflow (wFReDoW), which reduces the CPU time in the molecular docking simulations of FFR models to small molecules. It is based on the new workflow data pattern called self-adaptive multiple instances (P-SaMIs) and on a middleware built on Amazon EC2 instances. P-SaMI reduces the number of molecular docking simulations while the middleware speeds up the docking experiments using a High Performance Computing (HPC) environment on the cloud. The experimental results show a reduction in the total elapsed time of docking experiments and the quality of the new reduced receptor models produced by discarding the nonpromising conformations from an FFR model ruled by the P-SaMI data pattern. PMID:23691504

The Integration of CloudStack and OCCI/OpenNebula with DIRAC

NASA Astrophysics Data System (ADS)

Méndez Muñoz, Víctor; Fernández Albor, Víctor; Graciani Diaz, Ricardo; Casajús Ramo, Adriàn; Fernández Pena, Tomás; Merino Arévalo, Gonzalo; José Saborido Silva, Juan

2012-12-01

The increasing availability of Cloud resources is arising as a realistic alternative to the Grid as a paradigm for enabling scientific communities to access large distributed computing resources. The DIRAC framework for distributed computing is an easy way to efficiently access to resources from both systems. This paper explains the integration of DIRAC with two open-source Cloud Managers: OpenNebula (taking advantage of the OCCI standard) and CloudStack. These are computing tools to manage the complexity and heterogeneity of distributed data center infrastructures, allowing to create virtual clusters on demand, including public, private and hybrid clouds. This approach has required to develop an extension to the previous DIRAC Virtual Machine engine, which was developed for Amazon EC2, allowing the connection with these new cloud managers. In the OpenNebula case, the development has been based on the CernVM Virtual Software Appliance with appropriate contextualization, while in the case of CloudStack, the infrastructure has been kept more general, which permits other Virtual Machine sources and operating systems being used. In both cases, CernVM File System has been used to facilitate software distribution to the computing nodes. With the resulting infrastructure, the cloud resources are transparent to the users through a friendly interface, like the DIRAC Web Portal. The main purpose of this integration is to get a system that can manage cloud and grid resources at the same time. This particular feature pushes DIRAC to a new conceptual denomination as interware, integrating different middleware. Users from different communities do not need to care about the installation of the standard software that is available at the nodes, nor the operating system of the host machine which is transparent to the user. This paper presents an analysis of the overhead of the virtual layer, doing some tests to compare the proposed approach with the existing Grid solution. License Notice: Published under licence in Journal of Physics: Conference Series by IOP Publishing Ltd.

Comparative study of internet cloud and cloudlet over wireless mesh networks for real-time applications

NASA Astrophysics Data System (ADS)

Khan, Kashif A.; Wang, Qi; Luo, Chunbo; Wang, Xinheng; Grecos, Christos

2014-05-01

Mobile cloud computing is receiving world-wide momentum for ubiquitous on-demand cloud services for mobile users provided by Amazon, Google etc. with low capital cost. However, Internet-centric clouds introduce wide area network (WAN) delays that are often intolerable for real-time applications such as video streaming. One promising approach to addressing this challenge is to deploy decentralized mini-cloud facility known as cloudlets to enable localized cloud services. When supported by local wireless connectivity, a wireless cloudlet is expected to offer low cost and high performance cloud services for the users. In this work, we implement a realistic framework that comprises both a popular Internet cloud (Amazon Cloud) and a real-world cloudlet (based on Ubuntu Enterprise Cloud (UEC)) for mobile cloud users in a wireless mesh network. We focus on real-time video streaming over the HTTP standard and implement a typical application. We further perform a comprehensive comparative analysis and empirical evaluation of the application's performance when it is delivered over the Internet cloud and the cloudlet respectively. The study quantifies the influence of the two different cloud networking architectures on supporting real-time video streaming. We also enable movement of the users in the wireless mesh network and investigate the effect of user's mobility on mobile cloud computing over the cloudlet and Amazon cloud respectively. Our experimental results demonstrate the advantages of the cloudlet paradigm over its Internet cloud counterpart in supporting the quality of service of real-time applications.

BlueSky Cloud - rapid infrastructure capacity using Amazon's Cloud for wildfire emergency response

NASA Astrophysics Data System (ADS)

Haderman, M.; Larkin, N. K.; Beach, M.; Cavallaro, A. M.; Stilley, J. C.; DeWinter, J. L.; Craig, K. J.; Raffuse, S. M.

2013-12-01

During peak fire season in the United States, many large wildfires often burn simultaneously across the country. Smoke from these fires can produce air quality emergencies. It is vital that incident commanders, air quality agencies, and public health officials have smoke impact information at their fingertips for evaluating where fires and smoke are and where the smoke will go next. To address the need for this kind of information, the U.S. Forest Service AirFire Team created the BlueSky Framework, a modeling system that predicts concentrations of particle pollution from wildfires. During emergency response, decision makers use BlueSky predictions to make public outreach and evacuation decisions. The models used in BlueSky predictions are computationally intensive, and the peak fire season requires significantly more computer resources than off-peak times. Purchasing enough hardware to run the number of BlueSky Framework runs that are needed during fire season is expensive and leaves idle servers running the majority of the year. The AirFire Team and STI developed BlueSky Cloud to take advantage of Amazon's virtual servers hosted in the cloud. With BlueSky Cloud, as demand increases and decreases, servers can be easily spun up and spun down at a minimal cost. Moving standard BlueSky Framework runs into the Amazon Cloud made it possible for the AirFire Team to rapidly increase the number of BlueSky Framework instances that could be run simultaneously without the costs associated with purchasing and managing servers. In this presentation, we provide an overview of the features of BlueSky Cloud, describe how the system uses Amazon Cloud, and discuss the costs and benefits of moving from privately hosted servers to a cloud-based infrastructure.

Covariability in the Monthly Mean Convective and Radiative Diurnal Cycles in the Amazon

NASA Technical Reports Server (NTRS)

Dodson, Jason B.; Taylor, Patrick C.

2015-01-01

The diurnal cycle of convective clouds greatly influences the radiative energy balance in convectively active regions of Earth, through both direct presence, and the production of anvil and stratiform clouds. Previous studies show that the frequency and properties of convective clouds can vary on monthly timescales as a result of variability in the monthly mean atmospheric state. Furthermore, the radiative budget in convectively active regions also varies by up to 7 Wm-2 in convectively active regions. These facts suggest that convective clouds connect atmospheric state variability and radiation variability beyond clear sky effects alone. Previous research has identified monthly covariability between the diurnal cycle of CERES-observed top-of-atmosphere radiative fluxes and multiple atmospheric state variables from reanalysis over the Amazon region. ASVs that enhance (reduce) deep convection, such as CAPE (LTS), tend to shift the daily OLR and cloud albedo maxima earlier (later) in the day by 2-3 hr. We first test the analysis method using multiple reanalysis products for both the dry and wet seasons to further investigate the robustness of the preliminary results. We then use CloudSat data as an independent cloud observing system to further evaluate the relationships of cloud properties to variability in radiation and atmospheric states. While CERES can decompose OLR variability into clear sky and cloud effects, it cannot determine what variability in cloud properties lead to variability in the radiative cloud effects. Cloud frequency, cloud top height, and cloud microphysics all contribute to the cloud radiative effect, all of which are observable by CloudSat. In addition, CloudSat can also observe the presence and variability of deep convective cores responsible for the production of anvil clouds. We use these capabilities to determine the covariability of convective cloud properties and the radiative diurnal cycle.

Virtual machine provisioning, code management, and data movement design for the Fermilab HEPCloud Facility

NASA Astrophysics Data System (ADS)

Timm, S.; Cooper, G.; Fuess, S.; Garzoglio, G.; Holzman, B.; Kennedy, R.; Grassano, D.; Tiradani, A.; Krishnamurthy, R.; Vinayagam, S.; Raicu, I.; Wu, H.; Ren, S.; Noh, S.-Y.

2017-10-01

The Fermilab HEPCloud Facility Project has as its goal to extend the current Fermilab facility interface to provide transparent access to disparate resources including commercial and community clouds, grid federations, and HPC centers. This facility enables experiments to perform the full spectrum of computing tasks, including data-intensive simulation and reconstruction. We have evaluated the use of the commercial cloud to provide elasticity to respond to peaks of demand without overprovisioning local resources. Full scale data-intensive workflows have been successfully completed on Amazon Web Services for two High Energy Physics Experiments, CMS and NOνA, at the scale of 58000 simultaneous cores. This paper describes the significant improvements that were made to the virtual machine provisioning system, code caching system, and data movement system to accomplish this work. The virtual image provisioning and contextualization service was extended to multiple AWS regions, and to support experiment-specific data configurations. A prototype Decision Engine was written to determine the optimal availability zone and instance type to run on, minimizing cost and job interruptions. We have deployed a scalable on-demand caching service to deliver code and database information to jobs running on the commercial cloud. It uses the frontiersquid server and CERN VM File System (CVMFS) clients on EC2 instances and utilizes various services provided by AWS to build the infrastructure (stack). We discuss the architecture and load testing benchmarks on the squid servers. We also describe various approaches that were evaluated to transport experimental data to and from the cloud, and the optimal solutions that were used for the bulk of the data transport. Finally, we summarize lessons learned from this scale test, and our future plans to expand and improve the Fermilab HEP Cloud Facility.

Virtual Machine Provisioning, Code Management, and Data Movement Design for the Fermilab HEPCloud Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Timm, S.; Cooper, G.; Fuess, S.

The Fermilab HEPCloud Facility Project has as its goal to extend the current Fermilab facility interface to provide transparent access to disparate resources including commercial and community clouds, grid federations, and HPC centers. This facility enables experiments to perform the full spectrum of computing tasks, including data-intensive simulation and reconstruction. We have evaluated the use of the commercial cloud to provide elasticity to respond to peaks of demand without overprovisioning local resources. Full scale data-intensive workflows have been successfully completed on Amazon Web Services for two High Energy Physics Experiments, CMS and NOνA, at the scale of 58000 simultaneous cores.more » This paper describes the significant improvements that were made to the virtual machine provisioning system, code caching system, and data movement system to accomplish this work. The virtual image provisioning and contextualization service was extended to multiple AWS regions, and to support experiment-specific data configurations. A prototype Decision Engine was written to determine the optimal availability zone and instance type to run on, minimizing cost and job interruptions. We have deployed a scalable on-demand caching service to deliver code and database information to jobs running on the commercial cloud. It uses the frontiersquid server and CERN VM File System (CVMFS) clients on EC2 instances and utilizes various services provided by AWS to build the infrastructure (stack). We discuss the architecture and load testing benchmarks on the squid servers. We also describe various approaches that were evaluated to transport experimental data to and from the cloud, and the optimal solutions that were used for the bulk of the data transport. Finally, we summarize lessons learned from this scale test, and our future plans to expand and improve the Fermilab HEP Cloud Facility.« less

Taxonomy, Traits, and Environment Determine Isoprenoid Emission from an Evergreen Tropical forest.

NASA Astrophysics Data System (ADS)

Taylor, T.; Alves, E. G.; Tota, J.; Oliveira Junior, R. C.; Camargo, P. B. D.; Saleska, S. R.

2016-12-01

Volatile isoprenoid emissions from the leaves of tropical forest trees significantly affects atmospheric chemistry, aerosols, and cloud dynamics, as well as the physiology of the emitting leaves. Emission is associated with plant tolerance to heat and drought stress. Despite a potentially central role of isoprenoid emissions in tropical forest-climate interactions, we have a poor understanding of the relationship between emissions and ecological axes of forest function. We used a custom instrument to quantify leaf isoprenoid emission rates from over 200 leaves and 80 trees at a site in the eastern Brazilian Amazon. We related standardized leaf emission capacity (EC: leaf emission rate at 1000 PAR) to tree taxonomy, height, light environment, wood traits, and leaf traits. Taxonomy was the strongest predictor of EC, and non-emitters could be found throughout the canopy. But we found that environment and leaf carbon economics constrained the upper bound of EC. For example, the relationship between EC and specific leaf area (SLA; fresh leaf area / dry mass) is described by an envelope with a decreasing upper bound on EC as SLA increases (quantile regression: 85th quantile, p<0.01). That result suggests a limitation on emissions related to leaf carbon investment strategies. EC was highest in the mid-canopy, and in leaves growing under less direct light. While inferences of ecosystem emissions focus on environmental conditions in the canopy, our results suggest that sub-canopy leaves are more responsive. This work is allowing us to develop an ecological understanding of isoprenoid emissions from forests, the basis for a predictive model of emissions that depends on both environmental factors and biological emission capacity that is grounded in plant traits and phylogeny.

Quantifying the Sources of Intermodel Spread in Equilibrium Climate Sensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caldwell, Peter M.; Zelinka, Mark D.; Taylor, Karl E.

This paper clarifies the causes of intermodel differences in the global-average temperature response to doubled CO 2, commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant source of intermodel spread in ECS. This new approach also reveals that covariances between cloud feedback and forcing, between lapse rate andmore » longwave cloud feedbacks, and between albedo and shortwave cloud feedbacks play an important and previously underappreciated role in determining model differences in ECS. Finally, defining feedbacks based on fixed relative rather than specific humidity (as suggested by Held and Shell) reduces the covariances between processes and leads to more straightforward interpretations of results.« less

Quantifying the Sources of Intermodel Spread in Equilibrium Climate Sensitivity

DOE PAGES

Caldwell, Peter M.; Zelinka, Mark D.; Taylor, Karl E.; ...

2016-01-07

This paper clarifies the causes of intermodel differences in the global-average temperature response to doubled CO 2, commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant source of intermodel spread in ECS. This new approach also reveals that covariances between cloud feedback and forcing, between lapse rate andmore » longwave cloud feedbacks, and between albedo and shortwave cloud feedbacks play an important and previously underappreciated role in determining model differences in ECS. Finally, defining feedbacks based on fixed relative rather than specific humidity (as suggested by Held and Shell) reduces the covariances between processes and leads to more straightforward interpretations of results.« less

Amazon boundary layer aerosol concentration sustained by vertical transport during rainfall

DOE PAGES

Wang, Jian; Krejci, Radovan; Giangrande, Scott; ...

2016-10-24

The nucleation of atmospheric vapours is an important source of new aerosol particles that can subsequently grow to form cloud condensation nuclei in the atmosphere. Most field studies of atmospheric aerosols over continents are influenced by atmospheric vapours of anthropogenic origin and, in consequence, aerosol processes in pristine, terrestrial environments remain poorly understood. The Amazon rainforest is one of the few continental regions where aerosol particles and their precursors can be studied under near-natural conditions, but the origin of small aerosol particles that grow into cloud condensation nuclei in the Amazon boundary layer remains unclear. Here we present aircraft- andmore » ground-based measurements under clean conditions during the wet season in the central Amazon basin. We find that high concentrations of small aerosol particles (with diameters of less than 50 nanometres) in the lower free troposphere are transported from the free troposphere into the boundary layer during precipitation events by strong convective downdrafts and weaker downward motions in the trailing stratiform region. Lastly, this rapid vertical transport can help to maintain the population of particles in the pristine Amazon boundary layer, and may therefore influence cloud properties and climate under natural conditions.« less

Automating NEURON Simulation Deployment in Cloud Resources.

PubMed

Stockton, David B; Santamaria, Fidel

2017-01-01

Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the OpenStack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon's proprietary software. We describe the manual procedures and how to automate cloud operations. We describe extending our simulation automation software called NeuroManager (Stockton and Santamaria, Frontiers in Neuroinformatics, 2015), so that the user is capable of recruiting private cloud, public cloud, HPC, and local servers simultaneously with a simple common interface. We conclude by performing several studies in which we examine speedup, efficiency, total session time, and cost for sets of simulations of a published NEURON model.

The ACRIDICON-CHUVA observational study of tropical convective clouds and precipitation using the new German research aircraft HALO

NASA Astrophysics Data System (ADS)

Wendisch, Manfred; Pöschl, Ulrich; Andreae, Meinrat O.; Machado, Luiz A. T.; Albrecht, Rachel; Schlager, Hans; Rosenfeld, Daniel; Krämer, Martina

2015-04-01

An extensive airborne/ground-based measurement campaign to study tropical convective clouds is introduced. It was performed in Brazil with focus on the Amazon rainforest from 1 September to 4 October 2014. The project combined the joint German-Brazilian ACRIDICON (Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems) and CHUVA (Machado et al.2014) projects. ACRIDICON aimed at the quantification of aerosol-cloud-precipitation interactions and their thermodynamic, dynamic and radiative effects in convective cloud systems by in-situ aircraft observations and indirect measurements (aircraft, satellite, and ground-based). The ACRIDICON-CHUVA campaign was conducted in cooperation with the second Intensive Operational Phase (IOP) of the GOAmazon (Green Ocean Amazon) program. The focus in this presentation is on the airborne observations within ACRIDICON-CHUVA. The German HALO (High Altitude and Long-Range Research Aircraft) was based in Manaus (Amazonas State); it carried out 14 research flights (96 flight hours in total). HALO was equipped with remote sensing and in-situ instrumentation for meteorological, trace gas, aerosol, cloud, and precipitation measurements. Five mission objectives were pursued: (1) cloud vertical evolution (cloud profiling), (2) aerosol processing (inflow and outflow), (3) satellite validation, (4) vertical transport and mixing (tracer experiment), and (5) clouds over forested and deforested areas. The five cloud missions collected data in clean atmospheric conditions and in contrasting polluted (urban and biomass burning) environments.

Secure and Resilient Cloud Computing for the Department of Defense

DTIC Science & Technology

2015-11-16

platform as a service (PaaS), and software as a service ( SaaS )—that target system administrators, developers, and end-users respectively (see Table 2...interfaces (API) and services Medium Amazon Elastic MapReduce, MathWorks Cloud, Red Hat OpenShift SaaS Full-fledged applications Low Google gMail

Cloudiness over the Amazon rainforest: Meteorology and thermodynamics

NASA Astrophysics Data System (ADS)

Collow, Allison B. Marquardt; Miller, Mark A.; Trabachino, Lynne C.

2016-07-01

Comprehensive meteorological observations collected during GOAmazon2014/15 using the Atmospheric Radiation Measurement Mobile Facility no. 1 and assimilated observations from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 are used to document the seasonal cycle of cloudiness, thermodynamics, and precipitation above the Amazon rainforest. The reversal of synoptic-scale vertical motions modulates the transition between the wet and dry seasons. Ascending moist air during the wet season originates near the surface of the Atlantic Ocean and is advected into the Amazon rainforest, where it experiences convergence and, ultimately, precipitates. The dry season is characterized by weaker winds and synoptic-scale subsidence with little or no moisture convergence accompanying moisture advection. This combination results in the drying of the midtroposphere during June through October as indicated by a decrease in liquid water path, integrated water, and the vertical profile of water vapor mixing ratio. The vertical profile of cloud fraction exhibits a relatively consistent decline in cloud fraction from the lifting condensation level (LCL) to the freezing level where a minimum is observed, unlike many other tropical regions. Coefficients of determination between the LCL and cloud fractional coverage suggest a relatively robust relationship between the LCL and cloudiness beneath 5 km during the dry season (R2 = 0.42) but a weak relationship during the wet season (0.12).

Evaluation of the carbon content of aerosols from the burn- ing of biomass in the Brazilian Amazon using thermal, op- tical and thermal-optical analysis methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soto-Garcia, Lydia L.; Andreae, Meinrat O.; Andreae, Tracey W.

2011-06-03

Aerosol samples were collected at a pasture site in the Amazon Basin as part of the project LBA-SMOCC-2002 (Large-Scale Biosphere-Atmosphere Experiment in Amazonia - Smoke Aerosols, Clouds, Rainfall and Climate: Aerosols from Biomass Burning Perturb Global and Regional Climate). Sampling was conducted during the late dry season, when the aerosol composition was dominated by biomass burning emissions, especially in the submicron fraction. A 13-stage Dekati low-pressure impactor (DLPI) was used to collect particles with nominal aerodynamic diameters (D{sub p}) ranging from 0.03 to 0.10 m. Gravimetric analyses of the DLPI substrates and filters were performed to obtain aerosol mass concentrations.more » The concentrations of total, apparent elemental, and organic carbon (TC, EC{sub a}, and OC) were determined using thermal and thermal-optical analysis (TOA) methods. A light transmission method (LTM) was used to determine the concentration of equivalent black carbon (BC{sub e}) or the absorbing fraction at 880 nm for the size-resolved samples. During the dry period, due to the pervasive presence of fires in the region upwind of the sampling site, concentrations of fine aerosols (D{sub p} < 2.5 {mu}m: average 59.8 {mu}g m{sup -3}) were higher than coarse aerosols (D{sub p} > 2.5 {mu}m: 4.1 {mu}g m{sup -3}). Carbonaceous matter, estimated as the sum of the particulate organic matter (i.e., OC x 1.8) plus BC{sub e}, comprised more than 90% to the total aerosol mass. Concentrations of EC{sub a} (estimated by thermal analysis with a correction for charring) and BCe (estimated by LTM) averaged 5.2 {+-} 1.3 and 3.1 {+-} 0.8 {mu}g m{sup -3}, respectively. The determination of EC was improved by extracting water-soluble organic material from the samples, which reduced the average light absorption {angstrom} exponent of particles in the size range of 0.1 to 1.0 {mu}m from > 2.0 to approximately 1.2. The size-resolved BC{sub e} measured by the LTM showed a clear maximum between 0.4 and 0.6 m in diameter. The concentrations of OC and BC{sub e} varied diurnally during the dry period, and this variation is related to diurnal changes in boundary layer thickness and in fire frequency.« less

Cloud-based MOTIFSIM: Detecting Similarity in Large DNA Motif Data Sets.

PubMed

Tran, Ngoc Tam L; Huang, Chun-Hsi

2017-05-01

We developed the cloud-based MOTIFSIM on Amazon Web Services (AWS) cloud. The tool is an extended version from our web-based tool version 2.0, which was developed based on a novel algorithm for detecting similarity in multiple DNA motif data sets. This cloud-based version further allows researchers to exploit the computing resources available from AWS to detect similarity in multiple large-scale DNA motif data sets resulting from the next-generation sequencing technology. The tool is highly scalable with expandable AWS.

Unidata's Vision for Transforming Geoscience by Moving Data Services and Software to the Cloud

NASA Astrophysics Data System (ADS)

Ramamurthy, Mohan; Fisher, Ward; Yoksas, Tom

2015-04-01

Universities are facing many challenges: shrinking budgets, rapidly evolving information technologies, exploding data volumes, multidisciplinary science requirements, and high expectations from students who have grown up with smartphones and tablets. These changes are upending traditional approaches to accessing and using data and software. Unidata recognizes that its products and services must evolve to support new approaches to research and education. After years of hype and ambiguity, cloud computing is maturing in usability in many areas of science and education, bringing the benefits of virtualized and elastic remote services to infrastructure, software, computation, and data. Cloud environments reduce the amount of time and money spent to procure, install, and maintain new hardware and software, and reduce costs through resource pooling and shared infrastructure. Cloud services aimed at providing any resource, at any time, from any place, using any device are increasingly being embraced by all types of organizations. Given this trend and the enormous potential of cloud-based services, Unidata is taking moving to augment its products, services, data delivery mechanisms and applications to align with the cloud-computing paradigm. Specifically, Unidata is working toward establishing a community-based development environment that supports the creation and use of software services to build end-to-end data workflows. The design encourages the creation of services that can be broken into small, independent chunks that provide simple capabilities. Chunks could be used individually to perform a task, or chained into simple or elaborate workflows. The services will also be portable in the form of downloadable Unidata-in-a-box virtual images, allowing their use in researchers' own cloud-based computing environments. In this talk, we present a vision for Unidata's future in a cloud-enabled data services and discuss our ongoing efforts to deploy a suite of Unidata data services and tools in the Amazon EC2 and Microsoft Azure cloud environments, including the transfer of real-time meteorological data into its cloud instances, product generation using those data, and the deployment of TDS, McIDAS ADDE and AWIPS II data servers and the Integrated Data Server visualization tool.

A First Step Towards Network Security Virtualization: From Concept to Prototype

DTIC Science & Technology

2015-10-01

ec2 security groups. http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-network- security.html. [3] Jeffrey R. Ballard, Ian Rae, and Aditya...20] Matthew L. Meola Michael J. Freedman Jennifer Rexford Nate Foster, Rob Harrison and David Walker. Frenetic: A High-Level Langauge for OpenFlow

Aerosol Enhancements in the Upper Troposphere Over The Amazon Forest: Do Amazonian Clouds Produce Aerosols?

NASA Astrophysics Data System (ADS)

Andreae, M. O.; Afchine, A.; Albrecht, R. I.; Artaxo, P.; Borrmann, S.; Cecchini, M. A.; Costa, A.; Dollner, M.; Fütterer, D.; Järvinen, E.; Klimach, T.; Konemann, T.; Kraemer, M.; Krüger, M. L.; Machado, L.; Mertes, S.; Pöhlker, C.; Poeschl, U.; Sauer, D. N.; Schnaiter, M.; Schneider, J.; Schulz, C.; Spanu, A.; Walser, A.; Weinzierl, B.; Wendisch, M.

2015-12-01

The German-Brazilian cooperative aircraft campaign ACRIDICON-CHUVA (Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems) on the German research aircraft HALO took place over the Amazon Basin in September/October 2014, with the objective of studying tropical deep convective clouds over the Amazon rainforest and their interactions with trace gases, aerosol particles, and atmospheric radiation. The aircraft was equipped with about 30 remote sensing and in-situ instruments for meteorological, trace gas, aerosol, cloud, precipitation, and solar radiation measurements. Fourteen research flights were conducted during this campaign. Observations during ACRIDICON-CHUVA showed high aerosol concentrations in the upper troposphere (UT) over the Amazon Basin, with concentrations after normalization to standard conditions often exceeding those in the boundary layer (BL). This behavior was consistent between several aerosol metrics, including condensation nuclei (CN), cloud condensation nuclei (CCN), and chemical species mass concentrations. These UT aerosols were different in their composition and size distribution from the aerosol in the BL, making convective transport of particles unlikely as a source. The regions in the immediate outflow of deep convective clouds were found to be depleted in aerosol particles, whereas enhanced aerosol number and mass concentrations were found in UT regions that had experienced outflow from deep convection in the preceding 24-48 hours. This suggests that aerosol production takes place in the UT based on volatile and condensable material brought up by deep convection. Subsequently, downward mixing and transport of upper tropospheric aerosol may be a source of particles to the BL, where they increase in size by the condensation of biogenic volatile organic carbon (BVOC) oxidation products. This may be an important source of aerosol particles in the Amazonian BL, where aerosol nucleation and new particle formation has not been observed.

African Dust Fertilizing the Amazon Rainforest: An Assessment with Seven-year Record of CALIOP Measurements

NASA Astrophysics Data System (ADS)

Yu, H.; Chin, M.; Yuan, T.; Bian, H.; Prospero, J. M.; Omar, A. H.; Remer, L. A.; Winker, D. M.; Yang, Y.; Zhang, Y.; Zhang, Z.

2014-12-01

The productivity of Amazon rainforest is constrained by the availability of nutrients, in particular phosphorus (P). Deposition of transported African dust in boreal winter and spring is considered an important nutrient input for the Amazon Basin, though its magnitude is not well qunatified. This study provides a remote sensing observation-based estimate of dust deposition in the Amazon Basin using a 7-year (2007-2013) record of three dimensional (3D) distributions of aerosol in both cloud-free and above-cloud conditions from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). It is estimated that the 7-year average of dust deposition into the Amazon Basin amounts to 15.1 ~ 32.1 Tg a-1 (Tg = 1012 g). This imported dust could provide 0.012 ~ 0.025 Tg P a-1 or equivalent to 12 ~ 26 g P ha-1 a-1 to fertilize the Amazon rainforest, which largely compensates the hydrological loss of P. The CLAIOP-based estimate agrees better with estimates from in-situ measurements and model simulations than what has been reported in literature. The closer agreement benefits from a more realistic geographic definition of the Amazon Basin and inclusion of meridional dust transport calculation in addition to the 3D nature of CALIOP aerosol measurements. The trans-Atlantic transport and deposition of dust shows strong interannual variations that are found to correlate with the North Atlantic Oscillation index in the winter season and anticorrelate with the prior-year Sahel Precipitation Index on an annual basis. Uncertainties associated with the estimate will also be discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cecchini, Micael A.; Machado, Luiz A. T.; Comstock, Jennifer M.

The remote atmosphere over the Amazon can be similar to oceanic regions in terms of aerosol conditions and cloud type formations. This is especially true during the wet season. The main aerosol-related disturbances over the Amazon have both natural sources, such as dust transport from Africa, and anthropogenic sources, such as biomass burning or urban pollution. The present work considers the impacts of the latter on the microphysical properties of warm-phase clouds by analysing observations of the interactions between the Manaus pollution plume and its surroundings, as part of the GoAmazon2014/5 Experiment. The analysed period corresponds to the wet seasonmore » (specifically from February to March 2014 and corresponding to the first Intensive Operating Period (IOP1) of GoAmazon2014/5). The droplet size distributions reported are in the range 1 µm ≤ D≤50 µm in order to capture the processes leading up to the precipitation formation. The wet season largely presents a clean background atmosphere characterized by frequent rain showers. As such, the contrast between background clouds and those affected by the Manaus pollution can be observed and detailed. The focus is on the characteristics of the initial microphysical properties in cumulus clouds predominantly at their early stages. The pollution-affected clouds are found to have smaller effective diameters and higher droplet number concentrations. The differences range from 10 to 40% for the effective diameter and are as high as 1000% for droplet concentration for the same vertical levels. The growth rates of droplets with altitude are slower for pollution-affected clouds (2.90 compared to 5.59 µm km ₋1), as explained by the absence of bigger droplets at the onset of cloud development. Clouds under background conditions have higher concentrations of larger droplets (>20 µm) near the cloud base, which would contribute significantly to the growth rates through the collision–coalescence process. The overall shape of the droplet size distribution (DSD) does not appear to be predominantly determined by updraught strength, especially beyond the 20 µm range. The aerosol conditions play a major role in that case. However, the updraughts modulate the DSD concentrations and are responsible for the vertical transport of water in the cloud. The larger droplets found in background clouds are associated with weak water vapour competition and a bimodal distribution of droplet sizes in the lower levels of the cloud, which enables an earlier initiation of the collision–coalescence process. This study shows that the pollution produced by Manaus significantly affects warm-phase microphysical properties of the surrounding clouds by changing the initial DSD formation. The corresponding effects on ice-phase processes and precipitation formation will be the focus of future endeavours.« less

Assessing the Amazon Cloud Suitability for CLARREO's Computational Needs

NASA Technical Reports Server (NTRS)

Goldin, Daniel; Vakhnin, Andrei A.; Currey, Jon C.

2015-01-01

In this document we compare the performance of the Amazon Web Services (AWS), also known as Amazon Cloud, with the CLARREO (Climate Absolute Radiance and Refractivity Observatory) cluster and assess its suitability for computational needs of the CLARREO mission. A benchmark executable to process one month and one year of PARASOL (Polarization and Anistropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) data was used. With the optimal AWS configuration, adequate data-processing times, comparable to the CLARREO cluster, were found. The assessment of alternatives to the CLARREO cluster continues and several options, such as a NASA-based cluster, are being considered.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jian; Krejci, Radovan; Giangrande, Scott

The nucleation of atmospheric vapours is an important source of new aerosol particles that can subsequently grow to form cloud condensation nuclei in the atmosphere. Most field studies of atmospheric aerosols over continents are influenced by atmospheric vapours of anthropogenic origin and, in consequence, aerosol processes in pristine, terrestrial environments remain poorly understood. The Amazon rainforest is one of the few continental regions where aerosol particles and their precursors can be studied under near-natural conditions, but the origin of small aerosol particles that grow into cloud condensation nuclei in the Amazon boundary layer remains unclear. Here we present aircraft- andmore » ground-based measurements under clean conditions during the wet season in the central Amazon basin. We find that high concentrations of small aerosol particles (with diameters of less than 50 nanometres) in the lower free troposphere are transported from the free troposphere into the boundary layer during precipitation events by strong convective downdrafts and weaker downward motions in the trailing stratiform region. Lastly, this rapid vertical transport can help to maintain the population of particles in the pristine Amazon boundary layer, and may therefore influence cloud properties and climate under natural conditions.« less

Libraries in the Cloud: Making a Case for Google and Amazon

ERIC Educational Resources Information Center

Buck, Stephanie

2009-01-01

As news outlets create headlines such as "A Cloud & A Prayer," "The Cloud Is the Computer," and "Leveraging Clouds to Make You More Efficient," many readers have been left with cloud confusion. Many definitions exist for cloud computing, and a uniform definition is hard to find. In its most basic form, cloud…

Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5)

NASA Astrophysics Data System (ADS)

Martin, S. T.; Artaxo, P.; Machado, L. A. T.; Manzi, A. O.; Souza, R. A. F.; Schumacher, C.; Wang, J.; Andreae, M. O.; Barbosa, H. M. J.; Fan, J.; Fisch, G.; Goldstein, A. H.; Guenther, A.; Jimenez, J. L.; Pöschl, U.; Silva Dias, M. A.; Smith, J. N.; Wendisch, M.

2016-04-01

The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin for 2 years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, the pollution plume from the Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as the introduction to the special issue of GoAmazon2014/5, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G-1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the 2-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. The G-1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year-round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This introduction also organizes information relevant to many papers in the special issue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorologies relevant to the two IOPs are presented. Regional and long-range transport of emissions during the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5 as presented in this introduction, research articles including thematic overview articles are anticipated in this special issue to describe the detailed results and findings of the GoAmazon2014/5 Experiment.

The effect of atmospheric aerosol particles and clouds on net ecosystem exchange in the Amazon

NASA Astrophysics Data System (ADS)

Cirino, G. G.; Souza, R. A. F.; Adams, D. K.; Artaxo, P.

2014-07-01

Carbon cycling in the Amazon is closely linked to atmospheric processes and climate in the region as a consequence of the strong coupling between the atmosphere and biosphere. This work examines the effects of changes in net radiation due to atmospheric aerosol particles and clouds on the net ecosystem exchange (NEE) of CO2 in the Amazon region. Some of the major environmental factors affecting the photosynthetic activity of plants, such as air temperature and relative humidity, were also examined. An algorithm for clear-sky irradiance was developed and used to determine the relative irradiance, f, which quantifies the percentage of solar radiation absorbed and scattered due to atmospheric aerosol particles and clouds. Aerosol optical depth (AOD) was calculated from irradiances measured with the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, onboard the Terra and Aqua satellites, and was validated with ground-based AOD measurements from AERONET (Aerosol Robotic Network) sun photometers. Carbon fluxes were measured using eddy covariance technique at the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) flux towers. Two sites were studied: the Jaru Biological Reserve (RBJ), located in Rondonia, and the Cuieiras Biological Reserve at the K34 LBA tower (located in a preserved region in the central Amazon). Analysis was performed continuously from 1999 to 2009 at K34 and from 1999 to 2002 at RBJ, and includes wet, dry and transition seasons. In the Jaru Biological Reserve, a 29% increase in carbon uptake (NEE) was observed when the AOD ranged from 0.10 to 1.5 at 550 nm. In the Cuieiras Biological Reserve, the aerosol effect on NEE was smaller, accounting for an approximate 20% increase in NEE. High aerosol loading (AOD above 3 at 550 nm) or high cloud cover leads to reductions in solar flux and strong decreases in photosynthesis up to the point where NEE approaches zero. The observed increase in NEE is attributed to an enhancement (~50%) in the diffuse fraction of photosynthetic active radiation (PAR). The enhancement in diffuse PAR can be done through increases in aerosols and/or clouds. In the present study, it was not possible to separate these two components. Significant changes in air temperature and relative humidity resulting from changes in solar radiation fluxes under high aerosol loading were also observed at both sites. Considering the long-range transport of aerosols in the Amazon, the observed changes in NEE for these two sites may occur over large areas in the Amazon, significantly altering the carbon balance in the largest rainforest in the world.

Far-Reaching Impacts of African Dust- A Calipso Perspective

NASA Technical Reports Server (NTRS)

Yu, Hongbin; Chin, Mian; Yuan, Tianle; Bian, Huisheng; Prospero, Joseph; Omar, Ali; Remer, Lorraine; Winker, David; Yang, Yuekui; Zhang, Yan;

Development of Data-Assimilation-Quality MODIS and MISR Over Ocean Aerosol Products

DTIC Science & Technology

2009-08-01

fires to rainforests in order to clear land for agricultural purposes (Koren et al., 2004). During the Northern Hemisphere summer (June, July...atmosphere and dissipate clouds (Koren et al., 2004). Other studies have found that biomass-burning-generated aerosols over the Amazon basin could...Remer, and J. V. Martins, (2004), Measurement of the Effect of Amazon Smoke on Inhibition of Cloud Formation, Science 27 February 2004:Vol. 303. no

Photochemical ozone production in tropical squall line convection during NASA Global Tropospheric Experiment/Amazon Boundary Layer Experiment 2A

NASA Technical Reports Server (NTRS)

Pickering, Kenneth E.; Thompson, Anne M.; Tao, Wei-Kuo; Simpson, Joanne; Scala, John R.

1991-01-01

The role of convection was examined in trace gas transport and ozone production in a tropical dry season squall line sampled on August 3, 1985, during NASA Global Tropospheric Experiment/Amazon Boundary Layer Experiment 2A (NASA GTE/ABLE 2A) in Amazonia, Brazil. Two types of analyses were performed. Transient effects within the cloud are examined with a combination of two-dimensional cloud and one-dimensional photochemical modeling. Tracer analyses using the cloud model wind fields yield a series of cross sections of NO(x), CO, and O3 distribution during the lifetime of the cloud; these fields are used in the photochemical model to compute the net rate of O3 production. At noon, when the cloud was mature, the instantaneous ozone production potential in the cloud is between 50 and 60 percent less than in no-cloud conditions due to reduced photolysis and cloud scavenging of radicals. Analysis of cloud inflows and outflows is used to differentiate between air that is undisturbed and air that has been modified by the storm. These profiles are used in the photochemical model to examine the aftereffects of convective redistribution in the 24-hour period following the storm. Total tropospheric column O3 production changed little due to convection because so little NO(x) was available in the lower troposphere. However, the integrated O3 production potential in the 5- to 13-km layer changed from net destruction to net production as a result of the convection. The conditions of the August 3, 1985, event may be typical of the early part of the dry season in Amazonia, when only minimal amounts of pollution from biomass burning have been transported into the region.

The Green Ocean Amazon Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols, Clouds, and Rainfall over the Rain Forest

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, S. T.; Artaxo, P.; Machado, L.

The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment took place around the urban region of Manaus in central Amazonia across two years. The urban pollution plume was used to study the susceptibility of gases, aerosols, clouds, and rainfall to human activities in a tropical environment. Many aspects of air quality, weather, terrestrial ecosystems, and climate work differently in the tropics than in the more thoroughly studied USA, employed an unparalleled suite of measurements at nine ground sites and onboard two aircraft to investigate the flow of background air into Manaus, the emissions into the air over themore » city, and the advection of the pollution downwind of the city. Herein, to visualize this train of processes and its effects, observations aboard a low-flying aircraft are presented. Comparative measurements within and adjacent to the plume followed the emissions of biogenic volatile organic carbon compounds (BVOCs) from the tropical forest, their transformations by the atmospheric oxidant cycle, alterations of this cycle by the influence of the pollutants, transformations of the chemical products into aerosol particles, the relationship of these particles to cloud condensation nuclei (CCN) activity, and the differences in cloud properties and rainfall for background compared to polluted conditions. The observations of the GoAmazon2014/5 experiment illustrate how the hydrologic cycle, radiation balance, and carbon recycling may be affected by present-day as well as future economic development and pollution over the Amazonian tropical forest.« less

Electron-Cloud Build-Up: Theory and Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Furman, M. A.

We present a broad-brush survey of the phenomenology, history and importance of the electron-cloud effect (ECE). We briefly discuss the simulation techniques used to quantify the electron-cloud (EC) dynamics. Finally, we present in more detail an effective theory to describe the EC density build-up in terms of a few effective parameters. For further details, the reader is encouraged to refer to the proceedings of many prior workshops, either dedicated to EC or with significant EC contents, including the entire 'ECLOUD' series. In addition, the proceedings of the various flavors of Particle Accelerator Conferences contain a large number of EC-related publications.more » The ICFA Beam Dynamics Newsletter series contains one dedicated issue, and several occasional articles, on EC. An extensive reference database is the LHC website on EC.« less

Impacts of the Manaus pollution plume on the microphysical properties of Amazonian warm-phase clouds in the wet season

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cecchini, Micael A.; Machado, Luiz A. T.; Comstock, Jennifer M.

The remote atmosphere over the Amazon can be similar to oceanic regions in terms of aerosol conditions and cloud type formations. This is especially true during the wet season. The main aerosol-related disturbances over the Amazon have both natural sources, such as dust transport from Africa, and anthropogenic sources, such as biomass burning or urban pollution. The present work considers the impacts of the latter on the microphysical properties of warm-phase clouds by analyzing observations of the interactions between the Manaus pollution plume and its surroundings, as part of the GoAmazon2014/5 Experiment. The analyzed period corresponds to the wet seasonmore » (specifically from February to March 2014 and corresponding to the first Intensive Operating Period (IOP1) of GoAmazon2014/5). The droplet size distributions reported are in the range 1 µm ≤ D ≤ 50 µm in order to capture the processes leading up to the precipitation formation. The wet season largely presents a clean background atmosphere characterized by frequent rain showers. As such, the contrast between background clouds and those affected by the Manaus pollution can be observed and detailed. The focus is on the characteristics of the initial microphysical properties in cumulus clouds predominantly at their early stages. The pollution-affected clouds are found to have smaller effective diameters and higher droplet number concentrations. The differences range from 10 to 40 % for the effective diameter and are as high as 1000% for droplet concentration for the same vertical levels. The growth rates of droplets with altitude are slower for pollution-affected clouds (2.90 compared to 5.59 µm km –1), as explained by the absence of bigger droplets at the onset of cloud development. Clouds under background conditions have higher concentrations of larger droplets (> 20 µm) near the cloud base, which would contribute significantly to the growth rates through the collision–coalescence process. The overall shape of the droplet size distribution (DSD) does not appear to be predominantly determined by updraught strength, especially beyond the 20 µm range. The aerosol conditions play a major role in that case. However, the updraughts modulate the DSD concentrations and are responsible for the vertical transport of water in the cloud. The larger droplets found in background clouds are associated with weak water vapour competition and a bimodal distribution of droplet sizes in the lower levels of the cloud, which enables an earlier initiation of the collision–coalescence process. This paper shows that the pollution produced by Manaus significantly affects warm-phase microphysical properties of the surrounding clouds by changing the initial DSD formation. The corresponding effects on ice-phase processes and precipitation formation will be the focus of future endeavors.« less

Impacts of the Manaus pollution plume on the microphysical properties of Amazonian warm-phase clouds in the wet season

DOE PAGES

Cecchini, Micael A.; Machado, Luiz A. T.; Comstock, Jennifer M.; ...

2016-06-09

The remote atmosphere over the Amazon can be similar to oceanic regions in terms of aerosol conditions and cloud type formations. This is especially true during the wet season. The main aerosol-related disturbances over the Amazon have both natural sources, such as dust transport from Africa, and anthropogenic sources, such as biomass burning or urban pollution. The present work considers the impacts of the latter on the microphysical properties of warm-phase clouds by analyzing observations of the interactions between the Manaus pollution plume and its surroundings, as part of the GoAmazon2014/5 Experiment. The analyzed period corresponds to the wet seasonmore » (specifically from February to March 2014 and corresponding to the first Intensive Operating Period (IOP1) of GoAmazon2014/5). The droplet size distributions reported are in the range 1 µm ≤ D ≤ 50 µm in order to capture the processes leading up to the precipitation formation. The wet season largely presents a clean background atmosphere characterized by frequent rain showers. As such, the contrast between background clouds and those affected by the Manaus pollution can be observed and detailed. The focus is on the characteristics of the initial microphysical properties in cumulus clouds predominantly at their early stages. The pollution-affected clouds are found to have smaller effective diameters and higher droplet number concentrations. The differences range from 10 to 40 % for the effective diameter and are as high as 1000% for droplet concentration for the same vertical levels. The growth rates of droplets with altitude are slower for pollution-affected clouds (2.90 compared to 5.59 µm km –1), as explained by the absence of bigger droplets at the onset of cloud development. Clouds under background conditions have higher concentrations of larger droplets (> 20 µm) near the cloud base, which would contribute significantly to the growth rates through the collision–coalescence process. The overall shape of the droplet size distribution (DSD) does not appear to be predominantly determined by updraught strength, especially beyond the 20 µm range. The aerosol conditions play a major role in that case. However, the updraughts modulate the DSD concentrations and are responsible for the vertical transport of water in the cloud. The larger droplets found in background clouds are associated with weak water vapour competition and a bimodal distribution of droplet sizes in the lower levels of the cloud, which enables an earlier initiation of the collision–coalescence process. This paper shows that the pollution produced by Manaus significantly affects warm-phase microphysical properties of the surrounding clouds by changing the initial DSD formation. The corresponding effects on ice-phase processes and precipitation formation will be the focus of future endeavors.« less

NASA Enterprise Managed Cloud Computing (EMCC): Delivering an Initial Operating Capability (IOC) for NASA use of Commercial Infrastructure-as-a-Service (IaaS)

NASA Technical Reports Server (NTRS)

O'Brien, Raymond

2017-01-01

In 2016, Ames supported the NASA CIO in delivering an initial operating capability for Agency use of commercial cloud computing. This presentation provides an overview of the project, the services approach followed, and the major components of the capability that was delivered. The presentation is being given at the request of Amazon Web Services to a contingent representing the Brazilian Federal Government and Defense Organization that is interested in the use of Amazon Web Services (AWS). NASA is currently a customer of AWS and delivered the Initial Operating Capability using AWS as its first commercial cloud provider. The IOC, however, designed to also support other cloud providers in the future.

Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

2015-12-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, MODIS, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. HySDS is a Hybrid-Cloud Science Data System that has been developed and applied under NASA AIST, MEaSUREs, and ACCESS grants. HySDS uses the SciFlow workflow engine to partition analysis workflows into parallel tasks (e.g. segmenting by time or space) that are pushed into a durable job queue. The tasks are "pulled" from the queue by worker Virtual Machines (VM's) and executed in an on-premise Cloud (Eucalyptus or OpenStack) or at Amazon in the public Cloud or govCloud. In this way, years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the transferred data. We are using HySDS to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a MEASURES grant. We will present the architecture of HySDS, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. Our system demonstrates how one can pull A-Train variables (Levels 2 & 3) on-demand into the Amazon Cloud, and cache only those variables that are heavily used, so that any number of compute jobs can be executed "near" the multi-sensor data. Decade-long, multi-sensor studies can be performed without pre-staging data, with the researcher paying only his own Cloud compute bill.

Green Ocean Amazon 2014/15 Manaus Pollution Study Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keutsch, Frank N.

This work was part of the larger Green Ocean Amazon 2014/15 (GOAmazon 2014/15) experiment, which extended through the wet and dry seasons from January 2014 through December 2015 and which took place around the urban region of Manaus, Brazil in central Amazonia. This work was conducted as part of this experiment at the main U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility ground research site “T3” circa 100 km west of Manaus during two intensive operational periods, “IOP1” and “IOP2” (February 1 to March 31, 2014, and August 15 to October 15, 2014, respectively). Funding formore » this work was provided by the National Science Foundation AGS 1321987/1628491. The GoAmazon experiment was designed to enable the study of how aerosols and surface fluxes influence cloud cycles under clean conditions, as well as how aerosol and cloud life cycles, including cloud-aerosol-precipitation interactions, are influenced by pollutant outflow from a tropical megacity. These observations provide a data set vital to constrain tropical rain forest model parameterizations for organic aerosols, cloud and convection schemes, and terrestrial vegetation components and how these are perturbed by pollution. Research objectives specific to this work and the T3 ground site included studies of how outflow of pollution from Manaus modulated the photochemically driven conversion of emitted precursors to aerosol precursors and aerosol.« less

Campaign datasets for Observations and Modeling of the Green Ocean Amazon (GOAMAZON)

DOE Data Explorer

Martin,Scot; Mei,Fan; Alexander,Lizabeth; Artaxo,Paulo; Barbosa,Henrique; Bartholomew,Mary Jane; Biscaro,Thiago; Buseck,Peter; Chand,Duli; Comstock,Jennifer; Dubey,Manvendra; Godstein,Allen; Guenther,Alex; Hubbe,John; Jardine,Kolby; Jimenez,Jose-Luis; Kim,Saewung; Kuang,Chongai; Laskin,Alexander; Long,Chuck; Paralovo,Sarah; Petaja,Tuukka; Powers,Heath; Schumacher,Courtney; Sedlacek,Arthur; Senum,Gunnar; Smith,James; Shilling,John; Springston,Stephen; Thayer,Mitchell; Tomlinson,Jason; Wang,Jian; Xie,Shaocheng

2016-05-30

The hydrologic cycle of the Amazon Basin is one of the primary heat engines of the Southern Hemisphere. Any accurate climate model must succeed in a good description of the Basin, both in its natural state and in states perturbed by regional and global human activities. At the present time, however, tropical deep convection in a natural state is poorly understood and modeled, with insufficient observational data sets for model constraint. Furthermore, future climate scenarios resulting from human activities globally show the possible drying and the eventual possible conversion of rain forest to savanna in response to global climate change. Based on our current state of knowledge, the governing conditions of this catastrophic change are not defined. Human activities locally, including the economic development activities that are growing the population and the industry within the Basin, also have the potential to shift regional climate, most immediately by an increment in aerosol number and mass concentrations, and the shift is across the range of values to which cloud properties are most sensitive. The ARM Climate Research Facility in the Amazon Basin seeks to understand aerosol and cloud life cycles, particularly the susceptibility to cloud aerosol precipitation interactions, within the Amazon Basin.

A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gittens, Alex; Kottalam, Jey; Yang, Jiyan

We investigate the performance and scalability of the randomized CX low-rank matrix factorization and demonstrate its applicability through the analysis of a 1TB mass spectrometry imaging (MSI) dataset, using Apache Spark on an Amazon EC2 cluster, a Cray XC40 system, and an experimental Cray cluster. We implemented this factorization both as a parallelized C implementation with hand-tuned optimizations and in Scala using the Apache Spark high-level cluster computing framework. We obtained consistent performance across the three platforms: using Spark we were able to process the 1TB size dataset in under 30 minutes with 960 cores on all systems, with themore » fastest times obtained on the experimental Cray cluster. In comparison, the C implementation was 21X faster on the Amazon EC2 system, due to careful cache optimizations, bandwidth-friendly access of matrices and vector computation using SIMD units. We report these results and their implications on the hardware and software issues arising in supporting data-centric workloads in parallel and distributed environments.« less

Autonomic Management of Application Workflows on Hybrid Computing Infrastructure

DOE PAGES

Kim, Hyunjoo; el-Khamra, Yaakoub; Rodero, Ivan; ...

2011-01-01

In this paper, we present a programming and runtime framework that enables the autonomic management of complex application workflows on hybrid computing infrastructures. The framework is designed to address system and application heterogeneity and dynamics to ensure that application objectives and constraints are satisfied. The need for such autonomic system and application management is becoming critical as computing infrastructures become increasingly heterogeneous, integrating different classes of resources from high-end HPC systems to commodity clusters and clouds. For example, the framework presented in this paper can be used to provision the appropriate mix of resources based on application requirements and constraints.more » The framework also monitors the system/application state and adapts the application and/or resources to respond to changing requirements or environment. To demonstrate the operation of the framework and to evaluate its ability, we employ a workflow used to characterize an oil reservoir executing on a hybrid infrastructure composed of TeraGrid nodes and Amazon EC2 instances of various types. Specifically, we show how different applications objectives such as acceleration, conservation and resilience can be effectively achieved while satisfying deadline and budget constraints, using an appropriate mix of dynamically provisioned resources. Our evaluations also demonstrate that public clouds can be used to complement and reinforce the scheduling and usage of traditional high performance computing infrastructure.« less

Introduction: Observations and modeling of the Green Ocean Amazon (GoAmazon2014/5)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, S. T.; Artaxo, P.; Machado, L. A. T.

The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin for 2 years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, the pollution plume from themore » Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as the introduction to the special issue of GoAmazon2014/5, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G-1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the 2-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. In addition, the G-1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year-round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This introduction also organizes information relevant to many papers in the special issue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorologies relevant to the two IOPs are presented. Regional and long-range transport of emissions during the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5 as presented in this introduction, research articles including thematic overview articles are anticipated in this special issue to describe the detailed results and findings of the GoAmazon2014/5 Experiment.« less

Introduction: Observations and modeling of the Green Ocean Amazon (GoAmazon2014/5)

DOE PAGES

Martin, S. T.; Artaxo, P.; Machado, L. A. T.; ...

2016-04-19

The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin for 2 years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, the pollution plume from themore » Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as the introduction to the special issue of GoAmazon2014/5, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G-1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the 2-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. In addition, the G-1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year-round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This introduction also organizes information relevant to many papers in the special issue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorologies relevant to the two IOPs are presented. Regional and long-range transport of emissions during the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5 as presented in this introduction, research articles including thematic overview articles are anticipated in this special issue to describe the detailed results and findings of the GoAmazon2014/5 Experiment.« less

Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity from Observations

NASA Technical Reports Server (NTRS)

Marvel, Kate; Pincus, Robert; Schmidt, Gavin A.; Miller, Ron L.

2018-01-01

An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2-radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long-term sensitivity.

Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity From Observations

NASA Astrophysics Data System (ADS)

Marvel, Kate; Pincus, Robert; Schmidt, Gavin A.; Miller, Ron L.

2018-02-01

An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2 radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long-term sensitivity.

Folding Proteins at 500 ns/hour with Work Queue.

PubMed

Abdul-Wahid, Badi'; Yu, Li; Rajan, Dinesh; Feng, Haoyun; Darve, Eric; Thain, Douglas; Izaguirre, Jesús A

2012-10-01

Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour.

Folding Proteins at 500 ns/hour with Work Queue

PubMed Central

Abdul-Wahid, Badi’; Yu, Li; Rajan, Dinesh; Feng, Haoyun; Darve, Eric; Thain, Douglas; Izaguirre, Jesús A.

2014-01-01

Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour. PMID:25540799

Mouths of the Amazon River, Brazil, South America

NASA Image and Video Library

1992-08-08

STS046-80-009 (31 July-8 Aug. 1992) --- A view of the mouth of the Amazon River and the Amazon Delta shows a large sediment plume expanding outward into the Atlantic Ocean. The sediment plume can be seen hugging the coast north of the Delta. This is caused by the west-northwest flowing Guyana Current. The large island of Marajo is partially visible through the clouds.

Using Cloud-based Storage Technologies for Earth Science Data

NASA Astrophysics Data System (ADS)

Michaelis, A.; Readey, J.; Votava, P.

2016-12-01

Cloud based infrastructure may offer several key benefits of scalability, built in redundancy and reduced total cost of ownership as compared with a traditional data center approach. However, most of the tools and software systems developed for NASA data repositories were not developed with a cloud based infrastructure in mind and do not fully take advantage of commonly available cloud-based technologies. Object storage services are provided through all the leading public (Amazon Web Service, Microsoft Azure, Google Cloud, etc.) and private (Open Stack) clouds, and may provide a more cost-effective means of storing large data collections online. We describe a system that utilizes object storage rather than traditional file system based storage to vend earth science data. The system described is not only cost effective, but shows superior performance for running many different analytics tasks in the cloud. To enable compatibility with existing tools and applications, we outline client libraries that are API compatible with existing libraries for HDF5 and NetCDF4. Performance of the system is demonstrated using clouds services running on Amazon Web Services.

Biomedical cloud computing with Amazon Web Services.

PubMed

Fusaro, Vincent A; Patil, Prasad; Gafni, Erik; Wall, Dennis P; Tonellato, Peter J

2011-08-01

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.

Cloud-based Web Services for Near-Real-Time Web access to NPP Satellite Imagery and other Data

NASA Astrophysics Data System (ADS)

Evans, J. D.; Valente, E. G.

2010-12-01

We are building a scalable, cloud computing-based infrastructure for Web access to near-real-time data products synthesized from the U.S. National Polar-Orbiting Environmental Satellite System (NPOESS) Preparatory Project (NPP) and other geospatial and meteorological data. Given recent and ongoing changes in the the NPP and NPOESS programs (now Joint Polar Satellite System), the need for timely delivery of NPP data is urgent. We propose an alternative to a traditional, centralized ground segment, using distributed Direct Broadcast facilities linked to industry-standard Web services by a streamlined processing chain running in a scalable cloud computing environment. Our processing chain, currently implemented on Amazon.com's Elastic Compute Cloud (EC2), retrieves raw data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and synthesizes data products such as Sea-Surface Temperature, Vegetation Indices, etc. The cloud computing approach lets us grow and shrink computing resources to meet large and rapid fluctuations (twice daily) in both end-user demand and data availability from polar-orbiting sensors. Early prototypes have delivered various data products to end-users with latencies between 6 and 32 minutes. We have begun to replicate machine instances in the cloud, so as to reduce latency and maintain near-real time data access regardless of increased data input rates or user demand -- all at quite moderate monthly costs. Our service-based approach (in which users invoke software processes on a Web-accessible server) facilitates access into datasets of arbitrary size and resolution, and allows users to request and receive tailored and composite (e.g., false-color multiband) products on demand. To facilitate broad impact and adoption of our technology, we have emphasized open, industry-standard software interfaces and open source software. Through our work, we envision the widespread establishment of similar, derived, or interoperable systems for processing and serving near-real-time data from NPP and other sensors. A scalable architecture based on cloud computing ensures cost-effective, real-time processing and delivery of NPP and other data. Access via standard Web services maximizes its interoperability and usefulness.

In situ chemical composition measurement of individual cloud residue particles at a mountain site, southern China

NASA Astrophysics Data System (ADS)

Lin, Qinhao; Zhang, Guohua; Peng, Long; Bi, Xinhui; Wang, Xinming; Brechtel, Fred J.; Li, Mei; Chen, Duohong; Peng, Ping'an; Sheng, Guoying; Zhou, Zhen

2017-07-01

To investigate how atmospheric aerosol particles interact with chemical composition of cloud droplets, a ground-based counterflow virtual impactor (GCVI) coupled with a real-time single-particle aerosol mass spectrometer (SPAMS) was used to assess the chemical composition and mixing state of individual cloud residue particles in the Nanling Mountains (1690 m a. s. l. ), southern China, in January 2016. The cloud residues were classified into nine particle types: aged elemental carbon (EC), potassium-rich (K-rich), amine, dust, Pb, Fe, organic carbon (OC), sodium-rich (Na-rich) and Other. The largest fraction of the total cloud residues was the aged EC type (49.3 %), followed by the K-rich type (33.9 %). Abundant aged EC cloud residues that mixed internally with inorganic salts were found in air masses from northerly polluted areas. The number fraction (NF) of the K-rich cloud residues increased within southwesterly air masses from fire activities in Southeast Asia. When air masses changed from northerly polluted areas to southwesterly ocean and livestock areas, the amine particles increased from 0.2 to 15.1 % of the total cloud residues. The dust, Fe, Pb, Na-rich and OC particle types had a low contribution (0.5-4.1 %) to the total cloud residues. Higher fraction of nitrate (88-89 %) was found in the dust and Na-rich cloud residues relative to sulfate (41-42 %) and ammonium (15-23 %). Higher intensity of nitrate was found in the cloud residues relative to the ambient particles. Compared with nonactivated particles, nitrate intensity decreased in all cloud residues except for dust type. To our knowledge, this study is the first report on in situ observation of the chemical composition and mixing state of individual cloud residue particles in China.

Analysis of smoke and cloud impact on seasonal and interannual variations in normalized difference vegetation index in Amazon

NASA Astrophysics Data System (ADS)

Kobayashi, H.; Dye, D. G.

2004-12-01

Normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) is a unique measurement of long-term variations in global vegetation dynamics. The NDVI data have been used for the detection of the seasonal and interannual variations in vegetation. However, as reported in several studies, NDVI decreases with the increase in clouds and/or smoke aerosol contaminated in the pixels. This study assesses the smoke and clouds effect on long-term Global Inventory Modeling and Mapping Studies (GIMMS) and Pathfinder AVHRR Land (PAL) NDVI data in Amazon. This knowledge will help developing the correction method in the tropics in the future. To assess the smoke and cloud effects on GIMMS and PAL, we used another satellite-derived data sets; NDVI derived from SPOT/VEGETATION (VGT) data and Aerosol Index (AI) derived from Total Ozone Mapping Spectrometer (TOMS). Since April 1998, VGT has measured the earth surface globally including in Amazon. The advantage of the VGT is that it has blue channel where the smoke and cloud can be easily detected. By analyzing the VGT NDVI and comparing with the AVHRR-based NDVI, we inferred smoke and cloud effect on the AVHRR-based NDVI. From the results of the VGT analysis, we found the large NDVI seasonality in South and Southeastern Amazon. In these areas, the NDVI gradually increased from April to July and decreased from August to October. However the sufficient NDVI data were not existed from August to November when the smoke and cloud pixels were masked using blue reflectance. Thus it is said that the smoke and clouds mainly cause the large decreases in NDVI between August and November and NDVI has little vegetation signature in these months. Also we examined the interannual variations in NDVI and smoke aerosol. Then the decrease in NDVI is well consistent with the increase in the increase in AI. Our results suggest that the months between April and July are the most reliable season to monitor the vegetation.

Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5)

NASA Astrophysics Data System (ADS)

Martin, S. T.; Artaxo, P.; Machado, L. A. T.; Manzi, A. O.; Souza, R. A. F.; Schumacher, C.; Wang, J.; Andreae, M. O.; Barbosa, H. M. J.; Fan, J.; Fisch, G.; Goldstein, A. H.; Guenther, A.; Jimenez, J. L.; Pöschl, U.; Silva Dias, M. A.; Smith, J. N.; Wendisch, M.

2015-11-01

The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin during two years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, the pollution plume from the Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as the Introduction to the GoAmazon2014/5 Special Issue, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the two-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. The G1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This Introduction also organizes information relevant to many papers in the Special Issue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorologies relevant to the two IOPs are presented. Regional and long-range transport of emissions during the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5, as presented herein in this Introduction, research articles published in this Special Issue are anticipated in the near future to describe the detailed results and findings of the GoAmazon2014/5 Experiment.

Electron Cloud Effects in Accelerators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Furman, M.A.

Abstract We present a brief summary of various aspects of the electron-cloud effect (ECE) in accelerators. For further details, the reader is encouraged to refer to the proceedings of many prior workshops, either dedicated to EC or with significant EC contents, including the entire ?ECLOUD? series [1?22]. In addition, the proceedings of the various flavors of Particle Accelerator Conferences [23] contain a large number of EC-related publications. The ICFA Beam Dynamics Newsletter series [24] contains one dedicated issue, and several occasional articles, on EC. An extensive reference database is the LHC website on EC [25].

Modeling Optical and Radiative Properties of Clouds Constrained with CARDEX Observations

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Praveen, P. S.; Ramanathan, V.

2013-12-01

Carbonaceous aerosols (CA) have important effects on climate by directly absorbing solar radiation and indirectly changing cloud properties. These particles tend to be a complex mixture of graphitic carbon and organic compounds. The graphitic component, called as elemental carbon (EC), is characterized by significant absorption of solar radiation. Recent studies showed that organic carbon (OC) aerosols absorb strongly near UV region, and this faction is known as Brown Carbon (BrC). The indirect effect of CA can occur in two ways, first by changing the thermal structure of the atmosphere which further affects dynamical processes governing cloud life cycle; secondly, by acting as cloud condensation nuclei (CCN) that can change cloud radiative properties. In this work, cloud optical properties have been numerically estimated by accounting for CAEDEX (Cloud Aerosol Radiative Forcing Dynamics Experiment) observed cloud parameters and the physico-chemical and optical properties of aerosols. The aerosol inclusions in the cloud drop have been considered as core shell structure with core as EC and shell comprising of ammonium sulfate, ammonium nitrate, sea salt and organic carbon (organic acids, OA and brown carbon, BrC). The EC/OC ratio of the inclusion particles have been constrained based on observations. Moderate and heavy pollution events have been decided based on the aerosol number and BC concentration. Cloud drop's co-albedo at 550nm was found nearly identical for pure EC sphere inclusions and core-shell inclusions with all non-absorbing organics in the shell. However, co-albedo was found to increase for the drop having all BrC in the shell. The co-albedo of a cloud drop was found to be the maximum for all aerosol present as interstitial compare to 50% and 0% inclusions existing as interstitial aerosols. The co-albedo was found to be ~ 9.87e-4 for the drop with 100% inclusions existing as interstitial aerosols externally mixed with micron size mineral dust with 2% hematite content. The cloud spectral optical properties and the radiative properties for the aforesaid cases during CARDEX observations will be discussed in detail.

CCN numerical simulations for the GoAmazon with the OLAM model

NASA Astrophysics Data System (ADS)

Ramos-da-Silva, R.; Haas, R.; Barbosa, H. M.; Machado, L.

2015-12-01

Manaus is a large city in the center of the Amazon rainforest. The GoAmazon field project is exploring the region through various data collection and modeling to investigate in impacts of the urban polluted plume on the surrounding pristine areas. In this study a numerical model was applied to simulate the atmospheric dynamics and the Cloud Condensation Nucleai (CCN) concentrations evolution. Simulations with and without the urban plume was performed to identify its dynamics and local impacts. The results show that the land surface characteristics has important hole on the CCN distribution and rainfall over the region. At the south of Manaus the atmospheric dynamics is dominated by the cloud streets that are aligned with the trade winds and the Amazon River. At the north of Manaus, the Negro River produces the advection of a more stable atmosphere causing a higher CCN concentration on the boundary layer. Assuming a local high CCN concentration at the Manaus boundary layer region, the simulations show that the land-atmosphere interaction sets important dynamics on the plume. The model shows that the CCN plume moves along with the flow towards southwest of Manaus following the cloud streets and the river direction having the highest concentrations over the most stable water surface regions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Scot T.

The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin during two years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, themore » pollution plume from the Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as Introduction to the GoAmazon2014/5 Special Issue, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the two-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. The G1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This Introduction also organizes information relevant to many papers in the Special Issue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorology relevant to the two IOPs is presented. Regional and long-range transport of emissions for the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5, as presented herein in this Introduction, research articles published in this Special Issue are anticipated in the near future to describe the detailed results and findings of GoAmazon2014/5.« less

Electrification of precipitating systems over the Amazon: Physical processes of thunderstorm development

NASA Astrophysics Data System (ADS)

Albrecht, Rachel I.; Morales, Carlos A.; Silva Dias, Maria A. F.

2011-04-01

This study investigated the physical processes involved in the development of thunderstorms over southwestern Amazon by hypothesizing causalities for the observed cloud-to-ground lightning variability and the local environmental characteristics. Southwestern Amazon experiences every year a large variety of environmental factors, such as the gradual increase in atmospheric moisture, extremely high pollution due to biomass burning, and intense deforestation, which directly affects cloud development by differential surface energy partition. In the end of the dry period it was observed higher percentages of positive cloud-to-ground (+CG) lightning due to a relative increase in +CG dominated thunderstorms (positive thunderstorms). Positive (negative) thunderstorms initiated preferentially over deforested (forest) areas with higher (lower) cloud base heights, shallower (deeper) warm cloud depths, and higher (lower) convective potential available energy. These features characterized the positive (negative) thunderstorms as deeper (relatively shallower) clouds, stronger (relatively weaker) updrafts with enhanced (decreased) mixed and cold vertically integrated liquid. No significant difference between thunderstorms (negative and positive) and nonthunderstorms were observed in terms of atmospheric pollution, once the atmosphere was overwhelmed by pollution leading to an updraft-limited regime. However, in the wet season both negative and positive thunderstorms occurred during periods of relatively higher aerosol concentration and differentiated size distributions, suggesting an aerosol-limited regime where cloud electrification could be dependent on the aerosol concentration to suppress the warm and enhance the ice phase. The suggested causalities are consistent with the invoked hypotheses, but they are not observed facts; they are just hypotheses based on plausible physical mechanisms.

Spectrometry of Pasture Condition and Biogeochemistry in the Central Amazon

NASA Technical Reports Server (NTRS)

Asner, Gregory P.; Townsend, Alan R.; Bustamante, Mercedes M. C.

1999-01-01

Regional analyses of Amazon cattle pasture biogeochemistry are difficult due to the complexity of human, edaphic, biotic and climatic factors and persistent cloud cover in satellite observations. We developed a method to estimate key biophysical properties of Amazon pastures using hyperspectral reflectance data and photon transport inverse modeling. Remote estimates of live and senescent biomass were strongly correlated with plant-available forms of soil phosphorus and calcium. These results provide a basis for monitoring pasture condition and biogeochemistry in the Amazon Basin using spaceborne hyperspectral sensors.

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants.

PubMed

Elshazly, Hatem; Souilmi, Yassine; Tonellato, Peter J; Wall, Dennis P; Abouelhoda, Mohamed

2017-01-20

Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices. In this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers. MC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

Bootstrapping and Maintaining Trust in the Cloud

DTIC Science & Technology

2016-12-01

simultaneous cloud nodes. 1. INTRODUCTION The proliferation and popularity of infrastructure-as-a- service (IaaS) cloud computing services such as...Amazon Web Services and Google Compute Engine means more cloud tenants are hosting sensitive, private, and business critical data and applications in the...thousands of IaaS resources as they are elastically instantiated and terminated. Prior cloud trusted computing solutions address a subset of these features

SeReNA Project: studying aerosol interactions with cloud microphysics in the Amazon Basin

NASA Astrophysics Data System (ADS)

Correia, A. L.; Catandi, P. B.; Frigeri, F. F.; Ferreira, W. C.; Martins, J.; Artaxo, P.

2012-12-01

Cloud microphysics and its interaction with aerosols is a key atmospheric process for weather and climate. Interactions between clouds and aerosols can impact Earth's radiative balance, its hydrological and energetic cycles, and are responsible for a large fraction of the uncertainty in climatic models. On a planetary scale, the Amazon Basin is one of the most significant land sources of moisture and latent heat energy. Moreover, every year this region undergoes mearked seasonal shifts in its atmospheric state, transitioning from clean to heavily polluted conditions due to the occurrence of seasonal biomass burning fires, that emit large amounts of smoke to the atmosphere. These conditions make the Amazon Basin a special place to study aerosol-cloud interactions. The SeReNA Project ("Remote sensing of clouds and their interaction with aerosols", from the acronym in Portuguese, @SerenaProject on Twitter) is an ongoing effort to experimentally investigate the impact of aerosols upon cloud microphysics in Amazonia. Vertical profiles of droplet effective radius of water and ice particles, in single convective clouds, can be derived from measurements of the emerging radiation on cloud sides. Aerosol optical depth, cloud top properties, and meteorological parameters retrieved from satellites will be correlated with microphysical properties derived for single clouds. Maps of cloud brightness temperature will allow building temperature vs. effective radius profiles for hydrometeors in single clouds. Figure 1 shows an example extracted from Martins et al. (2011), illustrating a proof-of-concept for the kind of result expected within the framework for the SeReNA Project. The results to be obtained will help foster the quantitative knowledge about interactions between aerosols and clouds in a microphysical level. These interactions are a fundamental process in the context of global climatic changes, they are key to understanding basic processes within clouds and how aerosols can influence them. Reference: Martins et al. (2011) ACP, v.11, p.9485-9501. Available at: http://bit.ly/martinspaper Figure 1. Brightness temperature (left panel) and thermodynamic phase (right) of hydrometeors in the convective cloud shown in the middle panel. Extracted from Martins et al. (2011).

Experiences Building Globus Genomics: A Next-Generation Sequencing Analysis Service using Galaxy, Globus, and Amazon Web Services

PubMed Central

Madduri, Ravi K.; Sulakhe, Dinanath; Lacinski, Lukasz; Liu, Bo; Rodriguez, Alex; Chard, Kyle; Dave, Utpal J.; Foster, Ian T.

2014-01-01

We describe Globus Genomics, a system that we have developed for rapid analysis of large quantities of next-generation sequencing (NGS) genomic data. This system achieves a high degree of end-to-end automation that encompasses every stage of data analysis including initial data retrieval from remote sequencing centers or storage (via the Globus file transfer system); specification, configuration, and reuse of multi-step processing pipelines (via the Galaxy workflow system); creation of custom Amazon Machine Images and on-demand resource acquisition via a specialized elastic provisioner (on Amazon EC2); and efficient scheduling of these pipelines over many processors (via the HTCondor scheduler). The system allows biomedical researchers to perform rapid analysis of large NGS datasets in a fully automated manner, without software installation or a need for any local computing infrastructure. We report performance and cost results for some representative workloads. PMID:25342933

Experiences Building Globus Genomics: A Next-Generation Sequencing Analysis Service using Galaxy, Globus, and Amazon Web Services.

PubMed

Madduri, Ravi K; Sulakhe, Dinanath; Lacinski, Lukasz; Liu, Bo; Rodriguez, Alex; Chard, Kyle; Dave, Utpal J; Foster, Ian T

2014-09-10

We describe Globus Genomics, a system that we have developed for rapid analysis of large quantities of next-generation sequencing (NGS) genomic data. This system achieves a high degree of end-to-end automation that encompasses every stage of data analysis including initial data retrieval from remote sequencing centers or storage (via the Globus file transfer system); specification, configuration, and reuse of multi-step processing pipelines (via the Galaxy workflow system); creation of custom Amazon Machine Images and on-demand resource acquisition via a specialized elastic provisioner (on Amazon EC2); and efficient scheduling of these pipelines over many processors (via the HTCondor scheduler). The system allows biomedical researchers to perform rapid analysis of large NGS datasets in a fully automated manner, without software installation or a need for any local computing infrastructure. We report performance and cost results for some representative workloads.

Do Southern Ocean Cloud Feedbacks Matter for 21st Century Warming?

NASA Astrophysics Data System (ADS)

Frey, W. R.; Maroon, E. A.; Pendergrass, A. G.; Kay, J. E.

2017-12-01

Cloud phase improvements in a state-of-the-art climate model produce a large 1.5 K increase in equilibrium climate sensitivity (ECS, the surface warming in response to instantaneously doubled CO2) via extratropical shortwave cloud feedbacks. Here we show that the same model improvements produce only a small surface warming increase in a realistic 21st century emissions scenario. The small 21st century warming increase is attributed to extratropical ocean heat uptake. Southern Ocean mean-state circulation takes up heat while a slowdown in North Atlantic circulation acts as a feedback to slow surface warming. Persistent heat uptake by extratropical oceans implies that extratropical cloud biases may not be as important to 21st century warming as biases in other regions. Observational constraints on cloud phase and shortwave radiation that produce a large ECS increase do not imply large changes in 21st century warming.

Gas- and particle-phase chemical composition measurements onboard the G-1 research aircraft during the GoAmazon campaign.

NASA Astrophysics Data System (ADS)

Shilling, J.; Pekour, M. S.; Fortner, E.; Hubbe, J. M.; Longo, K.; Martin, S. T.; Mei, F.; Springston, S. R.; Tomlinson, J. M.; Wang, J.

2014-12-01

The Green Ocean Amazon (GoAmazon) campaign conducted from January 2014 - December 2015 in the vicinity of Manaus, Brazil, was designed to study the aerosol lifecycle and aerosol-cloud interactions in both pristine and anthropogenically influenced conditions. As part of this campaign, the DOE G-1 research aircraft was deployed from February 17th - March 25th 2014 and September 6th - October 5th 2014 to investigate aerosol and cloud properties aloft. An Aerodyne High Resolution Aerosol Mass Spectrometer (AMS) and an Ionicon Proton Transfer Reaction Mass Spectrometer (PTRMS) were part of the G-1 research aircraft payload and were used to investigate aerosol gas- and particle-phase chemical composition. Here we present preliminary analysis of the aerosol and gas phase chemical composition. PTR-MS measurements show that isoprene and its oxidation products are the dominant VOCs during research flights. HR-AMS measurements reveal that the particle phase is dominated by organic material with smaller concentrations of sulfate and nitrate observed. Organic particle concentrations are enhanced when encountering the urban plume from Manaus. During the wet season, we observe increased concentrations of organic particle when passing through low-altitude clouds. PMF analysis of the organic mass spectra shows that the chemical composition of the particles observed in-cloud is distinctly different from particles observed outside clouds. We will also compare measurements made during the wet and dry seasons.

Enabling a systems biology knowledgebase with gaggle and firegoose

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baliga, Nitin S.

The overall goal of this project was to extend the existing Gaggle and Firegoose systems to develop an open-source technology that runs over the web and links desktop applications with many databases and software applications. This technology would enable researchers to incorporate workflows for data analysis that can be executed from this interface to other online applications. The four specific aims were to (1) provide one-click mapping of genes, proteins, and complexes across databases and species; (2) enable multiple simultaneous workflows; (3) expand sophisticated data analysis for online resources; and enhance open-source development of the Gaggle-Firegoose infrastructure. Gaggle is anmore » open-source Java software system that integrates existing bioinformatics programs and data sources into a user-friendly, extensible environment to allow interactive exploration, visualization, and analysis of systems biology data. Firegoose is an extension to the Mozilla Firefox web browser that enables data transfer between websites and desktop tools including Gaggle. In the last phase of this funding period, we have made substantial progress on development and application of the Gaggle integration framework. We implemented the workspace to the Network Portal. Users can capture data from Firegoose and save them to the workspace. Users can create workflows to start multiple software components programmatically and pass data between them. Results of analysis can be saved to the cloud so that they can be easily restored on any machine. We also developed the Gaggle Chrome Goose, a plugin for the Google Chrome browser in tandem with an opencpu server in the Amazon EC2 cloud. This allows users to interactively perform data analysis on a single web page using the R packages deployed on the opencpu server. The cloud-based framework facilitates collaboration between researchers from multiple organizations. We have made a number of enhancements to the cmonkey2 application to enable and improve the integration within different environments, and we have created a new tools pipeline for generating EGRIN2 models in a largely automated way.« less

Radiative effects of biomass burning aerosols and cloudiness on seasonal carbon cycle in the Amazon region

NASA Astrophysics Data System (ADS)

Moreira, D. S.; Longo, K.; Freitas, S.; Mercado, L. M.; Miller, J. B.; Rosario, N. M. E. D.; Gatti, L.; Yamasoe, M. A.

2017-12-01

The Amazon region is characterized by high cloudiness, mainly due to convective clouds during most of the year due to the high humidity, and heat availability. However, during the Austral winter, the northward movement of the inter-tropical convergence zone (ITCZ) from its climatological position, significantly reducing cloudiness and precipitation, facilitating vegetation fires. Consequently, during these dry months, biomass burning aerosols contribute to relatively high values of aerosol optical depth (AOD) in Amazonia, typically exceeding 1.0 in the 550 nm wavelength. Both clouds and aerosols scatter solar radiation, reducing the direct irradiance and increasing the diffuse fraction that reaches the surface, decreasing near surface temperature and increasing photosynthetically active radiation (PAR) availability. This, in turn, affects energy and CO2 fluxes within the vegetation canopy. We applied an atmospheric model fully coupled to terrestrial carbon cycle model to assess the relative impact of biomass burning aerosols and clouds on CO2 fluxes in the Amazon region. Our results indicate that during most of the year, gross primary productivity (GPP) is high mainly due to high soil moisture and high values of the diffuse fraction of solar irradiation due to cloudiness. Therefore, heterotrophic and autotrophic respiration are both high, increasing the NEE values (i.e. reducing the net land sink). On the other hand, during the dry season, with a significant reduction of cloudiness, the biomass burning aerosol is mainly responsible for the increase in the diffuse fraction of solar irradiation and the GPP of the forest. However, the low soil moisture during the dry season, especially in the eastern Amazon, reduces heterotrophic and autotrophic respiration and thus compensates for reduced GPP compared to the wet season. Different reasons, an anthropogenic one (human induced fires during the dry season) and a natural one (cloudiness), lead to a somewhat stable value of NEE all year long in Amazonia.

Isotopic values of the Amazon headwaters in Peru: comparison of the wet upper Río Madre de Dios watershed with the dry Urubamba-Apurimac river system.

PubMed

Lambs, L; Horwath, A; Otto, T; Julien, F; Antoine, P-O

2012-04-15

The Amazon River is a huge network of long tributaries, and little is known about the headwaters. Here we present a study of one wet tropical Amazon forest side, and one dry and cold Atiplano plateau, originating from the same cordillera. The aim is to see how this difference affects the water characteristics. Different kind of water (spring, lake, river, rainfall) were sampled to determine their stable isotopes ratios (oxygen 18/16 and hydrogen 2/1) by continuous flow isotope ratio mass spectrometry (IRMS). These ratios coupled with chemical analysis enabled us to determine the origin of the water, the evaporation process and the water recycling over the Amazon plain forest and montane cloud forest. Our study shows that the water flowing in the upper Madre de Dios basin comes mainly from the foothill humid forest, with a characteristic water recycling process signature, and not from higher glacier melt. On the contrary, the water flowing in the Altiplano Rivers is mainly from glacier melts, with a high evaporation process. This snow and glacier are fed mainly by Atlantic moisture which transits over the large Amazon forest. The Atlantic moisture and its recycling over this huge tropical forest display a progressive isotopic gradient, as a function of distance from the ocean. At the level of the montane cloud forest and on the altiplano, respectively, additional water recycling and evaporation occur, but they are insignificant in the total water discharge. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.

Usability evaluation of cloud-based mapping tools for the display of very large datasets

NASA Astrophysics Data System (ADS)

Stotz, Nicole Marie

The elasticity and on-demand nature of cloud services have made it easier to create web maps. Users only need access to a web browser and the Internet to utilize cloud based web maps, eliminating the need for specialized software. To encourage a wide variety of users, a map must be well designed; usability is a very important concept in designing a web map. Fusion Tables, a new product from Google, is one example of newer cloud-based distributed GIS services. It allows for easy spatial data manipulation and visualization, within the Google Maps framework. ESRI has also introduced a cloud based version of their software, called ArcGIS Online, built on Amazon's EC2 cloud. Utilizing a user-centered design framework, two prototype maps were created with data from the San Diego East County Economic Development Council. One map was built on Fusion Tables, and another on ESRI's ArcGIS Online. A usability analysis was conducted and used to compare both map prototypes in term so of design and functionality. Load tests were also ran, and performance metrics gathered on both map prototypes. The usability analysis was taken by 25 geography students, and consisted of time based tasks and questions on map design and functionality. Survey participants completed the time based tasks for the Fusion Tables map prototype quicker than those of the ArcGIS Online map prototype. While response was generally positive towards the design and functionality of both prototypes, overall the Fusion Tables map prototype was preferred. For the load tests, the data set was broken into 22 groups for a total of 44 tests. While the Fusion Tables map prototype performed more efficiently than the ArcGIS Online prototype, differences are almost unnoticeable. A SWOT analysis was conducted for each prototype. The results from this research point to the Fusion Tables map prototype. A redesign of this prototype would incorporate design suggestions from the usability survey, while some functionality would need to be dropped. This is a free product and would therefore be the best option if cost is an issue, but this map may not be supported in the future.

SCIMITAR: Scalable Stream-Processing for Sensor Information Brokering

DTIC Science & Technology

2013-11-01

IaaS) cloud frameworks including Amazon Web Services and Eucalyptus . For load testing, we used The Grinder [9], a Java load testing framework that...internal Eucalyptus cluster which we could not scale as large as the Amazon environment due to a lack of computation resources. We recreated our

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

DOE PAGES

Holzman, Burt; Bauerdick, Lothar A. T.; Bockelman, Brian; ...

2017-09-29

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized bothmore » local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.« less

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holzman, Burt; Bauerdick, Lothar A. T.; Bockelman, Brian

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized bothmore » local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.« less

Trade Study: Storing NASA HDF5/netCDF-4 Data in the Amazon Cloud and Retrieving Data Via Hyrax Server Data Server

NASA Technical Reports Server (NTRS)

Habermann, Ted; Gallagher, James; Jelenak, Aleksandar; Potter, Nathan; Lee, Joe; Yang, Kent

2017-01-01

This study explored three candidate architectures with different types of objects and access paths for serving NASA Earth Science HDF5 data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the study were: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and netCDF4 data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3). Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades.We will describe the three architectures and the use cases along with performance results and recommendations for further work.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, Jiwen; Rosenfeld, Daniel; Zhang, Yuwei

Aerosol-cloud interaction remains the largest uncertainty in climate projections. Ultrafine aerosol particles (UAP; size <50nm) are considered too small to serve as cloud condensation nuclei conventionally. However, this study provides observational evidence to accompany insights from numerical simulations to support that deep convective clouds (DCCs) over Amazon have strong capability of nucleating UAP from an urban source and forming greater numbers of droplets, because fast drop coalescence in these DCCs reduces drop surface area available for condensation, leading to high vapor supersaturation. The additional droplets subsequently decrease supersaturation and release more condensational latent heating, a dominant contributor to convection intensification,more » whereas enhanced latent heat from ice-related processes plays a secondary role. Therefore, the addition of anthropogenic UAP may play a much greater role in modulating clouds than previously believed over the Amazon region and possibly in other relatively pristine regions such as maritime and forest locations.« less

Volatile isoprenoids as defense compounds during abiotic stress in tropical plants

NASA Astrophysics Data System (ADS)

Jardine, K.

2015-12-01

Emissions of volatile isoprenoids from tropical forests play central roles in atmospheric processes by fueling atmospheric chemistry resulting in modified aerosol and cloud lifecycles and their associated feedbacks with the terrestrial biosphere. However, the identities of tropical isoprenoids, their biological and environmental controls, and functions within plants and ecosystems remain highly uncertain. As part of the DOE ARM program's GoAmazon 2014/15 campaign, extensive field and laboratory observations of volatile isoprenoids are being conducted in the central Amazon. Here we report the results of our completed and ongoing activities at the ZF2 forest reserve in the central Amazon. Among the results of the research are the suprisingly high abundance of light-dependent volatile isoprenoid emissions across abundant tree genera in the Amazon in both primary and secondary forests, the discovery of highly reactive monoterpene emissions from Amazon trees, and evidence for the importance of volatile isoprenoids in protecting photosynthesis during oxidative stress under elevated temperatures including energy consumption and direct antioxidant functions and a tight connection betwen volatile isoprenoid emissions, photorespiration, and CO2 recycling within leaves. The results highlight the need to model allocation of carbon to isoprenoids during elevated temperature stress in the tropics.

Consolidation of cloud computing in ATLAS

NASA Astrophysics Data System (ADS)

Taylor, Ryan P.; Domingues Cordeiro, Cristovao Jose; Giordano, Domenico; Hover, John; Kouba, Tomas; Love, Peter; McNab, Andrew; Schovancova, Jaroslava; Sobie, Randall; ATLAS Collaboration

2017-10-01

Throughout the first half of LHC Run 2, ATLAS cloud computing has undergone a period of consolidation, characterized by building upon previously established systems, with the aim of reducing operational effort, improving robustness, and reaching higher scale. This paper describes the current state of ATLAS cloud computing. Cloud activities are converging on a common contextualization approach for virtual machines, and cloud resources are sharing monitoring and service discovery components. We describe the integration of Vacuum resources, streamlined usage of the Simulation at Point 1 cloud for offline processing, extreme scaling on Amazon compute resources, and procurement of commercial cloud capacity in Europe. Finally, building on the previously established monitoring infrastructure, we have deployed a real-time monitoring and alerting platform which coalesces data from multiple sources, provides flexible visualization via customizable dashboards, and issues alerts and carries out corrective actions in response to problems.

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venner, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; O'Brien, Raymond

2015-01-01

Cloud computing capabilities have rapidly expanded within the private sector, offering new opportunities for meteorological applications. Collaborations between NASA Marshall, NASA Ames, and contractor partners led to evaluations of private (NASA) and public (Amazon) resources for executing short-term NWP systems. Activities helped the Marshall team further understand cloud capabilities, and benchmark use of cloud resources for NWP and other applications

Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA

NASA Astrophysics Data System (ADS)

Machado, Luiz A. T.; Calheiros, Alan J. P.; Biscaro, Thiago; Giangrande, Scott; Silva Dias, Maria A. F.; Cecchini, Micael A.; Albrecht, Rachel; Andreae, Meinrat O.; Araujo, Wagner F.; Artaxo, Paulo; Borrmann, Stephan; Braga, Ramon; Burleyson, Casey; Eichholz, Cristiano W.; Fan, Jiwen; Feng, Zhe; Fisch, Gilberto F.; Jensen, Michael P.; Martin, Scot T.; Pöschl, Ulrich; Pöhlker, Christopher; Pöhlker, Mira L.; Ribaud, Jean-François; Rosenfeld, Daniel; Saraiva, Jaci M. B.; Schumacher, Courtney; Thalman, Ryan; Walter, David; Wendisch, Manfred

2018-05-01

This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. This study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weighted mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.

Fog and rain in the Amazon

PubMed Central

Anber, Usama; Gentine, Pierre; Wang, Shuguang; Sobel, Adam H.

2015-01-01

The diurnal and seasonal water cycles in the Amazon remain poorly simulated in general circulation models, exhibiting peak evapotranspiration in the wrong season and rain too early in the day. We show that those biases are not present in cloud-resolving simulations with parameterized large-scale circulation. The difference is attributed to the representation of the morning fog layer, and to more accurate characterization of convection and its coupling with large-scale circulation. The morning fog layer, present in the wet season but absent in the dry season, dramatically increases cloud albedo, which reduces evapotranspiration through its modulation of the surface energy budget. These results highlight the importance of the coupling between the energy and hydrological cycles and the key role of cloud albedo feedback for climates over tropical continents. PMID:26324902

Fog and rain in the Amazon

DOE PAGES

Anber, Usama; Gentine, Pierre; Wang, Shuguang; ...

2015-08-31

The diurnal and seasonal water cycles in the Amazon remain poorly simulated in general circulation models, exhibiting peak evapotranspiration in the wrong season and rain too early in the day. We show that those biases are not present in cloud-resolving simulations with parameterized large-scale circulation. The difference is attributed to the representation of the morning fog layer, and to more accurate characterization of convection and its coupling with large-scale circulation. The morning fog layer, present in the wet season but absent in the dry season, dramatically increases cloud albedo, which reduces evapotranspiration through its modulation of the surface energy budget.more » Finally, these results highlight the importance of the coupling between the energy and hydrological cycles and the key role of cloud albedo feedback for climates over tropical continents.« less

Security Risks of Cloud Computing and Its Emergence as 5th Utility Service

NASA Astrophysics Data System (ADS)

Ahmad, Mushtaq

Cloud Computing is being projected by the major cloud services provider IT companies such as IBM, Google, Yahoo, Amazon and others as fifth utility where clients will have access for processing those applications and or software projects which need very high processing speed for compute intensive and huge data capacity for scientific, engineering research problems and also e- business and data content network applications. These services for different types of clients are provided under DASM-Direct Access Service Management based on virtualization of hardware, software and very high bandwidth Internet (Web 2.0) communication. The paper reviews these developments for Cloud Computing and Hardware/Software configuration of the cloud paradigm. The paper also examines the vital aspects of security risks projected by IT Industry experts, cloud clients. The paper also highlights the cloud provider's response to cloud security risks.

Earth Observations taken by the Expedition 17 Crew

NASA Image and Video Library

2008-08-19

ISS017-E-013856 (19 Aug. 2008) --- Amazon River, Brazil is featured in this image photographed by an Expedition 17 crewmember on the International Space Station. This image shows the huge sunglint zone, common to oblique views from space, of the setting sun shining off the Amazon River and numerous lakes on its floodplain. About 150 kilometers of the sinuous Amazon course is shown here, as it appears about 1,000 kilometers from the Atlantic Ocean. The Uatuma River enters on the north side of the Amazon (top). A small side channel of the very large Madeira River enters the view from the left. Tupinambarama Island occupies the swampy wetlands between the Amazon and Madeira rivers. Sunglint images reveal great detail in waterbodies -- in this case the marked difference between the smooth outline of the Amazon and the jagged shoreline of the Uatuma River. The jagged shoreline results from valley sides being eroded in relatively hard rocks. The Uatuma River has since been dammed up by the sediment mass of the Amazon floodplain. Because the Amazon flows in its own soft sediment, its huge water discharge smooths the banks. Another dammed valley (known as a ria) is visible beneath the cirrus cloud of a storm (bottom). Although no smoke plumes from forest fires are visible in the view, two kinds of evidence show that there is smoke in the atmosphere. The coppery color of the sunglint is typically produced by smoke particles and other aerosols scattering yellow and red light. Second, a small patch of cloud (top right) casts a distinct shadow. The shadow, say scientists, is visible because so many particles in the surrounding sunlit parts of the atmosphere reflect light to the camera.

Higher absorbed solar radiation partly offset the negative effects of water stress on the photosynthesis of Amazon forests during the 2015 drought

NASA Astrophysics Data System (ADS)

Li, Xing; Xiao, Jingfeng; He, Binbin

2018-04-01

Amazon forests play an important role in the global carbon cycle and Earth’s climate. The vulnerability of Amazon forests to drought remains highly controversial. Here we examine the impacts of the 2015 drought on the photosynthesis of Amazon forests to understand how solar radiation and precipitation jointly control forest photosynthesis during the severe drought. We use a variety of gridded vegetation and climate datasets, including solar-induced chlorophyll fluorescence (SIF), photosynthetic active radiation (PAR), the fraction of absorbed PAR (APAR), leaf area index (LAI), precipitation, soil moisture, cloud cover, and vapor pressure deficit (VPD) in our analysis. Satellite-derived SIF observations provide a direct diagnosis of plant photosynthesis from space. The decomposition of SIF to SIF yield (SIFyield) and APAR (the product of PAR and fPAR) reveals the relative effects of precipitation and solar radiation on photosynthesis. We found that the drought significantly reduced SIFyield, the emitted SIF per photon absorbed. The higher APAR resulting from lower cloud cover and higher LAI partly offset the negative effects of water stress on the photosynthesis of Amazon forests, leading to a smaller reduction in SIF than in SIFyield and precipitation. We further found that SIFyield anomalies were more sensitive to precipitation and VPD anomalies in the southern regions of the Amazon than in the central and northern regions. Our findings shed light on the relative and combined effects of precipitation and solar radiation on photosynthesis, and can improve our understanding of the responses of Amazon forests to drought.

Bootstrapping and Maintaining Trust in the Cloud

DTIC Science & Technology

2016-12-01

proliferation and popularity of infrastructure-as-a- service (IaaS) cloud computing services such as Amazon Web Services and Google Compute Engine means...IaaS trusted computing system: • Secure Bootstrapping – the system should enable the tenant to securely install an initial root secret into each cloud ...elastically instantiated and terminated. Prior cloud trusted computing solutions address a subset of these features, but none achieve all. Excalibur [31] sup

AceCloud: Molecular Dynamics Simulations in the Cloud.

PubMed

Harvey, M J; De Fabritiis, G

2015-05-26

We present AceCloud, an on-demand service for molecular dynamics simulations. AceCloud is designed to facilitate the secure execution of large ensembles of simulations on an external cloud computing service (currently Amazon Web Services). The AceCloud client, integrated into the ACEMD molecular dynamics package, provides an easy-to-use interface that abstracts all aspects of interaction with the cloud services. This gives the user the experience that all simulations are running on their local machine, minimizing the learning curve typically associated with the transition to using high performance computing services.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Hyunwoo; Timm, Steven

We present a summary of how X.509 authentication and authorization are used with OpenNebula in FermiCloud. We also describe a history of why the X.509 authentication was needed in FermiCloud, and review X.509 authorization options, both internal and external to OpenNebula. We show how these options can be and have been used to successfully run scientific workflows on federated clouds, which include OpenNebula on FermiCloud and Amazon Web Services as well as other community clouds. We also outline federation options being used by other commercial and open-source clouds and cloud research projects.

A Highly Scalable Data Service (HSDS) using Cloud-based Storage Technologies for Earth Science Data

NASA Astrophysics Data System (ADS)

Michaelis, A.; Readey, J.; Votava, P.; Henderson, J.; Willmore, F.

2017-12-01

Cloud based infrastructure may offer several key benefits of scalability, built in redundancy, security mechanisms and reduced total cost of ownership as compared with a traditional data center approach. However, most of the tools and legacy software systems developed for online data repositories within the federal government were not developed with a cloud based infrastructure in mind and do not fully take advantage of commonly available cloud-based technologies. Moreover, services bases on object storage are well established and provided through all the leading cloud service providers (Amazon Web Service, Microsoft Azure, Google Cloud, etc…) of which can often provide unmatched "scale-out" capabilities and data availability to a large and growing consumer base at a price point unachievable from in-house solutions. We describe a system that utilizes object storage rather than traditional file system based storage to vend earth science data. The system described is not only cost effective, but shows a performance advantage for running many different analytics tasks in the cloud. To enable compatibility with existing tools and applications, we outline client libraries that are API compatible with existing libraries for HDF5 and NetCDF4. Performance of the system is demonstrated using clouds services running on Amazon Web Services.

Aerosol Production and Growth in the Upper Troposphere over the Amazon Forest Observed during ACRIDICON-CHUVA

NASA Astrophysics Data System (ADS)

Andreae, M. O.; Afchine, A.; Albrecht, R. I.; Artaxo, P.; Borrmann, S.; Cecchini, M. A.; Costa, A.; Fütterer, D.; Järvinen, E.; Klimach, T.; Konemann, T.; Kraemer, M.; Machado, L.; Mertes, S.; Pöhlker, C.; Pöhlker, M. L.; Poeschl, U.; Sauer, D. N.; Schnaiter, M.; Schneider, J.; Schulz, C.; Spanu, A.; Walser, A.; Wang, J.; Weinzierl, B.; Wendisch, M.

2016-12-01

Observations during ACRIDICON-CHUVA showed high aerosol concentrations in the upper troposphere (UT) over the Amazon Basin, with aerosol number concentrations after normalization to STP often exceeding those in the boundary layer (BL) by one or two orders of magnitude. The measurements were made during the German-Brazilian cooperative aircraft campaign ACRIDICON-CHUVA (Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems) on the German research aircraft HALO. The campaign took place over the Amazon Basin in September/October 2014, with the objective of studying tropical deep convective clouds over the Amazon rainforest and their interactions with trace gases, aerosol particles, and atmospheric radiation. Aerosol enhancements were consistently observed on all flights, using several aerosol metrics, including condensation nuclei (CN), cloud condensation nuclei (CCN), and chemical species mass concentrations. These UT aerosols were different in their composition and size distribution from the aerosol in the BL, making convective transport of particles unlikely as a source. The regions in the immediate outflow of deep convective clouds were depleted in aerosol particles, whereas dramatically enhanced small (<90 nm diameter) aerosol number concentrations were found in UT regions that had experienced outflow from deep convection in the preceding 24-48 hours. We also found elevated concentrations of larger (>90 nm) particles in the UT, which consisted mostly of organic matter and nitrate and were very effective CCN. Our findings suggest that aerosol production takes place in the UT from volatile material brought up by deep convection, which is converted to condensable species in the UT. Subsequently, downward mixing and transport of upper tropospheric aerosol may be a source of particles to the BL, where they increase in size by the condensation of biogenic volatile organic carbon (BVOC) oxidation products. This may be an important source of aerosol particles in the Amazonian BL, where aerosol nucleation and new particle formation has not been observed. We propose that this may have been the dominant process supplying secondary aerosols in the pristine atmosphere, making clouds the dominant control of both removal and production of atmospheric particles.

Cloud Computing Technologies Facilitate Earth Research

NASA Technical Reports Server (NTRS)

2015-01-01

Under a Space Act Agreement, NASA partnered with Seattle-based Amazon Web Services to make the agency's climate and Earth science satellite data publicly available on the company's servers. Users can access the data for free, but they can also pay to use Amazon's computing services to analyze and visualize information using the same software available to NASA researchers.

Long-term observations of cloud condensation nuclei in the Amazon rain forest

NASA Astrophysics Data System (ADS)

Pöhlker, Mira L.; Pöhlker, Christopher; Ditas, Florian; Klimach, Thomas; Hrabe de Angelis, Isabella; Brito, Joel; Carbone, Samara; Cheng, Yafang; Martin, Scot T.; Moran-Zuloaga, Daniel; Rose, Diana; Saturno, Jorge; Su, Hang; Thalman, Ryan; Walter, David; Wang, Jian; Barbosa, Henrique; Artaxo, Paulo; Andreae, Meinrat O.; Pöschl, Ulrich

2017-04-01

Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a full seasonal cycle (Mar 2014 - Feb 2015). The measurements provide a climatology of CCN properties characteristic of a remote central Amazonian rain forest site [1,2]. The CCN measurements were continuously cycled through 10 levels of supersaturation (S = 0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κAit = 0.14 ± 0.03), higher values for the accumulation mode (κAcc = 0.22 ± 0.05), and an overall mean value of κmean = 0.17 ± 0.06, consistent with high fractions of organic aerosol. The hygroscopicity parameter, κ, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes. For modelling purposes, we compare different approaches of predicting CCN number concentration and present a novel parameterization, which allows accurate CCN predictions based on a small set of input data. In addition, we analyzed the CCN short-term variability in relation to air mass changes as well as aerosol emission and transformation processes. The CCN short term variability is presented for selected case studies, which analyze particularly interesting and characteristic events/conditions in the Amazon region. References: [1] Andreae, M. O., et al. (2015), Atmos. Chem. Phys., 15, 10723-10776. [2] Pöhlker, M. L.., et al. (2016), Atmos. Chem. Phys., 16, 15709-15740.

Evaluating the cloud radiative forcing over East Asia during summer simulated by CMIP5 models

NASA Astrophysics Data System (ADS)

Lin, Z.; Wang, Y.; Liu, X.

2017-12-01

A large degree of uncertainty in global climate models (GCMs) can be attributed to the representation of clouds and its radiative forcing (CRF). In this study, the simulated CRFs, total cloud fraction (CF) and cloud properties over East Asia from 20 CMIP5 AMIP models are evaluated and compared with multiple satellite observations, and the possible causes for the CRF bias in the CMIP5 models are then investigated. Based on the satellite observation, strong Long wave CRF (LWCRF) and Short wave CRF (SWCRF) are found to be located over Southwestern China, with minimum SWCRF less than -130Wm-2 and this is associated with the large amount of cloud in the region. By contrast, weak CRFs are located over Northwest China and Western Pacific region because of less cloud amount. In Northeastern China, the strong SWCRF and week LWCRF can be found due to the dominant low-level cloud. In Eastern China, the CRFs is moderate due to the co-existence of the multi-layer cloud. CMIP5 models can basically capture the structure of CRFs in East Asia, with the spatial correlation coefficient between 0.5 and 0.9. But most models underestimate CRFs in East Asia, which is highly associated with the underestimation of cloud amount in the region. The performance of CMIP5 models varies in different part of East Asian region, with a larger deviation in Eastern China (EC). Further investigation suggests that, underestimation of the cloud amount in EC can lead to the weak bias of CRFs in EC, however, this CRF bias can be cancelled out by the overestimation effect of CRF due to excessive cloud optical depth (COD) simulated by the models. The annual cycle of simulated CRF over Eastern China is also examined, and it is found, CMIP models are unable to reproduce the northward migration of CRF in summer monsoon season, which is closely related with northward shift of East Asian summer monsoon rain belt.

Effect of Amazon Smoke on Cloud Microphysics and Albedo-Analysis from Satellite Imagery.

NASA Astrophysics Data System (ADS)

Kaufman, Yoram J.; Nakajima, Teruyuki

1993-04-01

NOAA Advanced Very High Resolution Radiometer images taken over the Brazilian Amazon Basin during the biomass burning season of 1987 are used to study the effect of smoke aerosol particles on the properties of low cumulus and stratocumulus clouds. The reflectance at a wavelength of 0.64 µm and the drop size, derived from the cloud reflectance at 3.75 µm, are studied for tens of thousands of clouds. The opacity of the smoke layer adjacent to each cloud is also monitored simultaneously. Though from satellite data it is impossible to derive all the parameters that influence cloud properties and smoke cloud interaction (e.g., detailed aerosol particles size distribution and chemistry, liquid water content, etc.); satellite data can be used to generate large-scale statistics of the properties of clouds and surrounding aerosol (e.g., smoke optical thickness, cloud-drop size, and cloud reflection of solar radiation) from which the interaction of aerosol with clouds can be surmised. In order to minimize the effect of variations in the precipitable water vapor and in other smoke and cloud properties, biomass burning in the tropics is chosen as the study topic, and the results are averaged for numerous clouds with the same ambient smoke optical thickness.It is shown in this study that the presence of dense smoke (an increase in the optical thickness from 0.1 to 2.0) can reduce the remotely sensed drop size of continental cloud drops from 15 to 9 µm. Due to both the high initial reflectance of clouds in the visible part of the spectrum and the presence of graphitic carbon, the average cloud reflectance at 0.64 µm is reduced from 0.71 to 0.68 for an increase in smoke optical thickness from 0.1 to 2.0. The measurements are compared to results from other years, and it is found that, as predicted, high concentration of aerosol particles causes a decrease in the cloud-drop size and that smoke darkens the bright Amazonian clouds. Comparison with theoretical computations based on Twomey's model show that by using the measured reduction in the cloud-drop size due to the presence of smoke it is possible to explain the reduction in the cloud reflectance at 0.64 µm for smoke imagery index of 0.02 to 0.03.Smoke particles are hygroscopic and have a similar size distribution to maritime and anthropogenic sulfuric aerosol particles. Therefore, these results may also be representative of the interaction of sulfuric particles with clouds.

Cloud Computing for Pharmacometrics: Using AWS, NONMEM, PsN, Grid Engine, and Sonic

PubMed Central

Sanduja, S; Jewell, P; Aron, E; Pharai, N

2015-01-01

Cloud computing allows pharmacometricians to access advanced hardware, network, and security resources available to expedite analysis and reporting. Cloud-based computing environments are available at a fraction of the time and effort when compared to traditional local datacenter-based solutions. This tutorial explains how to get started with building your own personal cloud computer cluster using Amazon Web Services (AWS), NONMEM, PsN, Grid Engine, and Sonic. PMID:26451333

Cloud Computing for Pharmacometrics: Using AWS, NONMEM, PsN, Grid Engine, and Sonic.

PubMed

Sanduja, S; Jewell, P; Aron, E; Pharai, N

2015-09-01

Cloud computing allows pharmacometricians to access advanced hardware, network, and security resources available to expedite analysis and reporting. Cloud-based computing environments are available at a fraction of the time and effort when compared to traditional local datacenter-based solutions. This tutorial explains how to get started with building your own personal cloud computer cluster using Amazon Web Services (AWS), NONMEM, PsN, Grid Engine, and Sonic.

Cloud Computing Security Issue: Survey

NASA Astrophysics Data System (ADS)

Kamal, Shailza; Kaur, Rajpreet

2011-12-01

Cloud computing is the growing field in IT industry since 2007 proposed by IBM. Another company like Google, Amazon, and Microsoft provides further products to cloud computing. The cloud computing is the internet based computing that shared recourses, information on demand. It provides the services like SaaS, IaaS and PaaS. The services and recourses are shared by virtualization that run multiple operation applications on cloud computing. This discussion gives the survey on the challenges on security issues during cloud computing and describes some standards and protocols that presents how security can be managed.

Ocean Heat Uptake Slows 21st Century Surface Warming Driven by Extratropical Cloud Feedbacks

NASA Astrophysics Data System (ADS)

Frey, W.; Maroon, E.; Pendergrass, A. G.; Kay, J. E.

2017-12-01

Equilibrium climate sensitivity (ECS), the warming in response to instantaneously doubled CO2, has long been used to compare climate models. In many models, ECS is well correlated with warming produced by transient forcing experiments. Modifications to cloud phase at high latitudes in a state-of-the-art climate model, the Community Earth System Model (CESM), produce a large increase in ECS (1.5 K) via extratropical cloud feedbacks. However, only a small surface warming increase occurs in a realistic 21st century simulation including a full-depth dynamic ocean and the "business as usual" RCP8.5 emissions scenario. In fact, the increase in surface warming is only barely above the internal variability-generated range in the CESM Large Ensemble. The small change in 21st century warming is attributed to subpolar ocean heat uptake in both hemispheres. In the Southern Ocean, the mean-state circulation takes up heat while in the North Atlantic a slowdown in circulation acts as a feedback to slow surface warming. These results show the importance of subpolar ocean heat uptake in controlling the pace of warming and demonstrate that ECS cannot be used to reliably infer transient warming when it is driven by extratropical feedbacks.

Cloud CPFP: a shotgun proteomics data analysis pipeline using cloud and high performance computing.

PubMed

Trudgian, David C; Mirzaei, Hamid

2012-12-07

We have extended the functionality of the Central Proteomics Facilities Pipeline (CPFP) to allow use of remote cloud and high performance computing (HPC) resources for shotgun proteomics data processing. CPFP has been modified to include modular local and remote scheduling for data processing jobs. The pipeline can now be run on a single PC or server, a local cluster, a remote HPC cluster, and/or the Amazon Web Services (AWS) cloud. We provide public images that allow easy deployment of CPFP in its entirety in the AWS cloud. This significantly reduces the effort necessary to use the software, and allows proteomics laboratories to pay for compute time ad hoc, rather than obtaining and maintaining expensive local server clusters. Alternatively the Amazon cloud can be used to increase the throughput of a local installation of CPFP as necessary. We demonstrate that cloud CPFP allows users to process data at higher speed than local installations but with similar cost and lower staff requirements. In addition to the computational improvements, the web interface to CPFP is simplified, and other functionalities are enhanced. The software is under active development at two leading institutions and continues to be released under an open-source license at http://cpfp.sourceforge.net.

The 3-D Tropical Convective Cloud Spectrum in AMIE Radar Observations and Global Climate Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schumacher, Courtney

2015-08-31

During the three years of this grant performance, the PI and her research group have made a number of significant contributions towards determining properties of tropical deep convective clouds and how models depict and respond to the heating associated with tropical convective systems. The PI has also been an active ARM/ASR science team member, including playing a significant role in AMIE and GoAmazon2014/5. She served on the DOE ASR radar science steering committee and was a joint chair of the Mesoscale Convective Organization group under the Cloud Life Cycle working group. This grant has funded a number of graduate students,more » many of them women, and the PI and her group have presented their DOE-supported work at various universities and national meetings. The PI and her group participated in the AMIE (2011-12) and GoAmazon2014/5 (2014-15) DOE field deployments that occurred in the tropical Indian Ocean and Brazilian Amazon, respectively. AMIE observational results (DePasquale et al. 2014, Feng et al. 2014, Ahmed and Schumacher 2015) focus on the variation and possible importance of Kelvin waves in various phases of the Madden-Julian Oscillation (MJO), on the synergy of the different wavelength radars deployed on Addu Atoll, and on the importance of humidity thresholds in the tropics on stratiform rain production. Much of the PIs GoAmazon2014/5 results to date relate to overviews of the observations made during the field campaign (Martin et al. 2015, 2016; Fuentes et al. 2016), but also include the introduction of the descending arm and its link to ozone transport from the mid-troposphere to the surface (Gerken et al. 2016). Vertical motion and mass flux profiles from GoAmazon (Giangrande et al. 2016) also show interesting patterns between seasons and provide targets for model simulations. Results from TWP-ICE (Schumacher et al. 2015), which took place in Darwin, Australia in 2006 show that vertical velocity retrievals from the profilers provide structure to better quantify the transition between convective, stratiform, and anvil cloud types.« less

Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA

DOE PAGES

Machado, Luiz A. T.; Calheiros, Alan J. P.; Biscaro, Thiago; ...

2018-05-07

This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. Here, this study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weightedmore » mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.« less

Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Machado, Luiz A. T.; Calheiros, Alan J. P.; Biscaro, Thiago

This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. Here, this study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weightedmore » mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.« less

Atmosphere-biosphere exchange of CO2 and O3 in the Central Amazon Forest

NASA Technical Reports Server (NTRS)

Fan, Song-Miao; Wofsy, Steven C.; Bakwin, Peter S.; Jacob, Daniel J.; Fitzjarrald, David R.

1990-01-01

An eddy correlation measurement of O3 deposition and CO2 exchange at a level 10 m above the canopy of the Amazon forest, conducted as part of the NASA/INPE ABLE2b mission during the wet season of 1987, is presented. It was found that the ecosystem exchange of CO2 undergoes a well-defined diurnal variation driven by the input of solar radiation. A curvilinear relationship was found between solar irradiance and uptake of CO2, with net CO2 uptake at a given solar irradiance equal to rates observed over forests in other climate zones. The carbon balance of the system appeared sensitive to cloud cover on the time scale of the experiment, suggesting that global carbon storage might be affected by changes in insolation associated with tropical climate fluctuations. The forest was found to be an efficient sink for O3 during the day, and evidence indicates that the Amazon forests could be a significant sink for global ozone during the nine-month wet period and that deforestation could dramatically alter O3 budgets.

How Continuous Observations of Shortwave Reflectance Spectra Can Narrow the Range of Shortwave Climate Feedbacks

NASA Astrophysics Data System (ADS)

Feldman, D.; Collins, W. D.; Wielicki, B. A.; Shea, Y.; Mlynczak, M. G.; Kuo, C.; Nguyen, N.

2017-12-01

Shortwave feedbacks are a persistent source of uncertainty for climate models and a large contributor to the diagnosed range of equilibrium climate sensitivity (ECS) for the international multi-model ensemble. The processes that contribute to these feedbacks affect top-of-atmosphere energetics and produce spectral signatures that may be time-evolving. We explore the value of such spectral signatures for providing an observational constraint on model ECS by simulating top-of-atmosphere shortwave reflectance spectra across much of the energetically-relevant shortwave bandpass (300 to 2500 nm). We present centennial-length shortwave hyperspectral simulations from low, medium and high ECS models that reported to the CMIP5 archive as part of an Observing System Simulation Experiment (OSSE) in support of the CLimate Absolute Radiance and Refractivity Observatory (CLARREO). Our framework interfaces with CMIP5 archive results and is agnostic to the choice of model. We simulated spectra from the INM-CM4 model (ECS of 2.08 °K/2xCO2), the MIROC5 model (ECS of 2.70 °K/2xCO2), and the CSIRO Mk3-6-0 (ECS of 4.08 °K/2xCO2) based on those models' integrations of the RCP8.5 scenario for the 21st Century. This approach allows us to explore how perfect data records can exclude models of lower or higher climate sensitivity. We find that spectral channels covering visible and near-infrared water-vapor overtone bands can potentially exclude a low or high sensitivity model with under 15 years' of absolutely-calibrated data. These different spectral channels are sensitive to model cloud radiative effect and cloud height changes, respectively. These unprecedented calculations lay the groundwork for spectral simulations of perturbed-physics ensembles in order to identify those shortwave observations that can help narrow the range in shortwave model feedbacks and ultimately help reduce the stubbornly-large range in model ECS.

Cloud computing for comparative genomics with windows azure platform.

PubMed

Kim, Insik; Jung, Jae-Yoon; Deluca, Todd F; Nelson, Tristan H; Wall, Dennis P

2012-01-01

Cloud computing services have emerged as a cost-effective alternative for cluster systems as the number of genomes and required computation power to analyze them increased in recent years. Here we introduce the Microsoft Azure platform with detailed execution steps and a cost comparison with Amazon Web Services.

Cloud Computing for Comparative Genomics with Windows Azure Platform

PubMed Central

Kim, Insik; Jung, Jae-Yoon; DeLuca, Todd F.; Nelson, Tristan H.; Wall, Dennis P.

2012-01-01

Cloud computing services have emerged as a cost-effective alternative for cluster systems as the number of genomes and required computation power to analyze them increased in recent years. Here we introduce the Microsoft Azure platform with detailed execution steps and a cost comparison with Amazon Web Services. PMID:23032609

Mouth of the Amazon River as seen from STS-58

NASA Image and Video Library

1993-10-30

STS058-107-083 (18 Oct.-1 Nov. 1993) --- A near-nadir view of the mouth of the Amazon River, that shows all signs of being a relatively healthy system, breathing and exhaling. The well-developed cumulus field over the forested areas on both the north and south sides of the river (the view is slightly to the west) shows that good evapotranspiration is underway. The change in the cloud field from the moisture influx from the Atlantic (the cloud fields over the ocean are parallel to the wind direction) to perpendicular cloud fields over the land surface are normal. This change in direction is caused by the increased surface roughness over the land area. The plume of the river, although turbid, is no more or less turbid than it has been reported since the Portuguese first rounded Brasil's coast at the end of the 15th Century.

Task 28: Web Accessible APIs in the Cloud Trade Study

NASA Technical Reports Server (NTRS)

Gallagher, James; Habermann, Ted; Jelenak, Aleksandar; Lee, Joe; Potter, Nathan; Yang, Muqun

2017-01-01

This study explored three candidate architectures for serving NASA Earth Science Hierarchical Data Format Version 5 (HDF5) data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the project are: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and Network Common Data Format Version 4 (netCDF4) data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3).Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades.

The fertilizing role of African dust in the Amazon rainforest: A first multiyear assessment based on data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations

NASA Astrophysics Data System (ADS)

Yu, Hongbin; Chin, Mian; Yuan, Tianle; Bian, Huisheng; Remer, Lorraine A.; Prospero, Joseph M.; Omar, Ali; Winker, David; Yang, Yuekui; Zhang, Yan; Zhang, Zhibo; Zhao, Chun

2015-03-01

The productivity of the Amazon rainforest is constrained by the availability of nutrients, in particular phosphorus (P). Deposition of long-range transported African dust is recognized as a potentially important but poorly quantified source of phosphorus. This study provides a first multiyear satellite-based estimate of dust deposition into the Amazon Basin using three-dimensional (3-D) aerosol measurements over 2007-2013 from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The 7 year average of dust deposition into the Amazon Basin is estimated to be 28 (8-48) Tg a-1 or 29 (8-50) kg ha-1 a-1. The dust deposition shows significant interannual variation that is negatively correlated with the prior-year rainfall in the Sahel. The CALIOP-based multiyear mean estimate of dust deposition matches better with estimates from in situ measurements and model simulations than a previous satellite-based estimate does. The closer agreement benefits from a more realistic geographic definition of the Amazon Basin and inclusion of meridional dust transport calculation in addition to the 3-D nature of CALIOP aerosol measurements. The imported dust could provide about 0.022 (0.006-0.037) Tg P of phosphorus per year, equivalent to 23 (7-39) g P ha-1 a-1 to fertilize the Amazon rainforest. This out-of-basin phosphorus input is comparable to the hydrological loss of phosphorus from the basin, suggesting an important role of African dust in preventing phosphorus depletion on timescales of decades to centuries.

Facilitating NASA Earth Science Data Processing Using Nebula Cloud Computing

NASA Astrophysics Data System (ADS)

Chen, A.; Pham, L.; Kempler, S.; Theobald, M.; Esfandiari, A.; Campino, J.; Vollmer, B.; Lynnes, C.

2011-12-01

Cloud Computing technology has been used to offer high-performance and low-cost computing and storage resources for both scientific problems and business services. Several cloud computing services have been implemented in the commercial arena, e.g. Amazon's EC2 & S3, Microsoft's Azure, and Google App Engine. There are also some research and application programs being launched in academia and governments to utilize Cloud Computing. NASA launched the Nebula Cloud Computing platform in 2008, which is an Infrastructure as a Service (IaaS) to deliver on-demand distributed virtual computers. Nebula users can receive required computing resources as a fully outsourced service. NASA Goddard Earth Science Data and Information Service Center (GES DISC) migrated several GES DISC's applications to the Nebula as a proof of concept, including: a) The Simple, Scalable, Script-based Science Processor for Measurements (S4PM) for processing scientific data; b) the Atmospheric Infrared Sounder (AIRS) data process workflow for processing AIRS raw data; and c) the GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (GIOVANNI) for online access to, analysis, and visualization of Earth science data. This work aims to evaluate the practicability and adaptability of the Nebula. The initial work focused on the AIRS data process workflow to evaluate the Nebula. The AIRS data process workflow consists of a series of algorithms being used to process raw AIRS level 0 data and output AIRS level 2 geophysical retrievals. Migrating the entire workflow to the Nebula platform is challenging, but practicable. After installing several supporting libraries and the processing code itself, the workflow is able to process AIRS data in a similar fashion to its current (non-cloud) configuration. We compared the performance of processing 2 days of AIRS level 0 data through level 2 using a Nebula virtual computer and a local Linux computer. The result shows that Nebula has significantly better performance than the local machine. Much of the difference was due to newer equipment in the Nebula than the legacy computer, which is suggestive of a potential economic advantage beyond elastic power, i.e., access to up-to-date hardware vs. legacy hardware that must be maintained past its prime to amortize the cost. In addition to a trade study of advantages and challenges of porting complex processing to the cloud, a tutorial was developed to enable further progress in utilizing the Nebula for Earth Science applications and understanding better the potential for Cloud Computing in further data- and computing-intensive Earth Science research. In particular, highly bursty computing such as that experienced in the user-demand-driven Giovanni system may become more tractable in a Cloud environment. Our future work will continue to focus on migrating more GES DISC's applications/instances, e.g. Giovanni instances, to the Nebula platform and making matured migrated applications to be in operation on the Nebula.

Low Cost, Scalable Proteomics Data Analysis Using Amazon's Cloud Computing Services and Open Source Search Algorithms

PubMed Central

Halligan, Brian D.; Geiger, Joey F.; Vallejos, Andrew K.; Greene, Andrew S.; Twigger, Simon N.

2009-01-01

One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step by step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center website (http://proteomics.mcw.edu/vipdac). PMID:19358578

Low cost, scalable proteomics data analysis using Amazon's cloud computing services and open source search algorithms.

PubMed

Halligan, Brian D; Geiger, Joey F; Vallejos, Andrew K; Greene, Andrew S; Twigger, Simon N

2009-06-01

One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step-by-step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center Web site ( http://proteomics.mcw.edu/vipdac ).

Cumulus cloud model estimates of trace gas transports

NASA Technical Reports Server (NTRS)

Garstang, Michael; Scala, John; Simpson, Joanne; Tao, Wei-Kuo; Thompson, A.; Pickering, K. E.; Harris, R.

1989-01-01

Draft structures in convective clouds are examined with reference to the results of the NASA Amazon Boundary Layer Experiments (ABLE IIa and IIb) and calculations based on a multidimensional time dependent dynamic and microphysical numerical cloud model. It is shown that some aspects of the draft structures can be calculated from measurements of the cloud environment. Estimated residence times in the lower regions of the cloud based on surface observations (divergence and vertical velocities) are within the same order of magnitude (about 20 min) as model trajectory estimates.

Aerosol characteristics and particle production in the upper troposphere over the Amazon Basin

NASA Astrophysics Data System (ADS)

Andreae, Meinrat O.; Afchine, Armin; Albrecht, Rachel; Amorim Holanda, Bruna; Artaxo, Paulo; Barbosa, Henrique M. J.; Borrmann, Stephan; Cecchini, Micael A.; Costa, Anja; Dollner, Maximilian; Fütterer, Daniel; Järvinen, Emma; Jurkat, Tina; Klimach, Thomas; Konemann, Tobias; Knote, Christoph; Krämer, Martina; Krisna, Trismono; Machado, Luiz A. T.; Mertes, Stephan; Minikin, Andreas; Pöhlker, Christopher; Pöhlker, Mira L.; Pöschl, Ulrich; Rosenfeld, Daniel; Sauer, Daniel; Schlager, Hans; Schnaiter, Martin; Schneider, Johannes; Schulz, Christiane; Spanu, Antonio; Sperling, Vinicius B.; Voigt, Christiane; Walser, Adrian; Wang, Jian; Weinzierl, Bernadett; Wendisch, Manfred; Ziereis, Helmut

2018-01-01

Airborne observations over the Amazon Basin showed high aerosol particle concentrations in the upper troposphere (UT) between 8 and 15 km altitude, with number densities (normalized to standard temperature and pressure) often exceeding those in the planetary boundary layer (PBL) by 1 or 2 orders of magnitude. The measurements were made during the German-Brazilian cooperative aircraft campaign ACRIDICON-CHUVA, where ACRIDICON stands for Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems and CHUVA is the acronym for Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (global precipitation measurement), on the German High Altitude and Long Range Research Aircraft (HALO). The campaign took place in September-October 2014, with the objective of studying tropical deep convective clouds over the Amazon rainforest and their interactions with atmospheric trace gases, aerosol particles, and atmospheric radiation. Aerosol enhancements were observed consistently on all flights during which the UT was probed, using several aerosol metrics, including condensation nuclei (CN) and cloud condensation nuclei (CCN) number concentrations and chemical species mass concentrations. The UT particles differed sharply in their chemical composition and size distribution from those in the PBL, ruling out convective transport of combustion-derived particles from the boundary layer (BL) as a source. The air in the immediate outflow of deep convective clouds was depleted of aerosol particles, whereas strongly enhanced number concentrations of small particles (< 90 nm diameter) were found in UT regions that had experienced outflow from deep convection in the preceding 5-72 h. We also found elevated concentrations of larger (> 90 nm) particles in the UT, which consisted mostly of organic matter and nitrate and were very effective CCN. Our findings suggest a conceptual model, where production of new aerosol particles takes place in the continental UT from biogenic volatile organic material brought up by deep convection and converted to condensable species in the UT. Subsequently, downward mixing and transport of upper tropospheric aerosol can be a source of particles to the PBL, where they increase in size by the condensation of biogenic volatile organic compound (BVOC) oxidation products. This may be an important source of aerosol particles for the Amazonian PBL, where aerosol nucleation and new particle formation have not been observed. We propose that this may have been the dominant process supplying secondary aerosol particles in the pristine atmosphere, making clouds the dominant control of both removal and production of atmospheric particles.

Aerosol characteristics and particle production in the upper troposphere over the Amazon Basin

DOE PAGES

Andreae, Meinrat O.; Afchine, Armin; Albrecht, Rachel; ...

2018-01-25

Airborne observations over the Amazon Basin showed high aerosol particle concentrations in the upper troposphere (UT) between 8 and 15 km altitude, with number densities (normalized to standard temperature and pressure) often exceeding those in the planetary boundary layer (PBL) by 1 or 2 orders of magnitude. The measurements were made during the German–Brazilian cooperative aircraft campaign ACRIDICON–CHUVA, where ACRIDICON stands for Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems and CHUVA is the acronym for Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (globalmore » precipitation measurement), on the German High Altitude and Long Range Research Aircraft (HALO). The campaign took place in September–October 2014, with the objective of studying tropical deep convective clouds over the Amazon rainforest and their interactions with atmospheric trace gases, aerosol particles, and atmospheric radiation. Aerosol enhancements were observed consistently on all flights during which the UT was probed, using several aerosol metrics, including condensation nuclei (CN) and cloud condensation nuclei (CCN) number concentrations and chemical species mass concentrations. The UT particles differed sharply in their chemical composition and size distribution from those in the PBL, ruling out convective transport of combustion-derived particles from the boundary layer (BL) as a source. The air in the immediate outflow of deep convective clouds was depleted of aerosol particles, whereas strongly enhanced number concentrations of small particles (< 90 nm diameter) were found in UT regions that had experienced outflow from deep convection in the preceding 5–72 h. We also found elevated concentrations of larger (> 90 nm) particles in the UT, which consisted mostly of organic matter and nitrate and were very effective CCN. Our findings suggest a conceptual model, where production of new aerosol particles takes place in the continental UT from biogenic volatile organic material brought up by deep convection and converted to condensable species in the UT. Subsequently, downward mixing and transport of upper tropospheric aerosol can be a source of particles to the PBL, where they increase in size by the condensation of biogenic volatile organic compound (BVOC) oxidation products. This may be an important source of aerosol particles for the Amazonian PBL, where aerosol nucleation and new particle formation have not been observed. We propose that this may have been the dominant process supplying secondary aerosol particles in the pristine atmosphere, making clouds the dominant control of both removal and production of atmospheric particles.« less

Aerosol characteristics and particle production in the upper troposphere over the Amazon Basin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andreae, Meinrat O.; Afchine, Armin; Albrecht, Rachel

Airborne observations over the Amazon Basin showed high aerosol particle concentrations in the upper troposphere (UT) between 8 and 15 km altitude, with number densities (normalized to standard temperature and pressure) often exceeding those in the planetary boundary layer (PBL) by 1 or 2 orders of magnitude. The measurements were made during the German–Brazilian cooperative aircraft campaign ACRIDICON–CHUVA, where ACRIDICON stands for Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems and CHUVA is the acronym for Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (globalmore » precipitation measurement), on the German High Altitude and Long Range Research Aircraft (HALO). The campaign took place in September–October 2014, with the objective of studying tropical deep convective clouds over the Amazon rainforest and their interactions with atmospheric trace gases, aerosol particles, and atmospheric radiation. Aerosol enhancements were observed consistently on all flights during which the UT was probed, using several aerosol metrics, including condensation nuclei (CN) and cloud condensation nuclei (CCN) number concentrations and chemical species mass concentrations. The UT particles differed sharply in their chemical composition and size distribution from those in the PBL, ruling out convective transport of combustion-derived particles from the boundary layer (BL) as a source. The air in the immediate outflow of deep convective clouds was depleted of aerosol particles, whereas strongly enhanced number concentrations of small particles (< 90 nm diameter) were found in UT regions that had experienced outflow from deep convection in the preceding 5–72 h. We also found elevated concentrations of larger (> 90 nm) particles in the UT, which consisted mostly of organic matter and nitrate and were very effective CCN. Our findings suggest a conceptual model, where production of new aerosol particles takes place in the continental UT from biogenic volatile organic material brought up by deep convection and converted to condensable species in the UT. Subsequently, downward mixing and transport of upper tropospheric aerosol can be a source of particles to the PBL, where they increase in size by the condensation of biogenic volatile organic compound (BVOC) oxidation products. This may be an important source of aerosol particles for the Amazonian PBL, where aerosol nucleation and new particle formation have not been observed. We propose that this may have been the dominant process supplying secondary aerosol particles in the pristine atmosphere, making clouds the dominant control of both removal and production of atmospheric particles.« less

Daytime turbulent exchange between the Amazon forest and the atmosphere

NASA Technical Reports Server (NTRS)

Fitzjarrald, David R.; Moore, Kathleen E.; Cabral, Osvaldo M. R.; Scolar, Jose; Manzi, Antonio O.; Deabreusa, Leonardo D.

1989-01-01

Detailed observations of turbulence just above and below the crown of the Amazon rain forest during the wet season are presented. The forest canopy is shown to remove high frequency turbulent fluctuations while passing lower frequencies. Filter characteristics of turbulent transfer into the Amazon rain forest canopy are quantified. Simple empirical relations that relate observed turbulent heat fluxes to horizontal wind variance are presented. Changes in the amount of turbulent coupling between the forest and the boundary layer associated with deep convective clouds are presented both as statistical averages and as a series of case studies. These convective processes during the rainy season are shown to alter the diurnal course of turbulent fluxes. In wake of giant coastal systems, no significant heat or moisture fluxes occur for up to a day after the event. Radar data is used to demonstrate that even small raining clouds are capable of evacuating the canopy of substances normally trapped by persistent static stability near the forest floor. Recovery from these events can take more than an hour, even during mid-day. In spite of the ubiquitous presence of clouds and frequent rain during this season, the average horizontal wind speed spectrum is well described by dry CBL similarity hypotheses originally found to apply in flat terrain.

Daytime turbulent exchange between the Amazon forest and the atmosphere

NASA Technical Reports Server (NTRS)

Fitzjarrald, David R.; Moore, Kathleen E.; Cabral, Osvaldo M. R.; Scolar, Jose; Manzi, Antonio

1990-01-01

Detailed observations of turbulence just above and below the crown of the Amazon rain forest during the wet season are presented. The forest canopy is shown to remove high frequency turbulent fluctuations while passing lower frequencies. Filter characteristics of turbulent transfer into the Amazon rain forest canopy are quantified. Simple empirical relations that relate observed turbulent heat fluxes to horizontal wind variance are presented. Changes in the amount of turbulent coupling between the forest and the boundary layer associated with deep convective clouds are presented both as statistical averages and as a series of case studies. These convective processes during the rainy season are shown to alter the diurnal course of turbulent fluxes. In wake of giant coastal systems, no significant heat or moisture fluxes occur for up to a day after the event. Radar data is used to demonstrate that even small raining clouds are capable of evacuating the canopy of substances normally trapped by persistent static stability near the forest floor. Recovery from these events can take more than an hour, even during mid-day. In spite of the ubiquitous presence of clouds and frequent rain during this season, the average horizontal wind speed spectrum is well described by dry CBL similarity hypotheses originally found to apply in flat terrain.

Earth Observation taken by the Expedition 20 crew

NASA Image and Video Library

2009-10-06

ISS020-E-047807 (6 Oct. 2009) --- Thunderstorms on the Brazilian horizon are featured in this image photographed by an Expedition 20 crew member on the International Space Station. A picturesque line of thunderstorms and numerous circular cloud patterns filled the view as the station crew members looked out at the limb and atmosphere (blue line on the horizon) of Earth. This region displayed in the photograph (top) includes an unstable, active atmosphere forming a large area of cumulonimbus clouds in various stages of development. The crew was looking west southwestward from the Amazon Basin, along the Rio Madeira, toward Bolivia when the image was taken. The distinctive circular patterns of the clouds in this view are likely caused by the aging of thunderstorms. Such ring structures often form during the final stages of a storm?s development as their centers collapse. Sunglint is visible on the waters of the Rio Madeira and Lago Acara in the Amazon Basin. Widespread haze over the basin gives the reflected light an orange hue. The Rio Madeira flows northward and joins the Amazon River on its path to the Atlantic Ocean. Scientists believe that a large smoke plume near the bottom center of the image may explain one source of the haze.

Cloud draft structure and trace gas transport

NASA Technical Reports Server (NTRS)

Scala, John R.; Tao, Wei-Kuo; Thompson, Anne M.; Simpson, Joanne; Garstang, Michael; Pickering, Kenneth E.; Browell, Edward V.; Sachse, Glen W.; Gregory, Gerald L.; Torres, Arnold L.

1990-01-01

During the second Amazon Boundary Layer Experiment (ABLE 2B), meteorological observations, chemical measurements, and model simulations are utilized in order to interpret convective cloud draft structure and to analyze its role in transport and vertical distribution of trace gases. One-dimensional photochemical model results suggest that the observed poststorm changes in ozone concentration can be attributed to convective transports rather than photochemical production and the results of a two-dimensional time-dependent cloud model simulation are presented for the May 6, 1987 squall system. The mesoscale convective system exhibited evidence of significant midlevel detrainment in addition to transports to anvil heights. Chemical measurements of O3 and CO obtained in the convective environment are used to predict photochemical production within the troposphere and to corroborate the cloud model results.

On the controls of deep convection and lightning in the Amazon

NASA Astrophysics Data System (ADS)

Albrecht, R. I.; Giangrande, S. E.; Wang, D.; Morales, C. A.; Pereira, R. F. O.; Machado, L.; Silva Dias, M. A. F.

2017-12-01

Local observations and remote sensing have been extensively used to unravel cloud distribution and life cycle but yet their representativeness in cloud resolve models (CRMs) and global climate models (GCMs) are still very poor. In addition, the complex cloud-aerosol-precipitation interactions (CAPI), as well as thermodynamics, dynamics and large scale controls on convection have been the focus of many studies in the last two decades but still no final answer has been reached on the overall impacts of these interactions and controls on clouds, especially on deep convection. To understand the environmental and CAPI controls of deep convection, cloud electrification and lightning activity in the pristine region of Amazon basin, in this study we use long term satellite and field campaign measurements to depict the characteristics of deep convection and the relationships between lightning and convective fluxes in this region. Precipitation and lightning activity from the Tropical Rainfall Measuring Mission (TRMM) satellite are combined with estimates of aerosol concentrations and reanalysis data to delineate the overall controls on thunderstorms. A more detailed analysis is obtained studying these controls on the relationship between lightning activity and convective mass fluxes using radar wind profiler and 3D total lightning during GoAmazon 2014/15 field campaign. We find evidences that the large scale conditions control the distribution of the precipitation, with widespread and more frequent mass fluxes of moderate intensity during the wet season, resulting in less vigorous convection and lower lightning activity. Under higher convective available potential energy, lightning is enhanced in polluted and background aerosol conditions. The relationships found in this study can be used in model parameterizations and ensemble evaluations of both lightning activity and lightning NOx from seasonal forecasting to climate projections and in a broader sense to Earth Climate System Modeling.

An Analysis of Cloud Computing with Amazon Web Services for the Atmospheric Science Data Center

NASA Astrophysics Data System (ADS)

Gleason, J. L.; Little, M. M.

2013-12-01

NASA science and engineering efforts rely heavily on compute and data handling systems. The nature of NASA science data is such that it is not restricted to NASA users, instead it is widely shared across a globally distributed user community including scientists, educators, policy decision makers, and the public. Therefore NASA science computing is a candidate use case for cloud computing where compute resources are outsourced to an external vendor. Amazon Web Services (AWS) is a commercial cloud computing service developed to use excess computing capacity at Amazon, and potentially provides an alternative to costly and potentially underutilized dedicated acquisitions whenever NASA scientists or engineers require additional data processing. AWS desires to provide a simplified avenue for NASA scientists and researchers to share large, complex data sets with external partners and the public. AWS has been extensively used by JPL for a wide range of computing needs and was previously tested on a NASA Agency basis during the Nebula testing program. Its ability to support the Langley Science Directorate needs to be evaluated by integrating it with real world operational needs across NASA and the associated maturity that would come with that. The strengths and weaknesses of this architecture and its ability to support general science and engineering applications has been demonstrated during the previous testing. The Langley Office of the Chief Information Officer in partnership with the Atmospheric Sciences Data Center (ASDC) has established a pilot business interface to utilize AWS cloud computing resources on a organization and project level pay per use model. This poster discusses an effort to evaluate the feasibility of the pilot business interface from a project level perspective by specifically using a processing scenario involving the Clouds and Earth's Radiant Energy System (CERES) project.

Application of microarray analysis on computer cluster and cloud platforms.

PubMed

Bernau, C; Boulesteix, A-L; Knaus, J

2013-01-01

Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

cryoem-cloud-tools: A software platform to deploy and manage cryo-EM jobs in the cloud.

PubMed

Cianfrocco, Michael A; Lahiri, Indrajit; DiMaio, Frank; Leschziner, Andres E

2018-06-01

Access to streamlined computational resources remains a significant bottleneck for new users of cryo-electron microscopy (cryo-EM). To address this, we have developed tools that will submit cryo-EM analysis routines and atomic model building jobs directly to Amazon Web Services (AWS) from a local computer or laptop. These new software tools ("cryoem-cloud-tools") have incorporated optimal data movement, security, and cost-saving strategies, giving novice users access to complex cryo-EM data processing pipelines. Integrating these tools into the RELION processing pipeline and graphical user interface we determined a 2.2 Å structure of ß-galactosidase in ∼55 hours on AWS. We implemented a similar strategy to submit Rosetta atomic model building and refinement to AWS. These software tools dramatically reduce the barrier for entry of new users to cloud computing for cryo-EM and are freely available at cryoem-tools.cloud. Copyright © 2018. Published by Elsevier Inc.

Cloud services for the Fermilab scientific stakeholders

DOE PAGES

Timm, S.; Garzoglio, G.; Mhashilkar, P.; ...

2015-12-23

As part of the Fermilab/KISTI cooperative research project, Fermilab has successfully run an experimental simulation workflow at scale on a federation of Amazon Web Services (AWS), FermiCloud, and local FermiGrid resources. We used the CernVM-FS (CVMFS) file system to deliver the application software. We established Squid caching servers in AWS as well, using the Shoal system to let each individual virtual machine find the closest squid server. We also developed an automatic virtual machine conversion system so that we could transition virtual machines made on FermiCloud to Amazon Web Services. We used this system to successfully run a cosmic raymore » simulation of the NOvA detector at Fermilab, making use of both AWS spot pricing and network bandwidth discounts to minimize the cost. On FermiCloud we also were able to run the workflow at the scale of 1000 virtual machines, using a private network routable inside of Fermilab. As a result, we present in detail the technological improvements that were used to make this work a reality.« less

Cloud services for the Fermilab scientific stakeholders

DOE Office of Scientific and Technical Information (OSTI.GOV)

Timm, S.; Garzoglio, G.; Mhashilkar, P.

As part of the Fermilab/KISTI cooperative research project, Fermilab has successfully run an experimental simulation workflow at scale on a federation of Amazon Web Services (AWS), FermiCloud, and local FermiGrid resources. We used the CernVM-FS (CVMFS) file system to deliver the application software. We established Squid caching servers in AWS as well, using the Shoal system to let each individual virtual machine find the closest squid server. We also developed an automatic virtual machine conversion system so that we could transition virtual machines made on FermiCloud to Amazon Web Services. We used this system to successfully run a cosmic raymore » simulation of the NOvA detector at Fermilab, making use of both AWS spot pricing and network bandwidth discounts to minimize the cost. On FermiCloud we also were able to run the workflow at the scale of 1000 virtual machines, using a private network routable inside of Fermilab. As a result, we present in detail the technological improvements that were used to make this work a reality.« less

VLBA DETERMINATION OF THE DISTANCE TO NEARBY STAR-FORMING REGIONS. IV. A PRELIMINARY DISTANCE TO THE PROTO-HERBIG AeBe STAR EC 95 IN THE SERPENS CORE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dzib, Sergio; Loinard, Laurent; Rodriguez, Luis F.

2010-08-01

Using the Very Long Base Array, we observed the young stellar object EC 95 in the Serpens cloud core at eight epochs from 2007 December to 2009 December. Two sources are detected in our field and are shown to form a tight binary system. The primary (EC 95a) is a 4-5 M {sub sun} proto-Herbig AeBe object (arguably the youngest such object known), whereas the secondary (EC 95b) is most likely a low-mass T Tauri star. Interestingly, both sources are non-thermal emitters. While T Tauri stars are expected to power a corona because they are convective while they go downmore » the Hayashi track, intermediate-mass stars approach the main sequence on radiative tracks. Thus, they are not expected to have strong superficial magnetic fields, and should not be magnetically active. We review several mechanisms that could produce the non-thermal emission of EC 95a and argue that the observed properties of EC 95a might be most readily interpreted if it possessed a corona powered by a rotation-driven convective layer. Using our observations, we show that the trigonometric parallax of EC 95 is {pi} = 2.41 {+-} 0.02 mas, corresponding to a distance of 414.9{sup +4.4} {sub -4.3} pc. We argue that this implies a distance to the Serpens core of 415 {+-} 5 pc and a mean distance to the Serpens cloud of 415 {+-} 25 pc. This value is significantly larger than previous estimates (d {approx} 260 pc) based on measurements of the extinction suffered by stars in the direction of Serpens. A possible explanation for this discrepancy is that these previous observations picked out foreground dust clouds associated with the Aquila Rift system rather than Serpens itself.« less

Cloud Surprises Discovered in Moving NASA EOSDIS Applications into Amazon Web Services… and #6 Will Shock You!

NASA Astrophysics Data System (ADS)

McLaughlin, B. D.; Pawloski, A. W.

2017-12-01

NASA ESDIS has been moving a variety of data ingest, distribution, and science data processing applications into a cloud environment over the last 2 years. As expected, there have been a number of challenges in migrating primarily on-premises applications into a cloud-based environment, related to architecture and taking advantage of cloud-based services. What was not expected is a number of issues that were beyond purely technical application re-architectures. From surprising network policy limitations, billing challenges in a government-based cost model, and obtaining certificates in an NASA security-compliant manner to working with multiple applications in a shared and resource-constrained AWS account, these have been the relevant challenges in taking advantage of a cloud model. And most surprising of all… well, you'll just have to wait and see the "gotcha" that caught our entire team off guard!

A PACS archive architecture supported on cloud services.

PubMed

Silva, Luís A Bastião; Costa, Carlos; Oliveira, José Luis

2012-05-01

Diagnostic imaging procedures have continuously increased over the last decade and this trend may continue in coming years, creating a great impact on storage and retrieval capabilities of current PACS. Moreover, many smaller centers do not have financial resources or requirements that justify the acquisition of a traditional infrastructure. Alternative solutions, such as cloud computing, may help address this emerging need. A tremendous amount of ubiquitous computational power, such as that provided by Google and Amazon, are used every day as a normal commodity. Taking advantage of this new paradigm, an architecture for a Cloud-based PACS archive that provides data privacy, integrity, and availability is proposed. The solution is independent from the cloud provider and the core modules were successfully instantiated in examples of two cloud computing providers. Operational metrics for several medical imaging modalities were tabulated and compared for Google Storage, Amazon S3, and LAN PACS. A PACS-as-a-Service archive that provides storage of medical studies using the Cloud was developed. The results show that the solution is robust and that it is possible to store, query, and retrieve all desired studies in a similar way as in a local PACS approach. Cloud computing is an emerging solution that promises high scalability of infrastructures, software, and applications, according to a "pay-as-you-go" business model. The presented architecture uses the cloud to setup medical data repositories and can have a significant impact on healthcare institutions by reducing IT infrastructures.

Crop classification and mapping based on Sentinel missions data in cloud environment

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.; Kussul, N.; Shelestov, A.; Vasiliev, V.

2017-12-01

Availability of high resolution satellite imagery (Sentinel-1/2/3, Landsat) over large territories opens new opportunities in agricultural monitoring. In particular, it becomes feasible to solve crop classification and crop mapping task at country and regional scale using time series of heterogenous satellite imagery. But in this case, we face with the problem of Big Data. Dealing with time series of high resolution (10 m) multispectral imagery we need to download huge volumes of data and then process them. The solution is to move "processing chain" closer to data itself to drastically shorten time for data transfer. One more advantage of such approach is the possibility to parallelize data processing workflow and efficiently implement machine learning algorithms. This could be done with cloud platform where Sentinel imagery are stored. In this study, we investigate usability and efficiency of two different cloud platforms Amazon and Google for crop classification and crop mapping problems. Two pilot areas were investigated - Ukraine and England. Google provides user friendly environment Google Earth Engine for Earth observation applications with a lot of data processing and machine learning tools already deployed. At the same time with Amazon one gets much more flexibility in implementation of his own workflow. Detailed analysis of pros and cons will be done in the presentation.

Cost-effective cloud computing: a case study using the comparative genomics tool, roundup.

PubMed

Kudtarkar, Parul; Deluca, Todd F; Fusaro, Vincent A; Tonellato, Peter J; Wall, Dennis P

2010-12-22

Comparative genomics resources, such as ortholog detection tools and repositories are rapidly increasing in scale and complexity. Cloud computing is an emerging technological paradigm that enables researchers to dynamically build a dedicated virtual cluster and may represent a valuable alternative for large computational tools in bioinformatics. In the present manuscript, we optimize the computation of a large-scale comparative genomics resource-Roundup-using cloud computing, describe the proper operating principles required to achieve computational efficiency on the cloud, and detail important procedures for improving cost-effectiveness to ensure maximal computation at minimal costs. Utilizing the comparative genomics tool, Roundup, as a case study, we computed orthologs among 902 fully sequenced genomes on Amazon's Elastic Compute Cloud. For managing the ortholog processes, we designed a strategy to deploy the web service, Elastic MapReduce, and maximize the use of the cloud while simultaneously minimizing costs. Specifically, we created a model to estimate cloud runtime based on the size and complexity of the genomes being compared that determines in advance the optimal order of the jobs to be submitted. We computed orthologous relationships for 245,323 genome-to-genome comparisons on Amazon's computing cloud, a computation that required just over 200 hours and cost $8,000 USD, at least 40% less than expected under a strategy in which genome comparisons were submitted to the cloud randomly with respect to runtime. Our cost savings projections were based on a model that not only demonstrates the optimal strategy for deploying RSD to the cloud, but also finds the optimal cluster size to minimize waste and maximize usage. Our cost-reduction model is readily adaptable for other comparative genomics tools and potentially of significant benefit to labs seeking to take advantage of the cloud as an alternative to local computing infrastructure.

Quantifying the microphysical impacts of fire aerosols on clouds in Indonesia using remote sensing observations

NASA Astrophysics Data System (ADS)

Tosca, M. G.; Diner, D. J.; Garay, M. J.; Kalashnikova, O. V.

2012-12-01

Fire-emitted aerosols modify cloud and precipitation dynamics by acting as cloud condensation nuclei in what is known as the first and second aerosol indirect effect. The cloud response to the indirect effect varies regionally and is not well understood in the highly convective tropics. We analyzed nine years (2003-2011) of aerosol data from the Multi-angle Imaging SpectroRadiometer (MISR), and fire emissions data from the Global Fire Emissions Database, version 3 (GFED3) over southeastern tropical Asia (Indonesia), and identified scenes that contained both a high atmospheric aerosol burden and large surface fire emissions. We then collected scenes from the Cloud Profiling Radar (CPR) on board the CLOUDSAT satellite that corresponded both spatially and temporally to the high-burning scenes from MISR, and identified differences in convective cloud dynamics over areas with varying aerosol optical depths. Differences in overpass times (MISR in the morning, CLOUDSAT in the afternoon) improved our ability to infer that changes in cloud dynamics were a response to increased or decreased aerosol emissions. Our results extended conclusions from initial studies over the Amazon that used remote sensing techniques to identify cloud fraction reductions in high burning areas (Koren et al., 2004; Rosenfeld, 1999) References Koren, I., Y.J. Kaufman, L.A. Remer and J.V. Martins (2004), Measurement of the effect of Amazon smoke on inhibition of cloud formation, Science, 303, 1342-1345 Rosenfeld, D. (1999), TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall, Gephys. Res. Lett., 26, 3105.

Making the most of cloud storage - a toolkit for exploitation by WLCG experiments

NASA Astrophysics Data System (ADS)

Alvarez Ayllon, Alejandro; Arsuaga Rios, Maria; Bitzes, Georgios; Furano, Fabrizio; Keeble, Oliver; Manzi, Andrea

2017-10-01

Understanding how cloud storage can be effectively used, either standalone or in support of its associated compute, is now an important consideration for WLCG. We report on a suite of extensions to familiar tools targeted at enabling the integration of cloud object stores into traditional grid infrastructures and workflows. Notable updates include support for a number of object store flavours in FTS3, Davix and gfal2, including mitigations for lack of vector reads; the extension of Dynafed to operate as a bridge between grid and cloud domains; protocol translation in FTS3; the implementation of extensions to DPM (also implemented by the dCache project) to allow 3rd party transfers over HTTP. The result is a toolkit which facilitates data movement and access between grid and cloud infrastructures, broadening the range of workflows suitable for cloud. We report on deployment scenarios and prototype experience, explaining how, for example, an Amazon S3 or Azure allocation can be exploited by grid workflows.

Tobacco Marketing, E-cigarette Susceptibility, and Perceptions among Adults.

PubMed

Nicksic, Nicole E; Snell, L Morgan; Rudy, Alyssa K; Cobb, Caroline O; Barnes, Andrew J

2017-09-01

Understanding the impact of tobacco marketing on e-cigarette (EC) susceptibility and perceptions is essential to inform efforts to mitigate tobacco product burden on public health. Data were collected online in 2016 from 634 conventional cigarette (CC) smokers and 393 non-smokers using a convenience sample from Amazon Mechanical Turk. Logistic regression models, stratified by smoking status and adjusted for socio-demographics, examined the relationship among tobacco advertisements and coupons, EC and CC susceptibility, and EC perceptions. Among non-smokers, increased exposure to tobacco advertising and receiving tobacco coupons was significantly related to measures of EC and CC susceptibility (p < .05). Older, more educated non-smokers had decreased odds of EC susceptibility (p < .05). Additionally, increased exposure to tobacco advertising was significantly associated with the perceptions of EC not containing nicotine and being less addictive than CC among smokers (p < .05). Increased exposure to tobacco advertising outlets could influence future EC and CC use in non-smokers and perceptions in smokers, while receiving coupons could affect EC and CC susceptibility among non-smokers. Future research is needed to determine whether policies to minimize exposure to tobacco marketing reduce EC use by decreasing susceptibility.

Low cost, high performance processing of single particle cryo-electron microscopy data in the cloud.

PubMed

Cianfrocco, Michael A; Leschziner, Andres E

2015-05-08

The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy (cryo-EM) as a technique to generate high-resolution structures. Calculating these structures requires high performance computing clusters, a resource that may be limiting to many likely cryo-EM users. To address this limitation and facilitate the spread of cryo-EM, we developed a publicly available 'off-the-shelf' computing environment on Amazon's elastic cloud computing infrastructure. This environment provides users with single particle cryo-EM software packages and the ability to create computing clusters with 16-480+ CPUs. We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters. Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM.

Modeling Aerosol Microphysical and Radiative Effects on Clouds and Implications for the Effects of Black and Brown Carbon on Clouds

NASA Astrophysics Data System (ADS)

Ten Hoeve, J. E.; Jacobson, M. Z.

2010-12-01

Satellite observational studies have found an increase in cloud fraction (CF) and cloud optical depth (COD) with increasing aerosol optical depth (AOD) followed by a decreasing CF/COD with increasing AOD at higher AODs over the Amazon Basin. The shape of this curve is similar to that of a boomerang, and thus the effect has been dubbed the "boomerang effect.” The increase in CF/COD with increasing AOD at low AODs is ascribed to the first and second indirect effects and is referred to as a microphysical effect of aerosols on clouds. The decrease in CF/COD at higher AODs is ascribed to enhanced warming of clouds due to absorbing aerosols, either as inclusions in drops or interstitially between drops. This is referred to as a radiative effect. To date, the interaction of the microphysical and radiative effects has not been simulated with a regional or global computer model. Here, we simulate the boomerang effect with the nested global-through-urban climate, air pollution, weather forecast model, GATOR-GCMOM, for the Amazon biomass burning season of 2006. We also compare the model with an extensive set of data, including satellite data from MODIS, TRMM, and CALIPSO, in situ surface observations, upper-air data, and AERONET data. Biomass burning emissions are obtained from the Global Fire Emissions Database (GFEDv2), and are combined with MODIS land cover data along with biomass burning emission factors. A high-resolution domain, nested within three increasingly coarser domains, is employed over the heaviest biomass burning region within the arc of deforestation. Modeled trends in cloud properties with aerosol loading compare well with MODIS observed trends, allowing causation of these observed correlations, including of the boomerang effect, to be determined by model results. The impact of aerosols on various cloud parameters, such as cloud optical thickness, cloud fraction, cloud liquid water/ice content, and precipitation, are shown through differences between simulations that include and exclude biomass burning emissions. This study suggests by cause and effect through numerical modeling that aerosol radiative effects counteract microphysical effects at high AODs, a result previously shown by correlation alone. As such, computer models that exclude treatment of cloud radiative effects are likely to overpredict the indirect effects of aerosols on clouds and underestimate the warming due to aerosols containing black carbon.

Cloudbus Toolkit for Market-Oriented Cloud Computing

NASA Astrophysics Data System (ADS)

Buyya, Rajkumar; Pandey, Suraj; Vecchiola, Christian

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.

NGScloud: RNA-seq analysis of non-model species using cloud computing.

PubMed

Mora-Márquez, Fernando; Vázquez-Poletti, José Luis; López de Heredia, Unai

2018-05-03

RNA-seq analysis usually requires large computing infrastructures. NGScloud is a bioinformatic system developed to analyze RNA-seq data using the cloud computing services of Amazon that permit the access to ad hoc computing infrastructure scaled according to the complexity of the experiment, so its costs and times can be optimized. The application provides a user-friendly front-end to operate Amazon's hardware resources, and to control a workflow of RNA-seq analysis oriented to non-model species, incorporating the cluster concept, which allows parallel runs of common RNA-seq analysis programs in several virtual machines for faster analysis. NGScloud is freely available at https://github.com/GGFHF/NGScloud/. A manual detailing installation and how-to-use instructions is available with the distribution. unai.lopezdeheredia@upm.es.

The Green Ocean: Precipitation Insights from the GoAmazon2014/5 Experiment

DOE PAGES

Wang, Die; Giangrande, Scott E.; Bartholomew, Mary Jane; ...

2018-02-07

This study summarizes the precipitation properties collected during the GoAmazon2014/5 campaign near Manaus in central Amazonia, Brazil. Precipitation breakdowns, summary radar rainfall relationships and self-consistency concepts from a coupled disdrometer and radar wind profiler measurements are presented. The properties of Amazon cumulus and associated stratiform precipitation are discussed, including segregations according to seasonal (Wet/Dry regime) variability, cloud echo-top height and possible aerosol influences on the apparent oceanic characteristics of the precipitation drop size distributions. Overall, we observe that the Amazon precipitation straddles behaviors found during previous U.S. Department of Energy Atmospheric Radiation Measurements program (ARM) tropical deployments, with distributions favoringmore » higher concentrations of smaller drops than ARM continental examples. Oceanic type precipitation characteristics are predominantly observed during the Amazon Wet seasons. Finally, an exploration of the controls on Wet season precipitation properties reveals that wind direction, as compared with other standard radiosonde thermodynamic parameters or aerosol count/regime classifications performed at the ARM site, provides a good indicator for those Wet season Amazon events having an oceanic character for their precipitation drop size distributions.« less

The Green Ocean: Precipitation Insights from the GoAmazon2014/5 Experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Die; Giangrande, Scott E.; Bartholomew, Mary Jane

This study summarizes the precipitation properties collected during the GoAmazon2014/5 campaign near Manaus in central Amazonia, Brazil. Precipitation breakdowns, summary radar rainfall relationships and self-consistency concepts from a coupled disdrometer and radar wind profiler measurements are presented. The properties of Amazon cumulus and associated stratiform precipitation are discussed, including segregations according to seasonal (Wet/Dry regime) variability, cloud echo-top height and possible aerosol influences on the apparent oceanic characteristics of the precipitation drop size distributions. Overall, we observe that the Amazon precipitation straddles behaviors found during previous U.S. Department of Energy Atmospheric Radiation Measurements program (ARM) tropical deployments, with distributions favoringmore » higher concentrations of smaller drops than ARM continental examples. Oceanic type precipitation characteristics are predominantly observed during the Amazon Wet seasons. Finally, an exploration of the controls on Wet season precipitation properties reveals that wind direction, as compared with other standard radiosonde thermodynamic parameters or aerosol count/regime classifications performed at the ARM site, provides a good indicator for those Wet season Amazon events having an oceanic character for their precipitation drop size distributions.« less

Mouths of the Amazon River, Brazil, South America

NASA Technical Reports Server (NTRS)

1991-01-01

Huge sediment loads from the interior of the country flow through the Mouths of the Amazon River, Brazil (0.5S, 50.0W). The river current carries hundreds of tons of sediment through the multiple outlets of the great river over 100 miles from shore before it is carried northward by oceanic currents. The characteristic 'fair weather cumulus' pattern of low clouds over the land but not over water may be observed in this scene.

Mouths of the Amazon River, Brazil, South America

NASA Image and Video Library

1991-08-11

Huge sediment loads from the interior of the country flow through the Mouths of the Amazon River, Brazil (0.5S, 50.0W). The river current carries hundreds of tons of sediment through the multiple outlets of the great river over 100 miles from shore before it is carried northward by oceanic currents. The characteristic "fair weather cumulus" pattern of low clouds over the land but not over water may be observed in this scene.

Towards Efficient Scientific Data Management Using Cloud Storage

NASA Technical Reports Server (NTRS)

He, Qiming

2013-01-01

A software prototype allows users to backup and restore data to/from both public and private cloud storage such as Amazon's S3 and NASA's Nebula. Unlike other off-the-shelf tools, this software ensures user data security in the cloud (through encryption), and minimizes users operating costs by using space- and bandwidth-efficient compression and incremental backup. Parallel data processing utilities have also been developed by using massively scalable cloud computing in conjunction with cloud storage. One of the innovations in this software is using modified open source components to work with a private cloud like NASA Nebula. Another innovation is porting the complex backup to- cloud software to embedded Linux, running on the home networking devices, in order to benefit more users.

Changes of cloudiness over tropical land during the past few decades and its link to global climate change

NASA Astrophysics Data System (ADS)

Arias, P.; Fu, R.; Li, W.

2007-12-01

Tropical forests play a key role in determining the global carbon-climate feedback in the 21st century. Changes in rainforest growth and mortality rates, especially in the deep and least perturbed forest areas, have been consistently observed across global tropics in recent years. Understanding the underlying causes of these changes, especially their links to the global climate change, is especially important in determining the future of the tropical rainforests in the 21st century. Previous studies have mostly focus on the potential influences from elevated atmospheric CO2 and increasing surface temperature. Because the rainforests in wet tropical region is often light limited, we explore whether cloudiness have changed, if so, whether it is consistent with that expected from changes in forest growth rate. We will report our observational analysis examining the trends in annual average shortwave (SW) downwelling radiation, total cloud cover, and cumulus cover over the tropical land regions and to link them with trends in convective available potencial energy (CAPE). ISCCP data and radiosonde records available from the Department of Atmospheric Sciences of the University of Wyoming (http://www.weather.uwyo.edu/upperair/sounding.html) are used to study the trends. The period for the trend analysis is 1984-2004 for the ISCCP data and 1980-2006 for the radiosondes. The results for the Amazon rainforest region suggest a decreasing trend in total cloud and convective cloud covers, which results in an increase in downwelling SW radiation at the surface. These changes of total and convective clouds are consistent with a trend of decreasing CAPE and an elevated Level of Free Convection (LFC) height, as obtained from the radiosondes. All the above mentioned trends are statistically significant based on the Mann-Kendall test with 95% of confidence. These results consistently suggest the downward surface solar radiation has been increasing since 1984, result from a decrease of convective and total cloudiness over the Southern Amazon basin, due to an increase of LFC and atmospheric thermodynamic stability. Such an increase of surface SW radiation probably has contributed to the increasing in growth rate for the forests in the Amazon forests. Currently, the same analysis is being applied using radiosonde data from the Comprehensive Aerological Reference Data Set (CARDS) over the Amazon and Congo basins and the Southeast Asia. Our objective is to identify changes in cloudiness over tropical land and identify its underlying causes, especially the link to changes in surface temperature and humidity.

Andes Altiplano, South America

NASA Image and Video Library

1991-08-11

STS043-151-159 (2-11 August 1991) --- This photograph looks westward over the high plateau of the southern Peruvian Andes west and north of Lake Titicaca (not in field of view). Lima, Peru lies under the clouds just north of the clear coastal area. Because the high Andes have been uplifted 10,000 to 13,000 feet during the past 20 million years, the rivers which cut down to the Pacific Ocean have gorges almost that deep, such as the Rio Ocona at the bottom of the photograph. The eastern slopes of the Andes are heavily forested, forming the headwaters of the Amazon system. Smoke from burning in the Amazon basin fills river valleys on the right side of the photograph. A Linhof camera was used to take this view.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qian, Yun; Gong, Daoyi; Fan, Jiwen

Long-term observational data reveal that both the frequency and amount of light rain have decreased in eastern China (EC) for 1956-2005 with high spatial coherency. This is different from the trend of total rainfall observed in EC, which decreases in northern EC and increases in southern EC. To examine the cause of the light rain trends, we analyzed the long-term variability of atmospheric water vapor and its correlation with light rain events. Results show very weak relationships between large-scale moisture transport and light rain in EC. This suggests that light rain trend in EC is not driven by large-scale circulationmore » changes. Because of human activities, pollutant emission has increased dramatically in China for the last few decades, leading to significant reductions in visibility between 1960 and 2000. Cloud-resolving model simulations show that aerosols corresponding to heavily polluted conditions can significantly increase the cloud droplet number concentration (CDNC) and reduce droplet sizes compared to pristine conditions. This can lead to a significant decline in raindrop concentration and delay raindrop formation because smaller cloud droplets are less efficient in the collision and coalescence processes. Together with weaker convection, the precipitation frequency and amount are significantly reduced in the polluted case. Satellite data also reveal higher CDNC and smaller droplet size over polluted land in EC relative to pristine regions, which is consistent with the model results. This evidence suggests that the significantly increased aerosol particles produced by air pollution are at least partly responsible for the decreased light rain events observed in China over the past fifty years.« less

Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE.

PubMed

Trinh, Quang M; Jen, Fei-Yang Arthur; Zhou, Ziru; Chu, Kar Ming; Perry, Marc D; Kephart, Ellen T; Contrino, Sergio; Ruzanov, Peter; Stein, Lincoln D

2013-07-22

Funded by the National Institutes of Health (NIH), the aim of the Model Organism ENCyclopedia of DNA Elements (modENCODE) project is to provide the biological research community with a comprehensive encyclopedia of functional genomic elements for both model organisms C. elegans (worm) and D. melanogaster (fly). With a total size of just under 10 terabytes of data collected and released to the public, one of the challenges faced by researchers is to extract biologically meaningful knowledge from this large data set. While the basic quality control, pre-processing, and analysis of the data has already been performed by members of the modENCODE consortium, many researchers will wish to reinterpret the data set using modifications and enhancements of the original protocols, or combine modENCODE data with other data sets. Unfortunately this can be a time consuming and logistically challenging proposition. In recognition of this challenge, the modENCODE DCC has released uniform computing resources for analyzing modENCODE data on Galaxy (https://github.com/modENCODE-DCC/Galaxy), on the public Amazon Cloud (http://aws.amazon.com), and on the private Bionimbus Cloud for genomic research (http://www.bionimbus.org). In particular, we have released Galaxy workflows for interpreting ChIP-seq data which use the same quality control (QC) and peak calling standards adopted by the modENCODE and ENCODE communities. For convenience of use, we have created Amazon and Bionimbus Cloud machine images containing Galaxy along with all the modENCODE data, software and other dependencies. Using these resources provides a framework for running consistent and reproducible analyses on modENCODE data, ultimately allowing researchers to use more of their time using modENCODE data, and less time moving it around.

Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE

PubMed Central

2013-01-01

Background Funded by the National Institutes of Health (NIH), the aim of the Model Organism ENCyclopedia of DNA Elements (modENCODE) project is to provide the biological research community with a comprehensive encyclopedia of functional genomic elements for both model organisms C. elegans (worm) and D. melanogaster (fly). With a total size of just under 10 terabytes of data collected and released to the public, one of the challenges faced by researchers is to extract biologically meaningful knowledge from this large data set. While the basic quality control, pre-processing, and analysis of the data has already been performed by members of the modENCODE consortium, many researchers will wish to reinterpret the data set using modifications and enhancements of the original protocols, or combine modENCODE data with other data sets. Unfortunately this can be a time consuming and logistically challenging proposition. Results In recognition of this challenge, the modENCODE DCC has released uniform computing resources for analyzing modENCODE data on Galaxy (https://github.com/modENCODE-DCC/Galaxy), on the public Amazon Cloud (http://aws.amazon.com), and on the private Bionimbus Cloud for genomic research (http://www.bionimbus.org). In particular, we have released Galaxy workflows for interpreting ChIP-seq data which use the same quality control (QC) and peak calling standards adopted by the modENCODE and ENCODE communities. For convenience of use, we have created Amazon and Bionimbus Cloud machine images containing Galaxy along with all the modENCODE data, software and other dependencies. Conclusions Using these resources provides a framework for running consistent and reproducible analyses on modENCODE data, ultimately allowing researchers to use more of their time using modENCODE data, and less time moving it around. PMID:23875683

Mobile healthcare information management utilizing Cloud Computing and Android OS.

PubMed

Doukas, Charalampos; Pliakas, Thomas; Maglogiannis, Ilias

2010-01-01

Cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. This work presents the implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing. The mobile application is developed using Google's Android operating system and provides management of patient health records and medical images (supporting DICOM format and JPEG2000 coding). The developed system has been evaluated using the Amazon's S3 cloud service. This article summarizes the implementation details and presents initial results of the system in practice.

Elastic Cloud Computing Infrastructures in the Open Cirrus Testbed Implemented via Eucalyptus

NASA Astrophysics Data System (ADS)

Baun, Christian; Kunze, Marcel

Cloud computing realizes the advantages and overcomes some restrictionsof the grid computing paradigm. Elastic infrastructures can easily be createdand managed by cloud users. In order to accelerate the research ondata center management and cloud services the OpenCirrusTM researchtestbed has been started by HP, Intel and Yahoo!. Although commercialcloud offerings are proprietary, Open Source solutions exist in the field ofIaaS with Eucalyptus, PaaS with AppScale and at the applications layerwith Hadoop MapReduce. This paper examines the I/O performance ofcloud computing infrastructures implemented with Eucalyptus in contrastto Amazon S3.

Measurements of the light-absorbing material inside cloud droplets and its effect on cloud albedo

NASA Technical Reports Server (NTRS)

Twohy, C. H.; Clarke, A. D.; Warren, Stephen G.; Radke, L. F.; Charleson, R. J.

1990-01-01

Most of the measurements of light-absorbing aerosol particles made previously have been in non-cloudy air and therefore provide no insight into aerosol effects on cloud properties. Here, researchers describe an experiment designed to measure light absorption exclusively due to substances inside cloud droplets, compare the results to related light absorption measurements, and evaluate possible effects on the albedo of clouds. The results of this study validate those of Twomey and Cocks and show that the measured levels of light-absorbing material are negligible for the radiative properties of realistic clouds. For the measured clouds, which appear to have been moderately polluted, the amount of elemental carbon (EC) present was insufficient to affect albedo. Much higher contaminant levels or much larger droplets than those measured would be necessary to significantly alter the radiative properties. The effect of the concentrations of EC actually measured on the albedo of snow, however, would be much more pronounced since, in contrast to clouds, snowpacks are usually optically semi-infinite and have large particle sizes.

Cloud Surprises in Moving NASA EOSDIS Applications into Amazon Web Services

NASA Technical Reports Server (NTRS)

Mclaughlin, Brett

2017-01-01

NASA ESDIS has been moving a variety of data ingest, distribution, and science data processing applications into a cloud environment over the last 2 years. As expected, there have been a number of challenges in migrating primarily on-premises applications into a cloud-based environment, related to architecture and taking advantage of cloud-based services. What was not expected is a number of issues that were beyond purely technical application re-architectures. We ran into surprising network policy limitations, billing challenges in a government-based cost model, and difficulty in obtaining certificates in an NASA security-compliant manner. On the other hand, this approach has allowed us to move a number of applications from local hosting to the cloud in a matter of hours (yes, hours!!), and our CMR application now services 95% of granule searches and an astonishing 99% of all collection searches in under a second. And most surprising of all, well, you'll just have to wait and see the realization that caught our entire team off guard!

Impact of aerosol microphysics on cloud optical properties and implications to atmospheric oxidation capacity

NASA Astrophysics Data System (ADS)

Luo, G.; Yu, F.

2016-12-01

GEOS-Chem, presently used by many research groups, is a state-of-the-art global 3-D model of atmospheric composition driven by assimilated meteorology from the Goddard Earth Observing System. Our comparisons of GEOS-5 cloud properties, used in photolysis rate calculation, with MODIS retrievals show that GEOS-5 underestimates cloud optical depth (COD) by a factor of more than 2 over most regions. Our further analysis indicates that the COD underestimation in the released GEOS-5 meteorology products is likely to be associated with the fact that the GEOS-5 doesn't take into account the impact of aerosol on cloud microphysics and optical properties. A new COD parameterization (called NewC thereafter), which re-calculates COD from GEOS-5 water content and GEOS-Chem simulated size-resolved particle properties, cloud condensation nuclei abundance, and cloud droplet number concentration, has been developed and incorporated into GEOS-Chem. The NewC increases the GEOS-5 derived annual mean CODs averaged between 60ºS and 60ºN from 2.0 to 4.3, in much better agreement with corresponding MODIS value of 4.3. The enhanced COD based on NewC scheme reduces global average boundary layer OH concentration by 9.8%. Zonal averaged OH is increased 3-7% above clouds due to backscattering and decreased 10-15% below clouds due to the attenuation of solar radiation. The global mean OH concentration simulated by GEOS-Chem driven by GEOS-5 COD and NewC COD are respectively12.9 × 105molec cm-3 and 12.2 × 105molec cm-3, with the latter closer to the ACCMIP multi-model estimation (11.7±1 105molec cm-3). Surface OH concentrations over major anthropogenic regions such as Eastern US, Europe, and East Asia decrease by up to -18.6%, -14.4%, and -19.9%, respectively. The relative change of surface OH concentration appears to be the largest over the Amazon rainforest, reaching up to -30%. After switching from old COD to NewC COD, GEOS-Chem simulated column HCHO and surface isoprene over Amazon region are enhanced 12% and 20%, respectively. At the Eastern United States, column HCHO and surface isoprene are enhanced 5% and 15%, respectively. Seasonal variation and regional characteristics of the impact of aerosol microphysics on cloud properties and implications to atmospheric oxidation capacity and tropospheric compositions will be discussed.

Impact of elevated CO2 concentration on dynamics of leaf photosynthesis in Fagus sylvatica is modulated by sky conditions.

PubMed

Urban, Otmar; Klem, Karel; Holišová, Petra; Šigut, Ladislav; Šprtová, Mirka; Teslová-Navrátilová, Petra; Zitová, Martina; Špunda, Vladimír; Marek, Michal V; Grace, John

2014-02-01

It has been suggested that atmospheric CO2 concentration and frequency of cloud cover will increase in future. It remains unclear, however, how elevated CO2 influences photosynthesis under complex clear versus cloudy sky conditions. Accordingly, diurnal changes in photosynthetic responses among beech trees grown at ambient (AC) and doubled (EC) CO2 concentrations were studied under contrasting sky conditions. EC stimulated the daily sum of fixed CO2 and light use efficiency under clear sky. Meanwhile, both these parameters were reduced under cloudy sky as compared with AC treatment. Reduction in photosynthesis rate under cloudy sky was particularly associated with EC-stimulated, xanthophyll-dependent thermal dissipation of absorbed light energy. Under clear sky, a pronounced afternoon depression of CO2 assimilation rate was found in sun-adapted leaves under EC compared with AC conditions. This was caused in particular by stomata closure mediated by vapour pressure deficit. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Virtual Facility at Fermilab: Infrastructure and Services Expand to Public Clouds

DOE PAGES

Timm, Steve; Garzoglio, Gabriele; Cooper, Glenn; ...

2016-02-18

In preparation for its new Virtual Facility Project, Fermilab has launched a program of work to determine the requirements for running a computation facility on-site, in public clouds, or a combination of both. This program builds on the work we have done to successfully run experimental workflows of 1000-VM scale both on an on-site private cloud and on Amazon AWS. To do this at scale we deployed dynamically launched and discovered caching services on the cloud. We are now testing the deployment of more complicated services on Amazon AWS using native load balancing and auto scaling features they provide. Themore » Virtual Facility Project will design and develop a facility including infrastructure and services that can live on the site of Fermilab, off-site, or a combination of both. We expect to need this capacity to meet the peak computing requirements in the future. The Virtual Facility is intended to provision resources on the public cloud on behalf of the facility as a whole instead of having each experiment or Virtual Organization do it on their own. We will describe the policy aspects of a distributed Virtual Facility, the requirements, and plans to make a detailed comparison of the relative cost of the public and private clouds. Furthermore, this talk will present the details of the technical mechanisms we have developed to date, and the plans currently taking shape for a Virtual Facility at Fermilab.« less

Virtual Facility at Fermilab: Infrastructure and Services Expand to Public Clouds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Timm, Steve; Garzoglio, Gabriele; Cooper, Glenn

In preparation for its new Virtual Facility Project, Fermilab has launched a program of work to determine the requirements for running a computation facility on-site, in public clouds, or a combination of both. This program builds on the work we have done to successfully run experimental workflows of 1000-VM scale both on an on-site private cloud and on Amazon AWS. To do this at scale we deployed dynamically launched and discovered caching services on the cloud. We are now testing the deployment of more complicated services on Amazon AWS using native load balancing and auto scaling features they provide. Themore » Virtual Facility Project will design and develop a facility including infrastructure and services that can live on the site of Fermilab, off-site, or a combination of both. We expect to need this capacity to meet the peak computing requirements in the future. The Virtual Facility is intended to provision resources on the public cloud on behalf of the facility as a whole instead of having each experiment or Virtual Organization do it on their own. We will describe the policy aspects of a distributed Virtual Facility, the requirements, and plans to make a detailed comparison of the relative cost of the public and private clouds. Furthermore, this talk will present the details of the technical mechanisms we have developed to date, and the plans currently taking shape for a Virtual Facility at Fermilab.« less

Hybrid cloud and cluster computing paradigms for life science applications

PubMed Central

2010-01-01

Background Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister. Results Comparisons of Amazon, Azure, and traditional Linux and Windows environments on common applications have shown encouraging performance and usability comparisons in several important non iterative cases. These are linked to MPI applications for final stages of the data analysis. Further we have released the open source Twister Iterative MapReduce and benchmarked it against basic MapReduce (Hadoop) and MPI in information retrieval and life sciences applications. Conclusions The hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications. Methods We used commercial clouds Amazon and Azure and the NSF resource FutureGrid to perform detailed comparisons and evaluations of different approaches to data intensive computing. Several applications were developed in MPI, MapReduce and Twister in these different environments. PMID:21210982

Hybrid cloud and cluster computing paradigms for life science applications.

PubMed

Qiu, Judy; Ekanayake, Jaliya; Gunarathne, Thilina; Choi, Jong Youl; Bae, Seung-Hee; Li, Hui; Zhang, Bingjing; Wu, Tak-Lon; Ruan, Yang; Ekanayake, Saliya; Hughes, Adam; Fox, Geoffrey

2010-12-21

Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister. Comparisons of Amazon, Azure, and traditional Linux and Windows environments on common applications have shown encouraging performance and usability comparisons in several important non iterative cases. These are linked to MPI applications for final stages of the data analysis. Further we have released the open source Twister Iterative MapReduce and benchmarked it against basic MapReduce (Hadoop) and MPI in information retrieval and life sciences applications. The hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications. We used commercial clouds Amazon and Azure and the NSF resource FutureGrid to perform detailed comparisons and evaluations of different approaches to data intensive computing. Several applications were developed in MPI, MapReduce and Twister in these different environments.

Integration of Cloud Technologies for Data Stewardship at the NOAA National Centers for Environmental Information (NCEI)

NASA Astrophysics Data System (ADS)

Casey, K. S.; Hausman, S. A.

2016-02-01

In the last year, the NOAA National Oceanographic Data Center (NODC) and its siblings, the National Climatic Data Center and National Geophysical Data Center, were merged into one organization, the NOAA National Centers for Environmental Information (NCEI). Combining its expertise under one management has helped NCEI accelerate its efforts to embrace and integrate private, public, and hybrid cloud environments into its range of data stewardship services. These services span a range of tiers, from basic, long-term preservation and access, through enhanced access and scientific quality control, to authoritative product development and international-level services. Throughout these tiers of stewardship, partnerships and pilot projects have been launched to identify technological and policy-oriented challenges, to establish solutions to these problems, and to highlight success stories for emulation during operational integration of the cloud into NCEI's data stewardship activities. Some of these pilot activities including data storage, access, and reprocessing in Amazon Web Services, the OneStop data discovery and access framework project, and a set of Cooperative Research and Development Agreements under the Big Data Project with Amazon, Google, IBM, Microsoft, and the Open Cloud Consortium. Progress in these efforts will be highlighted along with a future vision of how NCEI could leverage hybrid cloud deployments and federated systems across NOAA to enable effective data stewardship for its oceanographic, atmospheric, climatic, and geophysical Big Data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, Jiwen; Rosenfeld, Daniel; Zhang, Yuwei

Aerosol-cloud interactions remain the largest uncertainty in climate projections. Ultrafine aerosol particles smaller than 50 nanometers (UAP <50) can be abundant in the troposphere, but are conventionally considered too small to affect cloud formation. However, observational evidence and numerical simulations of deep convective clouds (DCCs) over the Amazon show that DCCs forming in a low aerosol environment can develop very large vapor supersaturation because fast droplet coalescence reduces integrated droplet surface area and subsequent condensation. UAP <50 from pollution plumes that are ingested into such clouds can be activated to form additional cloud droplets on which excess supersaturation condenses andmore » forms additional cloud water and latent heating, thus intensifying convective strength. This mechanism suggests a strong anthropogenic invigoration of DCCs in previously pristine regions of the world.« less

Satellite-Observed Vertical Structures of Clouds over the Amazon Basin

NASA Astrophysics Data System (ADS)

Wu, M.; Lee, J. E.

2017-12-01

The long wet season of the Amazon basin currently plays a critical role in the terrestrial ecosystem, regulating carbon balance and supporting high biodiversity. It has been argued that the land surface processes are important in maintaining high precipitation; yet, how the land-atmosphere interactions modulate the atmospheric processes are not completely understood. As a first step toward solving this problem, here we examine the vertical structures of clouds and the thermodynamics of the atmosphere over the entire basin at the different time of the year. We combine the vertical distribution of cloud water content from CloudSat, and the atmospheric thermodynamic conditions from the ECMWF ERA-interim reanalysis to compare and contrast the atmospheric condition at different time of the year-the wet, dry, and dry-to-wet transition seasons-and in different regions-ever-wet evergreen broadleaf forests, wet evergreen broadleaf forests with a dry season, and dry wooded grasslands/woodlands-following water stress gradient. In the ever-wet and wet regions, a large amount of cloud ice water is present in the upper atmosphere (above 11km) and convective available potential energy (CAPE) is high during the transition season, supporting the claim that the convective activity is strongest during the transition season. In the dry region, there are more cloud water above 8km over woodlands than over wooded grasslands during the dry and transition seasons, indicating the influence of the land cover. We also classified our data following the large-scale circulation pattern, and the CloudSat data support more deep convective activities in the wet and dry regions when the wind blows from the east during the wet and transition seasons. As a next step, we will focus more on linking the cloud structure to the large-scale circulation and surface processes.

Performance, Agility and Cost of Cloud Computing Services for NASA GES DISC Giovanni Application

NASA Astrophysics Data System (ADS)

Pham, L.; Chen, A.; Wharton, S.; Winter, E. L.; Lynnes, C.

2013-12-01

The NASA Goddard Earth Science Data and Information Services Center (GES DISC) is investigating the performance, agility and cost of Cloud computing for GES DISC applications. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure), one of the core applications at the GES DISC for online climate-related Earth science data access, subsetting, analysis, visualization, and downloading, was used to evaluate the feasibility and effort of porting an application to the Amazon Cloud Services platform. The performance and the cost of running Giovanni on the Amazon Cloud were compared to similar parameters for the GES DISC local operational system. A Giovanni Time-Series analysis of aerosol absorption optical depth (388nm) from OMI (Ozone Monitoring Instrument)/Aura was selected for these comparisons. All required data were pre-cached in both the Cloud and local system to avoid data transfer delays. The 3-, 6-, 12-, and 24-month data were used for analysis on the Cloud and local system respectively, and the processing times for the analysis were used to evaluate system performance. To investigate application agility, Giovanni was installed and tested on multiple Cloud platforms. The cost of using a Cloud computing platform mainly consists of: computing, storage, data requests, and data transfer in/out. The Cloud computing cost is calculated based on the hourly rate, and the storage cost is calculated based on the rate of Gigabytes per month. Cost for incoming data transfer is free, and for data transfer out, the cost is based on the rate in Gigabytes. The costs for a local server system consist of buying hardware/software, system maintenance/updating, and operating cost. The results showed that the Cloud platform had a 38% better performance and cost 36% less than the local system. This investigation shows the potential of cloud computing to increase system performance and lower the overall cost of system management.

Low cost, high performance processing of single particle cryo-electron microscopy data in the cloud

PubMed Central

Cianfrocco, Michael A; Leschziner, Andres E

2015-01-01

The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy (cryo-EM) as a technique to generate high-resolution structures. Calculating these structures requires high performance computing clusters, a resource that may be limiting to many likely cryo-EM users. To address this limitation and facilitate the spread of cryo-EM, we developed a publicly available ‘off-the-shelf’ computing environment on Amazon's elastic cloud computing infrastructure. This environment provides users with single particle cryo-EM software packages and the ability to create computing clusters with 16–480+ CPUs. We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters. Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM. DOI: http://dx.doi.org/10.7554/eLife.06664.001 PMID:25955969

Machine Learing Applications on a Radar Wind Profiler Deployment During the ARM GoAmazon2014/5 Campaign

NASA Astrophysics Data System (ADS)

Giangrande, S. E.; WANG, D.; Hardin, J. C.; Mitchell, J.

2017-12-01

As part of the 2 year Department of Energy Atmospheric Radiation Measurement (ARM) Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign, the ARM Mobile Facility (AMF) collected a unique set of observations in a region of strong climatic significance near Manacapuru, Brazil. An important example for the beneficial observational record obtained by ARM during this campaign was that of the Radar Wind Profiler (RWP). This dataset has been previously documented for providing critical convective cloud vertical air velocity retrievals and precipitation properties (e.g., calibrated reflectivity factor Z, rainfall rates) under a wide variety of atmospheric conditions. Vertical air motion estimates to within deep convective cores such as those available from this RWP system have been previously identified as critical constraints for ongoing global climate modeling activities and deep convective cloud process studies. As an extended deployment within this `green ocean' region, the RWP site and collocated AMF surface gauge instrumentation experienced a unique hybrid of tropical and continental precipitation conditions, including multiple wet and dry season precipitation regimes, convective and organized stratiform storm dynamics and contributions to rainfall accumulation, pristine aerosol conditions of the locale, as well as the effects of the Manaus, Brazil, mega city pollution plume. For hydrological applications and potential ARM products, machine learning methods developed using this dataset are explored to demonstrate advantages in geophysical retrievals when compared to traditional methods. Emphasis is on performance improvements when providing additional information on storm structure and regime or echo type classifications. Since deep convective cloud dynamic insights (core updraft/downdraft properties) are difficult to obtain directly by conventional radars that also observe radar reflectivity factor profiles similar to RWP systems, we also consider possible machine learning applications to inform on (statistical) proxy convective relationships between observed convective core dynamics and radar microphysical properties that are otherwise not easily related by clear physical process paths using existing radar networks.

The Impacts of Amazon Deforestation on Pacific Climate

NASA Astrophysics Data System (ADS)

Lindsey, Leah

Variability in eastern Pacific sea surface temperatures (SSTs) associated with the El Nino Southern Oscillation are known to affect Amazonian precipitation, but to what extent do changing Amazonian vegetation and rainfall impact eastern Pacific SST? The Amazon rainforest is threatened by many factors including climate change and clearing for agricultural reasons. Forest fires and dieback are more likely due to increased frequency and intensity of droughts in the region. It is possible that extensive Amazon deforestation can enhance El Nino conditions by weakening the Walker circulation. Correlations between annual rainfall rates over the Amazon and other atmospheric parameters (global precipitation, surface air temperature, low cloud amount, 500 hPa vertical velocity, surface winds, and 200 hPa winds) over the eastern Pacific indicate strong relationships among these fields. Maps of these correlations (teleconnection maps) reveal that when the Amazon is rainy SSTs in the central and eastern Pacific are cold, rainfall is suppressed over the central and eastern Pacific, low clouds are prominent over the eastern and southeastern Pacific, and subsidence over the central and eastern Pacific is enhanced. Precipitation in the Amazon is also consistent with a strong Walker circulation (La Nina conditions), manifest as strong correlations with the easterly surface and westerly 200 hPa zonal winds. Coupling between Amazon rainfall and these fields are seen in observations and model data. Correlations were calculated using data from observations, reanalysis data, two models under the Coupled Model Intercomparison Project/Atmospheric Model Intercomparison Project (CMIP5/AMIP), and an AMIP run with the model used in this study, the Community Earth System Model (CESM1.1.1). Although the correlations between Amazon precipitation and the aforementioned fields are strong, they do not show causality. In order to investigate the impact of tropical South American deforestation on the Pacific climate, numerical experiments were performed using the CESM. Amazon deforestation was studied in an idealized world where a single continent was covered in forest and then, in a separate simulation, covered in grassland. Four different sets of simulations were carried out: 1) the baseline idealized set-up with prescribed SST, 2) another with an Andes-like mountain range, 3) a simulation with a slab ocean model rather than prescribed SST, and 4) a simulation repeated with the standard Community Atmosphere Model (CAM4) replaced by the Superparameterized version (SP-CAM). The continent in these simulations was compared to the Amazon, and the ocean to the west of the continent was compared to the eastern Pacific. All of the simulations showed a strong warming of around 3-4°C over the continent going from forest to grassland. A notable decrease in precipitation over land of about 1-3 mm day-1 and increase to the west of the continent of about 1-2 mm day-1 was also observed in most of the simulations. The simulations with the slab ocean model showed enhanced precipitation changes with a corresponding decrease of 2-4 mm day-1 over land and increase of 3-5 mm day-1 west of the continent. Simulations that used the SP-CAM showed very small changes in precipitation, which was likely due to the decreased spin-up time allowed for these simulations. The decrease in the surface roughness and reduction in the evapotranspiration for the simulations with grassland contributed to these changes in surface temperature and precipitation. The conversion of forest to grassland in our experiments imply that deforestation can lead to weakening of the Walker circulation by weakening easterly surface winds and westerly upper tropospheric winds. These findings suggest that large-scale Amazon deforestation is capable of enhancing El Nino conditions.

Electron cloud buildup driving spontaneous vertical instabilities of stored beams in the Large Hadron Collider

NASA Astrophysics Data System (ADS)

Romano, Annalisa; Boine-Frankenheim, Oliver; Buffat, Xavier; Iadarola, Giovanni; Rumolo, Giovanni

2018-06-01

At the beginning of the 2016 run, an anomalous beam instability was systematically observed at the CERN Large Hadron Collider (LHC). Its main characteristic was that it spontaneously appeared after beams had been stored for several hours in collision at 6.5 TeV to provide data for the experiments, despite large chromaticity values and high strength of the Landau-damping octupole magnet. The instability exhibited several features characteristic of those induced by the electron cloud (EC). Indeed, when LHC operates with 25 ns bunch spacing, an EC builds up in a large fraction of the beam chambers, as revealed by several independent indicators. Numerical simulations have been carried out in order to investigate the role of the EC in the observed instabilities. It has been found that the beam intensity decay is unfavorable for the beam stability when LHC operates in a strong EC regime.

Analysis of the Diurnal Cycle of Precipitation and its Relation to Cloud Radiative Forcing Using TRMM Products

NASA Technical Reports Server (NTRS)

Randall, David A.; Fowler, Laura D.; Lin, Xin

1998-01-01

In order to improve our understanding of the interactions between clouds, radiation, and the hydrological cycle simulated in the Colorado State University General Circulation Model (CSU GCM), we focused our research on the analysis of the diurnal cycle of precipitation, top-of-the-atmosphere and surface radiation budgets, and cloudiness using 10-year long Atmospheric Model Intercomparison Project (AMIP) simulations. Comparisons the simulated diurnal cycle were made against the diurnal cycle of Earth Radiation Budget Experiment (ERBE) radiation budget and International Satellite Cloud Climatology Project (ISCCP) cloud products. This report summarizes our major findings over the Amazon Basin.

Further evidence for CCN aerosol concentrations determining the height of warm rain and ice initiation in convective clouds over the Amazon basin

NASA Astrophysics Data System (ADS)

Campos Braga, Ramon; Rosenfeld, Daniel; Weigel, Ralf; Jurkat, Tina; Andreae, Meinrat O.; Wendisch, Manfred; Pöschl, Ulrich; Voigt, Christiane; Mahnke, Christoph; Borrmann, Stephan; Albrecht, Rachel I.; Molleker, Sergej; Vila, Daniel A.; Machado, Luiz A. T.; Grulich, Lucas

2017-12-01

We have investigated how aerosols affect the height above cloud base of rain and ice hydrometeor initiation and the subsequent vertical evolution of cloud droplet size and number concentrations in growing convective cumulus. For this purpose we used in situ data of hydrometeor size distributions measured with instruments mounted on HALO aircraft during the ACRIDICON-CHUVA campaign over the Amazon during September 2014. The results show that the height of rain initiation by collision and coalescence processes (Dr, in units of meters above cloud base) is linearly correlated with the number concentration of droplets (Nd in cm-3) nucleated at cloud base (Dr ≈ 5 ṡ Nd). Additional cloud processes associated with Dr, such as GCCN, cloud, and mixing with ambient air and other processes, produce deviations of ˜ 21 % in the linear relationship, but it does not mask the clear relationship between Dr and Nd, which was also found at different regions around the globe (e.g., Israel and India). When Nd exceeded values of about 1000 cm-3, Dr became greater than 5000 m, and the first observed precipitation particles were ice hydrometeors. Therefore, no liquid water raindrops were observed within growing convective cumulus during polluted conditions. Furthermore, the formation of ice particles also took place at higher altitudes in the clouds in polluted conditions because the resulting smaller cloud droplets froze at colder temperatures compared to the larger drops in the unpolluted cases. The measured vertical profiles of droplet effective radius (re) were close to those estimated by assuming adiabatic conditions (rea), supporting the hypothesis that the entrainment and mixing of air into convective clouds is nearly inhomogeneous. Additional CCN activation on aerosol particles from biomass burning and air pollution reduced re below rea, which further inhibited the formation of raindrops and ice particles and resulted in even higher altitudes for rain and ice initiation.

Downward solar global irradiance at the surface in São Paulo city—The climatological effects of aerosol and clouds

NASA Astrophysics Data System (ADS)

Yamasoe, M. A.; do Rosário, N. M. E.; Barros, K. M.

2017-01-01

We analyzed the variability of downward solar irradiance reaching the surface at São Paulo city, Brazil, and estimated the climatological aerosol and cloud radiative effects. Eleven years of irradiance were analyzed, from 2005 to 2015. To distinguish the aerosol from the cloud effect, the radiative transfer code LibRadtran was used to calculate downward solar irradiance. Two runs were performed, one considering only ozone and water vapor daily variability, with AOD set to zero and the second allowing the three variables to change, according to mean climatological values. The difference of the 24 h mean irradiance calculated with and without aerosol resulted in the shortwave aerosol direct radiative effect, while the difference between the measured and calculated, including the aerosol, represented the cloud effect. Results showed that, climatologically, clouds can be 4 times more effective than aerosols. The cloud shortwave radiative effect presented a maximum reduction of about -170 W m-2 in January and a minimum in July, of -37 W m-2. The aerosol direct radiative effect was maximum in spring, when the transport of smoke from the Amazon and central parts of South America is frequent toward São Paulo. Around mid-September, the 24 h radiative effect due to aerosol only was estimated to be -50 W m-2. Throughout the rest of the year, the mean aerosol effect was around -20 W m-2 and was attributed to local urban sources. The effect of the cloud fraction on the cloud modification factor, defined as the ratio of all-sky irradiation to cloudless sky irradiation, showed dependence on the cloud height. Low clouds presented the highest impact while the presence of high clouds only almost did not affect solar transmittance, even in overcast conditions.

The Shallow-to-Deep Transition in Convective Clouds During GoAmazon 2014/5

NASA Astrophysics Data System (ADS)

Jensen, M. P.; Gostic, C.; Giangrande, S. E.; Mechem, D. B.; Ghate, V. P.; Toto, T.

2016-12-01

Nearly two years of observations from the ARM Mobile Facility (AMF) deployed at Manacapuru, Brazil during the GOAmazon 2014/5 campaign are analyzed to investigate the environmental conditions controlling the transition from shallow to deep convective clouds. The Active Remote Sensing of Clouds (ARSCL) product, which combines radar and lidar observations to produce best estimates of cloud locations in the vertical column is used to qualitatively define four subsets of convective cloud conditions: 1,2) Transition cases (wet season, dry season), where a period of shallow convective clouds is followed by a period of deep convective clouds and 2) Non-transition cases (wet season, dry season), where shallow convective clouds persist without any subsequent development. For these subsets, observations of the time varying thermodynamic properties of the atmosphere, including the surface heat and radiative fluxes, the profiles of atmospheric state variables, and the ECMWF-derived large-scale advective tendencies, are composited to define averaged properties for each transition state. Initial analysis indicates that the transition state strongly depends on the pre-dawn free-tropospheric humidity, the convective inhibition and surface temperature and humidity with little dependence on the convective available potential energy and surface heat fluxes. The composited environmental thermodynamics are then used to force large-eddy simulations for the four transition states to further evaluate the sensitivity of the transition to the composite thermodynamics versus the importance of larger-scale forcing.

Automating NEURON Simulation Deployment in Cloud Resources

PubMed Central

Santamaria, Fidel

2016-01-01

Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the Open-Stack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon’s proprietary software. We describe the manual procedures and how to automate cloud operations. We describe extending our simulation automation software called NeuroManager (Stockton and Santamaria, Frontiers in Neuroinformatics, 2015), so that the user is capable of recruiting private cloud, public cloud, HPC, and local servers simultaneously with a simple common interface. We conclude by performing several studies in which we examine speedup, efficiency, total session time, and cost for sets of simulations of a published NEURON model. PMID:27655341

Substantial convection and precipitation enhancements by ultrafine aerosol particles

DOE PAGES

Fan, Jiwen; Rosenfeld, Daniel; Zhang, Yuwei; ...

2018-01-26

Aerosol-cloud interactions remain the largest uncertainty in climate projections. Ultrafine aerosol particles smaller than 50 nanometers (UAP <50) can be abundant in the troposphere, but are conventionally considered too small to affect cloud formation. However, observational evidence and numerical simulations of deep convective clouds (DCCs) over the Amazon show that DCCs forming in a low aerosol environment can develop very large vapor supersaturation because fast droplet coalescence reduces integrated droplet surface area and subsequent condensation. UAP <50 from pollution plumes that are ingested into such clouds can be activated to form additional cloud droplets on which excess supersaturation condenses andmore » forms additional cloud water and latent heating, thus intensifying convective strength. This mechanism suggests a strong anthropogenic invigoration of DCCs in previously pristine regions of the world.« less

Improving microphysics in a convective parameterization: possibilities and limitations

NASA Astrophysics Data System (ADS)

Labbouz, Laurent; Heikenfeld, Max; Stier, Philip; Morrison, Hugh; Milbrandt, Jason; Protat, Alain; Kipling, Zak

2017-04-01

The convective cloud field model (CCFM) is a convective parameterization implemented in the climate model ECHAM6.1-HAM2.2. It represents a population of clouds within each ECHAM-HAM model column, simulating up to 10 different convective cloud types with individual radius, vertical velocities and microphysical properties. Comparisons between CCFM and radar data at Darwin, Australia, show that in order to reproduce both the convective cloud top height distribution and the vertical velocity profile, the effect of aerodynamic drag on the rising parcel has to be considered, along with a reduced entrainment parameter. A new double-moment microphysics (the Predicted Particle Properties scheme, P3) has been implemented in the latest version of CCFM and is compared to the standard single-moment microphysics and the radar retrievals at Darwin. The microphysical process rates (autoconversion, accretion, deposition, freezing, …) and their response to changes in CDNC are investigated and compared to high resolution CRM WRF simulations over the Amazon region. The results shed light on the possibilities and limitations of microphysics improvements in the framework of CCFM and in convective parameterizations in general.

Forecasting Excessive Rainfall and Low-Cloud Bases East of the Northern Andes and Mesoscale Convective Complex Movement in Central South America

DTIC Science & Technology

2003-03-01

wonderful companion and girlfriend . After all these years, you are the only one who truly stands by me unconditionally. Words can’t express my...than 300 ft, although rare, do occur mainly in the more moisture rich West Amazon Basin. Occurrences with bases less than 300 ft are about 3-5% in the...heating may not be a primary factor for time of convection probably due to smaller diurnal temperature differences in the moisture rich Amazon Basin

Giovanni in the Cloud: Earth Science Data Exploration in Amazon Web Services

NASA Astrophysics Data System (ADS)

Hegde, M.; Petrenko, M.; Smit, C.; Zhang, H.; Pilone, P.; Zasorin, A. A.; Pham, L.

2017-12-01

Giovanni (https://giovanni.gsfc.nasa.gov/giovanni/) is a popular online data exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of datacenters. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.

Giovanni in the Cloud: Earth Science Data Exploration in Amazon Web Services

NASA Technical Reports Server (NTRS)

Petrenko, Maksym; Hegde, Mahabal; Smit, Christine; Zhang, Hailiang; Pilone, Paul; Zasorin, Andrey A.; Pham, Long

2017-01-01

Giovanni is an exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of data centers. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.

An Elliptic Curve Based Schnorr Cloud Security Model in Distributed Environment

PubMed Central

Muthurajan, Vinothkumar; Narayanasamy, Balaji

2016-01-01

Cloud computing requires the security upgrade in data transmission approaches. In general, key-based encryption/decryption (symmetric and asymmetric) mechanisms ensure the secure data transfer between the devices. The symmetric key mechanisms (pseudorandom function) provide minimum protection level compared to asymmetric key (RSA, AES, and ECC) schemes. The presence of expired content and the irrelevant resources cause unauthorized data access adversely. This paper investigates how the integrity and secure data transfer are improved based on the Elliptic Curve based Schnorr scheme. This paper proposes a virtual machine based cloud model with Hybrid Cloud Security Algorithm (HCSA) to remove the expired content. The HCSA-based auditing improves the malicious activity prediction during the data transfer. The duplication in the cloud server degrades the performance of EC-Schnorr based encryption schemes. This paper utilizes the blooming filter concept to avoid the cloud server duplication. The combination of EC-Schnorr and blooming filter efficiently improves the security performance. The comparative analysis between proposed HCSA and the existing Distributed Hash Table (DHT) regarding execution time, computational overhead, and auditing time with auditing requests and servers confirms the effectiveness of HCSA in the cloud security model creation. PMID:26981584

An Elliptic Curve Based Schnorr Cloud Security Model in Distributed Environment.

PubMed

Muthurajan, Vinothkumar; Narayanasamy, Balaji

2016-01-01

Cloud computing requires the security upgrade in data transmission approaches. In general, key-based encryption/decryption (symmetric and asymmetric) mechanisms ensure the secure data transfer between the devices. The symmetric key mechanisms (pseudorandom function) provide minimum protection level compared to asymmetric key (RSA, AES, and ECC) schemes. The presence of expired content and the irrelevant resources cause unauthorized data access adversely. This paper investigates how the integrity and secure data transfer are improved based on the Elliptic Curve based Schnorr scheme. This paper proposes a virtual machine based cloud model with Hybrid Cloud Security Algorithm (HCSA) to remove the expired content. The HCSA-based auditing improves the malicious activity prediction during the data transfer. The duplication in the cloud server degrades the performance of EC-Schnorr based encryption schemes. This paper utilizes the blooming filter concept to avoid the cloud server duplication. The combination of EC-Schnorr and blooming filter efficiently improves the security performance. The comparative analysis between proposed HCSA and the existing Distributed Hash Table (DHT) regarding execution time, computational overhead, and auditing time with auditing requests and servers confirms the effectiveness of HCSA in the cloud security model creation.

Hydrogeochemistry of the Overland Flow in Soil at Agroecosystems in Eastern Amazon

NASA Astrophysics Data System (ADS)

Costa, C. F. G. D.; Figueiredo, R. O.; Oliveira, F. D. A.

2014-12-01

In the watershed of the Timboteua and Buiuna streams, northeast of Pará state, Amazon, it was characterized the overland flow dissolved material by some hydrogeochemical variables: electrical conductivity (EC), pH, chloride (Cl-), nitrate (NO3-), phosphate (PO43-), and sulfate (SO42-). In two small holder properties three overland flow experimental plots (1m2) were placed in each of the six evaluated ecosystems under similar biophysical conditions, totaling 18 plots. There was also installed three rainwater collectors and two rain gauges in a nearby area. In the rainy season were collected 234 samples of rainwater and overland flow. The evaluation of the measured variables promote the hydrogeochemical characterization of the overland flow at soil under chop-and-mulch and slash-and-burn practices in the different ecosystems found in the familiar agriculture of this watershed, in which it was identified some distinct hydrogeochemical characteristics of the overland flow. The lowest losses of NO3- (variation range = 0.07 to 2.57 μM) was found in agroecosystem - chop-and-mulch, this nutrient obtained higher values in agroecosystem - slash-and-burn (RQ). In agroecosystem (RQ) initially, there was a high value of PO43- (8.87 μM); EC (121 μS cm-1) and a subsequent sharp decline. Secondary successional forest (CP) of 20 years presented in overland flow pH 4.8 and EC 25 μS cm-1 (average 6 months), low loss of NO3- (0.2 μM) and PO43- (0.05 μM), and large range of variation of SO42- (0.7 to 21.5 μM). While Cl- and SO42- overland flow concentrations were affect by the rainfall variation, the increase of NO3- and PO43-concentrations were more related to the ecosystem management, with the first element responding to the presence of nitrogen-fixing species and the second responding to the burning practices. In summary: This study was efficient to characterize the hydrogeochemical of the overland flow and its relation to the altered ecosystems by Amazonian family farming.

The Role of Intraseasonal Variability in Supporting the Shallow-to-Deep Transition in the Amazon

NASA Astrophysics Data System (ADS)

Serra, Y. L.; Rowe, A.; Adams, D. K.; Barbosa, H. M.; Kiladis, G. N.

2016-12-01

The shallow-to-deep convective transition over land typically refers to the growth of the convective boundary layer after sunrise, followed by the development of cumulus congestus clouds in the late morning/early afternoon and transitioning to deep convective clouds in the late afternoon and early evening. Under favorable conditions, this diurnal convection can result in organized mesoscale convective systems (MCSs) that last through the following morning. While many studies have focused on improving this process in models, the shallow-to-deep transition remains poorly represented especially over land. The recent DOE ARM mobile facility deployment in the Amazon, launched as part of GOAmazon, along with a dense GNSS network supported by Universidade do Estado do Amazonas (UEA)/Instituto Nacional de Pesquisas Espaciais (INPE) and co-located with the CHUVA Project sites for GOAmazon, are used here to examine land-based convective processes in the tropics. In particular, this aspect of a larger study of the shallow-to-deep transition explores the role of large-scale intraseasonal wave activity in supporting the growth of MCSs over the GoAmazon region. These results will be placed in the context of local forcing mechanisms for convective growth over the region in ongoing work.

Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure

NASA Astrophysics Data System (ADS)

Asencio-Cortés, G.; Morales-Esteban, A.; Shang, X.; Martínez-Álvarez, F.

2018-06-01

Earthquake magnitude prediction is a challenging problem that has been widely studied during the last decades. Statistical, geophysical and machine learning approaches can be found in literature, with no particularly satisfactory results. In recent years, powerful computational techniques to analyze big data have emerged, making possible the analysis of massive datasets. These new methods make use of physical resources like cloud based architectures. California is known for being one of the regions with highest seismic activity in the world and many data are available. In this work, the use of several regression algorithms combined with ensemble learning is explored in the context of big data (1 GB catalog is used), in order to predict earthquakes magnitude within the next seven days. Apache Spark framework, H2 O library in R language and Amazon cloud infrastructure were been used, reporting very promising results.

GATECloud.net: a platform for large-scale, open-source text processing on the cloud.

PubMed

Tablan, Valentin; Roberts, Ian; Cunningham, Hamish; Bontcheva, Kalina

2013-01-28

Cloud computing is increasingly being regarded as a key enabler of the 'democratization of science', because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research--GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost-benefit analysis and usage evaluation.

Chemical Imaging and Stable Isotope Analysis of Atmospheric Particles by NanoSIMS (Invited)

NASA Astrophysics Data System (ADS)

Sinha, B.; Harris, E. J.; Pöhlker, C.; Wiedemann, K. T.; van Pinxteren, D.; Tilgner, A.; Fomba, K. W.; Schneider, J.; Roth, A.; Gnauk, T.; Fahlbusch, B.; Mertes, S.; Lee, T.; Collett, J. L.; Shiraiwa, M.; Gunthe, S. S.; Smith, M.; Artaxo, P. P.; Gilles, M.; Kilcoyne, A. L.; Moffet, R.; Weigand, M.; Martin, S. T.; Poeschl, U.; Andreae, M. O.; Hoppe, P.; Herrmann, H.; Borrmann, S.

2013-12-01

Chemical imaging analysis of the internal distribution of chemical compounds by a combination of SEM-EDX, and NanoSIMS allows investigating the physico-chemical properties and isotopic composition of individual aerosol particles. Stable sulphur isotope analysis provides insight into the sources, sinks and oxidation pathways of SO2 in the environment. Oxidation by OH radicals, O3 and H2O2 enriches the heavier isotope in the product sulphate, whereas oxidation by transition metal ions (TMI), hypohalites and hypohalous acids depletes the heavier isotope in the product sulphate. The isotope fractionation during SO2 oxidation by stabilized Criegee Intermediate radicals is unknown. We studied the relationship between aerosol chemical composition and predominant sulphate formation pathways in continental clouds in Central Europe and during the wet season in the Amazon rain forest. Sulphate formation in continental clouds in Central Europe was studied during HCCT-2010, a lagrangian-type field experiment, during which an orographic cloud was used as a natural flow-through reactor to study in-cloud aerosol processing (Harris et al. 2013). Sulphur isotopic compositions in SO2 and H2SO4 gas and particulate sulphate were measured and changes in the sulphur isotope composition of SO2 between the upwind and downwind measurement sites were used to determine the dominant SO2 chemical removal process occurring in the cloud. Changes in the isotopic composition of particulate sulphate revealed that transition metal catalysis pathway was the dominant SO2 oxidation pathway. This reaction occurred primarily on coarse mineral dust particles. Thus, sulphate produced due to in-cloud SO2 oxidation is removed relatively quickly from the atmosphere and has a minor climatic effect. The aerosol samples from the Amazonian rainforest, a pristine tropical environment, were collected during the rainy season. The samples were found to be dominated by SOA particles in the fine mode and primary biological aerosol particles in the coarse mode (Pöhlker et al. 2012). We applied STXM-NEXAFS analysis, SEM-EDX analysis and NanoSIMS analysis to investigate the morphology, chemical composition and isotopic composition of aerosol samples. Biogenic salt particles emitted from active biota in the rainforest were found to be enriched in the heavier sulphur isotope, whereas particles with a high organic mass fraction modified by condensation of VOC oxidation products and/or cloud processing were significantly depleted in the heavier sulphur isotope compared to the seed particles. This indicates either a depleted gas phase source of sulphur dioxide contributed to the sulphate formation via the H2O2, O3 or OH oxidation pathway or an unaccounted reaction pathway which depletes the heavier isotope in the product sulphate contributes to the secondary sulphate formation in the pristine Amazon rainforest. Harris, E., et al., Science 340, 727-730, 2013 Pöhlker, C., Science 337, 1075-1078, 2012

Properties of refractory BC containing particles during the ACRIDICON-CHUVA aircraft campaign in the Amazon basin

NASA Astrophysics Data System (ADS)

Holanda, Bruna; Pöhlker, Mira; Klimach, Thomas; Saturno, Jorge; Ditas, Florian; Ditas, Jeannine; Ma, Nan; Zhang, Yuxuan; Cheng, Yafang; Wendisch, Manfred; Machado, Luiz; Barbosa, Henrique; Pöhlker, Christopher; Artaxo, Paulo; Pöschl, Ulrich; Andreae, Meinrat

2017-04-01

Black carbon (BC) particles are emitted directly into the atmosphere by processes of incomplete combustion and therefore can be used as a tracer of atmospheric pollution. BC is considered one of the drivers of global warming due to its efficient absorption of solar and infra-red radiation (Bond et al., 2013). Depending on abundance and size, aerosols can also modify the characteristics of clouds and enhance or suppress precipitation (Pöschl et al., 2010). The BC particles can gain surface coatings by condensation of low and semi-volatile compounds, coagulation, and cloud processing. The inclusion of a non-absorbing coating influences the way that BC particles act as cloud nuclei and may increase their absorption through the lensing effect (Fuller et al., 1999). These aging processes change significantly the optical, chemical and physical properties of the particles, as well as their atmospheric lifetime, making BC a source of large uncertainties in current atmospheric models. Taking into account the complex dynamics of BC particles in the atmosphere, we are analyzing data from the ACRIDICON-CHUVA aircraft campaign, which took place in the Amazon basin, Brazil, during the dry season of 2014 (Wendisch et al., 2016). A detailed characterization of BC particles was done using the Single Particle Soot Photometer (SP2) instrument, which directly measures the mass of individual refractory BC particles (rBC). Additionally, the SP2 provides information about the size distribution of rBC cores and their associated coatings. These properties were measured covering a wide geographic area with different pollution conditions and at several levels of the atmosphere at high time resolution. The rBC concentrations change significantly with altitude and with the source of pollution, being a few nanograms per cubic meter for altitudes higher that 5 km. In the surroundings of Manaus city, the mean BC concentration was 0.7 μg/m3, with core sizes peaking at 180 nm. The highest BC mass values were observed over fresh biomass burning plumes (6 μg/m3) with smaller core sizes ( 150 nm). Moreover, in a specific flight (AC19) we identified an extended layer of pollution at 4 km altitude. Backward trajectories calculated using FLEXPART suggest that this pollution layer originated in Africa and has aged few days during its travel over the Atlantic. Similarities in the properties of rBC particles within the pollution and boundary layers suggest that the long range transport of pollution from Africa can be an important source of BC to the Amazonian atmosphere. Here we present first results from our analyses that characterize the various pollution aerosols and their properties in the Amazon basin. References Bond, T.C. et al., 2013. Bounding the role of black carbon in the climate system: A scientific assessment. Journal of Geophysical Research: Atmospheres, 118(11), pp.5380-5552. Fuller, K. A. et al., 1999. Effects of mixing on extinction by carbonaceous particles. Journal of Geophysical Research: Atmospheres, 104(D13), 15941-15954. Pöschl, U. et al., 2010. Rainforest aerosols as biogenic nuclei of clouds and precipitation in the Amazon. Science 329, 1513. Wendisch, M. et al., 2016. The ACRIDICON-CHUVA campaign: Studying tropical deep convective clouds and precipitation over Amazonia using the new German research aircraft HALO. Bull. Amer. Meteor. Soc.

Privacy authentication using key attribute-based encryption in mobile cloud computing

NASA Astrophysics Data System (ADS)

Mohan Kumar, M.; Vijayan, R.

2017-11-01

Mobile Cloud Computing is becoming more popular in nowadays were users of smartphones are getting increased. So, the security level of cloud computing as to be increased. Privacy Authentication using key-attribute based encryption helps the users for business development were the data sharing with the organization using the cloud in a secured manner. In Privacy Authentication the sender of data will have permission to add their receivers to whom the data access provided for others the access denied. In sender application, the user can choose the file which is to be sent to receivers and then that data will be encrypted using Key-attribute based encryption using AES algorithm. In which cipher created, and that stored in Amazon Cloud along with key value and the receiver list.

Observations and Modeling of the Green Ocean Amazon: Sounding Enhancement Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schumacher, Courtney

2016-05-01

The goal of this campaign was to provide higher temporal sampling of the vertical structure of the atmosphere during the two intensive observational periods (IOPs) of the GoAmazon 2014/15 campaign. The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s first ARM Mobile Facility (AMF1) baseline launches for 2014 and 2015 was 4 sondes/day at 2 am, 8 am, 2 pm, and 8 pm local time (LT) (6, 12, 18 and 0 Coordinated Universal Time [UTC]). However, rapid changes in boundary layer and free tropospheric temperature, humidity, and wind profiles happen throughout the diurnal cycle over Manaus,more » Brazil's complex forest canopy with resulting responses in aerosol, cloud, and precipitation characteristics. This campaign increased sampling to 5 sondes/day for the 2014 wet and dry season IOPs by adding a launch at 11 am (15 UTC) to capture rapid changes in boundary layer properties and convective cloud growth during that time. The extra launch also corresponded to the time of day the ARM Gulfstream (G-1) and German HALO aircraft most often flew, thus providing useful measurements of the large-scale environment during the flights. In addition, the extra launch will significantly add to the quality of AMF1 instrument retrievals and variational analysis forcing data set during the IOPs.« less

Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

NASA Astrophysics Data System (ADS)

Wilson, Brian; Manipon, Gerald; Hua, Hook; Fetzer, Eric

2014-05-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map-reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in a hybrid Cloud (private eucalyptus & public Amazon). Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Multi-year datasets are automatically "sharded" by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will present the architecture of SciReduce, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. We will also present a concept and prototype for staging NASA's A-Train Atmospheric datasets (Levels 2 & 3) in the Amazon Cloud so that any number of compute jobs can be executed "near" the multi-sensor data. Given such a system, multi-sensor climate studies over 10-20 years of data could be perform

Sharing Planetary-Scale Data in the Cloud

NASA Astrophysics Data System (ADS)

Sundwall, J.; Flasher, J.

2016-12-01

On 19 March 2015, Amazon Web Services (AWS) announced Landsat on AWS, an initiative to make data from the U.S. Geological Survey's Landsat satellite program freely available in the cloud. Because of Landsat's global coverage and long history, it has become a reference point for all Earth observation work and is considered the gold standard of natural resource satellite imagery. Within the first year of Landsat on AWS, the service served over a billion requests for Landsat imagery and metadata, globally. Availability of the data in the cloud has led to new product development by companies and startups including Mapbox, Esri, CartoDB, MathWorks, Development Seed, Trimble, Astro Digital, Blue Raster and Timbr.io. The model of staging data for analysis in the cloud established by Landsat on AWS has since been applied to high resolution radar data, European Space Agency satellite imagery, global elevation data and EPA air quality models. This session will provide an overview of lessons learned throughout these projects. It will demonstrate how cloud-based object storage is democratizing access to massive publicly-funded data sets that have previously only been available to people with access to large amounts of storage, bandwidth, and computing power. Technical discussion points will include: The differences between staging data for analysis using object storage versus file storage Using object stores to design simple RESTful APIs through thoughtful file naming conventions, header fields, and HTTP Range Requests Managing costs through data architecture and Amazon S3's "requester pays" feature Building tools that allow users to take their algorithm to the data in the cloud Using serverless technologies to display dynamic frontends for massive data sets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Godoi, R. H.M.; Barbosa, C. G.G.; Kurzlop, P.

Because of their proven adverse effects on human health and vegetation, and also considering their influence over the local and regional climate, inhalable fine particles (PM2.5) and NO 2, SO 2, and O 3 have been collected at the ARM site located in Manacapuru, Amazon, Brazil, as a part of the GoAmazon 2014/5 project. PM2.5 samples were analyzed through gravimetry, black carbon transmittance, elemental composition by energy dispersive x-ray fluorescence, and ionic concentration (cations) by ion chromatography. NO 2 and SO 2 samples were analyzed by ion chromatography, whereas O3 samples were analyzed through ultraviolet-vis spectrophotometry. Sampling of both particulatemore » and gaseous pollutants took place during the two intensive operation periods (IOP1 from February to March 2014, and IOP2 from August to October 2014). Results are interpreted both separately and as a whole with the specific goal of identifying compounds that could affect the population’s health and/or could act as cloud condensation nuclei. Chemical analysis supports the elucidation of the possible origins, transport mechanisms, health effects, and main effects of the assessed pollutants in those environments« less

Lidar observed seasonal variation of vertical canopy structure in the Amazon evergreen forests

NASA Astrophysics Data System (ADS)

Tang, H.; Dubayah, R.

2017-12-01

Both light and water are important environmental factors governing tree growth. Responses of tropical forests to their changes are complicated and can vary substantially across different spatial and temporal scales. Of particular interest is the dry-season greening-up of Amazon forests, a phenomenon undergoing considerable debates whether it is real or a "light illusion" caused by artifacts of passive optical remote sensing techniques. Here we analyze seasonal dynamic patterns of vertical canopy structure in the Amazon forests using lidar observations from NASA's Ice, Cloud, and and land Elevation Satellite (ICESat). We found that the net greening of canopy layer coincides with the wet-to-dry transition period, and its net browning occurs mostly at the late dry season. The understory also shows a seasonal cycle, but with an opposite variation to canopy and minimal correlation to seasonal variations in rainfall or radiation. Our results further suggest a potential interaction between canopy layers in the light regime that can optimize the growth of Amazon forests during the dry season. This light regime variability that exists in both spatial and temporal domains can better reveal the dry-season greening-up phenomenon, which appears less obvious when treating the Amazon forests as a whole.

New detections of embedded clusters in the Galactic halo

NASA Astrophysics Data System (ADS)

Camargo, D.; Bica, E.; Bonatto, C.

2016-09-01

Context. Until recently it was thought that high Galactic latitude clouds were a non-star-forming ensemble. However, in a previous study we reported the discovery of two embedded clusters (ECs) far away from the Galactic plane (~ 5 kpc). In our recent star cluster catalogue we provided additional high and intermediate latitude cluster candidates. Aims: This work aims to clarify whether our previous detection of star clusters far away from the disc represents just an episodic event or whether star cluster formation is currently a systematic phenomenon in the Galactic halo. We analyse the nature of four clusters found in our recent catalogue and report the discovery of three new ECs each with an unusually high latitude and distance from the Galactic disc midplane. Methods: The analysis is based on 2MASS and WISE colour-magnitude diagrams (CMDs), and stellar radial density profiles (RDPs). The CMDs are built by applying a field-star decontamination procedure, which uncovers the cluster's intrinsic CMD morphology. Results: All of these clusters are younger than 5 Myr. The high-latitude ECs C 932, C 934, and C 939 appear to be related to a cloud complex about 5 kpc below the Galactic disc, under the Local arm. The other clusters are above the disc, C 1074 and C 1100 with a vertical distance of ~3 kpc, C 1099 with ~ 2 kpc, and C 1101 with ~1.8 kpc. Conclusions: According to the derived parameters ECs located below and above the disc occur, which gives evidence of widespread star cluster formation throughout the Galactic halo. This study therefore represents a paradigm shift, by demonstrating that a sterile halo must now be understood as a host for ongoing star formation. The origin and fate of these ECs remain open. There are two possibilities for their origin, Galactic fountains or infall. The discovery of ECs far from the disc suggests that the Galactic halo is more actively forming stars than previously thought. Furthermore, since most ECs do not survive the infant mortality, stars may be raining from the halo into the disc, and/or the halo may be harbouring generations of stars formed in clusters like those detected in our survey.

Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce.

PubMed

Nellore, Abhinav; Wilks, Christopher; Hansen, Kasper D; Leek, Jeffrey T; Langmead, Ben

2016-08-15

Public archives contain thousands of trillions of bases of valuable sequencing data. More than 40% of the Sequence Read Archive is human data protected by provisions such as dbGaP. To analyse dbGaP-protected data, researchers must typically work with IT administrators and signing officials to ensure all levels of security are implemented at their institution. This is a major obstacle, impeding reproducibility and reducing the utility of archived data. We present a protocol and software tool for analyzing protected data in a commercial cloud. The protocol, Rail-dbGaP, is applicable to any tool running on Amazon Web Services Elastic MapReduce. The tool, Rail-RNA v0.2, is a spliced aligner for RNA-seq data, which we demonstrate by running on 9662 samples from the dbGaP-protected GTEx consortium dataset. The Rail-dbGaP protocol makes explicit for the first time the steps an investigator must take to develop Elastic MapReduce pipelines that analyse dbGaP-protected data in a manner compliant with NIH guidelines. Rail-RNA automates implementation of the protocol, making it easy for typical biomedical investigators to study protected RNA-seq data, regardless of their local IT resources or expertise. Rail-RNA is available from http://rail.bio Technical details on the Rail-dbGaP protocol as well as an implementation walkthrough are available at https://github.com/nellore/rail-dbgap Detailed instructions on running Rail-RNA on dbGaP-protected data using Amazon Web Services are available at http://docs.rail.bio/dbgap/ : anellore@gmail.com or langmea@cs.jhu.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Convective cloud vertical velocity and mass-flux characteristics from radar wind profiler observations during GoAmazon2014/5: VERTICAL VELOCITY GOAMAZON2014/5

DOE PAGES

Giangrande, Scott E.; Toto, Tami; Jensen, Michael P.; ...

2016-11-15

A radar wind profiler data set collected during the 2 year Department of Energy Atmospheric Radiation Measurement Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign is used to estimate convective cloud vertical velocity, area fraction, and mass flux profiles. Vertical velocity observations are presented using cumulative frequency histograms and weighted mean profiles to provide insights in a manner suitable for global climate model scale comparisons (spatial domains from 20 km to 60 km). Convective profile sensitivity to changes in environmental conditions and seasonal regime controls is also considered. Aggregate and ensemble average vertical velocity, convective area fraction, andmore » mass flux profiles, as well as magnitudes and relative profile behaviors, are found consistent with previous studies. Updrafts and downdrafts increase in magnitude with height to midlevels (6 to 10 km), with updraft area also increasing with height. Updraft mass flux profiles similarly increase with height, showing a peak in magnitude near 8 km. Downdrafts are observed to be most frequent below the freezing level, with downdraft area monotonically decreasing with height. Updraft and downdraft profile behaviors are further stratified according to environmental controls. These results indicate stronger vertical velocity profile behaviors under higher convective available potential energy and lower low-level moisture conditions. Sharp contrasts in convective area fraction and mass flux profiles are most pronounced when retrievals are segregated according to Amazonian wet and dry season conditions. During this deployment, wet season regimes favored higher domain mass flux profiles, attributed to more frequent convection that offsets weaker average convective cell vertical velocities.« less

Formation of a katabatic induced cold front at the east Andean slopes

NASA Astrophysics Data System (ADS)

Trachte, K.; Nauss, T.,; Rollenbeck, R.; Bendix, J.

2009-04-01

Within the DFG research unit 816, climate dynamics in a tropical mountain rain forest in the national reserve of the Reserva Biósfera de San Francisco in South Ecuador are investigated. Precipitation measurements in the mountain environment of the Estación Científica de San Francisco (ECSF) with a vertical rain radar profiler have been made over the last seven years. They reveal unexpected constant early morning rainfall events. On the basis of cloud top temperatures from corresponding GOES satellite imageries, a Mesoscale Convective System could be derived. Its formation region is located south-east of the ECSF in the Peruvian Amazon basin. The generation of the MCS is assumed to results from an interaction of both local and mesoscale conditions. Nocturnal drainage air from the Andean slopes and valleys confluences in the Amazon basin due to the concave lined terrain. This cold air converges with the warm-moist air of the Amazon inducing a local cold front. This process yields to deep convection resulting in a MCS. With the numerical model ARPS the hypothesized formation of a cloud cluster due to a katabatic induced cold front is shown in an ideal case study. Therefor an ideal terrain model representing the features of the Andes in the target area has been used. The simplification of the oprography concerns a concave lined slope and a valley draining into the basin. It describes the confluence of the cold drainage air due to the shape of the terrain. Inside the basin the generation of a local cold front is shown, which triggers the formation of a cloud cluster.

Trade Study: Storing NASA HDF5/netCDF-4 Data in the Amazon Cloud and Retrieving Data via Hyrax Server / THREDDS Data Server

NASA Technical Reports Server (NTRS)

Habermann, Ted; Jelenak, Aleksander; Lee, Joe; Yang, Kent; Gallagher, James; Potter, Nathan

2017-01-01

As part of the overall effort to understand implications of migrating ESDIS data and services to the cloud we are testing several common OPeNDAP and HDF use cases against three architectures for general performance and cost characteristics. The architectures include retrieving entire files, retrieving datasets using HTTP range gets, and retrieving elements of datasets (chunks) with HTTP range gets. We will describe these architectures and discuss our approach to estimating cost.

Substantial convection and precipitation enhancements by ultrafine aerosol particles

NASA Astrophysics Data System (ADS)

Fan, Jiwen; Rosenfeld, Daniel; Zhang, Yuwei; Giangrande, Scott E.; Li, Zhanqing; Machado, Luiz A. T.; Martin, Scot T.; Yang, Yan; Wang, Jian; Artaxo, Paulo; Barbosa, Henrique M. J.; Braga, Ramon C.; Comstock, Jennifer M.; Feng, Zhe; Gao, Wenhua; Gomes, Helber B.; Mei, Fan; Pöhlker, Christopher; Pöhlker, Mira L.; Pöschl, Ulrich; de Souza, Rodrigo A. F.

2018-01-01

Ultrafine aerosol particles (smaller than 50 nanometers in diameter) have been thought to be too small to affect cloud formation. Fan et al. show that this is not the case. They studied the effect of urban pollution transported into the otherwise nearly pristine atmosphere of the Amazon. Condensational growth of water droplets around the tiny particles releases latent heat, thereby intensifying atmospheric convection. Thus, anthropogenic ultrafine aerosol particles may exert a more important influence on cloud formation processes than previously believed.

Statistical properties of cloud and precipitation events in Central Amazonia using GoAmazon2014/5 data: revisiting deep convection timescales.

NASA Astrophysics Data System (ADS)

Pauliquevis, T.; Alves, C. F.; Barbosa, H. M.

2016-12-01

Previous studies in Amazon have shown a clear discrepance between models and observations of convection. From the observational stand point convection in Amazonia has a typical diurnal cycle, which is characterized by shallow convection and followed by shallow to deep transition (usually in early afternoon) and rain. Differently, numerical models based in cumulus parameterizations put heavy rain in the early hours of the morning. In this context, observations are crucial both to constraint as well to validate improvement in models. In this study we investigated statistical properties of clouds, precipitation and convection employing several instruments operated during GoAmazon2014/5-DOE/ARM at Manacapuru, AM (Brazil) combined with Cloud Top Temperature data obtained by GOES. Previous studies (e.g. Adams et al., 2013) defined deep convection events as connected to rapid CTT decrease, PWV increase (convergence) and precipitation. They also observed that the average deep convection event has two characteristic time-scales of its formation, in the sense that water vapor convergence begins to build 12 hs before precipitation, with an invigoration 4 hs before rain occur. In this study we revisited this approach using GoAmazon2014/5 measurement with special focus to its statistical variability. Preliminar results for the wet season of 2014 showed that events with rapid decrease in CTT were associated with 60% of the observed precipitation at ground. Defining t0 as the central time of CTT (rapid) decrease and analyzing only events with rain volume > 10 mm it was possible to observe that precipitation maximums distributed around t0 with mean difference Δ = 24 ± 82 minutes. Most of events presented several maxima (up to 16), and the general structure was similar to beatings in oscillatory systems. In several cases eve the first maximum of rain rate was 1 hour shifted from t0. In this presentation, the above results will be discussed combined with radiometer measurements (T, RH, LWP and PWV). Special attention to differences in diurnal cycles of convective and not convective days, as well as some mean vertical profiles of those variables.

Community Cloud Computing

NASA Astrophysics Data System (ADS)

Marinos, Alexandros; Briscoe, Gerard

Cloud Computing is rising fast, with its data centres growing at an unprecedented rate. However, this has come with concerns over privacy, efficiency at the expense of resilience, and environmental sustainability, because of the dependence on Cloud vendors such as Google, Amazon and Microsoft. Our response is an alternative model for the Cloud conceptualisation, providing a paradigm for Clouds in the community, utilising networked personal computers for liberation from the centralised vendor model. Community Cloud Computing (C3) offers an alternative architecture, created by combing the Cloud with paradigms from Grid Computing, principles from Digital Ecosystems, and sustainability from Green Computing, while remaining true to the original vision of the Internet. It is more technically challenging than Cloud Computing, having to deal with distributed computing issues, including heterogeneous nodes, varying quality of service, and additional security constraints. However, these are not insurmountable challenges, and with the need to retain control over our digital lives and the potential environmental consequences, it is a challenge we must pursue.

Using S3 cloud storage with ROOT and CvmFS

NASA Astrophysics Data System (ADS)

Arsuaga-Ríos, María; Heikkilä, Seppo S.; Duellmann, Dirk; Meusel, René; Blomer, Jakob; Couturier, Ben

2015-12-01

Amazon S3 is a widely adopted web API for scalable cloud storage that could also fulfill storage requirements of the high-energy physics community. CERN has been evaluating this option using some key HEP applications such as ROOT and the CernVM filesystem (CvmFS) with S3 back-ends. In this contribution we present an evaluation of two versions of the Huawei UDS storage system stressed with a large number of clients executing HEP software applications. The performance of concurrently storing individual objects is presented alongside with more complex data access patterns as produced by the ROOT data analysis framework. Both Huawei UDS generations show a successful scalability by supporting multiple byte-range requests in contrast with Amazon S3 or Ceph which do not support these commonly used HEP operations. We further report the S3 integration with recent CvmFS versions and summarize the experience with CvmFS/S3 for publishing daily releases of the full LHCb experiment software stack.

Unsupervised SBAS-DInSAR Processing of Space-borne SAR data for Earth Surface Displacement Time Series Generation

NASA Astrophysics Data System (ADS)

Casu, F.; de Luca, C.; Lanari, R.; Manunta, M.; Zinno, I.

2016-12-01

During the last 25 years, the Differential Synthetic Aperture Radar Interferometry (DInSAR) has played an important role for understanding the Earth's surface deformation and its dynamics. In particular, the large collections of SAR data acquired by a number of space-borne missions (ERS, ENVISAT, ALOS, RADARSAT, TerraSAR-X, COSMO-SkyMed) have pushed toward the development of advanced DInSAR techniques for monitoring the temporal evolution of the ground displacements with an high spatial density. Moreover, the advent of the Copernicus Sentinel-1 (S1) constellation is providing a further increase in the SAR data flow available to the Earth science community, due to its characteristics of global coverage strategy and free and open access data policy. Therefore, managing and storing such a huge amount of data, processing it in an effcient way and maximizing the available archives exploitation are becoming high priority issues. In this work we present some recent advances in the DInSAR field for dealing with the effective exploitation of the present and future SAR data archives. In particular, an efficient parallel SBAS implementation (namely P-SBAS) that takes benefit from high performance computing is proposed. Then, the P-SBAS migration to the emerging Cloud Computing paradigm is shown, together with extensive tests carried out in the Amazon's Elastic Cloud Compute (EC2) infrastructure. Finally, the integration of the P-SBAS processing chain within the ESA Geohazards Exploitation Platform (GEP), for setting up operational on-demand and systematic web tools, open to every user, aimed at automatically processing stacks of SAR data for the generation of SBAS displacement time series, is also illustrated. A number of experimental results obtained by using the ERS, ENVISAT and S1 data in areas characterized by volcanic, seismic and anthropogenic phenomena will be shown. This work is partially supported by: the DPC-CNR agreement, the EPOS-IP project and the ESA GEP project.

Aircraft measurements of aerosol properties during GoAmazon - G1 and HALO inter-comparison

NASA Astrophysics Data System (ADS)

Mei, F.; Cecchini, M. A.; Wang, J.; Tomlinson, J. M.; Comstock, J. M.; Hubbe, J. M.; Pekour, M. S.; Machado, L.; Wendisch, M.; Longo, K.; Martin, S. T.; Schmid, B.; Weinzierl, B.; Krüger, M. L.; Zöger, M.

2015-12-01

Currently, the indirect effects of atmospheric aerosols remain the most uncertain components in forcing of climate change over the industrial period (IPCC, 2013). This large uncertainty is partially a result of our incomplete understanding of the ability of particles to form cloud droplets under atmospherically relevant supersaturations. One objective of the US Department of Energy (DOE) Green Ocean Amazon Project (GoAmazon2014/5) is to understand the influence of the emission from Manaus, a tropical megacity, on aerosol size, concentration, and chemical composition, and their impact on cloud condensation nuclei (CCN) spectrum. The GoAmazon2014/5 study was an international campaign with the collaboration efforts from US, Brazil and Germany. During the intensive operation period, in the dry season (Sep. 1st - Oct. 10th, 2014), aerosol concentration, size distributions, and CCN spectra, both under pristine conditions and inside the Manaus plume, were characterized in-situ from the DOE Gulfstream-1 (G-1) research aircraft and German HALO aircraft during 4 coordinated flights on Sep. 9th, Sep. 16th, Sep 21st and Oct. 1st, 2014. During those four flights, aerosol number concentrations and CCN concentrations at two supersaturations (0.25% and 0.5%) were measured by condensation particle counters (CPCs) and a DMT dual column CCN counter onboard both G-1 and HALO. Aerosol size distribution was also measured by a Fast Integrated Mobility Spectrometer (FIMS) aboard the G-1 and is compared with the size distribution from Ultra High Sensitivity Aerosol Spectrometer - Airborne (UHSAS-A, DMT), which were deployed both on the G-1 and the HALO. Good agreement between the aerosol properties measured from the two aircraft has been achieved. The vertical profiles of aerosol size distribution and CCN spectrum will be discussed.

Multi-Resource Fair Queueing for Packet Processing

DTIC Science & Technology

2012-06-19

Huawei , Intel, MarkLogic, Microsoft, NetApp, Oracle, Quanta, Splunk, VMware and by DARPA (contract #FA8650-11-C-7136). Multi-Resource Fair Queueing for...Google PhD Fellowship, gifts from Amazon Web Services, Google, SAP, Blue Goji, Cisco, Cloud- era, Ericsson, General Electric, Hewlett Packard, Huawei

Open NASA Earth Exchange (OpenNEX): Strategies for enabling cross organization collaboration in the earth sciences

NASA Astrophysics Data System (ADS)

Michaelis, A.; Ganguly, S.; Nemani, R. R.; Votava, P.; Wang, W.; Lee, T. J.; Dungan, J. L.

2014-12-01

Sharing community-valued codes, intermediary datasets and results from individual efforts with others that are not in a direct funded collaboration can be a challenge. Cross organization collaboration is often impeded due to infrastructure security constraints, rigid financial controls, bureaucracy, and workforce nationalities, etc., which can force groups to work in a segmented fashion and/or through awkward and suboptimal web services. We show how a focused community may come together, share modeling and analysis codes, computing configurations, scientific results, knowledge and expertise on a public cloud platform; diverse groups of researchers working together at "arms length". Through the OpenNEX experimental workshop, users can view short technical "how-to" videos and explore encapsulated working environment. Workshop participants can easily instantiate Amazon Machine Images (AMI) or launch full cluster and data processing configurations within minutes. Enabling users to instantiate computing environments from configuration templates on large public cloud infrastructures, such as Amazon Web Services, may provide a mechanism for groups to easily use each others work and collaborate indirectly. Moreover, using the public cloud for this workshop allowed a single group to host a large read only data archive, making datasets of interest to the community widely available on the public cloud, enabling other groups to directly connect to the data and reduce the costs of the collaborative work by freeing other individual groups from redundantly retrieving, integrating or financing the storage of the datasets of interest.

Physical Mechanism, Spectral Detection, and Potential Mitigation of 3D Cloud Effects on OCO-2 Radiances and Retrievals

NASA Astrophysics Data System (ADS)

Cochrane, S.; Schmidt, S.; Massie, S. T.; Iwabuchi, H.; Chen, H.

2017-12-01

Analysis of multiple partially cloudy scenes as observed by OCO-2 in nadir and target mode (published previously and reviewed here) revealed that XCO2 retrievals are systematically biased in presence of scattered clouds. The bias can only partially be removed by applying more stringent filtering, and it depends on the degree of scene inhomogeneity as quantified with collocated MODIS/Aqua imagery. The physical reason behind this effect was so far not well understood because in contrast to cloud-mediated biases in imagery-derived aerosol retrievals, passive gas absorption spectroscopy products do not depend on the absolute radiance level and should therefore be less sensitive to 3D cloud effects and surface albedo variability. However, preliminary evidence from 3D radiative transfer calculations suggested that clouds in the vicinity of an OCO-2 footprint not only offset the reflected radiance spectrum, but introduce a spectrally dependent perturbation that affects absorbing channels disproportionately, and therefore bias the spectroscopy products. To understand the nature of this effect for a variety of scenes, we developed the OCO-2 radiance simulator, which uses the available information on a scene (e.g., MODIS-derived surface albedo, cloud distribution, and other parameters) as the basis for 3D radiative transfer calculations that can predict the radiances observed by OCO-2. We present this new tool and show examples of its utility for a few specific scenes. More importantly, we draw conclusions about the physical mechanism behind this 3D cloud effect on radiances and ultimately OCO-2 retrievals, which involves not only the clouds themselves but also the surface. Harnessed with this understanding, we can now detect cloud vicinity effects in the OCO-2 spectra directly, without actually running the 3D radiance simulator. Potentially, it is even possible to mitigate these effects and thus increase data harvest in regions with ubiquitous cloud cover such as the Amazon. We will discuss some of the hurdles one faces when using only OCO-2 spectra to accomplish this goal, but also that scene context from the other A-Train instruments and the new radiance simulator tool can help overcome some of them.

75 FR 30391 - Combined Notice of Filings #1

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-01

... Time on Tuesday, June 8, 2010. Docket Numbers: EC10-72-000. Applicants: Cloud County Wind Farm, LLC, Pioneer Prairie Wind Farm I, LLC, Arlington Wind Power Project LLC. Description: Application for... Power Project LLC, Cloud County Wind Farm, LLC, and Pioneer Prairie Wind Farm I, LLC. Filed Date: 05/20...

Robust relations between CCN and the vertical evolution of cloud drop size distribution in deep convective clouds

NASA Astrophysics Data System (ADS)

Freud, E.; Rosenfeld, D.; Andreae, M. O.; Costa, A. A.; Artaxo, P.

2008-03-01

In-situ measurements in convective clouds (up to the freezing level) over the Amazon basin show that smoke from deforestation fires prevents clouds from precipitating until they acquire a vertical development of at least 4 km, compared to only 1-2 km in clean clouds. The average cloud depth required for the onset of warm rain increased by ~350 m for each additional 100 cloud condensation nuclei per cm3 at a super-saturation of 0.5% (CCN0.5%). In polluted clouds, the diameter of modal liquid water content grows much slower with cloud depth (at least by a factor of ~2), due to the large number of droplets that compete for available water and to the suppressed coalescence processes. Contrary to what other studies have suggested, we did not observe this effect to reach saturation at 3000 or more accumulation mode particles per cm3. The CCN0.5% concentration was found to be a very good predictor for the cloud depth required for the onset of warm precipitation and other microphysical factors, leaving only a secondary role for the updraft velocities in determining the cloud drop size distributions. The effective radius of the cloud droplets (re) was found to be a quite robust parameter for a given environment and cloud depth, showing only a small effect of partial droplet evaporation from the cloud's mixing with its drier environment. This supports one of the basic assumptions of satellite analysis of cloud microphysical processes: the ability to look at different cloud top heights in the same region and regard their re as if they had been measured inside one well developed cloud. The dependence of re on the adiabatic fraction decreased higher in the clouds, especially for cleaner conditions, and disappeared at re≥~10 μm. We propose that droplet coalescence, which is at its peak when warm rain is formed in the cloud at re=~10 μm, continues to be significant during the cloud's mixing with the entrained air, cancelling out the decrease in re due to evaporation.

Robust relations between CCN and the vertical evolution of cloud drop size distribution in deep convective clouds

NASA Astrophysics Data System (ADS)

Freud, E.; Rosenfeld, D.; Andreae, M. O.; Costa, A. A.; Artaxo, P.

2005-10-01

In-situ measurements in convective clouds (up to the freezing level) over the Amazon basin show that smoke from deforestation fires prevents clouds from precipitating until they acquire a vertical development of at least 4 km, compared to only 1-2 km in clean clouds. The average cloud depth required for the onset of warm rain increased by ~350 m for each additional 100 cloud condensation nuclei per cm3 at a super-saturation of 0.5% (CCN0.5%). In polluted clouds, the diameter of modal liquid water content grows much slower with cloud depth (at least by a factor of ~2), due to the large number of droplets that compete for available water and to the suppressed coalescence processes. Contrary to what other studies have suggested, we did not observe this effect to reach saturation at 3000 or more accumulation mode particles per cm3. The CCN0.5% concentration was found to be a very good predictor for the cloud depth required for the onset of warm precipitation and other microphysical factors, leaving only a secondary role for the updraft velocities in determining the cloud drop size distributions. The effective radius of the cloud droplets (re) was found to be a quite robust parameter for a given environment and cloud depth, showing only a small effect of partial droplet evaporation from the cloud's mixing with its drier environment. This supports one of the basic assumptions of satellite analysis of cloud microphysical processes: the ability to look at different cloud top heights in the same region and regard their re as if they had been measured inside one well developed cloud. The dependence of re on the adiabatic fraction decreased higher in the clouds, especially for cleaner conditions, and disappeared at re≥~10 µm. We propose that droplet coalescence, which is at its peak when warm rain is formed in the cloud at re~10 µm, continues to be significant during the cloud's mixing with the entrained air, canceling out the decrease in re due to evaporation.

Scientific Data Storage for Cloud Computing

NASA Astrophysics Data System (ADS)

Readey, J.

2014-12-01

Traditionally data storage used for geophysical software systems has centered on file-based systems and libraries such as NetCDF and HDF5. In contrast cloud based infrastructure providers such as Amazon AWS, Microsoft Azure, and the Google Cloud Platform generally provide storage technologies based on an object based storage service (for large binary objects) complemented by a database service (for small objects that can be represented as key-value pairs). These systems have been shown to be highly scalable, reliable, and cost effective. We will discuss a proposed system that leverages these cloud-based storage technologies to provide an API-compatible library for traditional NetCDF and HDF5 applications. This system will enable cloud storage suitable for geophysical applications that can scale up to petabytes of data and thousands of users. We'll also cover other advantages of this system such as enhanced metadata search.

Increased Frequency of Large Blowdown Formation in Years With Hotter Dry Seasons in the Northwestern Amazon

NASA Astrophysics Data System (ADS)

Rifai, S. W.; Anderson, L. O.; Bohlman, S.

2015-12-01

Blowdowns, which are large tree mortality events caused by downbursts, create large pulses of carbon emissions in the short term and alter successional dynamics and species composition of forests, thus affecting long term biogeochemical cycling of tropical forests. Changing climate, especially increasing temperatures and frequency of extreme climate events, may cause changes in the frequency of blowdowns, but there has been little spatiotemporal analysis to associate the interannual variation in the frequency of blowdowns with annual climate parameters. We mapped blowdowns greater than 25 ha using a time series of Landsat images from 1984-2012 in the northwestern Amazon to estimate the annual size distribution of these blowdowns. The difference in forest area affected by blowdowns between the years with the highest and lowest blowdown activity were on the order of 10 - 30 times greater depending on location. Spatially, we found the probability of large blowdowns to be higher in regions with higher annual rainfall. Temporally, we found a positive correlation between the probability of large blowdown events and maximum dry season air temperature (R2 = 0.1-0.46). Mean and maximum blowdown size also increased with maximum dry season air temperature. The strength of these relationships varied between scene locations which may be related to cloud cover obscuring the land surface in the satellite images, or biophysical characteristics of the sites. Potentially, elevated dry season temperatures during the transition from the dry season to the wet season (October - December) may exacerbate atmospheric instabilities, which promote downburst occurrences. Most global circulation models predict dry season air temperatures to increase 2-5 ℃ in the northwestern Amazon by 2050. Should the blowdown disturbance regime continue increasing with elevated dry season temperatures, the northwestern Amazon is likely to experience more catastrophic tree mortality events which has direct consequences for both the carbon emissions and carbon storage capacity of the northwestern Amazon.

Orographic enhancement of rainfalls in the Rio San Francisco valley in southern Ecuador

NASA Astrophysics Data System (ADS)

Trachte, K.; Rollenbeck, R.; Bendix, J.

2012-04-01

In a tropical mountain rain forest in southern Ecuador diurnal dynamics of cloud development and precipitation behavior is investigated in the framework of the DFG research unit 816. With automatic climate stations and rain radar rainfalls in the Rio San Francisco valley are recorded. The observations showed the typical tropical late afternoon convective precipitation as well as local events such as mountain valley breezes and luv-lee effects. Additionally, the data revealed an unusually early morning peak that could be recognized as convective rainfalls. On the basis of GOES-E satellite imagery these rainfalls could be traced back to nocturnal convective clouds at the eastern Andes Mountains. There are some explanations for the occurrence of the clouds: One already examined mechanism is a katabatic induced cold front at the foothills of the Andes in the Peruvian Amazon basin. In this region the mountains form a quasi-concave configuration that contributes to a convergence of cold air drainage with subsequent convective activities. Another explanation for the events is the orographic enhancement by a local seeder-feeder mechanism. Mesoscale convective systems from the Amazon basin are transported to the west via the trade winds. At the Andes Mountains the complex and massive orography acts like a barrier to the clouds. The result is a disconnection of the upper part of the cloud from the lower part. The latter rains out at the eastern slopes and the upper cloud is transported further to the west. There it acts like a seeder to lower level clouds, i. e. the feeder. With the numerical model ARPS (Advanced Regional Prediction System) this procedure is investigated on the basis of two case studies. The events are detected and selected through the analysis of GOES-E brightness temperatures. They are also used to compare and validate the results of the model. Finally, the orographic enhancement of the clouds is examined. By using a vertically pointing radar the development of the resulting precipitation is analyzed and discussed in the context of a seeder-feeder mechanism.

Producing an Infrared Multiwavelength Galactic Plane Atlas Using Montage, Pegasus, and Amazon Web Services

NASA Astrophysics Data System (ADS)

Rynge, M.; Juve, G.; Kinney, J.; Good, J.; Berriman, B.; Merrihew, A.; Deelman, E.

2014-05-01

In this paper, we describe how to leverage cloud resources to generate large-scale mosaics of the galactic plane in multiple wavelengths. Our goal is to generate a 16-wavelength infrared Atlas of the Galactic Plane at a common spatial sampling of 1 arcsec, processed so that they appear to have been measured with a single instrument. This will be achieved by using the Montage image mosaic engine process observations from the 2MASS, GLIMPSE, MIPSGAL, MSX and WISE datasets, over a wavelength range of 1 μm to 24 μm, and by using the Pegasus Workflow Management System for managing the workload. When complete, the Atlas will be made available to the community as a data product. We are generating images that cover ±180° in Galactic longitude and ±20° in Galactic latitude, to the extent permitted by the spatial coverage of each dataset. Each image will be 5°x5° in size (including an overlap of 1° with neighboring tiles), resulting in an atlas of 1,001 images. The final size will be about 50 TBs. This paper will focus on the computational challenges, solutions, and lessons learned in producing the Atlas. To manage the computation we are using the Pegasus Workflow Management System, a mature, highly fault-tolerant system now in release 4.2.2 that has found wide applicability across many science disciplines. A scientific workflow describes the dependencies between the tasks and in most cases the workflow is described as a directed acyclic graph, where the nodes are tasks and the edges denote the task dependencies. A defining property for a scientific workflow is that it manages data flow between tasks. Applied to the galactic plane project, each 5 by 5 mosaic is a Pegasus workflow. Pegasus is used to fetch the source images, execute the image mosaicking steps of Montage, and store the final outputs in a storage system. As these workflows are very I/O intensive, care has to be taken when choosing what infrastructure to execute the workflow on. In our setup, we choose to use dynamically provisioned compute clusters running on the Amazon Elastic Compute Cloud (EC2). All our instances are using the same base image, which is configured to come up as a master node by default. The master node is a central instance from where the workflow can be managed. Additional worker instances are provisioned and configured to accept work assignments from the master node. The system allows for adding/removing workers in an ad hoc fashion, and could be run in large configurations. To-date we have performed 245,000 CPU hours of computing and generated 7,029 images and totaling 30 TB. With the current set up our runtime would be 340,000 CPU hours for the whole project. Using spot m2.4xlarge instances, the cost would be approximately $5,950. Using faster AWS instances, such as cc2.8xlarge could potentially decrease the total CPU hours and further reduce the compute costs. The paper will explore these tradeoffs.

Observations and Modeling of the Green Ocean Amazon 2014/15. CHUVA Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Machado, L. A. T.

2016-03-01

The physical processes inside clouds are one of the most unknown components of weather and climate systems. A description of cloud processes through the use of standard meteorological parameters in numerical models has to be strongly improved to accurately describe the characteristics of hydrometeors, latent heating profiles, radiative balance, air entrainment, and cloud updrafts and downdrafts. Numerical models have been improved to run at higher spatial resolutions where it is necessary to explicitly describe these cloud processes. For instance, to analyze the effects of global warming in a given region it is necessary to perform simulations taking into account allmore » of the cloud processes described above. Another important application that requires this knowledge is satellite precipitation estimation. The analysis will be performed focusing on the microphysical evolution and cloud life cycle, different precipitation estimation algorithms, the development of thunderstorms and lightning formation, processes in the boundary layer, and cloud microphysical modeling. This project intends to extend the knowledge of these cloud processes to reduce the uncertainties in precipitation estimation, mainly from warm clouds, and, consequently, improve knowledge of the water and energy budget and cloud microphysics.« less

Satellite Cloud and Radiative Property Processing and Distribution System on the NASA Langley ASDC OpenStack and OpenShift Cloud Platform

NASA Astrophysics Data System (ADS)

Nguyen, L.; Chee, T.; Palikonda, R.; Smith, W. L., Jr.; Bedka, K. M.; Spangenberg, D.; Vakhnin, A.; Lutz, N. E.; Walter, J.; Kusterer, J.

2017-12-01

Cloud Computing offers new opportunities for large-scale scientific data producers to utilize Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) IT resources to process and deliver data products in an operational environment where timely delivery, reliability, and availability are critical. The NASA Langley Research Center Atmospheric Science Data Center (ASDC) is building and testing a private and public facing cloud for users in the Science Directorate to utilize as an everyday production environment. The NASA SatCORPS (Satellite ClOud and Radiation Property Retrieval System) team processes and derives near real-time (NRT) global cloud products from operational geostationary (GEO) satellite imager datasets. To deliver these products, we will utilize the public facing cloud and OpenShift to deploy a load-balanced webserver for data storage, access, and dissemination. The OpenStack private cloud will host data ingest and computational capabilities for SatCORPS processing. This paper will discuss the SatCORPS migration towards, and usage of, the ASDC Cloud Services in an operational environment. Detailed lessons learned from use of prior cloud providers, specifically the Amazon Web Services (AWS) GovCloud and the Government Cloud administered by the Langley Managed Cloud Environment (LMCE) will also be discussed.

Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes

PubMed Central

Shringarpure, Suyash S.; Carroll, Andrew; De La Vega, Francisco M.; Bustamante, Carlos D.

2015-01-01

Population scale sequencing of whole human genomes is becoming economically feasible; however, data management and analysis remains a formidable challenge for many research groups. Large sequencing studies, like the 1000 Genomes Project, have improved our understanding of human demography and the effect of rare genetic variation in disease. Variant calling on datasets of hundreds or thousands of genomes is time-consuming, expensive, and not easily reproducible given the myriad components of a variant calling pipeline. Here, we describe a cloud-based pipeline for joint variant calling in large samples using the Real Time Genomics population caller. We deployed the population caller on the Amazon cloud with the DNAnexus platform in order to achieve low-cost variant calling. Using our pipeline, we were able to identify 68.3 million variants in 2,535 samples from Phase 3 of the 1000 Genomes Project. By performing the variant calling in a parallel manner, the data was processed within 5 days at a compute cost of $7.33 per sample (a total cost of $18,590 for completed jobs and $21,805 for all jobs). Analysis of cost dependence and running time on the data size suggests that, given near linear scalability, cloud computing can be a cheap and efficient platform for analyzing even larger sequencing studies in the future. PMID:26110529

RECOVER - An Automated Burned Area Emergency Response Decision Support System for Post-fire Rehabilitation Management of Savanna Ecosystems in the Western US

NASA Astrophysics Data System (ADS)

Weber, K.; Schnase, J. L.; Carroll, M.; Brown, M. E.; Gill, R.; Haskett, G.; Gardner, T.

2013-12-01

In partnership with the Department of Interior's Bureau of Land Management (BLM) and the Idaho Department of Lands (IDL), we are building and evaluating the RECOVER decision support system. RECOVER - which stands for Rehabilitation Capability Convergence for Ecosystem Recovery - is an automatically deployable, context-aware decision support system for savanna wildfires that brings together in a single application the information necessary for post-fire rehabilitation decision-making and long-term ecosystem monitoring. RECOVER uses state-of-the-art cloud-based data management technologies to improve performance, reduce cost, and provide site-specific flexibility for each fire. The RECOVER Server uses Integrated Rule-Oriented Data System (iRODS) data grid technology deployed in the Amazon Elastic Compute Cloud (EC2). The RECOVER Client is an Adobe Flex web map application that is able to provide a suite of convenient GIS analytical capabilities. In a typical use scenario, the RECOVER Server is provided a wildfire name and geospatial extent. The Server then automatically gathers Earth observational data and other relevant products from various geographically distributed data sources. The Server creates a database in the cloud where all relevant information about the wildfire is stored. This information is made available to the RECOVER Client and ultimately to fire managers through their choice of web browser. The Server refreshes the data throughout the burn and subsequent recovery period (3-5 years) with each refresh requiring two minutes to complete. Since remediation plans must be completed within 14 days of a fire's containment, RECOVER has the potential to significantly improve the decision-making process. RECOVER adds an important new dimension to post-fire decision-making by focusing on ecosystem rehabilitation in semiarid savannas. A novel aspect of RECOVER's approach involves the use of soil moisture estimates, which are an important but difficult-to-obtain element of post-fire rehabilitation planning. We will use downscaled soil moisture data from three primary observational sources to begin evaluation of soil moisture products and build the technology needed for RECOVER to use future SMAP products. As a result, RECOVER, BLM, and the fire applications community will be ready customers for data flowing out of new NASA missions, such as NPP, LDCM, and SMAP.

Spatial Variability of the Background Diurnal Cycle of Deep Convection around the GoAmazon2014/5 Field Campaign Sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burleyson, Casey D.; Feng, Zhe; Hagos, Samson M.

The Amazon rainforest is one of a few regions of the world where continental tropical deep convection occurs. The Amazon’s isolation makes it challenging to observe, but also creates a unique natural laboratory to study anthropogenic impacts on clouds and precipitation in an otherwise pristine environment. Extensive measurements were made upwind and downwind of the large city of Manaus, Brazil during the Observations and Modeling of the Green Ocean Amazon 2014-2015 (GoAmazon2014/5) field campaign. In this study, 15 years of high-resolution satellite data are analyzed to examine the spatial and diurnal variability of convection occurring around the GoAmazon2014/5 sites. Interpretationmore » of anthropogenic differences between the upwind (T0) and downwind (T1-T3) sites is complicated by naturally-occurring spatial variability between the sites. During the rainy season, the inland propagation of the previous day’s sea-breeze front happens to be in phase with the background diurnal cycle near Manaus, but is out of phase elsewhere. Enhanced convergence between the river-breezes and the easterly trade winds generates up to 10% more frequent deep convection at the GoAmazon2014/5 sites east of the river (T0a, T0t/k, and T1) compared to the T3 site which was located near the western bank. In general, the annual and diurnal cycles during 2014 were representative of the 2000-2013 distributions. The only exceptions were in March when the monthly mean rainrate was above the 95th percentile and September when both rain frequency and intensity were suppressed. The natural spatial variability must be accounted for before interpreting anthropogenically-induced differences among the GoAmazon2014/5 sites.« less

Long-term deforestation dynamics in the Brazilian Amazon-Uncovering historic frontier development along the Cuiabá-Santarém highway

NASA Astrophysics Data System (ADS)

Müller, Hannes; Griffiths, Patrick; Hostert, Patrick

2016-02-01

The great success of the Brazilian deforestation programme "PRODES digital" has shown the importance of annual deforestation information for understanding and mitigating deforestation and its consequences in Brazil. However, there is a lack of similar information on deforestation for the 1990s and 1980s. Such maps are essential to understand deforestation frontier development and related carbon emissions. This study aims at extending the deforestation mapping record backwards into the 1990s and 1980s for one of the major deforestation frontiers in the Amazon. We use an image compositing approach to transform 2224 Landsat images in a spatially continuous and cloud free annual time series of Tasseled Cap Wetness metrics from 1984 to 2012. We then employ a random forest classifier to derive annual deforestation patterns. Our final deforestation map has an overall accuracy of 85% with half of the overall deforestation being detected before the year 2000. The results show for the first time detailed patterns of the expanding deforestation frontier before the 2000s. The high degree of automatization exhibits the great potential for mapping the whole Amazon biome using long-term and freely accessible remote sensing collections, such as the Landsat archive and forthcoming Sentinel-2 data.

The Green Ocean Over the Amazon: Implications for Cloud Electrification

NASA Technical Reports Server (NTRS)

Williams, E.; Blakeslee, R.; Boccippio, D.; Arnold, James E. (Technical Monitor)

2001-01-01

A convective regime with distinct maritime characteristics (weak updraft, low CCN, prevalent coalescence and rainout, weak mixed phase reflectivity, low glaciation temperature, and little if any lightning) is documented over the Amazon basin of the South American continent, and dubbed the "green ocean". Radar, lightning, thermodynamic and AVHRR satellite observations are examined to shed light on the roles of updraft and aerosol in providing departures from the green ocean regime toward continental behavior. Extreme case studies are identified in which the updraft control is dominant and in which the aerosol control is dominant. The tentative conclusion gives importance to both updrafts and aerosol in shaping the electrification of tropical convection.

Hybrid Cloud Computing Environment for EarthCube and Geoscience Community

NASA Astrophysics Data System (ADS)

Yang, C. P.; Qin, H.

2016-12-01

The NSF EarthCube Integration and Test Environment (ECITE) has built a hybrid cloud computing environment to provides cloud resources from private cloud environments by using cloud system software - OpenStack and Eucalyptus, and also manages public cloud - Amazon Web Service that allow resource synchronizing and bursting between private and public cloud. On ECITE hybrid cloud platform, EarthCube and geoscience community can deploy and manage the applications by using base virtual machine images or customized virtual machines, analyze big datasets by using virtual clusters, and real-time monitor the virtual resource usage on the cloud. Currently, a number of EarthCube projects have deployed or started migrating their projects to this platform, such as CHORDS, BCube, CINERGI, OntoSoft, and some other EarthCube building blocks. To accomplish the deployment or migration, administrator of ECITE hybrid cloud platform prepares the specific needs (e.g. images, port numbers, usable cloud capacity, etc.) of each project in advance base on the communications between ECITE and participant projects, and then the scientists or IT technicians in those projects launch one or multiple virtual machines, access the virtual machine(s) to set up computing environment if need be, and migrate their codes, documents or data without caring about the heterogeneity in structure and operations among different cloud platforms.

Leveraging Cloud Technology to Provide a Responsive, Reliable and Scalable Backend for the Virtual Ice Sheet Laboratory Using the Ice Sheet System Model and Amazon's Elastic Compute Cloud

NASA Astrophysics Data System (ADS)

Perez, G. L.; Larour, E. Y.; Halkides, D. J.; Cheng, D. L. C.

2015-12-01

The Virtual Ice Sheet Laboratory(VISL) is a Cryosphere outreach effort byscientists at the Jet Propulsion Laboratory(JPL) in Pasadena, CA, Earth and SpaceResearch(ESR) in Seattle, WA, and the University of California at Irvine (UCI), with the goal of providing interactive lessons for K-12 and college level students,while conforming to STEM guidelines. At the core of VISL is the Ice Sheet System Model(ISSM), an open-source project developed jointlyat JPL and UCI whose main purpose is to model the evolution of the polar ice caps in Greenland and Antarctica. By using ISSM, VISL students have access tostate-of-the-art modeling software that is being used to conduct scientificresearch by users all over the world. However, providing this functionality isby no means simple. The modeling of ice sheets in response to sea and atmospheric temperatures, among many other possible parameters, requiressignificant computational resources. Furthermore, this service needs to beresponsive and capable of handling burst requests produced by classrooms ofstudents. Cloud computing providers represent a burgeoning industry. With majorinvestments by tech giants like Amazon, Google and Microsoft, it has never beeneasier or more affordable to deploy computational elements on-demand. This isexactly what VISL needs and ISSM is capable of. Moreover, this is a promisingalternative to investing in expensive and rapidly devaluing hardware.

On the Large-Scaling Issues of Cloud-based Applications for Earth Science Dat

NASA Astrophysics Data System (ADS)

Hua, H.

2016-12-01

Next generation science data systems are needed to address the incoming flood of data from new missions such as NASA's SWOT and NISAR where its SAR data volumes and data throughput rates are order of magnitude larger than present day missions. Existing missions, such as OCO-2, may also require high turn-around time for processing different science scenarios where on-premise and even traditional HPC computing environments may not meet the high processing needs. Additionally, traditional means of procuring hardware on-premise are already limited due to facilities capacity constraints for these new missions. Experiences have shown that to embrace efficient cloud computing approaches for large-scale science data systems requires more than just moving existing code to cloud environments. At large cloud scales, we need to deal with scaling and cost issues. We present our experiences on deploying multiple instances of our hybrid-cloud computing science data system (HySDS) to support large-scale processing of Earth Science data products. We will explore optimization approaches to getting best performance out of hybrid-cloud computing as well as common issues that will arise when dealing with large-scale computing. Novel approaches were utilized to do processing on Amazon's spot market, which can potentially offer 75%-90% costs savings but with an unpredictable computing environment based on market forces.

Genotyping in the cloud with Crossbow.

PubMed

Gurtowski, James; Schatz, Michael C; Langmead, Ben

2012-09-01

Crossbow is a scalable, portable, and automatic cloud computing tool for identifying SNPs from high-coverage, short-read resequencing data. It is built on Apache Hadoop, an implementation of the MapReduce software framework. Hadoop allows Crossbow to distribute read alignment and SNP calling subtasks over a cluster of commodity computers. Two robust tools, Bowtie and SOAPsnp, implement the fundamental alignment and variant calling operations respectively, and have demonstrated capabilities within Crossbow of analyzing approximately one billion short reads per hour on a commodity Hadoop cluster with 320 cores. Through protocol examples, this unit will demonstrate the use of Crossbow for identifying variations in three different operating modes: on a Hadoop cluster, on a single computer, and on the Amazon Elastic MapReduce cloud computing service.

Biogenic Potassium Salt Particles as Seeds for Secondary Organic Aerosol in the Amazon

NASA Astrophysics Data System (ADS)

Pöhlker, Christopher; Wiedemann, Kenia T.; Sinha, Bärbel; Shiraiwa, Manabu; Gunthe, Sachin S.; Smith, Mackenzie; Su, Hang; Artaxo, Paulo; Chen, Qi; Cheng, Yafang; Elbert, Wolfgang; Gilles, Mary K.; Kilcoyne, Arthur L. D.; Moffet, Ryan C.; Weigand, Markus; Martin, Scot T.; Pöschl, Ulrich; Andreae, Meinrat O.

2012-08-01

The fine particles serving as cloud condensation nuclei in pristine Amazonian rainforest air consist mostly of secondary organic aerosol. Their origin is enigmatic, however, because new particle formation in the atmosphere is not observed. Here, we show that the growth of organic aerosol particles can be initiated by potassium-salt-rich particles emitted by biota in the rainforest. These particles act as seeds for the condensation of low- or semi-volatile organic compounds from the atmospheric gas phase or multiphase oxidation of isoprene and terpenes. Our findings suggest that the primary emission of biogenic salt particles directly influences the number concentration of cloud condensation nuclei and affects the microphysics of cloud formation and precipitation over the rainforest.

Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research.

PubMed

Purawat, Shweta; Cowart, Charles; Amaro, Rommie E; Altintas, Ilkay

2017-05-01

The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC is an e-learning platform that empowers the biomedical community to develop, launch and share open training materials. It deploys hands-on software training toolboxes through virtualization technologies such as Amazon EC2 and Virtualbox. The BBDTC facilitates migration of courses across other course management platforms. The framework encourages knowledge sharing and content personalization through the playlist functionality that enables unique learning experiences and accelerates information dissemination to a wider community.

High-Resiliency and Auto-Scaling of Large-Scale Cloud Computing for OCO-2 L2 Full Physics Processing

NASA Astrophysics Data System (ADS)

Hua, H.; Manipon, G.; Starch, M.; Dang, L. B.; Southam, P.; Wilson, B. D.; Avis, C.; Chang, A.; Cheng, C.; Smyth, M.; McDuffie, J. L.; Ramirez, P.

2015-12-01

Next generation science data systems are needed to address the incoming flood of data from new missions such as SWOT and NISAR where data volumes and data throughput rates are order of magnitude larger than present day missions. Additionally, traditional means of procuring hardware on-premise are already limited due to facilities capacity constraints for these new missions. Existing missions, such as OCO-2, may also require high turn-around time for processing different science scenarios where on-premise and even traditional HPC computing environments may not meet the high processing needs. We present our experiences on deploying a hybrid-cloud computing science data system (HySDS) for the OCO-2 Science Computing Facility to support large-scale processing of their Level-2 full physics data products. We will explore optimization approaches to getting best performance out of hybrid-cloud computing as well as common issues that will arise when dealing with large-scale computing. Novel approaches were utilized to do processing on Amazon's spot market, which can potentially offer ~10X costs savings but with an unpredictable computing environment based on market forces. We will present how we enabled high-tolerance computing in order to achieve large-scale computing as well as operational cost savings.

Use of several Cloud Computing approaches for climate modelling: performance, costs and opportunities

NASA Astrophysics Data System (ADS)

Perez Montes, Diego A.; Añel Cabanelas, Juan A.; Wallom, David C. H.; Arribas, Alberto; Uhe, Peter; Caderno, Pablo V.; Pena, Tomas F.

2017-04-01

Cloud Computing is a technological option that offers great possibilities for modelling in geosciences. We have studied how two different climate models, HadAM3P-HadRM3P and CESM-WACCM, can be adapted in two different ways to run on Cloud Computing Environments from three different vendors: Amazon, Google and Microsoft. Also, we have evaluated qualitatively how the use of Cloud Computing can affect the allocation of resources by funding bodies and issues related to computing security, including scientific reproducibility. Our first experiments were developed using the well known ClimatePrediction.net (CPDN), that uses BOINC, over the infrastructure from two cloud providers, namely Microsoft Azure and Amazon Web Services (hereafter AWS). For this comparison we ran a set of thirteen month climate simulations for CPDN in Azure and AWS using a range of different virtual machines (VMs) for HadRM3P (50 km resolution over South America CORDEX region) nested in the global atmosphere-only model HadAM3P. These simulations were run on a single processor and took between 3 and 5 days to compute depending on the VM type. The last part of our simulation experiments was running WACCM over different VMS on the Google Compute Engine (GCE) and make a comparison with the supercomputer (SC) Finisterrae1 from the Centro de Supercomputacion de Galicia. It was shown that GCE gives better performance than the SC for smaller number of cores/MPI tasks but the model throughput shows clearly how the SC performance is better after approximately 100 cores (related with network speed and latency differences). From a cost point of view, Cloud Computing moves researchers from a traditional approach where experiments were limited by the available hardware resources to monetary resources (how many resources can be afforded). As there is an increasing movement and recommendation for budgeting HPC projects on this technology (budgets can be calculated in a more realistic way) we could see a shift on the trends over the next years to consolidate Cloud as the preferred solution.

Rainfall and its seasonality over the Amazon in the 21st century as assessed by the coupled models for the IPCC AR4

NASA Astrophysics Data System (ADS)

Li, Wenhong; Fu, Rong; Dickinson, Robert E.

2006-01-01

The global climate models for the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) predict very different changes of rainfall over the Amazon under the SRES A1B scenario for global climate change. Five of the eleven models predict an increase of annual rainfall, three models predict a decrease of rainfall, and the other three models predict no significant changes in the Amazon rainfall. We have further examined two models. The UKMO-HadCM3 model predicts an El Niño-like sea surface temperature (SST) change and warming in the northern tropical Atlantic which appear to enhance atmospheric subsidence and consequently reduce clouds over the Amazon. The resultant increase of surface solar absorption causes a stronger surface sensible heat flux and thus reduces relative humidity of the surface air. These changes decrease the rate and length of wet season rainfall and surface latent heat flux. This decreased wet season rainfall leads to drier soil during the subsequent dry season, which in turn can delay the transition from the dry to wet season. GISS-ER predicts a weaker SST warming in the western Pacific and the southern tropical Atlantic which increases moisture transport and hence rainfall in the Amazon. In the southern Amazon and Nordeste where the strongest rainfall increase occurs, the resultant higher soil moisture supports a higher surface latent heat flux during the dry and transition season and leads to an earlier wet season onset.

The Fertilizing Role of African Dust in the Amazon Rainforest: A First Multiyear Assessment Based on CALIPSO Lidar Observations

NASA Technical Reports Server (NTRS)

Yu, Hongbin; Chin, Mian; Yuan, Tianle; Bian, Huisheng; Remer, Lorraine A.; Prospero, Joseph M.; Omar, Ali; Winker, David; Yang, Yuekui; Zhang, Yan;

Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pöhlker, Mira L.; Pöhlker, Christopher; Ditas, Florian

Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a 1-year period and full seasonal cycle (March 2014–February 2015). Our measurements provide a climatology of CCN properties characteristic of a remote central Amazonian rain forest site.The CCN measurements were continuously cycled through 10 levels of supersaturation ( S=0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The mean critical diameters of CCN activation range from 43 nm at S = 1.10 % to 172more » nm at S = 0.11 %. Furthermore, the particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode ( κ Ait = 0.14 ± 0.03), higher values for the accumulation mode ( κ Acc = 0.22 ± 0.05), and an overall mean value of κ mean = 0.17 ± 0.06, consistent with high fractions of organic aerosol.The hygroscopicity parameter, κ, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. Here, we find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes.For modeling purposes, we compare different approaches of predicting CCN number concentration and present a novel parametrization, which allows accurate CCN predictions based on a small set of input data.« less

Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction

DOE PAGES

Pöhlker, Mira L.; Pöhlker, Christopher; Ditas, Florian; ...

2016-12-20

Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a 1-year period and full seasonal cycle (March 2014–February 2015). Our measurements provide a climatology of CCN properties characteristic of a remote central Amazonian rain forest site.The CCN measurements were continuously cycled through 10 levels of supersaturation ( S=0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The mean critical diameters of CCN activation range from 43 nm at S = 1.10 % to 172more » nm at S = 0.11 %. Furthermore, the particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode ( κ Ait = 0.14 ± 0.03), higher values for the accumulation mode ( κ Acc = 0.22 ± 0.05), and an overall mean value of κ mean = 0.17 ± 0.06, consistent with high fractions of organic aerosol.The hygroscopicity parameter, κ, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. Here, we find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes.For modeling purposes, we compare different approaches of predicting CCN number concentration and present a novel parametrization, which allows accurate CCN predictions based on a small set of input data.« less

Cloud computing geospatial application for water resources based on free and open source software and open standards - a prototype

NASA Astrophysics Data System (ADS)

Delipetrev, Blagoj

2016-04-01

Presently, most of the existing software is desktop-based, designed to work on a single computer, which represents a major limitation in many ways, starting from limited computer processing, storage power, accessibility, availability, etc. The only feasible solution lies in the web and cloud. This abstract presents research and development of a cloud computing geospatial application for water resources based on free and open source software and open standards using hybrid deployment model of public - private cloud, running on two separate virtual machines (VMs). The first one (VM1) is running on Amazon web services (AWS) and the second one (VM2) is running on a Xen cloud platform. The presented cloud application is developed using free and open source software, open standards and prototype code. The cloud application presents a framework how to develop specialized cloud geospatial application that needs only a web browser to be used. This cloud application is the ultimate collaboration geospatial platform because multiple users across the globe with internet connection and browser can jointly model geospatial objects, enter attribute data and information, execute algorithms, and visualize results. The presented cloud application is: available all the time, accessible from everywhere, it is scalable, works in a distributed computer environment, it creates a real-time multiuser collaboration platform, the programing languages code and components are interoperable, and it is flexible in including additional components. The cloud geospatial application is implemented as a specialized water resources application with three web services for 1) data infrastructure (DI), 2) support for water resources modelling (WRM), 3) user management. The web services are running on two VMs that are communicating over the internet providing services to users. The application was tested on the Zletovica river basin case study with concurrent multiple users. The application is a state-of-the-art cloud geospatial collaboration platform. The presented solution is a prototype and can be used as a foundation for developing of any specialized cloud geospatial applications. Further research will be focused on distributing the cloud application on additional VMs, testing the scalability and availability of services.

The HEPiX Virtualisation Working Group: Towards a Grid of Clouds

NASA Astrophysics Data System (ADS)

Cass, Tony

2012-12-01

The use of virtual machine images, as for example with Cloud services such as Amazon's Elastic Compute Cloud, is attractive for users as they have a guaranteed execution environment, something that cannot today be provided across sites participating in computing grids such as the Worldwide LHC Computing Grid. However, Grid sites often operate within computer security frameworks which preclude the use of remotely generated images. The HEPiX Virtualisation Working Group was setup with the objective to enable use of remotely generated virtual machine images at Grid sites and, to this end, has introduced the idea of trusted virtual machine images which are guaranteed to be secure and configurable by sites such that security policy commitments can be met. This paper describes the requirements and details of these trusted virtual machine images and presents a model for their use to facilitate the integration of Grid- and Cloud-based computing environments for High Energy Physics.

Research on Key Technologies of Cloud Computing

NASA Astrophysics Data System (ADS)

Zhang, Shufen; Yan, Hongcan; Chen, Xuebin

With the development of multi-core processors, virtualization, distributed storage, broadband Internet and automatic management, a new type of computing mode named cloud computing is produced. It distributes computation task on the resource pool which consists of massive computers, so the application systems can obtain the computing power, the storage space and software service according to its demand. It can concentrate all the computing resources and manage them automatically by the software without intervene. This makes application offers not to annoy for tedious details and more absorbed in his business. It will be advantageous to innovation and reduce cost. It's the ultimate goal of cloud computing to provide calculation, services and applications as a public facility for the public, So that people can use the computer resources just like using water, electricity, gas and telephone. Currently, the understanding of cloud computing is developing and changing constantly, cloud computing still has no unanimous definition. This paper describes three main service forms of cloud computing: SAAS, PAAS, IAAS, compared the definition of cloud computing which is given by Google, Amazon, IBM and other companies, summarized the basic characteristics of cloud computing, and emphasized on the key technologies such as data storage, data management, virtualization and programming model.

Changes in the physico-chemical properties of Amazonian aerosols from background conditions due to urban impacts in Central Amazonia.

NASA Astrophysics Data System (ADS)

Artaxo, P.; Barbosa, H. M.; Brito, J.; Carbone, S.; Fiorese, C.; Andre, B.; Rizzo, L. V.; Ditas, F.; Pöhlker, C.; Pöhlker, M. L.; Saturno, J.; Holanda, B. A.; Wang, J.; Souza, R. A. F. D.; Machado, L.; Andreae, M. O.; Martin, S. T.

2016-12-01

The GoAmazon 2014/15 experiment (Observations and Modeling of the Green Ocean Amazon) was a great opportunity to study how urbanization can change aerosol properties under pristine conditions in a tropical rain forest. The experiment took place from January 2014 to December 2015 in the vicinity of Manaus, Brazil, where several sampling stations were operated. Natural biogenic aerosol properties were studied in 3 sampling stations upwind of Manaus (ATTO (T0a), ZF2 (T0z) and EMBRAPA (T0e)). Urban impacted aerosols were analysed in two downwind sampling stations at Tiwa (T2) and Manacapuru (T3). Properties analysed were size distribution, scattering and absorption, composition, vertical profiles and others. Remote sensing measurements were done using AERONET and MODIS, while extensive ground based measurements were done in all sampling stations. Remote sensing measurements shows important changes in aerosol optical depth (AOD), especially in the aerosol absorption component. It was also observed a reduction in cloud droplet size downwind of Manaus for liquid phase clouds. Changes in particle number and size were also very significant, that reflected in changes in the aerosol radiative forcing (RF) before and after Manaus plume. In the dry season, an average RF of -24 w/m² was observed upwind, while -17 w/m² was observed downwind, due to large scale biomass burning aerosols. Single scattering albedo (SSA) at 550 nm changed from a high value of 0.96 upwind to 0.84 downwind due to the increase in absorbing aerosols in the wet season. In the dry season, SSA at 550nm changed from 0.95 to 0.87. Aerosol composition showed a large dominance of organic aerosols for all sites, accounting for 65-75% of PM1 non refractory aerosol. Most of these were secondary organic aerosol (SOA), with very low sulfate and nitrate concentrations. The influence of the Manaus plume on aerosol properties was more intense during the wet season, because in the dry season a significant amount of large scale biomass burning aerosol was observed for all GoAmazon 2014/15 sites.

The Ethics of Cloud Computing.

PubMed

de Bruin, Boudewijn; Floridi, Luciano

2017-02-01

Cloud computing is rapidly gaining traction in business. It offers businesses online services on demand (such as Gmail, iCloud and Salesforce) and allows them to cut costs on hardware and IT support. This is the first paper in business ethics dealing with this new technology. It analyzes the informational duties of hosting companies that own and operate cloud computing datacentres (e.g., Amazon). It considers the cloud services providers leasing 'space in the cloud' from hosting companies (e.g., Dropbox, Salesforce). And it examines the business and private 'clouders' using these services. The first part of the paper argues that hosting companies, services providers and clouders have mutual informational (epistemic) obligations to provide and seek information about relevant issues such as consumer privacy, reliability of services, data mining and data ownership. The concept of interlucency is developed as an epistemic virtue governing ethically effective communication. The second part considers potential forms of government restrictions on or proscriptions against the development and use of cloud computing technology. Referring to the concept of technology neutrality, it argues that interference with hosting companies and cloud services providers is hardly ever necessary or justified. It is argued, too, however, that businesses using cloud services (e.g., banks, law firms, hospitals etc. storing client data in the cloud) will have to follow rather more stringent regulations.

Anthropogenic Effects on the Mixing State of Aerosols over Manaus during the Green Ocean Amazon (GoAmazon) Campaign

NASA Astrophysics Data System (ADS)

Fraund, M. W.; Pham, D.; Harder, T.; O'Brien, R.; Wang, B.; Laskin, A.; Gilles, M. K.; Moffet, R.

2015-12-01

The role that anthropogenic aerosols play in cloud formation is uncertain and contributes largely to the uncertainty in predicting future climate. One region of particular importance is the Amazon rainforest, which accounts for over half of the world's rainforest. During GoAmazon2014/15 IOP2, aerosol samples were collected at multiple sites in and around the rapidly growing industrial city of Manaus in the Amazon basin. Manaus is of scientific interest due to the pristine nature of the surrounding rainforest and the high levels of pollution coming from the city in the form of SO2, NOx, and soot. Some sites, such as the Terrestrial Ecosystem Science center (TES, also designated ZF2) located to the north of Manaus, represent air masses which have not interacted with emissions from the city. The comparison of pristine atmosphere with heavy pollution allows both for the determination of a natural baseline level of pollutants, as well as the study of pollutant's impact on the conversion of biogenic volatile organic compounds to secondary organic aerosols. Towards this goal, samples from ZF2 and other unpolluted sites will be compared to samples from the Atmospheric Radiation Measurement (ARM) climate research facility in Manacapuru (T3), which is southwest (downwind) of Manaus. Spatially resolved spectra were recorded at the sub-particle level using scanning transmission X-ray microscopy (STXM) at the carbon, nitrogen, and oxygen K-absorption edges. Scanning electron microscopy coupled with energy dispersive x-ray spectroscopy (SEM/EDX) was also performed on to characterize higher Z elements. These two techniques together will allow for the mass fraction of atmospherically relevant elements to be determined on a per-particle basis. We will apply established procedures to determine the mixing state index for samples collected at ZF2 and T3 using elemental mass fractions. Preliminary results will be presented which focus on investigating the difference between mixing state indices of aerosol samples collected at these two sites, due to anthropogenic emissions. The ultimate goal is to use the mixing state index as a parameter which enables changes in the composition of aerosols to be modeled with more certainty as well enabling a quantification of the anthropogenic effects on natural rainforest atmosphere.

Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research.

PubMed

Purawat, Shweta; Cowart, Charles; Amaro, Rommie E; Altintas, Ilkay

2016-06-01

The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC collaborative is an e-learning platform that supports the biomedical community to access, develop and deploy open training materials. The BBDTC supports Big Data skill training for biomedical scientists at all levels, and from varied backgrounds. The natural hierarchy of courses allows them to be broken into and handled as modules . Modules can be reused in the context of multiple courses and reshuffled, producing a new and different, dynamic course called a playlist . Users may create playlists to suit their learning requirements and share it with individual users or the wider public. BBDTC leverages the maturity and design of the HUBzero content-management platform for delivering educational content. To facilitate the migration of existing content, the BBDTC supports importing and exporting course material from the edX platform. Migration tools will be extended in the future to support other platforms. Hands-on training software packages, i.e., toolboxes , are supported through Amazon EC2 and Virtualbox virtualization technologies, and they are available as: ( i ) downloadable lightweight Virtualbox Images providing a standardized software tool environment with software packages and test data on their personal machines, and ( ii ) remotely accessible Amazon EC2 Virtual Machines for accessing biomedical big data tools and scalable big data experiments. At the moment, the BBDTC site contains three open Biomedical big data training courses with lecture contents, videos and hands-on training utilizing VM toolboxes, covering diverse topics. The courses have enhanced the hands-on learning environment by providing structured content that users can use at their own pace. A four course biomedical big data series is planned for development in 2016.

PROBING ELECTRON-CAPTURE SUPERNOVAE: X-RAY BINARIES IN STARBURSTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Linden, T.; Sepinsky, J. F.; Kalogera, V.

We develop population models of high-mass X-ray binaries (HMXBs) formed after bursts of star formation and we investigate the effect of electron-capture supernovae (ECS) of massive ONeMg white dwarfs and the hypothesis that ECS events are associated with typically low supernova kicks imparted to the nascent neutron stars. We identify an interesting ECS bump in the time evolution of HMXB numbers; this bump is caused by significantly increased production of wind-fed HMXBs 20-60 Myr post-starburst. The amplitude and age extent of the ECS bump depend on the strength of ECS kicks and the mass range of ECS progenitors. We alsomore » find that ECS-HMXBs form through a specific evolutionary channel that is expected to lead to binaries with Be donors in wide orbits. These characteristics, along with their sensitivity to ECS properties, provide us with an intriguing opportunity to probe ECS physics and progenitors through studies of starbursts of different ages. Specifically, the case of the Small Magellanic Cloud, with a significant observed population of Be-HMXBs and starburst activity 30-60 Myr ago, arises as a promising laboratory for understanding the role of ECS in neutron star formation.« less

NOAA's Big Data Partnership at the National Centers for Environmental Information

NASA Astrophysics Data System (ADS)

Kearns, E. J.

2015-12-01

In April of 2015, the U.S. Department of Commerce announced NOAA's Big Data Partnership (BDP) with Amazon Web Services, Google Cloud Platform, IBM, Microsoft Corp., and the Open Cloud Consortium through Cooperative Research and Development Agreements. Recent progress on the activities with these Partners at the National Centers for Environmental Information (NCEI) will be presented. These activities include the transfer of over 350 TB of NOAA's archived data from NCEI's tape-based archive system to BDP cloud providers; new opportunities for data mining and investigation; application of NOAA's data maturity and stewardship concepts to the BDP; and integration of both archived and near-realtime data streams into a synchronized, distributed data system. Both lessons learned and future opportunities for the environmental data community will be presented.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anber, Usama; Gentine, Pierre; Wang, Shuguang

The diurnal and seasonal water cycles in the Amazon remain poorly simulated in general circulation models, exhibiting peak evapotranspiration in the wrong season and rain too early in the day. We show that those biases are not present in cloud-resolving simulations with parameterized large-scale circulation. The difference is attributed to the representation of the morning fog layer, and to more accurate characterization of convection and its coupling with large-scale circulation. The morning fog layer, present in the wet season but absent in the dry season, dramatically increases cloud albedo, which reduces evapotranspiration through its modulation of the surface energy budget.more » Finally, these results highlight the importance of the coupling between the energy and hydrological cycles and the key role of cloud albedo feedback for climates over tropical continents.« less

Observations of atmospheric monoaromatic hydrocarbons at urban, semi-urban and forest environments in the Amazon region

NASA Astrophysics Data System (ADS)

Paralovo, Sarah L.; Borillo, Guilherme C.; Barbosa, Cybelli G. G.; Godoi, Ana Flavia L.; Yamamoto, Carlos I.; de Souza, Rodrigo A. F.; Andreoli, Rita V.; Costa, Patrícia S.; Almeida, Gerson P.; Manzi, Antonio O.; Pöhlker, Christopher; Yáñez-Serrano, Ana M.; Kesselmeier, Jürgen; Godoi, Ricardo H. M.

2016-03-01

The Amazon region is one of the most significant natural ecosystems on the planet. Of special interest as a major study area is the interface between the forest and Manaus city, a state capital in Brazil embedded in the heart of the Amazon forest. In view of the interactions between natural and anthropogenic processes, an integrated experiment was conducted measuring the concentrations of the volatile organic compounds (VOCs) benzene, toluene, ethylbenzene and meta, ortho, para-xylene (known as BTEX), all of them regarded as pollutants with harmful effects on human health and vegetation and acting also as important precursors of tropospheric ozone. Furthermore, these compounds also take part in the formation of secondary organic aerosols, which can influence the pattern of cloud formation, and thus the regional water cycle and climate. The samples were collected in 2012/2013 at three different sites: (i) The Amazon Tall Tower Observatory (ATTO), a pristine rain forest region in the central Amazon Basin; (ii) Manacapuru, a semi-urban site located southwest and downwind of Manaus as a preview of the Green Ocean Amazon Experiment (GoAmazon 2014/15); and (iii) the city of Manaus (distributed over three sites). Results indicate that there is an increase in pollutant concentrations with increasing proximity to urban areas. For instance, the benzene concentration ranges were 0.237-19.6 (Manaus), 0.036-0.948 (Manacapuru) and 0.018-0.313 μg m-3 (ATTO). Toluene ranges were 0.700-832 (Manaus), 0.091-2.75 μg m-3 (Manacapuru) and 0.011-4.93 (ATTO). For ethylbenzene, they were 0.165-447 (Manaus), 0.018-1.20 μg m-3 (Manacapuru) and 0.047-0.401 (ATTO). Some indication was found for toluene to be released from the forest. No significant difference was found between the BTEX levels measured in the dry season and the wet seasons. Furthermore, it was observed that, in general, the city of Manaus seems to be less impacted by these pollutants than other cities in Brazil and in other countries, near the coastline or on the continent. A risk analysis for the health of Manaus' population was performed and indicated that the measured concentrations posed a risk for development of chronic diseases and cancer for the population of Manaus.

Effects of Convective Transport on the Budget of Amazonian Aerosol under Background Conditions

NASA Astrophysics Data System (ADS)

Wang, J.; Krejci, R.; Giangrande, S. E.; Kuang, C.; Barbosa, H. M.; Brito, J.; Carbone, S.; Chi, X.; Comstock, J. M.; Ditas, F.; Lavric, J. V.; Manninen, H. E.; Mei, F.; Moran, D.; Pöhlker, C.; Pöhlker, M. L.; Saturno, J.; Schmid, B.; Souza, R. A. F. D.; Springston, S. R.; Tomlinson, J. M.; Toto, T.; Walter, D.; Wimmer, D.; Smith, J. N.; Machado, L.; Artaxo, P.; Andreae, M. O.; Martin, S. T.

2016-12-01

Aerosol particles can strongly influence the radiative properties of clouds, and they represent one of the largest uncertainties in computer simulations of climate change. The large uncertainty is in large part due to a poor understanding of processes under natural conditions, which serves as the baseline to measure change against. Understanding the processes under natural conditions is critical for a reliable assessment and quantification of ongoing and future climate change. The Amazon rainforest is one of the few continental regions where aerosol particles and their precursors can be studied under near-natural conditions. Here we examine the aerosol number and CCN budget under background conditions in the Amazon basin using data collected during the Observations and Modeling of the Green Ocean Amazon (GoAmazon 2014/5) campaign, which took place from January 2014 to December 2015 near Manaus, Brazil. The aerosol size spectrum was observed at the Amazon Tall Tower Observatory (ATTO), 150 km upwind of Manaus, and its variation with convection and precipitation during the wet season is presented. Air masses arriving at the ATTO during the wet season are typically brought by the northeasterly trade winds and travel across at least 1000 km of undeveloped tropical rainforest, therefore are generally clean. Also shown are vertical profiles of aerosol observed onboard the DOE Gulfstream-1 research aircraft. The impact of convective transport on the budget of boundary layer aerosol and CCN under the background conditions is discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Hongbin; Chin, Mian; Yuan, Tianle

The productivity of the Amazon rainforest is constrained by the availability of nutrients, in particular phosphorus (P). Deposition of long-range transported African dust is recognized as a potentially important but poorly quantified source of phosphorus. This study provides a first multiyear satellite-based estimate of dust deposition into the Amazon Basin using three dimensional (3D) aerosol measurements over 2007-2013 from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The 7-year average of dust deposition into the Amazon Basin is estimated to be 28 (8~48) Tg a -1 or 29 (8~50) kg ha -1 a -1. The dust deposition shows significant interannual variationmore » that is negatively correlated with the prior-year rainfall in the Sahel. The CALIOP-based multi-year mean estimate of dust deposition matches better with estimates from in-situ measurements and model simulations than a previous satellite-based estimate does. The closer agreement benefits from a more realistic geographic definition of the Amazon Basin and inclusion of meridional dust transport calculation in addition to the 3D nature of CALIOP aerosol measurements. The imported dust could provide about 0.022 (0.006~0.037) Tg P of phosphorus per year, equivalent to 23 (7~39) g P ha -1 a -1 to fertilize the Amazon rainforest. This out-of-Basin P input is comparable to the hydrological loss of P from the Basin, suggesting an important role of African dust in preventing phosphorus depletion on time scales of decades to centuries.« less

Characterizing Temporal and Spatial Changes in Land Surface Temperature across the Amazon Basin using Thermal and Infrared Satellite Data

NASA Astrophysics Data System (ADS)

Cak, A. D.

2017-12-01

The Amazon Basin has faced innumerable pressures in recent years, including logging, mining and resource extraction, agricultural expansion, road building, and urbanization. These changes have drastically altered the landscape, transforming a predominantly forested environment into a mosaic of different types of land cover. The resulting fragmentation has caused dramatic and negative impacts on its structure and function, including on biodiversity and the transfer of water and energy to and from soil, vegetation, and the atmosphere (e.g., evapotranspiration). Because evapotranspiration from forested areas, which is affected by factors including temperature and water availability, plays a significant role in water dynamics in the Amazon Basin, measuring land surface temperature (LST) across the region can provide a dynamic assessment of hydrological, vegetation, and land use and land cover changes. It can also help to identify widespread urban development, which often has a higher LST signal relative to surrounding vegetation. Here, we discuss results from work to measure and identify drivers of change in LST across the entire Amazon Basin through analysis of past and current thermal and infrared satellite imagery. We leverage cloud computing resources in new ways to allow for more efficient analysis of imagery over the Amazon Basin across multiple years and multiple sensors. We also assess potential drivers of change in LST using spatial and multivariate statistical analyses with additional data sources of land cover, urban development, and demographics.

Processing ARM VAP data on an AWS cluster

NASA Astrophysics Data System (ADS)

Martin, T.; Macduff, M.; Shippert, T.

2017-12-01

The Atmospheric Radiation Measurement (ARM) Data Management Facility (DMF) manages over 18,000 processes and 1.3 TB of data each day. This includes many Value Added Products (VAPs) that make use of multiple instruments to produce the derived products that are scientifically relevant. A thermodynamic and cloud profile VAP is being developed to provide input to the ARM Large-eddy simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project (https://www.arm.gov/capabilities/vaps/lasso-122) . This algorithm is CPU intensive and the processing requirements exceeded the available DMF computing capacity. Amazon Web Service (AWS) along with CfnCluster was investigated to see how it would perform. This cluster environment is cost effective and scales dynamically based on demand. We were able to take advantage of autoscaling which allowed the cluster to grow and shrink based on the size of the processing queue. We also were able to take advantage of the Amazon Web Services spot market to further reduce the cost. Our test was very successful and found that cloud resources can be used to efficiently and effectively process time series data. This poster will present the resources and methodology used to successfully run the algorithm.

Cloud-based NEXRAD Data Processing and Analysis for Hydrologic Applications

NASA Astrophysics Data System (ADS)

Seo, B. C.; Demir, I.; Keem, M.; Goska, R.; Weber, J.; Krajewski, W. F.

2016-12-01

The real-time and full historical archive of NEXRAD Level II data, covering the entire United States from 1991 to present, recently became available on Amazon cloud S3. This provides a new opportunity to rebuild the Hydro-NEXRAD software system that enabled users to access vast amounts of NEXRAD radar data in support of a wide range of research. The system processes basic radar data (Level II) and delivers radar-rainfall products based on the user's custom selection of features such as space and time domain, river basin, rainfall product space and time resolution, and rainfall estimation algorithms. The cloud-based new system can eliminate prior challenges faced by Hydro-NEXRAD data acquisition and processing: (1) temporal and spatial limitation arising from the limited data storage; (2) archive (past) data ingestion and format conversion; and (3) separate data processing flow for the past and real-time Level II data. To enhance massive data processing and computational efficiency, the new system is implemented and tested for the Iowa domain. This pilot study begins by ingesting rainfall metadata and implementing Hydro-NEXRAD capabilities on the cloud using the new polarimetric features, as well as the existing algorithm modules and scripts. The authors address the reliability and feasibility of cloud computation and processing, followed by an assessment of response times from an interactive web-based system.

Scaling predictive modeling in drug development with cloud computing.

PubMed

Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

2015-01-26

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

Lidar Comparison for GoAmazon 2014/15 Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barbosa, Henrique MJ; Barja, B; Landulfo, E

2016-04-01

The Observations and Modeling of the Green Ocean Amazon 2014/15 (GoAmazon 2014/15) experiment uses the city of Manaus, Amazonas (AM), Brazil, in the setting of the surrounding green ocean as a natural laboratory for understanding the effects of present and future anthropogenic pollution on the aerosol and cloud life cycle in the tropics. The U.S. Department of Energy (DOE) supported this experiment through the deployment of the Atmospheric Radiation Measurement (ARM) Climate Research Facility’s first Mobile Facility (AMF-1) in the city of Manacapuru, which is 100 km downwind of Manaus, from January 1 2014 to December 31 2015. During themore » second Intensive Operational Period (IOP) from August 15 to October 15 2014, three lidar systems were operated simultaneously at different experimental sites, and an instrument comparison campaign was carried out during the period October 4 to 10, during which the mobile lidar system from Instituto de Pesquisas Energéticas e Nucleares-Universidade de São Paulo was brought from the T2 site (Iranduba) to the other sites (T3 [Manacapuru] and then T0e-Embrapa). In this report we present the data collected by the mobile lidar system at the DOE-ARM site and compare its measurements with those from the micro-pulse lidar system running at that site.« less

NOAA's Big Data Partnership and Applications to Ocean Sciences

NASA Astrophysics Data System (ADS)

Kearns, E. J.

2016-02-01

New opportunities for the distribution of NOAA's oceanographic and other environmental data are being explored through NOAA's Big Data Partnership (BDP) with Amazon Web Services, Google Cloud Platform, IBM, Microsoft Corp. and the Open Cloud Consortium. This partnership was established in April 2015 through Cooperative Research and Development Agreements, and is seeking new, financially self-sustaining collaborations between the Partners and the federal government centered upon NOAA's data and their potential value in the information marketplace. We will discuss emerging opportunities for collaboration among businesses and NOAA, progress in making NOAA's ocean data more widely accessible through the Partnerships, and applications based upon this access to NOAA's data.

Amazon Deforestation Fires Increase Plant Productivity through Changes in Diffuse Radiation

NASA Astrophysics Data System (ADS)

Rap, A.; Reddington, C.; Spracklen, D. V.; Mercado, L.; Haywood, J. M.; Bonal, D.; Butt, N.; Phillips, O.

2013-12-01

Over the past few decades a large increase in carbon storage has been observed in undisturbed forests across Amazonia. The reason for such a sink is unclear, although many possible mechanisms have been suggested, including changes in temperature, carbon dioxide, precipitation, clouds, and solar radiation. In this work we focus on one such mechanism, namely the increase in plant photosynthesis due to changes in diffuse radiation caused by atmospheric aerosols from large-scale deforestation fires that now occur throughout the Amazon region. We estimate that this mechanism has increased dry season (August-September) net primary productivity (NPP) by up to 30% across wide regions of the Amazon. We conclude that aerosol from deforestation fires may be responsible for a substantial fraction of the Amazon carbon sink that has been observed. Our approach is based on the combined use of three models: (i) the Global Model of Aerosol Processes (GLOMAP), (ii) the Edwards-Slingo radiation model, and (iii) the UK Met Office JULES land-surface scheme, constrained against in-situ aerosol and radiation observation datasets from several Amazonian sites. A 10 year (1999-2008) GLOMAP simulation using GFED3 biomass burning emissions is first evaluated against aerosol observations, indicating that the model is able to capture the Amazon aerosol seasonality, with enhanced concentrations during the dry season driven by biomass burning. The radiation scheme is then shown to be in good agreement with total and diffuse radiation in-situ observations, the model being able to capture the high total and low diffuse radiation flux in the dry season, as well as the low total and high diffuse radiation flux in the wet season. We then use our modelling framework to quantify the contribution of deforestation fires to diffuse/direct radiation fraction and forest productivity. We calculate that deforestation fires increase dry season diffuse radiation by up to 60% or 30 Wm-2. Finally, we use the JULES model to show that this increase in diffuse radiation is responsible for a substantial growth in gross primary productivity (GPP), enhancing Amazon-wide dry-season GPP by 5% with local increases of up to 15%. Most of this GPP response results in an increase in NPP, estimated in the dry season at 10% across the Amazon with local increases as large as 30%. This substantial NPP enhancement spatially matches observed increases in forest biomass storage across the Amazon. We thus suggest that deforestation fires have an important impact on the Amazon carbon budget and attempt to estimate the fraction of the observed forest carbon sink that can be attributed to this mechanism. Change [%] in diffuse radiation due to deforestation

Managing a tier-2 computer centre with a private cloud infrastructure

NASA Astrophysics Data System (ADS)

Bagnasco, Stefano; Berzano, Dario; Brunetti, Riccardo; Lusso, Stefano; Vallero, Sara

2014-06-01

In a typical scientific computing centre, several applications coexist and share a single physical infrastructure. An underlying Private Cloud infrastructure eases the management and maintenance of such heterogeneous applications (such as multipurpose or application-specific batch farms, Grid sites, interactive data analysis facilities and others), allowing dynamic allocation resources to any application. Furthermore, the maintenance of large deployments of complex and rapidly evolving middleware and application software is eased by the use of virtual images and contextualization techniques. Such infrastructures are being deployed in some large centres (see e.g. the CERN Agile Infrastructure project), but with several open-source tools reaching maturity this is becoming viable also for smaller sites. In this contribution we describe the Private Cloud infrastructure at the INFN-Torino Computer Centre, that hosts a full-fledged WLCG Tier-2 centre, an Interactive Analysis Facility for the ALICE experiment at the CERN LHC and several smaller scientific computing applications. The private cloud building blocks include the OpenNebula software stack, the GlusterFS filesystem and the OpenWRT Linux distribution (used for network virtualization); a future integration into a federated higher-level infrastructure is made possible by exposing commonly used APIs like EC2 and OCCI.

SU-E-T-628: A Cloud Computing Based Multi-Objective Optimization Method for Inverse Treatment Planning.

PubMed

Na, Y; Suh, T; Xing, L

2012-06-01

Multi-objective (MO) plan optimization entails generation of an enormous number of IMRT or VMAT plans constituting the Pareto surface, which presents a computationally challenging task. The purpose of this work is to overcome the hurdle by developing an efficient MO method using emerging cloud computing platform. As a backbone of cloud computing for optimizing inverse treatment planning, Amazon Elastic Compute Cloud with a master node (17.1 GB memory, 2 virtual cores, 420 GB instance storage, 64-bit platform) is used. The master node is able to scale seamlessly a number of working group instances, called workers, based on the user-defined setting account for MO functions in clinical setting. Each worker solved the objective function with an efficient sparse decomposition method. The workers are automatically terminated if there are finished tasks. The optimized plans are archived to the master node to generate the Pareto solution set. Three clinical cases have been planned using the developed MO IMRT and VMAT planning tools to demonstrate the advantages of the proposed method. The target dose coverage and critical structure sparing of plans are comparable obtained using the cloud computing platform are identical to that obtained using desktop PC (Intel Xeon® CPU 2.33GHz, 8GB memory). It is found that the MO planning speeds up the processing of obtaining the Pareto set substantially for both types of plans. The speedup scales approximately linearly with the number of nodes used for computing. With the use of N nodes, the computational time is reduced by the fitting model, 0.2+2.3/N, with r̂2>0.99, on average of the cases making real-time MO planning possible. A cloud computing infrastructure is developed for MO optimization. The algorithm substantially improves the speed of inverse plan optimization. The platform is valuable for both MO planning and future off- or on-line adaptive re-planning. © 2012 American Association of Physicists in Medicine.

Assessment of radargrammetric DSMs from TerraSAR-X Stripmap images in a mountainous relief area of the Amazon region

NASA Astrophysics Data System (ADS)

de Oliveira, Cleber Gonzales; Paradella, Waldir Renato; da Silva, Arnaldo de Queiroz

The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or re-mapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSAR-X Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Carajás Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of systematic error (bias) and precision. The test for significant systematic error was based on the Student's-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuracy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuracy. Thus, the use of TerraSAR-X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available topographic information is rare or presents low quality.

The conversion of CESR to operate as the Test Accelerator, CesrTA. Part 3: Electron cloud diagnostics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Billing, M. G.; Conway, J. V.; Crittenden, J. A.

Cornell's electron/positron storage ring (CESR) was modified over a series of accelerator shutdowns beginning in May 2008, which substantially improves its capability for research and development for particle accelerators. CESR's energy span from 1.8 to 5.6 GeV with both electrons and positrons makes it ideal for the study of a wide spectrum of accelerator physics issues and instrumentation related to present light sources and future lepton damping rings. Additionally a number of these are also relevant for the beam physics of proton accelerators. This paper is the third in a series of four describing the conversion of CESR to themore » test accelerator, CESRTA. The first two papers discuss the overall plan for the conversion of the storage ring to an instrument capable of studying advanced accelerator physics issues [1] and the details of the vacuum system upgrades [2]. This paper focuses on the necessary development of new instrumentation, situated in four dedicated experimental regions, capable of studying such phenomena as electron clouds (ECs) and methods to mitigate EC effects. The fourth paper in this series describes the vacuum system modifications of the superconducting wigglers to accommodate the diagnostic instrumentation for the study of EC behavior within wigglers. Lastly, while the initial studies of CESRTA focused on questions related to the International Linear Collider damping ring design, CESRTA is a very versatile storage ring, capable of studying a wide range of accelerator physics and instrumentation questions.« less

The conversion of CESR to operate as the Test Accelerator, CesrTA. Part 3: Electron cloud diagnostics

DOE PAGES

Billing, M. G.; Conway, J. V.; Crittenden, J. A.; ...

2016-04-28

Cornell's electron/positron storage ring (CESR) was modified over a series of accelerator shutdowns beginning in May 2008, which substantially improves its capability for research and development for particle accelerators. CESR's energy span from 1.8 to 5.6 GeV with both electrons and positrons makes it ideal for the study of a wide spectrum of accelerator physics issues and instrumentation related to present light sources and future lepton damping rings. Additionally a number of these are also relevant for the beam physics of proton accelerators. This paper is the third in a series of four describing the conversion of CESR to themore » test accelerator, CESRTA. The first two papers discuss the overall plan for the conversion of the storage ring to an instrument capable of studying advanced accelerator physics issues [1] and the details of the vacuum system upgrades [2]. This paper focuses on the necessary development of new instrumentation, situated in four dedicated experimental regions, capable of studying such phenomena as electron clouds (ECs) and methods to mitigate EC effects. The fourth paper in this series describes the vacuum system modifications of the superconducting wigglers to accommodate the diagnostic instrumentation for the study of EC behavior within wigglers. Lastly, while the initial studies of CESRTA focused on questions related to the International Linear Collider damping ring design, CESRTA is a very versatile storage ring, capable of studying a wide range of accelerator physics and instrumentation questions.« less

Analysis of cloud-based solutions on EHRs systems in different scenarios.

PubMed

Fernández-Cardeñosa, Gonzalo; de la Torre-Díez, Isabel; López-Coronado, Miguel; Rodrigues, Joel J P C

2012-12-01

Nowadays with the growing of the wireless connections people can access all the resources hosted in the Cloud almost everywhere. In this context, organisms can take advantage of this fact, in terms of e-Health, deploying Cloud-based solutions on e-Health services. In this paper two Cloud-based solutions for different scenarios of Electronic Health Records (EHRs) management system are proposed. We have researched articles published between the years 2005 and 2011 about the implementation of e-Health services based on the Cloud in Medline. In order to analyze the best scenario for the deployment of Cloud Computing two solutions for a large Hospital and a network of Primary Care Health centers have been studied. Economic estimation of the cost of the implementation for both scenarios has been done via the Amazon calculator tool. As a result of this analysis two solutions are suggested depending on the scenario: To deploy a Cloud solution for a large Hospital a typical Cloud solution in which are hired just the needed services has been assumed. On the other hand to work with several Primary Care Centers it's suggested the implementation of a network, which interconnects these centers with just one Cloud environment. Finally it's considered the fact of deploying a hybrid solution: in which EHRs with images will be hosted in the Hospital or Primary Care Centers and the rest of them will be migrated to the Cloud.

Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jensen, Michael P; Giangrande, Scott E; Bartholomew, Mary Jane

The Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) [http://www.arm.gov/campaigns/osc2013rwpcf] campaign was scheduled to take place from 15 July 2013 through 15 July 2015 (or until shipped for the next U.S. Department of Energy Atmospheric Radiation Measurement [ARM] Climate Research Facility first Mobile Facility [AMF1] deployment). The campaign involved the deployment of the AMF1 Scintec 915 MHz Radar Wind Profiler (RWP) at BNL, in conjunction with several other ARM, BNL and National Weather Service (NWS) instruments. The two main scientific foci of the campaign were: 1) To provide profiles of the horizontal wind to be used tomore » test and validate short-term cloud advection forecasts for solar-energy applications and 2) to provide vertical profiling capabilities for the study of dynamics (i.e., vertical velocity) and hydrometeors in winter storms. This campaign was a serendipitous opportunity that arose following the deployment of the RWP at the Two-Column Aerosol Project (TCAP) campaign in Cape Cod, Massachusetts and restriction from participation in the Green Ocean Amazon 2014/15 (GoAmazon 2014/15) campaign due to radio-frequency allocation restriction for international deployments. The RWP arrived at BNL in the fall of 2013, but deployment was delayed until fall of 2014 as work/safety planning and site preparation were completed. The RWP further encountered multiple electrical failures, which eventually required several shipments of instrument power supplies and the final amplifier to the vendor to complete repairs. Data collection began in late January 2015. The operational modes of the RWP were changed such that in addition to collecting traditional profiles of the horizontal wind, a vertically pointing mode was also included for the purpose of precipitation sensing and estimation of vertical velocities. The RWP operated well until the end of the campaign in July 2015 and collected observations for more than 20 precipitation events.« less

Trace gas exchanges and transports over the Amazonian rain forest

NASA Technical Reports Server (NTRS)

Garstang, Michael; Greco, Steve; Scala, John; Harriss, Robert; Browell, Edward; Sachse, Glenn; Simpson, Joanne; Tao, Wei-Kuo; Torres, Arnold

1986-01-01

Early results are presented from a program to model deep convective transport of chemical species by means of in situ data collection and numerical models. Data were acquired during the NASA GTE Amazon Boundary Layer Experiment in July-August 1985. Airborne instrumentation, including a UV-DIAL system, collected data on the O3, CO, NO, temperature and water vapor profiles from the surface to 400 mb altitude, while GOES imagery tracked convective clouds over the study area. A two-dimensional cloud model with small amplitude random temperature fluctuations at low levels, which simulated thermals, was used to describe the movements of the chemical species sensed in the convective atmosphere. The data was useful for evaluating the accuracy of the cloud model, which in turn was effective in describing the circulation of the chemical species.

Cloud-based adaptive exon prediction for DNA analysis.

PubMed

Putluri, Srinivasareddy; Zia Ur Rahman, Md; Fathima, Shaik Yasmeen

2018-02-01

Cloud computing offers significant research and economic benefits to healthcare organisations. Cloud services provide a safe place for storing and managing large amounts of such sensitive data. Under conventional flow of gene information, gene sequence laboratories send out raw and inferred information via Internet to several sequence libraries. DNA sequencing storage costs will be minimised by use of cloud service. In this study, the authors put forward a novel genomic informatics system using Amazon Cloud Services, where genomic sequence information is stored and accessed for processing. True identification of exon regions in a DNA sequence is a key task in bioinformatics, which helps in disease identification and design drugs. Three base periodicity property of exons forms the basis of all exon identification techniques. Adaptive signal processing techniques found to be promising in comparison with several other methods. Several adaptive exon predictors (AEPs) are developed using variable normalised least mean square and its maximum normalised variants to reduce computational complexity. Finally, performance evaluation of various AEPs is done based on measures such as sensitivity, specificity and precision using various standard genomic datasets taken from National Center for Biotechnology Information genomic sequence database.

Signal and image processing algorithm performance in a virtual and elastic computing environment

NASA Astrophysics Data System (ADS)

Bennett, Kelly W.; Robertson, James

2013-05-01

The U.S. Army Research Laboratory (ARL) supports the development of classification, detection, tracking, and localization algorithms using multiple sensing modalities including acoustic, seismic, E-field, magnetic field, PIR, and visual and IR imaging. Multimodal sensors collect large amounts of data in support of algorithm development. The resulting large amount of data, and their associated high-performance computing needs, increases and challenges existing computing infrastructures. Purchasing computer power as a commodity using a Cloud service offers low-cost, pay-as-you-go pricing models, scalability, and elasticity that may provide solutions to develop and optimize algorithms without having to procure additional hardware and resources. This paper provides a detailed look at using a commercial cloud service provider, such as Amazon Web Services (AWS), to develop and deploy simple signal and image processing algorithms in a cloud and run the algorithms on a large set of data archived in the ARL Multimodal Signatures Database (MMSDB). Analytical results will provide performance comparisons with existing infrastructure. A discussion on using cloud computing with government data will discuss best security practices that exist within cloud services, such as AWS.

Status of the DIRAC Project

NASA Astrophysics Data System (ADS)

Casajus, A.; Ciba, K.; Fernandez, V.; Graciani, R.; Hamar, V.; Mendez, V.; Poss, S.; Sapunov, M.; Stagni, F.; Tsaregorodtsev, A.; Ubeda, M.

2012-12-01

The DIRAC Project was initiated to provide a data processing system for the LHCb Experiment at CERN. It provides all the necessary functionality and performance to satisfy the current and projected future requirements of the LHCb Computing Model. A considerable restructuring of the DIRAC software was undertaken in order to turn it into a general purpose framework for building distributed computing systems that can be used by various user communities in High Energy Physics and other scientific application domains. The CLIC and ILC-SID detector projects started to use DIRAC for their data production system. The Belle Collaboration at KEK, Japan, has adopted the Computing Model based on the DIRAC system for its second phase starting in 2015. The CTA Collaboration uses DIRAC for the data analysis tasks. A large number of other experiments are starting to use DIRAC or are evaluating this solution for their data processing tasks. DIRAC services are included as part of the production infrastructure of the GISELA Latin America grid. Similar services are provided for the users of the France-Grilles and IBERGrid National Grid Initiatives in France and Spain respectively. The new communities using DIRAC started to provide important contributions to its functionality. Among recent additions can be mentioned the support of the Amazon EC2 computing resources as well as other Cloud management systems; a versatile File Replica Catalog with File Metadata capabilities; support for running MPI jobs in the pilot based Workload Management System. Integration with existing application Web Portals, like WS-PGRADE, is demonstrated. In this paper we will describe the current status of the DIRAC Project, recent developments of its framework and functionality as well as the status of the rapidly evolving community of the DIRAC users.

Developing cloud applications using the e-Science Central platform.

PubMed

Hiden, Hugo; Woodman, Simon; Watson, Paul; Cala, Jacek

2013-01-28

This paper describes the e-Science Central (e-SC) cloud data processing system and its application to a number of e-Science projects. e-SC provides both software as a service (SaaS) and platform as a service for scientific data management, analysis and collaboration. It is a portable system and can be deployed on both private (e.g. Eucalyptus) and public clouds (Amazon AWS and Microsoft Windows Azure). The SaaS application allows scientists to upload data, edit and run workflows and share results in the cloud, using only a Web browser. It is underpinned by a scalable cloud platform consisting of a set of components designed to support the needs of scientists. The platform is exposed to developers so that they can easily upload their own analysis services into the system and make these available to other users. A representational state transfer-based application programming interface (API) is also provided so that external applications can leverage the platform's functionality, making it easier to build scalable, secure cloud-based applications. This paper describes the design of e-SC, its API and its use in three different case studies: spectral data visualization, medical data capture and analysis, and chemical property prediction.

Developing cloud applications using the e-Science Central platform

PubMed Central

Hiden, Hugo; Woodman, Simon; Watson, Paul; Cala, Jacek

2013-01-01

This paper describes the e-Science Central (e-SC) cloud data processing system and its application to a number of e-Science projects. e-SC provides both software as a service (SaaS) and platform as a service for scientific data management, analysis and collaboration. It is a portable system and can be deployed on both private (e.g. Eucalyptus) and public clouds (Amazon AWS and Microsoft Windows Azure). The SaaS application allows scientists to upload data, edit and run workflows and share results in the cloud, using only a Web browser. It is underpinned by a scalable cloud platform consisting of a set of components designed to support the needs of scientists. The platform is exposed to developers so that they can easily upload their own analysis services into the system and make these available to other users. A representational state transfer-based application programming interface (API) is also provided so that external applications can leverage the platform's functionality, making it easier to build scalable, secure cloud-based applications. This paper describes the design of e-SC, its API and its use in three different case studies: spectral data visualization, medical data capture and analysis, and chemical property prediction. PMID:23230161

A Web-Based GIS for Reporting Water Usage in the High Plains Underground Water Conservation District

NASA Astrophysics Data System (ADS)

Jia, M.; Deeds, N.; Winckler, M.

2012-12-01

The High Plains Underground Water Conservation District (HPWD) is the largest and oldest of the Texas water conservation districts, and oversees approximately 1.7 million irrigated acres. Recent rule changes have motivated HPWD to develop a more automated system to allow owners and operators to report well locations, meter locations, meter readings, the association between meters and wells, and contiguous acres. INTERA, Inc. has developed a web-based interactive system for HPWD water users to report water usage and for the district to better manage its water resources. The HPWD web management system utilizes state-of-the-art GIS techniques, including cloud-based Amazon EC2 virtual machine, ArcGIS Server, ArcSDE and ArcGIS Viewer for Flex, to support web-based water use management. The system enables users to navigate to their area of interest using a well-established base-map and perform a variety of operations and inquiries against their spatial features. The application currently has six components: user privilege management, property management, water meter registration, area registration, meter-well association and water use report. The system is composed of two main databases: spatial database and non-spatial database. With the help of Adobe Flex application at the front end and ArcGIS Server as the middle-ware, the spatial feature geometry and attributes update will be reflected immediately in the back end. As a result, property owners, along with the HPWD staff, collaborate together to weave the fabric of the spatial database. Interactions between the spatial and non-spatial databases are established by Windows Communication Foundation (WCF) services to record water-use report, user-property associations, owner-area associations, as well as meter-well associations. Mobile capabilities will be enabled in the near future for field workers to collect data and synchronize them to the spatial database. The entire solution is built on a highly scalable cloud server to dynamically allocate the computational resources so as to reduce the cost on security and hardware maintenance. In addition to the default capabilities provided by ESRI, customizations include 1) enabling interactions between spatial and non-spatial databases, 2) providing role-based feature editing, 3) dynamically filtering spatial features on the map based on user accounts and 4) comprehensive data validation.

Fluvial carbon export from a lowland Amazonian rainforest in relation to atmospheric fluxes

NASA Astrophysics Data System (ADS)

Vihermaa, Leena E.; Waldron, Susan; Domingues, Tomas; Grace, John; Cosio, Eric G.; Limonchi, Fabian; Hopkinson, Chris; da Rocha, Humberto Ribeiro; Gloor, Emanuel

2016-12-01

We constructed a whole carbon budget for a catchment in the Western Amazon Basin, combining drainage water analyses with eddy covariance (EC) measured terrestrial CO2 fluxes. As fluvial C export can represent permanent C export it must be included in assessments of whole site C balance, but it is rarely done. The footprint area of the flux tower is drained by two small streams ( 5-7 km2) from which we measured the dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), particulate organic carbon (POC) export, and CO2 efflux. The EC measurements showed the site C balance to be +0.7 ± 9.7 Mg C ha-1 yr-1 (a source to the atmosphere) and fluvial export was 0.3 ± 0.04 Mg C ha-1 yr-1. Of the total fluvial loss 34% was DIC, 37% DOC, and 29% POC. The wet season was most important for fluvial C export. There was a large uncertainty associated with the EC results and with previous biomass plot studies (-0.5 ± 4.1 Mg C ha-1 yr-1); hence, it cannot be concluded with certainty whether the site is C sink or source. The fluvial export corresponds to only 3-7% of the uncertainty related to the site C balance; thus, other factors need to be considered to reduce the uncertainty and refine the estimated C balance. However, stream C export is significant, especially for almost neutral sites where fluvial loss may determine the direction of the site C balance. The fate of C downstream then dictates the overall climate impact of fluvial export.

Evaluating Emergent Constraints for Equilibrium Climate Sensitivity

DOE PAGES

Caldwell, Peter M.; Zelinka, Mark D.; Klein, Stephen A.

2018-04-23

Emergent constraints are quantities that are observable from current measurements and have skill predicting future climate. Here, this study explores 19 previously proposed emergent constraints related to equilibrium climate sensitivity (ECS; the global-average equilibrium surface temperature response to CO 2 doubling). Several constraints are shown to be closely related, emphasizing the importance for careful understanding of proposed constraints. A new method is presented for decomposing correlation between an emergent constraint and ECS into terms related to physical processes and geographical regions. Using this decomposition, one can determine whether the processes and regions explaining correlation with ECS correspond to the physicalmore » explanation offered for the constraint. Shortwave cloud feedback is generally found to be the dominant contributor to correlations with ECS because it is the largest source of intermodel spread in ECS. In all cases, correlation results from interaction between a variety of terms, reflecting the complex nature of ECS and the fact that feedback terms and forcing are themselves correlated with each other. For 4 of the 19 constraints, the originally proposed explanation for correlation is borne out by our analysis. These four constraints all predict relatively high climate sensitivity. The credibility of six other constraints is called into question owing to correlation with ECS coming mainly from unexpected sources and/or lack of robustness to changes in ensembles. Another six constraints lack a testable explanation and hence cannot be confirmed. Lastly, the fact that this study casts doubt upon more constraints than it confirms highlights the need for caution when identifying emergent constraints from small ensembles.« less

Evaluating Emergent Constraints for Equilibrium Climate Sensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caldwell, Peter M.; Zelinka, Mark D.; Klein, Stephen A.

Emergent constraints are quantities that are observable from current measurements and have skill predicting future climate. Here, this study explores 19 previously proposed emergent constraints related to equilibrium climate sensitivity (ECS; the global-average equilibrium surface temperature response to CO 2 doubling). Several constraints are shown to be closely related, emphasizing the importance for careful understanding of proposed constraints. A new method is presented for decomposing correlation between an emergent constraint and ECS into terms related to physical processes and geographical regions. Using this decomposition, one can determine whether the processes and regions explaining correlation with ECS correspond to the physicalmore » explanation offered for the constraint. Shortwave cloud feedback is generally found to be the dominant contributor to correlations with ECS because it is the largest source of intermodel spread in ECS. In all cases, correlation results from interaction between a variety of terms, reflecting the complex nature of ECS and the fact that feedback terms and forcing are themselves correlated with each other. For 4 of the 19 constraints, the originally proposed explanation for correlation is borne out by our analysis. These four constraints all predict relatively high climate sensitivity. The credibility of six other constraints is called into question owing to correlation with ECS coming mainly from unexpected sources and/or lack of robustness to changes in ensembles. Another six constraints lack a testable explanation and hence cannot be confirmed. Lastly, the fact that this study casts doubt upon more constraints than it confirms highlights the need for caution when identifying emergent constraints from small ensembles.« less

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest.

PubMed

Wu, Jin; Kobayashi, Hideki; Stark, Scott C; Meng, Ran; Guan, Kaiyu; Tran, Ngoc Nguyen; Gao, Sicong; Yang, Wei; Restrepo-Coupe, Natalia; Miura, Tomoaki; Oliviera, Raimundo Cosme; Rogers, Alistair; Dye, Dennis G; Nelson, Bruce W; Serbin, Shawn P; Huete, Alfredo R; Saleska, Scott R

2018-03-01

Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun-sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate-phenology relationships in the tropics. No claim to original US Government works New Phytologist © 2017 New Phytologist Trust.

The future of the Amazon: new perspectives from climate, ecosystem and social sciences.

PubMed

Betts, Richard A; Malhi, Yadvinder; Roberts, J Timmons

2008-05-27

The potential loss or large-scale degradation of the tropical rainforests has become one of the iconic images of the impacts of twenty-first century environmental change and may be one of our century's most profound legacies. In the Amazon region, the direct threat of deforestation and degradation is now strongly intertwined with an indirect challenge we are just beginning to understand: the possibility of substantial regional drought driven by global climate change. The Amazon region hosts more than half of the world's remaining tropical forests, and some parts have among the greatest concentrations of biodiversity found anywhere on Earth. Overall, the region is estimated to host about a quarter of all global biodiversity. It acts as one of the major 'flywheels' of global climate, transpiring water and generating clouds, affecting atmospheric circulation across continents and hemispheres, and storing substantial reserves of biomass and soil carbon. Hence, the ongoing degradation of Amazonia is a threat to local climate stability and a contributor to the global atmospheric climate change crisis. Conversely, the stabilization of Amazonian deforestation and degradation would be an opportunity for local adaptation to climate change, as well as a potential global contributor towards mitigation of climate change. However, addressing deforestation in the Amazon raises substantial challenges in policy, governance, sustainability and economic science. This paper introduces a theme issue dedicated to a multidisciplinary analysis of these challenges.

A Meteorological Overview of the TC4 Mission

NASA Technical Reports Server (NTRS)

Pfister, L.; Selkirk, H. B.; Starr, D. O.; Rosenlof, K.; Newman, P. F.

2010-01-01

The TC4 mission in Central America during summer 2007 examined convective transport into the tropical Upper Troposphere/Lower Stratosphere (UTLS) and the evolution of cirrus clouds. The tropical tropopause layer (TTL) circulation is dominated by the Asian monsoon anticyclone and westward winds that stretch from the western Pacific into the Atlantic. During TC4, TTL westward flow over Central America was stronger than normal. Incidence of cold clouds over the Central American region was the third lowest out of 34 years sampled. The major factor was an incipient La Nina, specifically anomalously cold temperatures off the Pacific Coast of South America. Weakness in the low level Caribbean jet caused a shift in the coldest clouds from the Caribbean to the Pacific side of Central America. The character of tropopause temperature variability was that of upward propagating waves generated by local and nonlocal convection. These waves produced tropopause temperature variations of 3 K, with peak-to-peak variations of 8 K. At low levels in Central America, flow from the Sahara desert predominated; further south, the air came from the Amazon region. Convectively influenced air in the upper troposphere came from Central America, the northern Amazon region, the Atlantic ITCZ, and the North American monsoon. In the TTL, Asian and African convection affected the observed air masses. North of 10N in the Central American TTL, African and Asian convection may have contributed as much to the air masses as Central and South American convection. South of 8N, Asian and African convection had far less impact.

Reduced anthropogenic aerosol radiative forcing caused by biogenic new particle formation

PubMed Central

Sengupta, Kamalika; Duplissy, Jonathan; Frege, Carla; Williamson, Christina; Heinritzi, Martin; Simon, Mario; Yan, Chao; Almeida, João; Tröstl, Jasmin; Nieminen, Tuomo; Ortega, Ismael K.; Wagner, Robert; Dunne, Eimear M.; Adamov, Alexey; Amorim, Antonio; Bernhammer, Anne-Kathrin; Bianchi, Federico; Breitenlechner, Martin; Brilke, Sophia; Chen, Xuemeng; Craven, Jill S.; Dias, Antonio; Ehrhart, Sebastian; Fischer, Lukas; Flagan, Richard C.; Franchin, Alessandro; Fuchs, Claudia; Guida, Roberto; Hakala, Jani; Hoyle, Christopher R.; Jokinen, Tuija; Junninen, Heikki; Kangasluoma, Juha; Kim, Jaeseok; Krapf, Manuel; Kürten, Andreas; Laaksonen, Ari; Lehtipalo, Katrianne; Makhmutov, Vladimir; Mathot, Serge; Molteni, Ugo; Monks, Sarah A.; Onnela, Antti; Peräkylä, Otso; Piel, Felix; Petäjä, Tuukka; Praplan, Arnaud P.; Pringle, Kirsty J.; Richards, Nigel A. D.; Rissanen, Matti P.; Rondo, Linda; Sarnela, Nina; Scott, Catherine E.; Seinfeld, John H.; Sharma, Sangeeta; Sipilä, Mikko; Steiner, Gerhard; Stozhkov, Yuri; Stratmann, Frank; Tomé, Antonio; Virtanen, Annele; Vogel, Alexander Lucas; Wagner, Andrea C.; Wagner, Paul E.; Weingartner, Ernest; Wimmer, Daniela; Winkler, Paul M.; Ye, Penglin; Zhang, Xuan; Hansel, Armin; Worsnop, Douglas R.; Baltensperger, Urs; Kulmala, Markku; Curtius, Joachim

2016-01-01

The magnitude of aerosol radiative forcing caused by anthropogenic emissions depends on the baseline state of the atmosphere under pristine preindustrial conditions. Measurements show that particle formation in atmospheric conditions can occur solely from biogenic vapors. Here, we evaluate the potential effect of this source of particles on preindustrial cloud condensation nuclei (CCN) concentrations and aerosol–cloud radiative forcing over the industrial period. Model simulations show that the pure biogenic particle formation mechanism has a much larger relative effect on CCN concentrations in the preindustrial atmosphere than in the present atmosphere because of the lower aerosol concentrations. Consequently, preindustrial cloud albedo is increased more than under present day conditions, and therefore the cooling forcing of anthropogenic aerosols is reduced. The mechanism increases CCN concentrations by 20–100% over a large fraction of the preindustrial lower atmosphere, and the magnitude of annual global mean radiative forcing caused by changes of cloud albedo since 1750 is reduced by 0.22 W m−2 (27%) to −0.60 W m−2. Model uncertainties, relatively slow formation rates, and limited available ambient measurements make it difficult to establish the significance of a mechanism that has its dominant effect under preindustrial conditions. Our simulations predict more particle formation in the Amazon than is observed. However, the first observation of pure organic nucleation has now been reported for the free troposphere. Given the potentially significant effect on anthropogenic forcing, effort should be made to better understand such naturally driven aerosol processes. PMID:27790989

Reduced anthropogenic aerosol radiative forcing caused by biogenic new particle formation

NASA Astrophysics Data System (ADS)

Gordon, Hamish; Sengupta, Kamalika; Rap, Alexandru; Duplissy, Jonathan; Frege, Carla; Williamson, Christina; Heinritzi, Martin; Simon, Mario; Yan, Chao; Almeida, João; Tröstl, Jasmin; Nieminen, Tuomo; Ortega, Ismael K.; Wagner, Robert; Dunne, Eimear M.; Adamov, Alexey; Amorim, Antonio; Bernhammer, Anne-Kathrin; Bianchi, Federico; Breitenlechner, Martin; Brilke, Sophia; Chen, Xuemeng; Craven, Jill S.; Dias, Antonio; Ehrhart, Sebastian; Fischer, Lukas; Flagan, Richard C.; Franchin, Alessandro; Fuchs, Claudia; Guida, Roberto; Hakala, Jani; Hoyle, Christopher R.; Jokinen, Tuija; Junninen, Heikki; Kangasluoma, Juha; Kim, Jaeseok; Kirkby, Jasper; Krapf, Manuel; Kürten, Andreas; Laaksonen, Ari; Lehtipalo, Katrianne; Makhmutov, Vladimir; Mathot, Serge; Molteni, Ugo; Monks, Sarah A.; Onnela, Antti; Peräkylä, Otso; Piel, Felix; Petäjä, Tuukka; Praplan, Arnaud P.; Pringle, Kirsty J.; Richards, Nigel A. D.; Rissanen, Matti P.; Rondo, Linda; Sarnela, Nina; Schobesberger, Siegfried; Scott, Catherine E.; Seinfeld, John H.; Sharma, Sangeeta; Sipilä, Mikko; Steiner, Gerhard; Stozhkov, Yuri; Stratmann, Frank; Tomé, Antonio; Virtanen, Annele; Vogel, Alexander Lucas; Wagner, Andrea C.; Wagner, Paul E.; Weingartner, Ernest; Wimmer, Daniela; Winkler, Paul M.; Ye, Penglin; Zhang, Xuan; Hansel, Armin; Dommen, Josef; Donahue, Neil M.; Worsnop, Douglas R.; Baltensperger, Urs; Kulmala, Markku; Curtius, Joachim; Carslaw, Kenneth S.

2016-10-01

The magnitude of aerosol radiative forcing caused by anthropogenic emissions depends on the baseline state of the atmosphere under pristine preindustrial conditions. Measurements show that particle formation in atmospheric conditions can occur solely from biogenic vapors. Here, we evaluate the potential effect of this source of particles on preindustrial cloud condensation nuclei (CCN) concentrations and aerosol-cloud radiative forcing over the industrial period. Model simulations show that the pure biogenic particle formation mechanism has a much larger relative effect on CCN concentrations in the preindustrial atmosphere than in the present atmosphere because of the lower aerosol concentrations. Consequently, preindustrial cloud albedo is increased more than under present day conditions, and therefore the cooling forcing of anthropogenic aerosols is reduced. The mechanism increases CCN concentrations by 20-100% over a large fraction of the preindustrial lower atmosphere, and the magnitude of annual global mean radiative forcing caused by changes of cloud albedo since 1750 is reduced by 0.22 W m-2 (27%) to -0.60 W m-2. Model uncertainties, relatively slow formation rates, and limited available ambient measurements make it difficult to establish the significance of a mechanism that has its dominant effect under preindustrial conditions. Our simulations predict more particle formation in the Amazon than is observed. However, the first observation of pure organic nucleation has now been reported for the free troposphere. Given the potentially significant effect on anthropogenic forcing, effort should be made to better understand such naturally driven aerosol processes.

Oyster Fisheries App

NASA Technical Reports Server (NTRS)

Perez Guerrero, Geraldo A.; Armstrong, Duane; Underwood, Lauren

2015-01-01

This project is creating a cloud-enabled, HTML 5 web application to help oyster fishermen and state agencies apply Earth science to improve the management of this important natural and economic resource. The Oyster Fisheries app gathers and analyzes environmental and water quality information, and alerts fishermen and resources managers about problems in oyster fishing waters. An intuitive interface based on Google Maps displays the geospatial information and provides familiar interactive controls to the users. Alerts can be tailored to notify users when conditions in specific leases or public fishing areas require attention. The app is hosted on the Amazon Web Services cloud. It is being developed and tested using some of the latest web development tools such as web components and Polymer.

Enhancing data utilization through adoption of cloud-based data architectures (Invited Paper 211869)

NASA Astrophysics Data System (ADS)

Kearns, E. J.

2017-12-01

A traditional approach to data distribution and utilization of open government data involves continuously moving those data from a central government location to each potential user, who would then utilize them on their local computer systems. An alternate approach would be to bring those users to the open government data, where users would also have access to computing and analytics capabilities that would support data utilization. NOAA's Big Data Project is exploring such an alternate approach through an experimental collaboration with Amazon Web Services, Google Cloud Platform, IBM, Microsoft Azure, and the Open Commons Consortium. As part of this ongoing experiment, NOAA is providing open data of interest which are freely hosted by the Big Data Project Collaborators, who provide a variety of cloud-based services and capabilities to enable utilization by data users. By the terms of the agreement, the Collaborators may charge for those value-added services and processing capacities to recover their costs to freely host the data and to generate profits if so desired. Initial results have shown sustained increases in data utilization from 2 to over 100 times previously-observed access patterns from traditional approaches. Significantly increased utilization speed as compared to the traditional approach has also been observed by NOAA data users who have volunteered their experiences on these cloud-based systems. The potential for implementing and sustaining the alternate cloud-based approach as part of a change in operational data utilization strategies will be discussed.

Model-Driven Engineering: Automatic Code Generation and Beyond

DTIC Science & Technology

2015-03-01

and Weblogic as well as cloud environments such as Mi- crosoft Azure and Amazon Web Services®. Finally, while the generated code has dependencies on...code generation in the context of the full system lifecycle from development to sustainment. Acquisition programs in govern- ment or large commercial...Acquirers are concerned with the full system lifecycle, and they need confidence that the development methods will enable the system to meet the functional

Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2

NASA Astrophysics Data System (ADS)

Oishi, Yu; Ishida, Haruma; Nakajima, Takashi Y.; Nakamura, Ryosuke; Matsunaga, Tsuneo

2018-05-01

The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009 to measure global atmospheric CO2 and CH4 concentrations. GOSAT is equipped with two sensors: the Thermal And Near infrared Sensor for carbon Observations (TANSO)-Fourier transform spectrometer (FTS) and TANSO-Cloud and Aerosol Imager (CAI). The presence of clouds in the instantaneous field of view of the FTS leads to incorrect estimates of the concentrations. Thus, the FTS data suspected to have cloud contamination must be identified by a CAI cloud discrimination algorithm and rejected. Conversely, overestimating clouds reduces the amount of FTS data that can be used to estimate greenhouse gas concentrations. This is a serious problem in tropical rainforest regions, such as the Amazon, where the amount of useable FTS data is small because of cloud cover. Preparations are continuing for the launch of the GOSAT-2 in fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing CAI cloud discrimination algorithm: Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1). A new cloud discrimination algorithm using a support vector machine (CLAUDIA3) was developed and presented in another paper. Although the use of visual inspection of clouds as a standard for judging is not practical for screening a full satellite data set, it has the advantage of allowing for locally optimized thresholds, while CLAUDIA1 and -3 use common global thresholds. Thus, the accuracy of visual inspection is better than that of these algorithms in most regions, with the exception of snow- and ice-covered surfaces, where there is not enough spectral contrast to identify cloud. In other words, visual inspection results can be used as truth data for accuracy evaluation of CLAUDIA1 and -3. For this reason visual inspection can be used for the truth metric for the cloud discrimination verification exercise. In this study, we compared CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types, and evaluated the accuracy of CLAUDIA3-CAI by comparing both CLAUDIA1-CAI and CLAUDIA3-CAI with visual inspection (400 × 400 pixels) of the same CAI images in tropical rainforests. Comparative results between CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types indicated that CLAUDIA3-CAI had a tendency to identify bright surface and optically thin clouds. However, CLAUDIA3-CAI had a tendency to misjudge the edges of clouds compared with CLAUDIA1-CAI. The accuracy of CLAUDIA3-CAI was approximately 89.5 % in tropical rainforests, which is greater than that of CLAUDIA1-CAI (85.9 %) for the test cases presented here.

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis.

PubMed

Weber, Nick; Liou, David; Dommer, Jennifer; MacMenamin, Philip; Quiñones, Mariam; Misner, Ian; Oler, Andrew J; Wan, Joe; Kim, Lewis; Coakley McCarthy, Meghan; Ezeji, Samuel; Noble, Karlynn; Hurt, Darrell E

2018-04-15

Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools. The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions. https://nephele.niaid.nih.gov and https://github.com/niaid/Nephele. darrell.hurt@nih.gov.

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis

PubMed Central

Weber, Nick; Liou, David; Dommer, Jennifer; MacMenamin, Philip; Quiñones, Mariam; Misner, Ian; Oler, Andrew J; Wan, Joe; Kim, Lewis; Coakley McCarthy, Meghan; Ezeji, Samuel; Noble, Karlynn; Hurt, Darrell E

2018-01-01

Abstract Motivation Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools. Results The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions. Availability and implementation https://nephele.niaid.nih.gov and https://github.com/niaid/Nephele Contact darrell.hurt@nih.gov PMID:29028892

Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline.

PubMed

Reid, Jeffrey G; Carroll, Andrew; Veeraraghavan, Narayanan; Dahdouli, Mahmoud; Sundquist, Andreas; English, Adam; Bainbridge, Matthew; White, Simon; Salerno, William; Buhay, Christian; Yu, Fuli; Muzny, Donna; Daly, Richard; Duyk, Geoff; Gibbs, Richard A; Boerwinkle, Eric

2014-01-29

Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results. To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts. By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples.

Evaluating Commercial and Private Cloud Services for Facility-Scale Geodetic Data Access, Analysis, and Services

NASA Astrophysics Data System (ADS)

Meertens, C. M.; Boler, F. M.; Ertz, D. J.; Mencin, D.; Phillips, D.; Baker, S.

2017-12-01

UNAVCO, in its role as a NSF facility for geodetic infrastructure and data, has succeeded for over two decades using on-premises infrastructure, and while the promise of cloud-based infrastructure is well-established, significant questions about suitability of such infrastructure for facility-scale services remain. Primarily through the GeoSciCloud award from NSF EarthCube, UNAVCO is investigating the costs, advantages, and disadvantages of providing its geodetic data and services in the cloud versus using UNAVCO's on-premises infrastructure. (IRIS is a collaborator on the project and is performing its own suite of investigations). In contrast to the 2-3 year time scale for the research cycle, the time scale of operation and planning for NSF facilities is for a minimum of five years and for some services extends to a decade or more. Planning for on-premises infrastructure is deliberate, and migrations typically take months to years to fully implement. Migrations to a cloud environment can only go forward with similar deliberate planning and understanding of all costs and benefits. The EarthCube GeoSciCloud project is intended to address the uncertainties of facility-level operations in the cloud. Investigations are being performed in a commercial cloud environment (Amazon AWS) during the first year of the project and in a private cloud environment (NSF XSEDE resource at the Texas Advanced Computing Center) during the second year. These investigations are expected to illuminate the potential as well as the limitations of running facility scale production services in the cloud. The work includes running parallel equivalent cloud-based services to on premises services and includes: data serving via ftp from a large data store, operation of a metadata database, production scale processing of multiple months of geodetic data, web services delivery of quality checked data and products, large-scale compute services for event post-processing, and serving real time data from a network of 700-plus GPS stations. The evaluation is based on a suite of metrics that we have developed to elucidate the effectiveness of cloud-based services in price, performance, and management. Services are currently running in AWS and evaluation is underway.

Mechanisms for the formation and growth of nanometer-sized particles in the Amazon: New insights from GoAmazon2014 and the Tapajos Upwind Forest Flux Study (TUFFS).

NASA Astrophysics Data System (ADS)

Smith, J. N.; Park, J. H.; Kuang, C.; Bustillos, J. O. V.; Souza, R. A. F. D.; Wiedemann, K. T.; Munger, J. W.; Wofsy, S. C.; Rizzo, L. V.; Artaxo, P.; Martin, S. T.; Seco, R.; Kim, S.; Guenther, A. B.; Batalha, S. S. A.; Alves, E. G.; Tota, J.

2014-12-01

The Amazon rainforest is a unique and important place for studying aerosol formation and its impacts on atmospheric chemistry and climate. In remote areas, the atmosphere is characterized by low particle number concentrations and high humidity; perturbations in the particle number concentrations and climate-relevant physical and chemical properties could therefore have a great impact on cloud formation and thus on regional climate and precipitation. While it was previously believed that new particle formation occurs rarely in the Amazon, observations in the Amazon of a sustained steady-state particle number concentration, along with an abundance of dry and wet surfaces upon which particles may deposit, imply that sources of new particles must exist in this region. We present observations from two studies, GOAmazon2014 and Tapajos Upwind Forest Flux Study (TUFFS), which seek to identify and quantify the sources of aerosol particles in the Amazon. Measurements of the chemical composition of 20 - 100 nm diameter aerosol particles at the T3 measurement site during the wet and dry season campaigns of GOAmazon2014 show the presence of inorganic ions such as potassium ion and sulfate, as well as organic ion such as oxalate, in ambient nanoparticles. These observations, combined with 1.5 - 300 nm diameter particle number size distributions and trace gas measurements of organic compounds and sulfuric acid, are used to determine the relative importance of sulfuric acid, organic compounds, and primary biological particle emissions to nanoparticle formation and growth. Observations of 3 - 100 nm diameter particle number size distributions at the KM67 tower site during TUFFS show frequent new particle formation events during the wet season in April, transitioning to a scenario of less frequent events in July at the onset of the dry season. These observations highlight the regional nature of new particle formation in the Amazon, and suggest that additional observations at a variety of locales are needed to fully understand the roles of new particle formation in this region.

Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery

NASA Astrophysics Data System (ADS)

Nascimento, Wilson R.; Souza-Filho, Pedro Walfir M.; Proisy, Christophe; Lucas, Richard M.; Rosenqvist, Ake

2013-01-01

Mapping and monitoring mangrove ecosystems is a crucial objective for tropical countries, particularly where human disturbance occurs and because of uncertainties associated with sea level and climatic fluctuation. In many tropical regions, such efforts have focused largely on the use of optical data despite low capture rates because of persistent cloud cover. Recognizing the ability of Synthetic Aperture Radar (SAR) for providing cloud-free observations, this study investigated the use of JERS-1 SAR and ALOS PALSAR data, acquired in 1996 and 2008 respectively, for mapping the extent of mangroves along the Brazilian coastline, from east of the Amazon River mouth, Pará State, to the Bay of São José in Maranhão. For each year, an object-orientated classification of major land covers (mangrove, secondary vegetation, gallery and swamp forest, open water, intermittent lakes and bare areas) was performed with the resulting maps then compared to quantify change. Comparison with available ground truth data indicated a general accuracy in the 2008 image classification of all land covers of 96% (kappa = 90.6%, tau = 92.6%). Over the 12 year period, the area of mangrove increased by 718.6 km2 from 6705 m2 to 7423.60 km2, with 1931.0 km² of expansion and 1213 km² of erosion noted; 5493 km² remained unchanged in extent. The general accuracy relating to changes in mangroves was 83.3% (Kappa 66.1%; tau 66.7%). The study confirmed that these mangroves constituted the largest continuous belt globally and were experiencing significant change because of the dynamic coastal environment and the influence of sedimentation from the Amazon River along the shoreline. The study recommends continued observations using combinations of SAR and optical data to establish trends in mangrove distributions and implications for provision of ecosystem services (e.g., fish/invertebrate nurseries, carbon storage and coastal protection).

Amazon Rain Forest Classification Using J-ERS-1 SAR Data

NASA Technical Reports Server (NTRS)

Freeman, A.; Kramer, C.; Alves, M.; Chapman, B.

1994-01-01

The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jardine, Kolby

In conjunction with the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility GoAmazon campaign, the Terrestrial Ecosystem Science (TES)-funded Green Ocean Amazon (GoAmazon 2014/15) terrestrial ecosystem project (Geco) was designed to: • evaluate the strengths and weaknesses of leaf-level algorithms for biogenic volatile organic compounds (BVOCs) emissions in Amazon forests near Manaus, Brazil, and • conduct mechanistic field studies to characterize biochemical and physiological processes governing leaf- and landscape-scale tropical forest BVOC emissions, and the influence of environmental drivers that are expected to change with a warming climate. Through a close interaction between modeling and observationalmore » activities, including the training of MS and PhD graduate students, post-doctoral students, and technicians at the National Institute for Amazon Research (INPA), the study aimed at improving the representation of BVOC-mediated biosphere-atmosphere interactions and feedbacks under a warming climate. BVOCs can form cloud condensation nuclei (CCN) that influence precipitation dynamics and modify the quality of down welling radiation for photosynthesis. However, our ability to represent these coupled biosphere-atmosphere processes in Earth system models suffers from poor understanding of the functions, identities, quantities, and seasonal patterns of BVOC emissions from tropical forests as well as their biological and environmental controls. The Model of Emissions of Gases and Aerosols from Nature (MEGAN), the current BVOC sub-model of the Community Earth System Model (CESM), was evaluated to explore mechanistic controls over BVOC emissions. Based on that analysis, a combination of observations and experiments were studied in forests near Manaus, Brazil, to test existing parameterizations and algorithm structures in MEGAN. The model was actively modified as needed to improve tropical BVOC emission simulations on a regional scale.« less

Analyzing the Effect of Intraseasonal Meteorological Variability and Land Cover on Aerosol-Cloud Interactions During the Amazonian Biomass Burning Season

NASA Technical Reports Server (NTRS)

TenHoeve, J. E.; Remer, L. A.; Jacobson, M. Z.

2010-01-01

High resolution aerosol, cloud, water vapor, and atmospheric profile data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are utilized to examine the impact of aerosols on clouds during the Amazonian biomass burning season in Rondnia, Brazil. It is found that increasing background column water vapor (CWV) throughout this transition season between the Amazon dry and wet seasons exerts a strong effect on cloud properties. As a result, aerosol-cloud correlations should be stratified by column water vapor to achieve a more accurate assessment of the effect of aerosols on clouds. Previous studies ignored the systematic changes to meteorological factors during the transition season, leading to possible misinterpretation of their results. Cloud fraction is shown generally to increase with aerosol optical depth (AOD) for both low and high values of column water vapor, whereas the relationship between cloud optical depth (COD) and AOD exhibits a different relationship. COD increases with AOD until AOD approx. 0.25 due to the first indirect (microphysical) effect. At higher values of AOD, COD is found to decrease with increasing AOD, which may be due to: (1) the inhibition of cloud development by absorbing aerosols (radiative effect) and/or (2) a retrieval artifact in which the measured reflectance in the visible is less than expected from a cloud top either from the darkening of clouds through the addition of carbonaceous biomass burning aerosols or subpixel dark surface contamination in the measured cloud reflectance. If (1) is a contributing mechanism, as we suspect, then a linear relationship between the indirect effect and increasing AOD, assumed in a majority of GCMs, is inaccurate since these models do not include treatment of aerosol absorption in and around clouds. The effect of aerosols on both column water vapor and clouds over varying land surface types is also analyzed. The study finds that the difference in column water vapor between forest and pasture is not correlated with aerosol loading, supporting the assumption that temporal variation of column water vapor is primarily a function of the larger-scale meteorology. However, a difference in the response of cloud fraction to increasing AOD is observed between forest and pasture. This suggests that dissimilarities between other meteorological factors, such as atmospheric stability, are likely to have an impact on aerosol-cloud correlations between different land-cover types.

Integrating multiple scientific computing needs via a Private Cloud infrastructure

NASA Astrophysics Data System (ADS)

Bagnasco, S.; Berzano, D.; Brunetti, R.; Lusso, S.; Vallero, S.

2014-06-01

In a typical scientific computing centre, diverse applications coexist and share a single physical infrastructure. An underlying Private Cloud facility eases the management and maintenance of heterogeneous use cases such as multipurpose or application-specific batch farms, Grid sites catering to different communities, parallel interactive data analysis facilities and others. It allows to dynamically and efficiently allocate resources to any application and to tailor the virtual machines according to the applications' requirements. Furthermore, the maintenance of large deployments of complex and rapidly evolving middleware and application software is eased by the use of virtual images and contextualization techniques; for example, rolling updates can be performed easily and minimizing the downtime. In this contribution we describe the Private Cloud infrastructure at the INFN-Torino Computer Centre, that hosts a full-fledged WLCG Tier-2 site and a dynamically expandable PROOF-based Interactive Analysis Facility for the ALICE experiment at the CERN LHC and several smaller scientific computing applications. The Private Cloud building blocks include the OpenNebula software stack, the GlusterFS filesystem (used in two different configurations for worker- and service-class hypervisors) and the OpenWRT Linux distribution (used for network virtualization). A future integration into a federated higher-level infrastructure is made possible by exposing commonly used APIs like EC2 and by using mainstream contextualization tools like CloudInit.

Preliminary characterization of submicron secondary aerosol in the amazon forest - ATTO station

NASA Astrophysics Data System (ADS)

Carbone, S.; Ferreira De Brito, J.; Andreae, M. O.; Pöhlker, C.; Chi, X.; Saturno, J.; Barbosa, H. M.; Artaxo, P.

2014-12-01

Biogenic secondary organic aerosol particles are investigated in the Amazon in the context of the GoAmazon Project. The forest naturally emits a large number of gaseous compounds; they are called the volatile organic compounds (VOCs). They are emitted through processes that are not totally understood. Part of those gaseous compounds are converted into aerosol particles, which affect the biogeochemical cycles, the radiation balance, the mechanisms involving cloud formation and evolution, among few other important effects. In this study the aerosol life-cycle is investigated at the ATTO station, which is located about 150 km northeast of Manaus, with emphasis on the natural organic component and its impacts in the ecosystem. To achieve these objectives physical and chemical aerosol properties have been investigated, such as the chemical composition with aerosol chemical speciation monitor (ACSM), nanoparticle size distribution (using the SMPS - Scanning Mobility Particle Sizer), optical properties with measurements of scattering and absorption (using nephelometers and aethalometers). Those instruments have been operating continuously since February 2014 together with trace gases (O3, CO2, CO, SO2 and NOx) analyzers and additional meteorological instruments. On average PM1 (the sum of black carbon, organic and inorganic ions) totalized 1.0±0.3 μg m-3, where the organic fraction was dominant (75%). During the beginning of the dry season (July/August) the organic aerosol presented a moderate oxygenated character with the oxygen to carbon ratio (O:C) of 0.7. In the wet season some episodes containing significant amount of chloride and backward wind trajectories suggest aerosol contribution from the Atlantic Ocean. A more comprehensive analysis will include an investigation of the different oxidized fractions of the organic aerosol and optical properties.

Design Patterns to Achieve 300x Speedup for Oceanographic Analytics in the Cloud

NASA Astrophysics Data System (ADS)

Jacob, J. C.; Greguska, F. R., III; Huang, T.; Quach, N.; Wilson, B. D.

2017-12-01

We describe how we achieve super-linear speedup over standard approaches for oceanographic analytics on a cluster computer and the Amazon Web Services (AWS) cloud. NEXUS is an open source platform for big data analytics in the cloud that enables this performance through a combination of horizontally scalable data parallelism with Apache Spark and rapid data search, subset, and retrieval with tiled array storage in cloud-aware NoSQL databases like Solr and Cassandra. NEXUS is the engine behind several public portals at NASA and OceanWorks is a newly funded project for the ocean community that will mature and extend this capability for improved data discovery, subset, quality screening, analysis, matchup of satellite and in situ measurements, and visualization. We review the Python language API for Spark and how to use it to quickly convert existing programs to use Spark to run with cloud-scale parallelism, and discuss strategies to improve performance. We explain how partitioning the data over space, time, or both leads to algorithmic design patterns for Spark analytics that can be applied to many different algorithms. We use NEXUS analytics as examples, including area-averaged time series, time averaged map, and correlation map.

Cloud-based adaptive exon prediction for DNA analysis

PubMed Central

Putluri, Srinivasareddy; Fathima, Shaik Yasmeen

2018-01-01

Cloud computing offers significant research and economic benefits to healthcare organisations. Cloud services provide a safe place for storing and managing large amounts of such sensitive data. Under conventional flow of gene information, gene sequence laboratories send out raw and inferred information via Internet to several sequence libraries. DNA sequencing storage costs will be minimised by use of cloud service. In this study, the authors put forward a novel genomic informatics system using Amazon Cloud Services, where genomic sequence information is stored and accessed for processing. True identification of exon regions in a DNA sequence is a key task in bioinformatics, which helps in disease identification and design drugs. Three base periodicity property of exons forms the basis of all exon identification techniques. Adaptive signal processing techniques found to be promising in comparison with several other methods. Several adaptive exon predictors (AEPs) are developed using variable normalised least mean square and its maximum normalised variants to reduce computational complexity. Finally, performance evaluation of various AEPs is done based on measures such as sensitivity, specificity and precision using various standard genomic datasets taken from National Center for Biotechnology Information genomic sequence database. PMID:29515813

The NOAA Big Data Project: NEXRAD on the Cloud

NASA Astrophysics Data System (ADS)

Sundwall, Jed; Bouffler, Brendan

2016-04-01

Last year, the US National Oceanic and Atmospheric Administration (NOAA) made headlines when it entered into a research agreement with Amazon Web Services (AWS) to explore sustainable models to increase the output of open NOAA data. Publicly available NOAA data drives multi-billion dollar industries and critical research efforts. Under this new agreement, AWS and its Data Alliance collaborators are looking at ways to push more NOAA data to the cloud and build an ecosystem of innovation around it. In this presentation, we will provide a brief overview of the NOAA Big Data Project and the AWS Data Alliance, then dive into a specific example of data that has been made available (high resolution Doppler radar from the NEXRAD system) and early use cases.

The NOAA Big Data Project: NEXRAD on the Cloud

NASA Astrophysics Data System (ADS)

Gold, A.; Weber, J.

2015-12-01

This past April, the US National Oceanic and Atmospheric Administration (NOAA) made headlines when it entered into a research agreement with Amazon Web Services (AWS) to explore sustainable models to increase the output of open NOAA data. Publicly available NOAA data drives multi-billion dollar industries and critical research efforts. Under this new agreement, AWS and its Data Alliance collaborators are looking at ways to push more NOAA data to the cloud and build an ecosystem of innovation around it. In this presentation, we will provide a brief overview of the NOAA Big Data Project and the AWS Data Alliance, then dive into a specific example of data that has been made available (high resolution Doppler radar from the NEXRAD system) and early use cases.

Excess electron is trapped in a large single molecular cage C60F60.

PubMed

Wang, Yin-Feng; Li, Zhi-Ru; Wu, Di; Sun, Chia-Chung; Gu, Feng-Long

2010-01-15

A new kind of solvated electron systems, sphere-shaped e(-)@C60F60 (I(h)) and capsule-shaped e(-)@C60F60 (D6h), in contrast to the endohedral complex M@C60, is represented at the B3LYP/6-31G(d) + dBF (diffusive basis functions) density functional theory. It is proven, by examining the singly occupied molecular orbital (SOMO) and the spin density map of e(-)@C60F60, that the excess electron is indeed encapsulated inside the C60F60 cage. The shape of the electron cloud in SOMO matches with the shape of C60F60 cage. These cage-like single molecular solvated electrons have considerably large vertical electron detachment energies VDE of 4.95 (I(h)) and 4.67 eV (D6h) at B3LYP/6-31+G(3df) + dBF level compared to the VDE of 3.2 eV for an electron in bulk water (Coe et al., Int Rev Phys Chem 2001, 20, 33) and that of 3.66 eV for e(-)@C20F20 (Irikura, J Phys Chem A 2008, 112, 983), which shows their higher stability. The VDE of the sphere-shaped e(-)@C60F60 (I(h)) is greater than that of the capsule-shaped e(-)@C60F60 (D6h), indicating that the excess electron prefers to reside in the cage with the higher symmetry to form the more stable solvated electron. It is also noticed that the cage size [7.994 (I(h)), 5.714 and 9.978 A (D6h) in diameter] is much larger than that (2.826 A) of (H2O)20- dodecahedral cluster (Khan, Chem Phys Lett 2005, 401, 85). Copyright 2009 Wiley Periodicals, Inc.

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia

PubMed Central

de Oliveira, Gabriel; Brunsell, Nathaniel A.; Moraes, Elisabete C.; Bertani, Gabriel; dos Santos, Thiago V.; Shimabukuro, Yosio E.; Aragão, Luiz E. O. C.

2016-01-01

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001–December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance. PMID:27347957

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia.

PubMed

de Oliveira, Gabriel; Brunsell, Nathaniel A; Moraes, Elisabete C; Bertani, Gabriel; Dos Santos, Thiago V; Shimabukuro, Yosio E; Aragão, Luiz E O C

2016-06-24

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001-December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.

Model My Watershed and BiG CZ Data Portal: Interactive geospatial analysis and hydrological modeling web applications that leverage the Amazon cloud for scientists, resource managers and students

NASA Astrophysics Data System (ADS)

Aufdenkampe, A. K.; Mayorga, E.; Tarboton, D. G.; Sazib, N. S.; Horsburgh, J. S.; Cheetham, R.

2016-12-01

The Model My Watershed Web app (http://wikiwatershed.org/model/) was designed to enable citizens, conservation practitioners, municipal decision-makers, educators, and students to interactively select any area of interest anywhere in the continental USA to: (1) analyze real land use and soil data for that area; (2) model stormwater runoff and water-quality outcomes; and (3) compare how different conservation or development scenarios could modify runoff and water quality. The BiG CZ Data Portal is a web application for scientists for intuitive, high-performance map-based discovery, visualization, access and publication of diverse earth and environmental science data via a map-based interface that simultaneously performs geospatial analysis of selected GIS and satellite raster data for a selected area of interest. The two web applications share a common codebase (https://github.com/WikiWatershed and https://github.com/big-cz), high performance geospatial analysis engine (http://geotrellis.io/ and https://github.com/geotrellis) and deployment on the Amazon Web Services (AWS) cloud cyberinfrastructure. Users can use "on-the-fly" rapid watershed delineation over the national elevation model to select their watershed or catchment of interest. The two web applications also share the goal of enabling the scientists, resource managers and students alike to share data, analyses and model results. We will present these functioning web applications and their potential to substantially lower the bar for studying and understanding our water resources. We will also present work in progress, including a prototype system for enabling citizen-scientists to register open-source sensor stations (http://envirodiy.org/mayfly/) to stream data into these systems, so that they can be reshared using Water One Flow web services.

Characterization of the Temporal-Spatial Variability of Trans-Atlantic Dust Transport Based on CALIPSO Lidar Measurements

NASA Technical Reports Server (NTRS)

Yu, Hongbin

2015-01-01

The trans-Atlantic dust transport has important implications for human and ecosystem health, the terrestrial and oceanic biogeochemical cycle, weather systems, and climate. A reliable assessment of these influences requires the characterization of dust distributions in three dimensions and over long time periods. We provide an observation-based multiyear estimate of trans-Atlantic dust transport by using a 7-year (2007 - 2013) lidar record from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) in both cloud-free and above-cloud conditions. We estimate that on a basis of the 7-year average and integration over 10S - 30N, 182 Tg a-1 dust leaves the coast of North Africa at 15W, of which 132 Tg a-1 and 43 Tg a-1 reaches 35W and 75W, respectively. These flux estimates have an overall known uncertainty of (45 - 70). The 7-year average of dust deposition into the Amazon Basin is estimated to be 28 (8 - 48) Tg a-1 or 29 (8 - 50) kg ha-1 a-1. This imported dust could provide about 0.022 (0.006 - 0.037) Tg P of phosphorus per year, equivalent to 23 (7 - 39) g P ha-1 a-1 to fertilize the Amazon rainforest, which is comparable to the loss of phosphorus to rainfall. Significant seasonal variations are observed in both the magnitude of total dust transport and its meridional and vertical distributions. The observed large interannual variability of annual dust transport is highly anti-correlated with the prior-year Sahel Precipitation Index. Comparisons of CALIPSO measurements with surface-based observations and model simulations will also be discussed.

Identifying the impact of G-quadruplexes on Affymetrix 3' arrays using cloud computing.

PubMed

Memon, Farhat N; Owen, Anne M; Sanchez-Graillet, Olivia; Upton, Graham J G; Harrison, Andrew P

2010-01-15

A tetramer quadruplex structure is formed by four parallel strands of DNA/ RNA containing runs of guanine. These quadruplexes are able to form because guanine can Hoogsteen hydrogen bond to other guanines, and a tetrad of guanines can form a stable arrangement. Recently we have discovered that probes on Affymetrix GeneChips that contain runs of guanine do not measure gene expression reliably. We associate this finding with the likelihood that quadruplexes are forming on the surface of GeneChips. In order to cope with the rapidly expanding size of GeneChip array datasets in the public domain, we are exploring the use of cloud computing to replicate our experiments on 3' arrays to look at the effect of the location of G-spots (runs of guanines). Cloud computing is a recently introduced high-performance solution that takes advantage of the computational infrastructure of large organisations such as Amazon and Google. We expect that cloud computing will become widely adopted because it enables bioinformaticians to avoid capital expenditure on expensive computing resources and to only pay a cloud computing provider for what is used. Moreover, as well as financial efficiency, cloud computing is an ecologically-friendly technology, it enables efficient data-sharing and we expect it to be faster for development purposes. Here we propose the advantageous use of cloud computing to perform a large data-mining analysis of public domain 3' arrays.

Central Brazil

NASA Technical Reports Server (NTRS)

2002-01-01

This Moderate resolution Imaging Spectroradiometer (MODIS) true-color image was acquired on October 19, 2000, over a region in Brazil large enough to show much of the country's diverse landscape. Spanning some 8.5 million square kilometers (3.2 million square miles), Brazil is by far the largest South American nation--both in terms of land and population. The region known as the Amazon Basin lies to the northwest (upper left) and extends well beyond the northern and western edges of this scene. Typically, from this perspective Amazonia appears as a lush, dark green carpet due to the thick canopy of vegetation growing there. Some of the Amazon Basin is visible in this image, but much is obscured by clouds (bright white pixels), as is the Amazon River. This region is home to countless plant and animal species and some 150,000 native South Americans. The clusters of square and rectangular patterns toward the center of the image (light green or reddish-brown pixels) are where people have cleared away trees and vegetation to make room for development and agriculture. Toward the western side of the scene there is considerable haze and smoke from widespread biomass burning in parts of Brazil and Bolivia, which shares its eastern border with Brazil. Toward the east in this image is the highland, or 'cerrado,' region, which is more sparsely vegetated and has a somewhat drier climate than the Amazon Basin. The capital city, Brasilia, lies within this region just southwest of the Geral de Goias Mountains (orangish pixels running north-south). There are two large water reservoirs visible in this scene--the Sobradinho Reservoir about 800 km (500 miles) northeast of Brasilia, and the Paranaiba about 500 km (300 miles) southwest of Brasilia. MODIS flies aboard NASA's Terra spacecraft. Image courtesy Brian Montgomery, Reto Stockli, and Robert Simmon, based on data from the MODIS Science Team.

Global Variability of Mesoscale Convective System Anvil Structure from A-Train Satellite Data

NASA Technical Reports Server (NTRS)

Yuan, Jian; Houze, Robert A.

2010-01-01

Mesoscale convective systems (MCSs) in the tropics produce extensive anvil clouds, which significantly affect the transfer of radiation. This study develops an objective method to identify MCSs and their anvils by combining data from three A-train satellite instruments: Moderate Resolution Imaging Spectroradiometer (MODIS) for cloud-top size and coldness, Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for rain area size and intensity, and CloudSat for horizontal and vertical dimensions of anvils. The authors distinguish three types of MCSs: small and large separated MCSs and connected MCSs. The latter are MCSs sharing a contiguous rain area. Mapping of the objectively identified MCSs shows patterns of MCSs that are consistent with previous studies of tropical convection, with separated MCSs dominant over Africa and the Amazon regions and connected MCSs favored over the warm pool of the Indian and west Pacific Oceans. By separating the anvil from the raining regions of MCSs, this study leads to quantitative global maps of anvil coverage. These maps are consistent with the MCS analysis, and they lay the foundation for estimating the global radiative effects of anvil clouds. CloudSat radar data show that the modal thickness of MCS anvils is about 4-5 km. Anvils are mostly confined to within 1.5-2 times the equivalent radii of the primary rain areas of the MCSs. Over the warm pool, they may extend out to about 5 times the rain area radii. The warm ocean MCSs tend to have thicker non-raining and lightly raining anvils near the edges

What are the greenhouse gas observing system requirements for reducing fundamental biogeochemical process uncertainty? Amazon wetland CH4 emissions as a case study

NASA Astrophysics Data System (ADS)

Bloom, A. Anthony; Lauvaux, Thomas; Worden, John; Yadav, Vineet; Duren, Riley; Sander, Stanley P.; Schimel, David S.

2016-12-01

Understanding the processes controlling terrestrial carbon fluxes is one of the grand challenges of climate science. Carbon cycle process controls are readily studied at local scales, but integrating local knowledge across extremely heterogeneous biota, landforms and climate space has proven to be extraordinarily challenging. Consequently, top-down or integral flux constraints at process-relevant scales are essential to reducing process uncertainty. Future satellite-based estimates of greenhouse gas fluxes - such as CO2 and CH4 - could potentially provide the constraints needed to resolve biogeochemical process controls at the required scales. Our analysis is focused on Amazon wetland CH4 emissions, which amount to a scientifically crucial and methodologically challenging case study. We quantitatively derive the observing system (OS) requirements for testing wetland CH4 emission hypotheses at a process-relevant scale. To distinguish between hypothesized hydrological and carbon controls on Amazon wetland CH4 production, a satellite mission will need to resolve monthly CH4 fluxes at a ˜ 333 km resolution and with a ≤ 10 mg CH4 m-2 day-1 flux precision. We simulate a range of low-earth orbit (LEO) and geostationary orbit (GEO) CH4 OS configurations to evaluate the ability of these approaches to meet the CH4 flux requirements. Conventional LEO and GEO missions resolve monthly ˜ 333 km Amazon wetland fluxes at a 17.0 and 2.7 mg CH4 m-2 day-1 median uncertainty level. Improving LEO CH4 measurement precision by 2 would only reduce the median CH4 flux uncertainty to 11.9 mg CH4 m-2 day-1. A GEO mission with targeted observing capability could resolve fluxes at a 2.0-2.4 mg CH4 m-2 day-1 median precision by increasing the observation density in high cloud-cover regions at the expense of other parts of the domain. We find that residual CH4 concentration biases can potentially reduce the ˜ 5-fold flux CH4 precision advantage of a GEO mission to a ˜ 2-fold advantage (relative to a LEO mission). For residual CH4 bias correlation lengths of 100 km, the GEO can nonetheless meet the ≤ 10 mg CH4 m-2 day-1 requirements for systematic biases ≤ 10 ppb. Our study demonstrates that process-driven greenhouse gas OS simulations can enhance conventional uncertainty reduction assessments by quantifying the OS characteristics required for testing biogeochemical process hypotheses.

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.

PubMed

Ragothaman, Anjani; Boddu, Sairam Chowdary; Kim, Nayong; Feinstein, Wei; Brylinski, Michal; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

PubMed Central

Ragothaman, Anjani; Feinstein, Wei; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. PMID:24995285

Satellite-based estimation of cloud-base updrafts for convective clouds and stratocumulus

NASA Astrophysics Data System (ADS)

Zheng, Y.; Rosenfeld, D.; Li, Z.

2017-12-01

Updraft speeds of thermals have always been notoriously difficult to measure, despite significant roles they play in transporting pollutants and in cloud formation and precipitation. To our knowledge, no attempt to date has been made to estimate updraft speed from satellite information. In this study, we introduce three methods of retrieving updraft speeds at cloud base () for convective clouds and marine stratocumulus with VIIRS onboard Suomi-NPP satellite. The first method uses ground-air temperature difference to characterize the surface sensible heat flux, which is found to be correlated with updraft speeds measured by the Doppler lidar over the Southern Great Plains (SGP). Based on the relationship, we use the satellite-retrieved surface skin temperature and reanalysis surface air temperature to estimate the updrafts. The second method is based on a good linear correlation between cloud base height and updrafts, which was found over the SGP, the central Amazon, and on board a ship sailing between Honolulu and Los Angeles. We found a universal relationship for both land and ocean. The third method is for marine stratocumulus. A statistically significant relationship between Wb and cloud-top radiative cooling rate (CTRC) is found from measurements over northeastern Pacific and Atlantic. Based on this relation, satellite- and reanalysis-derived CTRC is utilized to infer the Wb of stratocumulus clouds. Evaluations against ground-based Doppler lidar measurements show estimation errors of 24%, 21% and 22% for the three methods, respectively.

Does Cloud Computing in the Atmospheric Sciences Make Sense? A case study of hybrid cloud computing at NASA Langley Research Center

NASA Astrophysics Data System (ADS)

Nguyen, L.; Chee, T.; Minnis, P.; Spangenberg, D.; Ayers, J. K.; Palikonda, R.; Vakhnin, A.; Dubois, R.; Murphy, P. R.

2014-12-01

The processing, storage and dissemination of satellite cloud and radiation products produced at NASA Langley Research Center are key activities for the Climate Science Branch. A constellation of systems operates in sync to accomplish these goals. Because of the complexity involved with operating such intricate systems, there are both high failure rates and high costs for hardware and system maintenance. Cloud computing has the potential to ameliorate cost and complexity issues. Over time, the cloud computing model has evolved and hybrid systems comprising off-site as well as on-site resources are now common. Towards our mission of providing the highest quality research products to the widest audience, we have explored the use of the Amazon Web Services (AWS) Cloud and Storage and present a case study of our results and efforts. This project builds upon NASA Langley Cloud and Radiation Group's experience with operating large and complex computing infrastructures in a reliable and cost effective manner to explore novel ways to leverage cloud computing resources in the atmospheric science environment. Our case study presents the project requirements and then examines the fit of AWS with the LaRC computing model. We also discuss the evaluation metrics, feasibility, and outcomes and close the case study with the lessons we learned that would apply to others interested in exploring the implementation of the AWS system in their own atmospheric science computing environments.

Optical and geometrical properties of cirrus clouds in Amazonia derived from 1 year of ground-based lidar measurements

NASA Astrophysics Data System (ADS)

Gouveia, Diego A.; Barja, Boris; Barbosa, Henrique M. J.; Seifert, Patric; Baars, Holger; Pauliquevis, Theotonio; Artaxo, Paulo

2017-03-01

Cirrus clouds cover a large fraction of tropical latitudes and play an important role in Earth's radiation budget. Their optical properties, altitude, vertical and horizontal coverage control their radiative forcing, and hence detailed cirrus measurements at different geographical locations are of utmost importance. Studies reporting cirrus properties over tropical rain forests like the Amazon, however, are scarce. Studies with satellite profilers do not give information on the diurnal cycle, and the satellite imagers do not report on the cloud vertical structure. At the same time, ground-based lidar studies are restricted to a few case studies. In this paper, we derive the first comprehensive statistics of optical and geometrical properties of upper-tropospheric cirrus clouds in Amazonia. We used 1 year (July 2011 to June 2012) of ground-based lidar atmospheric observations north of Manaus, Brazil. This dataset was processed by an automatic cloud detection and optical properties retrieval algorithm. Upper-tropospheric cirrus clouds were observed more frequently than reported previously for tropical regions. The frequency of occurrence was found to be as high as 88 % during the wet season and not lower than 50 % during the dry season. The diurnal cycle shows a minimum around local noon and maximum during late afternoon, associated with the diurnal cycle of precipitation. The mean values of cirrus cloud top and base heights, cloud thickness, and cloud optical depth were 14.3 ± 1.9 (SD) km, 12.9 ± 2.2 km, 1.4 ± 1.1 km, and 0.25 ± 0.46, respectively. Cirrus clouds were found at temperatures down to -90 °C. Frequently cirrus were observed within the tropical tropopause layer (TTL), which are likely associated to slow mesoscale uplifting or to the remnants of overshooting convection. The vertical distribution was not uniform, and thin and subvisible cirrus occurred more frequently closer to the tropopause. The mean lidar ratio was 23.3 ± 8.0 sr. However, for subvisible cirrus clouds a bimodal distribution with a secondary peak at about 44 sr was found suggesting a mixed composition. A dependence of the lidar ratio with cloud temperature (altitude) was not found, indicating that the clouds are vertically well mixed. The frequency of occurrence of cirrus clouds classified as subvisible (τ < 0. 03) were 41.6 %, whilst 37.8 % were thin cirrus (0. 03 < τ < 0. 3) and 20.5 % opaque cirrus (τ > 0. 3). Hence, in central Amazonia not only a high frequency of cirrus clouds occurs, but also a large fraction of subvisible cirrus clouds. This high frequency of subvisible cirrus clouds may contaminate aerosol optical depth measured by sun photometers and satellite sensors to an unknown extent.

Impact of a drier Early-Mid-Holocene climate upon Amazonian forests.

PubMed

Mayle, Francis E; Power, Mitchell J

2008-05-27

This paper uses a palaeoecological approach to examine the impact of drier climatic conditions of the Early-Mid-Holocene (ca 8000-4000 years ago) upon Amazonia's forests and their fire regimes. Palaeovegetation (pollen data) and palaeofire (charcoal) records are synthesized from 20 sites within the present tropical forest biome, and the underlying causes of any emergent patterns or changes are explored by reference to independent palaeoclimate data and present-day patterns of precipitation, forest cover and fire activity across Amazonia. During the Early-Mid-Holocene, Andean cloud forest taxa were replaced by lowland tree taxa as the cloud base rose while lowland ecotonal areas, which are presently covered by evergreen rainforest, were instead dominated by savannahs and/or semi-deciduous dry forests. Elsewhere in the Amazon Basin there is considerable spatial and temporal variation in patterns of vegetation disturbance and fire, which probably reflects the complex heterogeneous patterns in precipitation and seasonality across the basin, and the interactions between climate change, drought- and fire susceptibility of the forests, and Palaeo-Indian land use. Our analysis shows that the forest biome in most parts of Amazonia appears to have been remarkably resilient to climatic conditions significantly drier than those of today, despite widespread evidence of forest burning. Only in ecotonal areas is there evidence of biome replacement in the Holocene. From this palaeoecological perspective, we argue against the Amazon forest 'dieback' scenario simulated for the future.

Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost?

PubMed

Madhyastha, Tara M; Koh, Natalie; Day, Trevor K M; Hernández-Fernández, Moises; Kelley, Austin; Peterson, Daniel J; Rajan, Sabreena; Woelfer, Karl A; Wolf, Jonathan; Grabowski, Thomas J

2017-01-01

The contribution of this paper is to identify and describe current best practices for using Amazon Web Services (AWS) to execute neuroimaging workflows "in the cloud." Neuroimaging offers a vast set of techniques by which to interrogate the structure and function of the living brain. However, many of the scientists for whom neuroimaging is an extremely important tool have limited training in parallel computation. At the same time, the field is experiencing a surge in computational demands, driven by a combination of data-sharing efforts, improvements in scanner technology that allow acquisition of images with higher image resolution, and by the desire to use statistical techniques that stress processing requirements. Most neuroimaging workflows can be executed as independent parallel jobs and are therefore excellent candidates for running on AWS, but the overhead of learning to do so and determining whether it is worth the cost can be prohibitive. In this paper we describe how to identify neuroimaging workloads that are appropriate for running on AWS, how to benchmark execution time, and how to estimate cost of running on AWS. By benchmarking common neuroimaging applications, we show that cloud computing can be a viable alternative to on-premises hardware. We present guidelines that neuroimaging labs can use to provide a cluster-on-demand type of service that should be familiar to users, and scripts to estimate cost and create such a cluster.

Evaluation of a multi-scale WRF-CAM5 simulation during the 2010 East Asian Summer Monsoon

DOE PAGES

Campbell, Patrick; Zhang, Yang; Wang, Kai; ...

2017-09-08

The Weather Research and Forecasting model with Chemistry (WRF-Chem) with the physics package of the Community Atmosphere Model Version 5 (CAM5) has been applied at multiple scales over Eastern China (EC) and the Yangtze River Delta (YRD) to evaluate how increased horizontal resolution with physics designed for a coarser resolution climate model impacts aerosols and clouds, and the resulting precipitation characteristics and performance during the 2010 East Asian Summer Monsoon (EASM). Despite large underpredictions in surface aerosol concentrations and aerosol optical depth, there is good spatial agreement with surface observations of chemical predictions, and increasing spatial resolution tends to improvemore » performance. Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD. Increasing model horizontal resolution tends to reduce model bias and error for precipitation predictions. The surface and column aerosol loading is maximized between about 32°N and 42°N in early to mid-May during the 2010 EASM, and then shifts north while decreasing in magnitude during July and August. Changing model resolution moderately changes the spatiotemporal relationships between aerosols, cloud properties, and precipitation during the EASM, thus demonstrating the importance of model grid resolution in simulating EASM circulation and rainfall patterns over EC and the YRD. In conclusion, results from this work demonstrate the capability and limitations in the aerosol, cloud, and precipitation representation of WRF-CAM5 for regional-scale applications down to relatively fine horizontal resolutions. Further WRF-CAM5 model development and application in this area is needed.« less

Evaluation of a multi-scale WRF-CAM5 simulation during the 2010 East Asian Summer Monsoon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campbell, Patrick; Zhang, Yang; Wang, Kai

The Weather Research and Forecasting model with Chemistry (WRF-Chem) with the physics package of the Community Atmosphere Model Version 5 (CAM5) has been applied at multiple scales over Eastern China (EC) and the Yangtze River Delta (YRD) to evaluate how increased horizontal resolution with physics designed for a coarser resolution climate model impacts aerosols and clouds, and the resulting precipitation characteristics and performance during the 2010 East Asian Summer Monsoon (EASM). Despite large underpredictions in surface aerosol concentrations and aerosol optical depth, there is good spatial agreement with surface observations of chemical predictions, and increasing spatial resolution tends to improvemore » performance. Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD. Increasing model horizontal resolution tends to reduce model bias and error for precipitation predictions. The surface and column aerosol loading is maximized between about 32N and 42N in early to mid-May during the 2010 EASM, and then shifts north while decreasing in magnitude during July and August. Changing model resolution moderately changes the spatiotemporal relationships between aerosols, cloud properties, and precipitation during the EASM, thus demonstrating the importance of model grid resolution in simulating EASM circulation and rainfall patterns over EC and the YRD. Results from this work demonstrate the capability and limitations in the aerosol, cloud, and precipitation representation of WRF-CAM5 for regional-scale applications down to relatively fine horizontal resolutions. Further WRF-CAM5 model development and application in this area is needed.« less

Evaluation of a multi-scale WRF-CAM5 simulation during the 2010 East Asian Summer Monsoon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campbell, Patrick; Zhang, Yang; Wang, Kai

The Weather Research and Forecasting model with Chemistry (WRF-Chem) with the physics package of the Community Atmosphere Model Version 5 (CAM5) has been applied at multiple scales over Eastern China (EC) and the Yangtze River Delta (YRD) to evaluate how increased horizontal resolution with physics designed for a coarser resolution climate model impacts aerosols and clouds, and the resulting precipitation characteristics and performance during the 2010 East Asian Summer Monsoon (EASM). Despite large underpredictions in surface aerosol concentrations and aerosol optical depth, there is good spatial agreement with surface observations of chemical predictions, and increasing spatial resolution tends to improvemore » performance. Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD. Increasing model horizontal resolution tends to reduce model bias and error for precipitation predictions. The surface and column aerosol loading is maximized between about 32°N and 42°N in early to mid-May during the 2010 EASM, and then shifts north while decreasing in magnitude during July and August. Changing model resolution moderately changes the spatiotemporal relationships between aerosols, cloud properties, and precipitation during the EASM, thus demonstrating the importance of model grid resolution in simulating EASM circulation and rainfall patterns over EC and the YRD. In conclusion, results from this work demonstrate the capability and limitations in the aerosol, cloud, and precipitation representation of WRF-CAM5 for regional-scale applications down to relatively fine horizontal resolutions. Further WRF-CAM5 model development and application in this area is needed.« less

GoAmazon 2014/15. SRI-PTR-ToFMS Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guenther, A.

Our science team, including Dr. Alex Guenther (previously at Pacific Northwest National Laboratory (PNNL) and now at the University of California, Irvine) Dr. Saewung Kim and Dr. Roger Seco, and Dr. Jim Smith (previously at NCAR and now at UC Irvine), deployed a selected reagent ion – proton transfer reaction – time-of-flight mass spectrometer (SRI-PTR-TOFMS) to the T3 site during the GoAmazon study. One of the major uncertainties in climate model simulations is the effects of aerosols on radiative forcing, and a better understanding of the factors controlling aerosol distributions and life cycle is urgently needed. Aerosols contribute directly tomore » the Earth’s radiation balance by scattering or absorbing light as a function of their physical properties and indirectly through particle-cloud interactions that lead to cloud formation and the modification of cloud properties. On a global scale, the dominant source of organic aerosol is biogenic volatile organic compounds (BVOC) emitted from terrestrial ecosystems. These organic aerosols are a major part of the total mass of all airborne particles and are currently not sufficiently represented in climate models. To incorporate quantitatively the effects of BVOCs and their oxidation products on biogenic organic aerosol (BOA) requires parameterization of their production in terrestrial ecosystems and their atmospheric transformations. This project was designed to reduce the gaps in our understanding of how these processes control BVOCs and BOAs, and their impact on climate. This was accomplished by wet and dry season measurements at the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility T3 site along with a comprehensive suite of complementary measurements. The specific goals were to 1) measure and mechanistically understand the factors affecting aerosol distributions over a tropical rain forest, especially the effects of anthropogenic pollution as a perturbation to the natural state, and 2) develop and implement an upscaling analysis from this new data set and knowledge of aerosol distributions to prognosticate possible climatic impacts of present-day urban pollution and possibly greater pollution in the future.« less

Impact of Biomass Burning Aerosols on the Biosphere over Amazonia

NASA Astrophysics Data System (ADS)

Malavelle, F.; Haywood, J.; Mercado, L.; Folberth, G.; Bellouin, N.

2014-12-01

Biomass burning (BB) smoke from deforestation and the burning of agricultural waste emit a complex cocktail of aerosol particles and gases. BB emissions show a regional hotspot over South America on the edges of Amazonia. These major perturbations and impacts on surface temperature, surface fluxes, chemistry, radiation, rainfall, may have significant consequent impacts on the Amazon rainforest, the largest and most productive carbon store on the planet. There is therefore potential for very significant interaction and interplay between aerosols, clouds, radiation and the biosphere in the region. Terrestrial carbon production (i.e. photosynthesis) is intimately tied to the supply of photosynthetically active radiation (PAR - i.e. wavelengths between 300-690 nm). PAR in sufficient intensity and duration is critical for plant growth. However, if a decrease in total radiation is accompanied by an increase in the component of diffuse radiation, plant productivity may increase due to higher light use efficiency per unit of PAR and less photosynthetic saturation. This effect, sometimes referred as diffuse light fertilization effect, could have increased the global land carbon sink by approximately one quarter during the global dimming period and is expected to be a least as important locally. By directly interacting with radiation, BB aerosols significantly reduce the total amount of PAR available to plant canopies. In addition, BB aerosols also play a centre role in cloud formation because they provide the necessary cloud condensation nuclei, hence indirectly altering the water cycle and the components and quantity of PAR. In this presentation, we use the recent observations from the South American Biomass Burning Analysis (SAMBBA) to explore the impact of radiation changes on the carbon cycle in the Amazon region caused by BB emissions. A parameterisation of the impact of diffuse and direct radiation upon photosynthesis rates and net primary productivity in the biosphere has been implemented within a fully coupled Earth System Model, namely the UK Met Office Hadley Centre HadGEM2-ES model. We present results from ten-year experiments (2000-2010) designed to investigate the sensitivity of the terrestrial biosphere to the burden and absorbing nature of Amazonian BB aerosols.

Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003-2013

NASA Astrophysics Data System (ADS)

Anderson, Martha C.; Zolin, Cornelio A.; Hain, Christopher R.; Semmens, Kathryn; Tugrul Yilmaz, M.; Gao, Feng

2015-07-01

Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and functioning have been attributed to cloud and aerosol contamination of shortwave reflectance composites, particularly over the rainforested regions of the Amazon basin which are subject to prolonged periods of cloud cover and episodes of intense biomass burning. This study compares timeseries of satellite-derived maps of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and precipitation from the Tropical Rainfall Mapping Mission (TRMM) with a diagnostic Evaporative Stress Index (ESI) retrieved using thermal infrared remote sensing over South America for the period 2003-2013. This period includes several severe droughts and floods that occurred both over the Amazon and over unforested savanna and agricultural areas in Brazil. Cross-correlations between absolute values and standardized anomalies in monthly LAI and precipitation composites as well as the actual-to-reference evapotranspiration (ET) ratio used in the ESI were computed for representative forested and agricultural regions. The correlation analyses reveal strong apparent anticorrelation between MODIS LAI and TRMM precipitation anomalies over the Amazon, but better coupling over regions vegetated with shorter grass and crop canopies. The ESI was more consistently correlated with precipitation patterns over both landcover types. Temporal comparisons between ESI and TRMM anomalies suggest longer moisture buffering timescales in the deeper rooted rainforest systems. Diagnostic thermal-based retrievals of ET and ET anomalies, such as used in the ESI, provide independent information on the impacts of extreme hydrologic events on vegetation health in comparison with VI and precipitation-based drought indicators, and used in concert may provide a more reliable evaluation of natural and managed ecosystem response to variable climate regimes.

Open Source Surrogate Safety Assessment Model, 2017 Enhancement and Update: SSAM Version 3.0 [Tech Brief

DOT National Transportation Integrated Search

2016-11-17

The ETFOMM (Enhanced Transportation Flow Open Source Microscopic Model) Cloud Service (ECS) is a software product sponsored by the U.S. Department of Transportation in conjunction with the Microscopic Traffic Simulation Models and SoftwareAn Op...

The response of thunderstorms and lightning to smoke from Amazonian fires

NASA Astrophysics Data System (ADS)

Altaratz, Orit; Koren, Ilan; Yair, Yoav; Price, Colin

2010-05-01

The effects of man-made aerosols on clouds are long believed to be a key component for model predictions of climate change, yet are one of the least understood. High aerosol concentrations can change the convection intensity and hence the electrical activity of thunderclouds. Focusing on the Amazon dry season in Brazil, where thousands of man-made forest fires inject smoke into the atmosphere, we studied the aerosol effects on thunderclouds and lightning. We used the ground-based World-Wide Lightning Location Network (WWLLN) measurements together with Aqua-MODIS remotely-sensed aerosol and cloud data to study the relationship between aerosol loading and lightning flash occurrence. We present evidence for the transition between two regimes, representing opposing effects of aerosols on clouds. The first is the microphysical effect which is manifested in an increase in convective intensity (and therefore in electrical activity), followed by the radiative effect that becomes dominant with the increase in aerosol loading leading to a decrease in convective intensity, manifested in lower lightning activity.

Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline

PubMed Central

2014-01-01

Background Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results. Results To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts. Conclusions By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples. PMID:24475911

Confluence of the Amazon and Topajos Rivers, Brazil, South America

NASA Image and Video Library

1991-08-11

This view shows the confluence of the Amazon and the Topajos Rivers at Santarem, Brazil (2.0S, 55.0W). The Am,azon flows from lower left to upper right of the photo. Below the river juncture of the Amazon and Tapajos, there is considerable deforestation activity along the Trans-Amazon Highway.

Optimizing the Use of Storage Systems Provided by Cloud Computing Environments

NASA Astrophysics Data System (ADS)

Gallagher, J. H.; Potter, N.; Byrne, D. A.; Ogata, J.; Relph, J.

2013-12-01

Cloud computing systems present a set of features that include familiar computing resources (albeit augmented to support dynamic scaling of processing power) bundled with a mix of conventional and unconventional storage systems. The linux base on which many Cloud environments (e.g., Amazon) are based make it tempting to assume that any Unix software will run efficiently in this environment efficiently without change. OPeNDAP and NODC collaborated on a short project to explore how the S3 and Glacier storage systems provided by the Amazon Cloud Computing infrastructure could be used with a data server developed primarily to access data stored in a traditional Unix file system. Our work used the Amazon cloud system, but we strived for designs that could be adapted easily to other systems like OpenStack. Lastly, we evaluated different architectures from a computer security perspective. We found that there are considerable issues associated with treating S3 as if it is a traditional file system, even though doing so is conceptually simple. These issues include performance penalties because using a software tool that emulates a traditional file system to store data in S3 performs poorly when compared to a storing data directly in S3. We also found there are important benefits beyond performance to ensuring that data written to S3 can directly accessed without relying on a specific software tool. To provide a hierarchical organization to the data stored in S3, we wrote 'catalog' files, using XML. These catalog files map discrete files to S3 access keys. Like a traditional file system's directories, the catalogs can also contain references to other catalogs, providing a simple but effective hierarchy overlaid on top of S3's flat storage space. An added benefit to these catalogs is that they can be viewed in a web browser; our storage scheme provides both efficient access for the data server and access via a web browser. We also looked at the Glacier storage system and found that the system's response characteristics are very different from a traditional file system or database; it behaves like a near-line storage system. To be used by a traditional data server, the underlying access protocol must support asynchronous accesses. This is because the Glacier system takes a minimum of four hours to deliver any data object, so systems built with the expectation of instant access (i.e., most web systems) must be fundamentally changed to use Glacier. Part of a related project has been to develop an asynchronous access mode for OPeNDAP, and we have developed a design using that new addition to the DAP protocol with Glacier as a near-line mass store. In summary, we found that both S3 and Glacier require special treatment to be effectively used by a data server. It is important to add (new) interfaces to data servers that enable them to use these storage devices through their native interfaces. We also found that our designs could easily map to a cloud environment based on OpenStack. Lastly, we noted that while these designs invited more liberal use of remote references for data objects, that can expose software to new security risks.

ScipionCloud: An integrative and interactive gateway for large scale cryo electron microscopy image processing on commercial and academic clouds.

PubMed

Cuenca-Alba, Jesús; Del Cano, Laura; Gómez Blanco, Josué; de la Rosa Trevín, José Miguel; Conesa Mingo, Pablo; Marabini, Roberto; S Sorzano, Carlos Oscar; Carazo, Jose María

2017-10-01

New instrumentation for cryo electron microscopy (cryoEM) has significantly increased data collection rate as well as data quality, creating bottlenecks at the image processing level. Current image processing model of moving the acquired images from the data source (electron microscope) to desktops or local clusters for processing is encountering many practical limitations. However, computing may also take place in distributed and decentralized environments. In this way, cloud is a new form of accessing computing and storage resources on demand. Here, we evaluate on how this new computational paradigm can be effectively used by extending our current integrative framework for image processing, creating ScipionCloud. This new development has resulted in a full installation of Scipion both in public and private clouds, accessible as public "images", with all the required preinstalled cryoEM software, just requiring a Web browser to access all Graphical User Interfaces. We have profiled the performance of different configurations on Amazon Web Services and the European Federated Cloud, always on architectures incorporating GPU's, and compared them with a local facility. We have also analyzed the economical convenience of different scenarios, so cryoEM scientists have a clearer picture of the setup that is best suited for their needs and budgets. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Eo-1 Hyperion Measures Canopy Drought Stress In Amazonia

NASA Technical Reports Server (NTRS)

Asner, Gregory P.; Nepstad, Daniel; Cardinot, Gina; Moutinho, Paulo; Harris, Thomas; Ray, David

2004-01-01

The central, south and southeast portions of the Amazon Basin experience a period of decreased cloud cover and precipitation from June through November. There are likely important effects of seasonal and interannual rainfall variation on forest leaf area index, canopy water stress, productivity and regional carbon cycling in the Amazon. While both ground and spaceborne studies of precipitation continue to improve, there has been almost no progress made in observing forest canopy responses to rainfall variability in the humid tropics. This shortfall stems from the large stature of the vegetation and great spatial extent of tropical forests, both of which strongly impede field studies of forest responses to water availability. Those few studies employing satellite measures of canopy responses to seasonal and interannual drought (e.g., Bohlman et al. 1998, Asner et al. 2000) have been limited by the spectral resolution and sampling available from Landsat and AVHRR sensors. We report on a study combining the first landscape-level, managed drought experiment in Amazon tropical forest with the first spaceborne imaging spectrometer observations of this experimental area. Using extensive field data on rainfall inputs, soil water content, and both leaf and canopy responses, we test the hypothesis that spectroscopic signatures unique to hyperspectral observations can be used to quantify relative differences in canopy stress resulting from water availability.

Biomass burning CCN enhance the dynamics of a mesoscale convective system over the La Plata Basin: a numerical approach

NASA Astrophysics Data System (ADS)

Camponogara, Gláuber; Assunção Faus da Silva Dias, Maria; Carrió, Gustavo G.

2018-02-01

High aerosol loadings are discharged into the atmosphere every year by biomass burning in the Amazon and central Brazil during the dry season (July-December). These particles, suspended in the atmosphere, can be carried via a low-level jet toward the La Plata Basin, one of the largest hydrographic basins in the world. Once they reach this region, the aerosols can affect mesoscale convective systems (MCSs), whose frequency is higher during the spring and summer over the basin. The present study is one of the first that seeks to understand the microphysical effects of biomass burning aerosols from the Amazon Basin on mesoscale convective systems over the La Plata Basin. We performed numerical simulations initialized with idealized cloud condensation nuclei (CCN) profiles for an MCS case observed over the La Plata Basin on 21 September 2010. The experiments reveal an important link between CCN number concentration and MCS dynamics, where stronger downdrafts were observed under higher amounts of aerosols, generating more updraft cells in response. Moreover, the simulations show higher amounts of precipitation as the CCN concentration increases. Despite the model's uncertainties and limitations, these results represent an important step toward the understanding of possible impacts on the Amazon biomass burning aerosols over neighboring regions such as the La Plata Basin.

Abstracting application deployment on Cloud infrastructures

NASA Astrophysics Data System (ADS)

Aiftimiei, D. C.; Fattibene, E.; Gargana, R.; Panella, M.; Salomoni, D.

2017-10-01

Deploying a complex application on a Cloud-based infrastructure can be a challenging task. In this contribution we present an approach for Cloud-based deployment of applications and its present or future implementation in the framework of several projects, such as “!CHAOS: a cloud of controls” [1], a project funded by MIUR (Italian Ministry of Research and Education) to create a Cloud-based deployment of a control system and data acquisition framework, “INDIGO-DataCloud” [2], an EC H2020 project targeting among other things high-level deployment of applications on hybrid Clouds, and “Open City Platform”[3], an Italian project aiming to provide open Cloud solutions for Italian Public Administrations. We considered to use an orchestration service to hide the complex deployment of the application components, and to build an abstraction layer on top of the orchestration one. Through Heat [4] orchestration service, we prototyped a dynamic, on-demand, scalable platform of software components, based on OpenStack infrastructures. On top of the orchestration service we developed a prototype of a web interface exploiting the Heat APIs. The user can start an instance of the application without having knowledge about the underlying Cloud infrastructure and services. Moreover, the platform instance can be customized by choosing parameters related to the application such as the size of a File System or the number of instances of a NoSQL DB cluster. As soon as the desired platform is running, the web interface offers the possibility to scale some infrastructure components. In this contribution we describe the solution design and implementation, based on the application requirements, the details of the development of both the Heat templates and of the web interface, together with possible exploitation strategies of this work in Cloud data centers.

DNA Sequencing Technologies within the Chemical and Biological Defense Enterprise: How to Position the Department of Defense to Maximize the Use of These Emerging Technologies - JUPITR

DTIC Science & Technology

2015-07-01

sharing, the Pathosphere.org open-system architecture is now deployed and available on the Amazon cloud . The site has over 100 users from...SUPPLEMENTARY NOTES 14. ABSTRACT: To generate accurate and actionable disease data that can be integrated into the broader Department of Defense...PREFACE The work described in this report was authorized under the Joint United States Forces Korea Portal and Integrated Threat Recognition

Now and Next-Generation Sequencing Techniques: Future of Sequence Analysis Using Cloud Computing

PubMed Central

Thakur, Radhe Shyam; Bandopadhyay, Rajib; Chaudhary, Bratati; Chatterjee, Sourav

2012-01-01

Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed “cloud computing”) has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows. PMID:23248640

Now and next-generation sequencing techniques: future of sequence analysis using cloud computing.

PubMed

Thakur, Radhe Shyam; Bandopadhyay, Rajib; Chaudhary, Bratati; Chatterjee, Sourav

2012-01-01

Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed "cloud computing") has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows.

Implementation and use of a highly available and innovative IaaS solution: the Cloud Area Padovana

NASA Astrophysics Data System (ADS)

Aiftimiei, C.; Andreetto, P.; Bertocco, S.; Biasotto, M.; Dal Pra, S.; Costa, F.; Crescente, A.; Dorigo, A.; Fantinel, S.; Fanzago, F.; Frizziero, E.; Gulmini, M.; Michelotto, M.; Sgaravatto, M.; Traldi, S.; Venaruzzo, M.; Verlato, M.; Zangrando, L.

2015-12-01

While in the business world the cloud paradigm is typically implemented purchasing resources and services from third party providers (e.g. Amazon), in the scientific environment there's usually the need of on-premises IaaS infrastructures which allow efficient usage of the hardware distributed among (and owned by) different scientific administrative domains. In addition, the requirement of open source adoption has led to the choice of products like OpenStack by many organizations. We describe a use case of the Italian National Institute for Nuclear Physics (INFN) which resulted in the implementation of a unique cloud service, called ’Cloud Area Padovana’, which encompasses resources spread over two different sites: the INFN Legnaro National Laboratories and the INFN Padova division. We describe how this IaaS has been implemented, which technologies have been adopted and how services have been configured in high-availability (HA) mode. We also discuss how identity and authorization management were implemented, adopting a widely accepted standard architecture based on SAML2 and OpenID: by leveraging the versatility of those standards the integration with authentication federations like IDEM was implemented. We also discuss some other innovative developments, such as a pluggable scheduler, implemented as an extension of the native OpenStack scheduler, which allows the allocation of resources according to a fair-share based model and which provides a persistent queuing mechanism for handling user requests that can not be immediately served. Tools, technologies, procedures used to install, configure, monitor, operate this cloud service are also discussed. Finally we present some examples that show how this IaaS infrastructure is being used.

InSAR Deformation Time Series Processed On-Demand in the Cloud

NASA Astrophysics Data System (ADS)

Horn, W. B.; Weeden, R.; Dimarchi, H.; Arko, S. A.; Hogenson, K.

2017-12-01

During this past year, ASF has developed a cloud-based on-demand processing system known as HyP3 (http://hyp3.asf.alaska.edu/), the Hybrid Pluggable Processing Pipeline, for Synthetic Aperture Radar (SAR) data. The system makes it easy for a user who doesn't have the time or inclination to install and use complex SAR processing software to leverage SAR data in their research or operations. One such processing algorithm is generation of a deformation time series product, which is a series of images representing ground displacements over time, which can be computed using a time series of interferometric SAR (InSAR) products. The set of software tools necessary to generate this useful product are difficult to install, configure, and use. Moreover, for a long time series with many images, the processing of just the interferograms can take days. Principally built by three undergraduate students at the ASF DAAC, the deformation time series processing relies the new Amazon Batch service, which enables processing of jobs with complex interconnected dependencies in a straightforward and efficient manner. In the case of generating a deformation time series product from a stack of single-look complex SAR images, the system uses Batch to serialize the up-front processing, interferogram generation, optional tropospheric correction, and deformation time series generation. The most time consuming portion is the interferogram generation, because even for a fairly small stack of images many interferograms need to be processed. By using AWS Batch, the interferograms are all generated in parallel; the entire process completes in hours rather than days. Additionally, the individual interferograms are saved in Amazon's cloud storage, so that when new data is acquired in the stack, an updated time series product can be generated with minimal addiitonal processing. This presentation will focus on the development techniques and enabling technologies that were used in developing the time series processing in the ASF HyP3 system. Data and process flow from job submission through to order completion will be shown, highlighting the benefits of the cloud for each step.

Distributed MRI reconstruction using Gadgetron-based cloud computing.

PubMed

Xue, Hui; Inati, Souheil; Sørensen, Thomas Sangild; Kellman, Peter; Hansen, Michael S

2015-03-01

To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency. The Gadgetron framework was extended with new software components that enable an arbitrary number of Gadgetron instances to collaborate on a reconstruction task. This cloud-enabled version of the Gadgetron was deployed on three different distributed computing platforms ranging from a heterogeneous collection of commodity computers to the commercial Amazon Elastic Compute Cloud. The Gadgetron cloud was used to provide nonlinear, compressed sensing reconstruction on a clinical scanner with low reconstruction latency (eg, cardiac and neuroimaging applications). The proposed setup was able to handle acquisition and 11 -SPIRiT reconstruction of nine high temporal resolution real-time, cardiac short axis cine acquisitions, covering the ventricles for functional evaluation, in under 1 min. A three-dimensional high-resolution brain acquisition with 1 mm(3) isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min. A distributed computing enabled Gadgetron provides a scalable way to improve reconstruction performance using commodity cluster computing. Nonlinear, compressed sensing reconstruction can be deployed clinically with low image reconstruction latency. © 2014 Wiley Periodicals, Inc.

Equilibrium Climate Sensitivity Obtained From Multimillennial Runs of Two GFDL Climate Models

NASA Astrophysics Data System (ADS)

Paynter, D.; Frölicher, T. L.; Horowitz, L. W.; Silvers, L. G.

2018-02-01

Equilibrium climate sensitivity (ECS), defined as the long-term change in global mean surface air temperature in response to doubling atmospheric CO2, is usually computed from short atmospheric simulations over a mixed layer ocean, or inferred using a linear regression over a short-time period of adjustment. We report the actual ECS from multimillenial simulations of two Geophysical Fluid Dynamics Laboratory (GFDL) general circulation models (GCMs), ESM2M, and CM3 of 3.3 K and 4.8 K, respectively. Both values are 1 K higher than estimates for the same models reported in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change obtained by regressing the Earth's energy imbalance against temperature. This underestimate is mainly due to changes in the climate feedback parameter (-α) within the first century after atmospheric CO2 has stabilized. For both GCMs it is possible to estimate ECS with linear regression to within 0.3 K by increasing CO2 at 1% per year to doubling and using years 51-350 after CO2 is constant. We show that changes in -α differ between the two GCMs and are strongly tied to the changes in both vertical velocity at 500 hPa (ω500) and estimated inversion strength that the GCMs experience during the progression toward the equilibrium. This suggests that while cloud physics parametrizations are important for determining the strength of -α, the substantially different atmospheric state resulting from a changed sea surface temperature pattern may be of equal importance.

Saharan Dust Fertilizing Atlantic Ocean and Amazon Rainforest via Long-range Transport and Deposition: A Perspective from Multiyear Satellite Measurements

NASA Astrophysics Data System (ADS)

Yu, H.; Chin, M.; Yuan, T.; Bian, H.; Remer, L. A.; Prospero, J. M.; Omar, A. H.; Winker, D. M.; Yang, Y.; Zhang, Y.; Zhang, Z.; Zhao, C.

2015-12-01

Massive dust emitted from Sahara desert is carried by trade winds across the tropical Atlantic Ocean, reaching the Amazon Rainforest and Caribbean Sea. Airborne dust degrades air quality and interacts with radiation and clouds. Dust falling to land and ocean adds essential nutrients that could increase the productivity of terrestrial and aquatic ecosystems and modulate the biogeochemical cycles and climate. The resultant climate change will feed back on the production of dust in Sahara desert and its subsequent transport and deposition. Understanding the connections among the remote ecosystems requires an accurate quantification of dust transport and deposition flux on large spatial and temporal scales, in which satellite remote sensing can play an important role. We provide the first multiyear satellite-based estimates of altitude-resolved across-Atlantic dust transport and deposition based on eight-year (2007-2014) record of aerosol three-dimensional distributions from the CALIPSO lidar. On a basis of the 8-year average, 179 Tg (million tons) of dust leaves the coast of North Africa and is transported across Atlantic Ocean, of which 102, 20, and 28 Tg of dust is deposited into the tropical Atlantic Ocean, Caribbean Sea, and Amazon Rainforest, respectively. The dust deposition adds 4.3 Tg of iron and 0.1 Tg of phosphorus to the tropical Atlantic Ocean and Caribbean Sea where the productivity of marine ecosystem depends on the availability of these nutrients. The 28 Tg of dust provides about 0.022 Tg of phosphorus to Amazon Rainforest yearly that replenishes the leak of this plant-essential nutrient by rains and flooding, suggesting an important role of Saharan dust in maintaining the productivity of Amazon rainforest on timescales of decades or centuries. We will also discuss seasonal and interannual variations of the dust transport and deposition, and comparisons of the CALIOP-based estimates with model simulations.

Year-round Source Contributions of Fossil Fuel and Biomass Combustion to Elemental Carbon on the North Slope Alaska Utilizing Radiocarbon Analysis

NASA Astrophysics Data System (ADS)

Barrett, T. E.; Gustafsson, O.; Winiger, P.; Moffett, C.; Back, J.; Sheesley, R. J.

2015-12-01

It is well documented that the Arctic has undergone rapid warming at an alarming rate over the past century. Black carbon (BC) affects the radiative balance of the Arctic directly and indirectly through the absorption of incoming solar radiation and by providing a source of cloud and ice condensation nuclei. Among atmospheric aerosols, BC is the most efficient absorber of light in the visible spectrum. The solar absorbing efficiency of BC is amplified when it is internally mixed with sulfates. Furthermore, BC plumes that are fossil fuel dominated have been shown to be approximately 100% more efficient warming agents than biomass burning dominated plumes. The renewal of offshore oil and gas exploration in the Arctic, specifically in the Chukchi Sea, will introduce new BC sources to the region. This study focuses on the quantification of fossil fuel and biomass combustion sources to atmospheric elemental carbon (EC) during a year-long sampling campaign in the North Slope Alaska. Samples were collected at the Department of Energy Atmospheric Radiation Measurement (ARM) climate research facility in Barrow, AK, USA. Particulate matter (PM10) samples collected from July 2012 to June 2013 were analyzed for EC and sulfate concentrations combined with radiocarbon (14C) analysis of the EC fraction. Radiocarbon analysis distinguishes fossil fuel and biomass burning contributions based on large differences in end members between fossil and contemporary carbon. To perform isotope analysis on EC, it must be separated from the organic carbon fraction of the sample. Separation was achieved by trapping evolved CO2 produced during EC combustion in a cryo-trap utilizing liquid nitrogen. Radiocarbon results show an average fossil contribution of 85% to atmospheric EC, with individual samples ranging from 47% to 95%. Source apportionment results will be combined with back trajectory (BT) analysis to assess geographic source region impacts on the EC burden in the western Arctic.

A microphysical pathway analysis to investigate aerosol effects on convective clouds

NASA Astrophysics Data System (ADS)

Heikenfeld, Max; White, Bethan; Labbouz, Laurent; Stier, Philip

2017-04-01

The impact of aerosols on ice- and mixed-phase processes in convective clouds remains highly uncertain, which has strong implications for estimates of the role of aerosol-cloud interactions in the climate system. The wide range of interacting microphysical processes are still poorly understood and generally not resolved in global climate models. To understand and visualise these processes and to conduct a detailed pathway analysis, we have added diagnostic output of all individual process rates for number and mass mixing ratios to two commonly-used cloud microphysics schemes (Thompson and Morrison) in WRF. This allows us to investigate the response of individual processes to changes in aerosol conditions and the propagation of perturbations throughout the development of convective clouds. Aerosol effects on cloud microphysics could strongly depend on the representation of these interactions in the model. We use different model complexities with regard to aerosol-cloud interactions ranging from simulations with different levels of fixed cloud droplet number concentration (CDNC) as a proxy for aerosol, to prognostic CDNC with fixed modal aerosol distributions. Furthermore, we have implemented the HAM aerosol model in WRF-chem to also perform simulations with a fully interactive aerosol scheme. We employ a hierarchy of simulation types to understand the evolution of cloud microphysical perturbations in atmospheric convection. Idealised supercell simulations are chosen to present and test the analysis methods for a strongly confined and well-studied case. We then extend the analysis to large case study simulations of tropical convection over the Amazon rainforest. For both cases we apply our analyses to individually tracked convective cells. Our results show the impact of model uncertainties on the understanding of aerosol-convection interactions and have implications for improving process representation in models.

CloudNeo: a cloud pipeline for identifying patient-specific tumor neoantigens.

PubMed

Bais, Preeti; Namburi, Sandeep; Gatti, Daniel M; Zhang, Xinyu; Chuang, Jeffrey H

2017-10-01

We present CloudNeo, a cloud-based computational workflow for identifying patient-specific tumor neoantigens from next generation sequencing data. Tumor-specific mutant peptides can be detected by the immune system through their interactions with the human leukocyte antigen complex, and neoantigen presence has recently been shown to correlate with anti T-cell immunity and efficacy of checkpoint inhibitor therapy. However computing capabilities to identify neoantigens from genomic sequencing data are a limiting factor for understanding their role. This challenge has grown as cancer datasets become increasingly abundant, making them cumbersome to store and analyze on local servers. Our cloud-based pipeline provides scalable computation capabilities for neoantigen identification while eliminating the need to invest in local infrastructure for data transfer, storage or compute. The pipeline is a Common Workflow Language (CWL) implementation of human leukocyte antigen (HLA) typing using Polysolver or HLAminer combined with custom scripts for mutant peptide identification and NetMHCpan for neoantigen prediction. We have demonstrated the efficacy of these pipelines on Amazon cloud instances through the Seven Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfaces for running and editing, infrastructure for workflow sharing and version tracking, and access to TCGA data. The CWL implementation is at: https://github.com/TheJacksonLaboratory/CloudNeo. For users who have obtained licenses for all internal software, integrated versions in CWL and on the Seven Bridges Cancer Genomics Cloud platform (https://cgc.sbgenomics.com/, recommended version) can be obtained by contacting the authors. jeff.chuang@jax.org. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Seasonal aerosol characteristics in the Amazon rain forest

NASA Astrophysics Data System (ADS)

Baars, H.; Althausen, D.; Ansmann, A.; Engelmann, R.; Heese, B.; Müller, D.; Pauliquevis, T.; Souza, R.; Artaxo, P.

2012-04-01

For the first time in Amazonia, continuous measurements of the vertical aerosol structure were carried out in the framework of EUCAARI (European Integrated Project on Aerosol, Cloud, Climate, Air Quality Interactions) and AMAZE-08 (Amazonian Aerosol Characterization Experiment). The observations were performed 60 km north of Manaus, Brazil (at 2° 35.5' S and 60° 2.3' W) in the central northern part of the Amazon rain forest from January to November 2008 with the automated multi-wavelength-Raman-polarization-lidar PollyXT. With this instrument, vertical profiles of the particle backscatter coefficient at 355, 532, and 1064 nm, of the particle extinction coefficient at 355 and 532 nm, and of the particle linear depolarization ratio at 355 nm can be determined. During the 10-months observational period, measurements were performed on 211 days resulting in more than 2500 hours of tropospheric aerosol and cloud profile observations. The analysis of the long-term data set revealed strong differences in the aerosol characteristics between the wet and the dry season. In the wet season, very clean atmospheric conditions occurred in ca. 50% of all observation cases. During these clean conditions, the aerosol optical depth (AOD) at 532 nm was less than 0.05 and the aerosol was trapped in the lowermost 2 km of the troposphere. However, also intrusions of Saharan dust and African biomass-burning aerosol (BBA) - characterized by a significantly increased AOD and particle depolarization ratio - were observed in about one third (32%) of all lidar observations. These African aerosol plumes extended usually from the surface up to about 3.5 km agl. During the dry season, BBA from fires on the South American continent was the dominant aerosol species. The mean AOD of the dry season was found to be a factor of 3 higher than the mean AOD of the wet season (0.26 compared to 0.08 at 532 nm). This is due to the high BBA concentration in the atmosphere. Maximum AOD values were less than 0.55 and hence show that the lidar location was not in the direct vicinity of fire events. An AOD below 0.1 was observed in only 7% of all cases in the dry season 2008. Significantly different geometrical, optical, and microphysical properties of BBA (e.g., vertical layering, extinction-to-backscatter ratio, Ångström exponent, effective radius, single-scattering albedo) were observed in dependence of the burning conditions, transport time, etc. The measurements also revealed that BBA can easily mix up to 3-5 km height and thus has the potential to affect cloud microphysics.

Smoke, Clouds, and Radiation-Brazil (SCAR-B) Experiment

NASA Technical Reports Server (NTRS)

Kaufman, Y. J.; Hobbs, P. V.; Kirchoff, V. W. J. H.; Artaxo, P.; Remer, L. A.; Holben, B. N.; King, M. D.; Ward, D. E.; Prins, E. M.; Longo, K. M.;

Use of Cloud Computing to Calibrate a Highly Parameterized Model

NASA Astrophysics Data System (ADS)

Hayley, K. H.; Schumacher, J.; MacMillan, G.; Boutin, L.

2012-12-01

We present a case study using cloud computing to facilitate the calibration of a complex and highly parameterized model of regional groundwater flow. The calibration dataset consisted of many (~1500) measurements or estimates of static hydraulic head, a high resolution time series of groundwater extraction and disposal rates at 42 locations and pressure monitoring at 147 locations with a total of more than one million raw measurements collected over a ten year pumping history, and base flow estimates at 5 surface water monitoring locations. This modeling project was undertaken to assess the sustainability of groundwater withdrawal and disposal plans for insitu heavy oil extraction in Northeast Alberta, Canada. The geological interpretations used for model construction were based on more than 5,000 wireline logs collected throughout the 30,865 km2 regional study area (RSA), and resulted in a model with 28 slices, and 28 hydro stratigraphic units (average model thickness of 700 m, with aquifers ranging from a depth of 50 to 500 m below ground surface). The finite element FEFLOW model constructed on this geological interpretation had 331,408 nodes and required 265 time steps to simulate the ten year transient calibration period. This numerical model of groundwater flow required 3 hours to run on a on a server with two, 2.8 GHz processers and 16 Gb. RAM. Calibration was completed using PEST. Horizontal and vertical hydraulic conductivity as well as specific storage for each unit were independent parameters. For the recharge and the horizontal hydraulic conductivity in the three aquifers with the most transient groundwater use, a pilot point parameterization was adopted. A 7*7 grid of pilot points was defined over the RSA that defined a spatially variable horizontal hydraulic conductivity or recharge field. A 7*7 grid of multiplier pilot points that perturbed the more regional field was then superimposed over the 3,600 km2 local study area (LSA). The pilot point multipliers were implemented so a higher resolution of spatial variability could be obtained where there was a higher density of observation data. Five geologic boundaries were modeled with a specified flux boundary condition and the transfer rate was used as an adjustable parameter for each of these boundaries. This parameterization resulted in 448 parameters for calibration. In the project planning stage it was estimated that the calibration might require as much 15,000 hours (1.7 years) of computing. In an effort to complete the calibration in a timely manner, the inversion was parallelized and implemented on as many as 250 computing nodes located on Amazon's EC2 servers. The results of the calibration provided a better fit to the data than previous efforts with homogenous parameters, and the highly parameterized approach facilitated subspace Monte Carlo analysis for predictive uncertainty. This scale of cloud computing is relatively new for the hydrogeology community and at the time of implementation it was believed to be the first implementation of FEFLOW model at this scale. While the experience provided several challenges, the implementation was successful and provides some valuable learning for future efforts.

Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost?

PubMed Central

Madhyastha, Tara M.; Koh, Natalie; Day, Trevor K. M.; Hernández-Fernández, Moises; Kelley, Austin; Peterson, Daniel J.; Rajan, Sabreena; Woelfer, Karl A.; Wolf, Jonathan; Grabowski, Thomas J.

2017-01-01

The contribution of this paper is to identify and describe current best practices for using Amazon Web Services (AWS) to execute neuroimaging workflows “in the cloud.” Neuroimaging offers a vast set of techniques by which to interrogate the structure and function of the living brain. However, many of the scientists for whom neuroimaging is an extremely important tool have limited training in parallel computation. At the same time, the field is experiencing a surge in computational demands, driven by a combination of data-sharing efforts, improvements in scanner technology that allow acquisition of images with higher image resolution, and by the desire to use statistical techniques that stress processing requirements. Most neuroimaging workflows can be executed as independent parallel jobs and are therefore excellent candidates for running on AWS, but the overhead of learning to do so and determining whether it is worth the cost can be prohibitive. In this paper we describe how to identify neuroimaging workloads that are appropriate for running on AWS, how to benchmark execution time, and how to estimate cost of running on AWS. By benchmarking common neuroimaging applications, we show that cloud computing can be a viable alternative to on-premises hardware. We present guidelines that neuroimaging labs can use to provide a cluster-on-demand type of service that should be familiar to users, and scripts to estimate cost and create such a cluster. PMID:29163119

Regional Application of an Ecosystem Production Model for Studies of Biogeochemistry in the...

NASA Technical Reports Server (NTRS)

Potter, C. S.; Klooster, S.; Brooks, V.; Peterson, David L. (Technical Monitor)

1997-01-01

The degree to which primary production, soil carbon, and trace gas fluxes in tropical forests of the Amazon are limited by moisture availability and other environmental factors was examined using an ecosystem modeling application for the country of Brazil. A regional geographic information system (GIS) serves as the data source of climate drivers, satellite images, land cover, and soil properties for input to the NASA Ames-CASA (Carnegie-Ames-Stanford Approach) model over a 8-km grid resolution. Simulation results supports the hypothesis that net primary production (NPP) is limited by cloud interception of solar radiation over the humid northwestern portion of the region. Peak annual rates for NPP of nearly 1.4 kg C m-2yr -1are localized in the seasonally dry eastern Amazon in areas that we assume are primarily deep-rooted evergreen forest cover. Regional effects of forest conversion on NPP and soil carbon content are indicated in the model results, especially in seasonally dry areas. Comparison of model flux predictions along selected eco-climatic transects reveal moisture, soil, and land use controls on gradients of ecosystem production and soil trace gas emissions (CO2, N2O, and NO). These results are used to formulate a series of research hypotheses for testing in the next phase of regional modeling, which includes recalibration of the light-use efficiency term in CASA using field measurements of NPP, and refinements of vegetation index and soil property (texture and potential rooting depth) maps for the region.

Land-use versus natural controls on soil fertility in the Subandean Amazon, Peru.

PubMed

Lindell, Lina; Aström, Mats; Oberg, Tomas

2010-01-15

Deforestation to amplify the agricultural frontier is a serious threat to the Amazon forest. Strategies to attain and maintain satisfactory soil fertility, which requires knowledge of spatial and temporal changes caused by land-use, are important for reaching sustainable development. This study highlights these issues by evaluating the relative effects of agricultural land-use and natural factors on chemical fertility of Inceptisols on redbed lithologies in the Subandean Amazon. Macro and micronutrients were determined in topsoil and subsoil in the vicinity of two villages at a total of 80 sites including pastures, coffee plantations, swidden fields, secondary forest and, as a reference, adjacent primary forest. Differences in soil fertility between the land cover classes were investigated by principal component analysis (PCA) and partial least squares regression (PLSR). Primary forest soil was found to be chemically similar to that of coffee plantations, pastures and secondary forests. There were no significant differences between soils of these land cover types in terms of plant nutrients (e.g. N, P, K, Ca, Mg, Mo, Mn, Zn, Cu and Co) or other fertility indicators (OM, pH, BS, EC, CECe and exchangeable acidity). The parent material (as indicated by texture and sample geographical origin) and the slope of the sampled sites were stronger controls on soil fertility than land cover type. Elevated concentrations of a few nutrients (NO(3) and K) were, however detected in soils of swidden fields. Despite being fertile (higher CECe, Ca and P) compared to Oxisols and Ultisols in the Amazon lowland, the Subandean soils frequently showed deficiencies in several nutrients (e.g. P, K, NO(3), Cu and Zn), and high levels of free Al at acidic sites. This paper concludes that deforestation and agricultural land-use has not introduced lasting chemical changes in the studied Subandean soils that are significant in comparison to the natural variability. Copyright 2009 Elsevier B.V. All rights reserved.

Biomass burning in the Amazon region: Aerosol source apportionment and associated health risk assessment

NASA Astrophysics Data System (ADS)

de Oliveira Alves, Nilmara; Brito, Joel; Caumo, Sofia; Arana, Andrea; de Souza Hacon, Sandra; Artaxo, Paulo; Hillamo, Risto; Teinilä, Kimmo; Batistuzzo de Medeiros, Silvia Regina; de Castro Vasconcellos, Pérola

2015-11-01

The Brazilian Amazon represents about 40% of the world's remaining tropical rainforest. However, human activities have become important drivers of disturbance in that region. The majority of forest fire hotspots in the Amazon arc due to deforestation are impacting the health of the local population of over 10 million inhabitants. In this study we characterize western Amazonia biomass burning emissions through the quantification of 14 Polycyclic Aromatic Hydrocarbons (PAHs), Organic Carbon, Elemental Carbon and unique tracers of biomass burning such as levoglucosan. From the PAHs dataset a toxic equivalence factor is calculated estimating the carcinogenic and mutagenic potential of biomass burning emissions during the studied period. Peak concentration of PM10 during the dry seasons was observed to reach 60 μg m-3 on the 24 h average. Conversely, PM10 was relatively constant throughout the wet season indicating an overall stable balance between aerosol sources and sinks within the filter sampling resolution. Similar behavior is identified for OC and EC components. Levoglucosan was found in significant concentrations (up to 4 μg m-3) during the dry season. Correspondingly, the estimated lung cancer risk calculated during the dry seasons largely exceeded the WHO health-based guideline. A source apportionment study was carried out through the use of Absolute Principal Factor Analysis (APFA), identifying a three-factor solution. The biomass burning factor is found to be the dominating aerosol source, having 75.4% of PM10 loading. The second factor depicts an important contribution of several PAHs without a single source class and therefore was considered as mixed sources factor, contributing to 6.3% of PM10. The third factor was mainly associated with fossil fuel combustion emissions, contributing to 18.4% of PM10. This work enhances the knowledge of aerosol sources and its impact on climate variability and local population, on a site representative of the deforestation which occupies a significant fraction of the Amazon basin.

Cost Optimal Elastic Auto-Scaling in Cloud Infrastructure

NASA Astrophysics Data System (ADS)

Mukhopadhyay, S.; Sidhanta, S.; Ganguly, S.; Nemani, R. R.

2014-12-01

Today, elastic scaling is critical part of leveraging cloud. Elastic scaling refers to adding resources only when it is needed and deleting resources when not in use. Elastic scaling ensures compute/server resources are not over provisioned. Today, Amazon and Windows Azure are the only two platform provider that allow auto-scaling of cloud resources where servers are automatically added and deleted. However, these solution falls short of following key features: A) Requires explicit policy definition such server load and therefore lacks any predictive intelligence to make optimal decision; B) Does not decide on the right size of resource and thereby does not result in cost optimal resource pool. In a typical cloud deployment model, we consider two types of application scenario: A. Batch processing jobs → Hadoop/Big Data case B. Transactional applications → Any application that process continuous transactions (Requests/response) In reference of classical queuing model, we are trying to model a scenario where servers have a price and capacity (size) and system can add delete servers to maintain a certain queue length. Classical queueing models applies to scenario where number of servers are constant. So we cannot apply stationary system analysis in this case. We investigate the following questions 1. Can we define Job queue and use the metric to define such a queue to predict the resource requirement in a quasi-stationary way? Can we map that into an optimal sizing problem? 2. Do we need to get into a level of load (CPU/Data) on server level to characterize the size requirement? How do we learn that based on Job type?

The 20-22 January 2007 Snow Events over Canada: Microphysical Properties

NASA Technical Reports Server (NTRS)

Tao. W.K.; Shi, J.J.; Matsui, T.; Hao, A.; Lang, S.; Peters-Lidard, C.; Skofronick-Jackson, G.; Petersen, W.; Cifelli, R.; Rutledge, S.

2009-01-01

One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve precipitation measurements in mid- and high-latitudes during cold seasons through the use of high-frequency passive microwave radiometry. Toward this end, the Weather Research and Forecasting (WRF) model with the Goddard microphysics scheme is coupled with a Satellite Data Simulation Unit (WRF-SDSU) that has been developed to facilitate over-land snowfall retrieval algorithms by providing a virtual cloud library and microwave brightness temperature (Tb) measurements consistent with the GPM Microwave Imager (GMI). This study tested the Goddard cloud microphysics scheme in WRF for snowstorm events (January 20-22, 2007) that took place over the Canadian CloudSAT/CALIPSO Validation Project (C3VP) ground site (Centre for Atmospheric Research Experiments - CARE) in Ontario, Canada. In this paper, the performance of the Goddard cloud microphysics scheme both with 2ice (ice and snow) and 3ice (ice, snow and graupel) as well as other WRF microphysics schemes will be presented. The results are compared with data from the Environment Canada (EC) King Radar, an operational C-band radar located near the CARE site. In addition, the WRF model output is used to drive the Goddard SDSU to calculate radiances and backscattering signals consistent with direct satellite observations for evaluating the model results.

Physiological Responses of Two Epiphytic Bryophytes to Nitrogen, Phosphorus and Sulfur Addition in a Subtropical Montane Cloud Forest

PubMed Central

Chen, Xi; Liu, Wen-yao; Song, Liang; Li, Su; Wu, Yi; Shi, Xian-meng; Huang, Jun-biao; Wu, Chuan-sheng

2016-01-01

Atmospheric depositions pose significant threats to biodiversity and ecosystem function. However, the underlying physiological mechanisms are not well understood, and few studies have considered the combined effects and interactions of multiple pollutants. This in situ study explored the physiological responses of two epiphytic bryophytes to combined addition of nitrogen, phosphorus and sulfur. We investigated the electrical conductivity (EC), total chlorophyll concentration (Chl), nutrient stoichiometry and chlorophyll fluorescence signals in a subtropical montane cloud forest in south-west China. The results showed that enhanced fertilizer additions imposed detrimental effects on bryophytes, and the combined enrichment of simulated fertilization exerted limited synergistic effects in their natural environments. On the whole, EC, Chl, the effective quantum yield of photosystem II (ΦPSII) and photochemical quenching (qP) were the more reliable indicators of increased artificial fertilization. However, conclusions on nutrient stoichiometry should be drawn cautiously concerning the saturation uptake and nutrient interactions in bryophytes. Finally, we discuss the limitations of prevailing fertilization experiments and emphasize the importance of long-term data available for future investigations. PMID:27560190

Single-particle Analyses of Compositions, Morphology, and Viscosity of Aerosol Particles Collected During GoAmazon2014

NASA Astrophysics Data System (ADS)

Adachi, K.; Gong, Z.; Bateman, A. P.; Martin, S. T.; Cirino, G. G.; Artaxo, P.; Sedlacek, A. J., III; Buseck, P. R.

2014-12-01

Single-particle analysis using transmission electron microscopy (TEM) shows composition and morphology of individual aerosol particles collected during the GoAmazon2014 campaign. These TEM results indicate aerosol types and mixing states, both of which are important for evaluating particle optical properties and cloud condensation nuclei activity. The samples were collected at the T3 site, which is located in the Amazon forest with influences from the urban pollution plume from Manaus. Samples were also collected from the T0 site, which is in the middle of the jungle with minimal to no influences of anthropogenic sources. The aerosol particles mainly originated from 1) anthropogenic pollution (e.g., nanosphere soot, sulfate), 2) biogenic emissions (e.g., primary biogenic particles, organic aerosols), and 3) long-range transport (e.g., sea salts). We found that the biogenic organic aerosol particles contain homogeneously distributed potassium. Particle viscosity is important for evaluating gas-particle interactions and atmospheric chemistry for the particles. Viscosity can be estimated from the rebounding behavior at controlled relative humidities, i.e., highly viscous particles display less rebound on a plate than low-viscosity particles. We collected 1) aerosol particles from a plate (non-rebounded), 2) those that had rebounded from the plate and were then captured onto an adjacent sampling plate, and 3) particles from ambient air using a separate impactor sampler. Preliminary results show that more than 90% of non-rebounded particles consisted of nanosphere soot with or without coatings. The coatings mostly consisted of organic matter. Although rebounded particles also contain nanosphere soot (number fraction 64-69%), they were mostly internally mixed with sulfate, organic matter, or their mixtures. TEM tilted images suggested that the rebounded particles were less deformed on the substrate, whereas the non-rebounded particles were more deformed, which could reflect differences in their viscosity.

Consistency of vegetation index seasonality across the Amazon rainforest

NASA Astrophysics Data System (ADS)

Maeda, Eduardo Eiji; Moura, Yhasmin Mendes; Wagner, Fabien; Hilker, Thomas; Lyapustin, Alexei I.; Wang, Yujie; Chave, Jérôme; Mõttus, Matti; Aragão, Luiz E. O. C.; Shimabukuro, Yosio

2016-10-01

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.

Consistency of Vegetation Index Seasonality Across the Amazon Rainforest

NASA Technical Reports Server (NTRS)

Maeda, Eduardo Eiji; Moura, Yhasmin Mendes; Wagner, Fabien; Hilker, Thomas; Lyapustin, Alexei I.; Wang, Yujie; Chave, Jerome; Mottus, Matti; Aragao, Luiz E.O.C.; Shimabukuro, Yosio

2016-01-01

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.

Simulation of Water Sources and Precipitation Recycling for the MacKenzie, Mississippi and Amazon River Basins

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Chern, Jiun-Dar

2005-01-01

An atmospheric general circulation model simulation for 1948-1997 of the water budgets for the MacKenzie, Mississippi and Amazon River basins is presented. In addition to the water budget, we include passive tracers to identify the geographic sources of water for the basins, and the analysis focuses on the mechanisms contributing to precipitation recycling in each basin. While each basin s precipitation recycling has a strong dependency on evaporation during the mean annual cycle, the interannual variability of the recycling shows important relationships with the atmospheric circulation. The MacKenzie River basin has only a weak interannual dependency on evaporation, where the variations in zonal moisture transport from the Pacific Ocean can affect the basin water cycle. On the other hand, the Mississippi River basin has strong interannual dependencies on evaporation. While the precipitation recycling weakens with increased low level jet intensity, the evaporation variations exert stronger influence in providing water vapor for convective precipitation at the convective cloud base. High precipitation recycling is also found to be partly connected to warm SSTs in the tropical Pacific Ocean. The Amazon River basin evaporation exhibits small interannual variations, so that the interannual variations of precipitation recycling are related to atmospheric moisture transport from the tropical south Atlantic Ocean. Increasing SSTs over the 50-year period are causing increased easterly transport across the basin. As moisture transport increases, the Amazon precipitation recycling decreases (without real time varying vegetation changes). In addition, precipitation recycling from a bulk diagnostic method is compared to the passive tracer method used in the analysis. While the mean values are different, the interannual variations are comparable between each method. The methods also exhibit similar relationships to the terms of the basin scale water budgets.

Cloud Condensation Nuclei and Chemical Composition of size-resolved particles in a Brazilian megacity: Effect of NPF event, biomass burning and sea salt from remote regions on the CCN properties

NASA Astrophysics Data System (ADS)

Souto-Oliveira, Carlos; de Fátima Andrade, Maria; Kumar, Prashant; Lopes, Fabio; Babinski, Marly; Landulfo, Eduado; Vara-Vela, Angel

2016-04-01

Atmospheric aerosol particles are an important source of cloud condensation nuclei (CCN). Their microphysics and chemical composition can directly affect development of clouds and precipitation process1,2. Only a few studies in Latin American have reported the impact of urban aerosol on the formation of CCN and their contribution to global climate change3. In this study, we simultaneously measured size distributed particle number concentration (PNC), CCN, black carbon (BC) and elemental concentrations (EC) in aerosol samples from São Paulo city. The PNC was measured by DMPS (model 3936) operated with a DMA (model 3080) and CPC (TSI, model 3010). The CCN was measuredby a single-column continuous-flow stream-wise thermal gradient CCN chamber (DMT CCNC-100). The BC and EC were determined in polycarbonate filter collected by Cascade Impactor (MOUDI-MSP), using a smoke stain reflectometer and an ED-XRF (EDX 700; Shimadzu), respectively. During the study period, which was August to September 2014, four events of new particle formation (NPF), characterizing secondary process of aerosol formation were noted. The total PNC varied between 1106 and 29168 cm-3, while CCN presented concentrations of 206 to 12761 cm-3for SS=1.0%. The PNC showed different concentrations during diurnal and nocturnal periods with average of 16392±7811 cm-3 and 6874±3444cm-3, respectively. The activated ratio (CCN/CN) presented diurnal and nocturnal values of 0.19±0.10 and 0.41±0.18, while apparent activation diameter (Dact,a) was estimated to be 110±29 and 71±28 nm (SS=0.6%), respectively. Combining EC and BC results with air mass trajectory analysis (Lidar aerosol profiles and Hysplit air trajectories), apportionment events were identified for sea salt and biomass burning from coastal and continental regions, respectively. The nocturnal AR and Dact,apresented values of 0.46±0.11 and 49±15 nm (SS=0.6%) for sea salt events as opposed to 0.33±0.14 and 64±30 nm (SS=0.6%) during biomass burning events. Although statistically not robust, it was observed diurnal and nocturnal tendencies for CCN properties (AR, Dact,a), which were accompanied by small variability for sea salt and biomass burning events. References [1] Andreae et al. (2004). Science, 303, 1337-1342. [2] Andreae, M. O., and Rosenfeld, D. (2008). Earth-Science Reviews, 89, 13-41. [3] Almeida et al. Atmospheric Chemistry and Physics, 14, 7559-7572.

Black carbon over the Amazon during SAMBBA: it gets everywhere

NASA Astrophysics Data System (ADS)

Morgan, W.; Allan, J. D.; Flynn, M.; Darbyshire, E.; Liu, D.; Szpek, K.; Langridge, J.; Johnson, B. T.; Haywood, J.; Longo, K.; Artaxo, P.; Coe, H.

2014-12-01

Biomass burning represents a major source of Black Carbon (BC) aerosol to the atmosphere, which can result in major perturbations to weather, climate and ecosystem development. Large uncertainties in these impacts prevail, particularly on regional scales. One such region is the Amazon Basin, where large, intense and frequent burning occurs on an annual basis during the dry season. Absorption by atmospheric aerosols is underestimated by models over South America, which points to significant uncertainties relating to BC aerosol properties. Results from the South American Biomass Burning Analysis (SAMBBA) field experiment, which took place during September and October 2012 over Brazil on-board the UK Facility for Airborne Atmospheric Measurement (FAAM) BAe-146 research aircraft, are presented here. Aerosol chemical composition was measured by a DMT Single Particle Soot Photometer (SP2) and an Aerodyne Aerosol Mass Spectrometer (AMS). The physical, chemical and optical properties of BC-containing particles across the region will be characterised, with particular emphasis on the vertical distribution. BC was ubiquitous across the region, with measurements extending from heavily deforested regions in the Western Amazon Basin, through to agricultural fires in the Cerrado (Savannah-like) region and more pristine areas over the Amazon Rainforest. Measurements in the vicinity of Manaus (a city located deep into the jungle) were also conducted. BC concentrations peaked within the boundary layer at a height of around 1.5km. BC-containing particles were found to be rapidly coated in the near-field, with little evidence for additional coating upon advection and dilution. Biomass burning layers within the free troposphere were routinely observed. BC-containing particles within such layers were typically associated with less coating than those within the boundary layer, suggestive of wet removal of more coated BC particles. The importance of such properties in relation to the optical properties of BC and its resultant impact will be investigated. The prevalence of elevated biomass burning layers above the frequent build-up of shallow cumulus clouds during the afternoon will also be characterised. This will provide improved constraint upon the highly uncertain impact of biomass burning aerosol over the region.

Brazil-USA Collaborative Research: Modifications by Anthropogenic Pollution of the Natural Atmospheric Chemistry and Particle Microphysics of the Tropical Rain Forest During the GoAmazon Intensive Operating Periods (IOPs)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jimenez, Jose-Luis; Day, Douglas A.; Martin, Scot T.

Manaus, a city of nearly two million people, represents an isolated urban area having a distinct urban pollution plume within the otherwise pristine Amazon Basin. The plume has high concentrations of oxides of nitrogen and sulfur, carbon monoxide, particle concentrations, and soot, among other pollutants. Critically, the distinct plume in the setting of the surrounding tropical rain forest serves as a natural laboratory to allow direct comparisons between periods of pollution influence to those of pristine conditions. The funded activity of this report is related to the Brazil-USA collaborative project during the two Intensive Operating Periods (wet season, 1 Febmore » - 31 Mar 2014; dry season, 15 Aug - 15 Oct 2014) of GoAmazon2014/5. The project addresses key science questions regarding the modification of the natural atmospheric chemistry and particle microphysics of the forest by present and future anthropogenic pollution. The first objective of the project was to understand and quantify the interactions of biogenic and anthropogenic emissions with respect to the production of secondary organic material. In clean conditions in the Amazon basin, secondary organic material dominates the diameter distribution of the submicron particles. How and why is the diameter distribution shifted by pollution? The second objective followed from the first in that, although the diameter distribution is dominated by secondary organic material, the actual source of new particle production remains uncertain (i.e., the number concentration). The second objective was to test the hypothesis that new particles under natural conditions are produced as a result of evaporation of primary particles emitted by fungal spores as well as to investigate any shifts in this mechanism under pollution conditions, e.g., in consequence to the high concentrations of SO 2 in the pollution plume. Combined, the number-diameter distribution is the key connection to upscaling to the effects of aerosol particles on clouds and climate. Understanding this upscaling under clean and pollution conditions, including differences, was the third objective.« less

Public-Private Partnership: Joint recommendations to improve downloads of large Earth observation data

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Murphy, K. J.; Baynes, K.; Lynnes, C.

2016-12-01

With the volume of Earth observation data expanding rapidly, cloud computing is quickly changing the way Earth observation data is processed, analyzed, and visualized. The cloud infrastructure provides the flexibility to scale up to large volumes of data and handle high velocity data streams efficiently. Having freely available Earth observation data collocated on a cloud infrastructure creates opportunities for innovation and value-added data re-use in ways unforeseen by the original data provider. These innovations spur new industries and applications and spawn new scientific pathways that were previously limited due to data volume and computational infrastructure issues. NASA, in collaboration with Amazon, Google, and Microsoft, have jointly developed a set of recommendations to enable efficient transfer of Earth observation data from existing data systems to a cloud computing infrastructure. The purpose of these recommendations is to provide guidelines against which all data providers can evaluate existing data systems and be used to improve any issues uncovered to enable efficient search, access, and use of large volumes of data. Additionally, these guidelines ensure that all cloud providers utilize a common methodology for bulk-downloading data from data providers thus preventing the data providers from building custom capabilities to meet the needs of individual cloud providers. The intent is to share these recommendations with other Federal agencies and organizations that serve Earth observation to enable efficient search, access, and use of large volumes of data. Additionally, the adoption of these recommendations will benefit data users interested in moving large volumes of data from data systems to any other location. These data users include the cloud providers, cloud users such as scientists, and other users working in a high performance computing environment who need to move large volumes of data.

Environmental risk perception from visual cues: the psychophysics of tornado risk perception

NASA Astrophysics Data System (ADS)

Dewitt, Barry; Fischhoff, Baruch; Davis, Alexander; Broomell, Stephen B.

2015-12-01

Lay judgments of environmental risks are central to both immediate decisions (e.g., taking shelter from a storm) and long-term ones (e.g., building in locations subject to storm surges). Using methods from quantitative psychology, we provide a general approach to studying lay perceptions of environmental risks. As a first application of these methods, we investigate a setting where lay decisions have not taken full advantage of advances in natural science understanding: tornado forecasts in the US and Canada. Because official forecasts are imperfect, members of the public must often evaluate the risks on their own, by checking environmental cues (such as cloud formations) before deciding whether to take protective action. We study lay perceptions of cloud formations, demonstrating an approach that could be applied to other environmental judgments. We use signal detection theory to analyse how well people can distinguish tornadic from non-tornadic clouds, and multidimensional scaling to determine how people make these judgments. We find that participants (N = 400 recruited from Amazon Mechanical Turk) have heuristics that generally serve them well, helping participants to separate tornadic from non-tornadic clouds, but which also lead them to misjudge the tornado risk of certain cloud types. The signal detection task revealed confusion regarding shelf clouds, mammatus clouds, and clouds with upper- and mid-level tornadic features, which the multidimensional scaling task suggested was the result of participants focusing on the darkness of the weather scene and the ease of discerning its features. We recommend procedures for training (e.g., for storm spotters) and communications (e.g., tornado warnings) that will reduce systematic misclassifications of tornadicity arising from observers’ reliance on otherwise useful heuristics.

Fast Deployment on the Cloud of Integrated Postgres, API and a Jupyter Notebook for Geospatial Collaboration

NASA Astrophysics Data System (ADS)

Fatland, R.; Tan, A.; Arendt, A. A.

2016-12-01

We describe a Python-based implementation of a PostgreSQL database accessed through an Application Programming Interface (API) hosted on the Amazon Web Services public cloud. The data is geospatial and concerns hydrological model results in the glaciated catchment basins of southcentral and southeast Alaska. This implementation, however, is intended to be generalized to other forms of geophysical data, particularly data that is intended to be shared across a collaborative team or publicly. An example (moderate-size) dataset is provided together with the code base and a complete installation tutorial on GitHub. An enthusiastic scientist with some familiarity with software installation can replicate the example system in two hours. This installation includes database, API, a test Client and a supporting Jupyter Notebook, specifically oriented towards Python 3 and markup text to comprise an executable paper. The installation 'on the cloud' often engenders discussion and consideration of cloud cost and safety. By treating the process as somewhat "cookbook" we hope to first demonstrate the feasibility of the proposition. A discussion of cost and data security is provided in this presentation and in the accompanying tutorial/documentation. This geospatial data system case study is part of a larger effort at the University of Washington to enable research teams to take advantage of the public cloud to meet challenges in data management and analysis.

MR-Tandem: parallel X!Tandem using Hadoop MapReduce on Amazon Web Services.

PubMed

Pratt, Brian; Howbert, J Jeffry; Tasman, Natalie I; Nilsson, Erik J

2012-01-01

MR-Tandem adapts the popular X!Tandem peptide search engine to work with Hadoop MapReduce for reliable parallel execution of large searches. MR-Tandem runs on any Hadoop cluster but offers special support for Amazon Web Services for creating inexpensive on-demand Hadoop clusters, enabling search volumes that might not otherwise be feasible with the compute resources a researcher has at hand. MR-Tandem is designed to drop in wherever X!Tandem is already in use and requires no modification to existing X!Tandem parameter files, and only minimal modification to X!Tandem-based workflows. MR-Tandem is implemented as a lightly modified X!Tandem C++ executable and a Python script that drives Hadoop clusters including Amazon Web Services (AWS) Elastic Map Reduce (EMR), using the modified X!Tandem program as a Hadoop Streaming mapper and reducer. The modified X!Tandem C++ source code is Artistic licensed, supports pluggable scoring, and is available as part of the Sashimi project at http://sashimi.svn.sourceforge.net/viewvc/sashimi/trunk/trans_proteomic_pipeline/extern/xtandem/. The MR-Tandem Python script is Apache licensed and available as part of the Insilicos Cloud Army project at http://ica.svn.sourceforge.net/viewvc/ica/trunk/mr-tandem/. Full documentation and a windows installer that configures MR-Tandem, Python and all necessary packages are available at this same URL. brian.pratt@insilicos.com

Earth observations during Space Shuttle flight STS-29 - Discovery's voyage to the earth

NASA Technical Reports Server (NTRS)

Lulla, Kamlesh; Helfert, Michael; Whitehead, Victor; Amsbury, David; Coats, Michael; Blaha, John; Buchli, James; Springer, Robert; Bagian, James

1989-01-01

The environmental, geologic, meteorologic, and oceanographic phenomena documented by earth photography during the Space Shuttle STS-29 mission are reviewed. A map of the nadir point positions of earth-viewing photographs from the mission is given and color photographs of various regions are presented. The mission photographs include atmospheric dust and smoke over parts of Africa and Asia, Sahelian water sites, center pivot irrigation fields in the Middle East, urban smog over Mexico City, isolated burning in the Bolivian Amazon, and various ocean features and cloud formations.

OpenNEX, a private-public partnership in support of the national climate assessment

NASA Astrophysics Data System (ADS)

Nemani, R. R.; Wang, W.; Michaelis, A.; Votava, P.; Ganguly, S.

2016-12-01

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX is funded as an enabling tool for sustaining the national climate assessment. Over the past five years, researchers have used the NEX platform and produced a number of data sets highly relevant to the National Climate Assessment. These include high-resolution climate projections using different downscaling techniques and trends in historical climate from satellite data. To enable a broader community in exploiting the above datasets, the NEX team partnered with public cloud providers to create the OpenNEX platform. OpenNEX provides ready access to NEX data holdings on a number of public cloud platforms along with pertinent analysis tools and workflows in the form of Machine Images and Docker Containers, lectures and tutorials by experts. We will showcase some of the applications of OpenNEX data and tools by the community on Amazon Web Services, Google Cloud and the NEX Sandbox.

One-Click Data Analysis Software for Science Operations

NASA Astrophysics Data System (ADS)

Navarro, Vicente

2015-12-01

One of the important activities of ESA Science Operations Centre is to provide Data Analysis Software (DAS) to enable users and scientists to process data further to higher levels. During operations and post-operations, Data Analysis Software (DAS) is fully maintained and updated for new OS and library releases. Nonetheless, once a Mission goes into the "legacy" phase, there are very limited funds and long-term preservation becomes more and more difficult. Building on Virtual Machine (VM), Cloud computing and Software as a Service (SaaS) technologies, this project has aimed at providing long-term preservation of Data Analysis Software for the following missions: - PIA for ISO (1995) - SAS for XMM-Newton (1999) - Hipe for Herschel (2009) - EXIA for EXOSAT (1983) Following goals have guided the architecture: - Support for all operations, post-operations and archive/legacy phases. - Support for local (user's computer) and cloud environments (ESAC-Cloud, Amazon - AWS). - Support for expert users, requiring full capabilities. - Provision of a simple web-based interface. This talk describes the architecture, challenges, results and lessons learnt gathered in this project.

Statistical Characteristics of Mesoscale Convective Systems over the Middle Reaches area of the Yellow River During 2005-2014

NASA Astrophysics Data System (ADS)

Zhao, Guixiang

2017-04-01

Based on the hourly TBB and cloud images of FY-2E, meteorological observation data, and NCEP reanalysis data with 1°×1° spatial resolution from May to October during 2005-2014, the climatic characteristics of mesoscale convective systems (MCS) over the middle reaches area of the Yellow River were analyzed, including mesoscale convective complex (MCC), persistent elongated convective systems (PECS), meso-βscale MCC (MβCCS) and Meso-βscale PECS (MβECS). The results are as follows: (1) MCS tended to occur over the middle and south of Gansu, the middle and south of Shanxi, the middle and north of Shaanxi, and the border of Shanxi, Shaanxi and Inner Mongolia. MCS over the middle reaches area of the Yellow River formed in May to October, and was easy to develop the mature in summer. MCC and MβECS were main MCS causing precipitation in summer. (2) The daily variation of MCS was obvious, and usually formed and matured in the afternoon and the evening to early morning of the next day. Most MCS generated fast and dissipated slowly, and were mainly move to the easterly and southeasterly, but the moving of round shape MCS was less than the elongated shape's. (3) The average TBB for the round shape MCS was lower than the elongated shape MCS. The development of MCC was most vigorous and strong, and it was the strongest in August, while that of MβECS wasn't obviously influenced by the seasonal change. The average eccentricity of the mature MCC and PECS over the middle reaches area of the Yellow River was greater than that in USA, and the former was greater than in the lower reaches area of the Yellow River, while the latter was smaller. (4) The characteristics of rainfall caused by MCS were complex over the middle reaches area of the Yellow River, and there were obvious regional difference. There was wider, stronger and longer precipitation when the multiple MCS merged. The rainfall in the center of cloud area was obviously greater than in other region of cloud area. The heavy rain mainly occurred in the left and backward quadrant of MCS. The most precipitation intensity of MCS was generally greater than 50 mm•h-1. The ratios of rain areas and cloud areas for the different types and regions MCS were significantly different. (5) There were obvious inter-annual variation characteristics of MCS. The number of MCS was more in 2011 and less in 2009 than the normal year, and the circulation situation in 2011 was nearly opposite to 2009, which were related not only to the subtropical high, geopotential height anomaly on 500 hPa in the middle latitude and transportation and gather of warm and moisture airflow in lower layer but also to the cold vortex systems on 500 hPa.

DOE Office of Scientific and Technical Information (OSTI.GOV)

M. P. Jensen; Giangrande, S. E.; Bartholomew, M. J.

The Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) [http://www.arm.gov/campaigns/osc2013rwpcf] campaign was scheduled to take place from 15 July 2013 through 15 July 2015 (or until shipped for the next U.S. Department of Energy Atmospheric Radiation Measurement [ARM] Climate Research Facility first Mobile Facility [AMF1] deployment). The campaign involved the deployment of the AMF1 Scintec 915 MHz Radar Wind Profiler (RWP) at BNL, in conjunction with several other ARM, BNL and National Weather Service (NWS) instruments. The two main scientific foci of the campaign were: 1) To provide profiles of the horizontal wind to be used tomore » test and validate short-term cloud advection forecasts for solar-energy applications and 2) to provide vertical profiling capabilities for the study of dynamics (i.e., vertical velocity) and hydrometeors in winter storms. This campaign was a serendipitous opportunity that arose following the deployment of the RWP at the Two-Column Aerosol Project (TCAP) campaign in Cape Cod, Massachusetts and restriction from participation in the Green Ocean Amazon 2014/15 (GoAmazon 2014/15) campaign due to radio-frequency allocation restriction for international deployments. The RWP arrived at BNL in the fall of 2013, but deployment was delayed until fall of 2014 as work/safety planning and site preparation were completed. The RWP further encountered multiple electrical failures, which eventually required several shipments of instrument power supplies and the final amplifier to the vendor to complete repairs. Data collection began in late January 2015. The operational modes of the RWP were changed such that in addition to collecting traditional profiles of the horizontal wind, a vertically pointing mode was also included for the purpose of precipitation sensing and estimation of vertical velocities. The RWP operated well until the end of the campaign in July 2015 and collected observations for more than 20 precipitation events.« less

Individual tree detection in intact forest and degraded forest areas in the north region of Mato Grosso State, Brazilian Amazon

NASA Astrophysics Data System (ADS)

Santos, E. G.; Jorge, A.; Shimabukuro, Y. E.; Gasparini, K.

2017-12-01

The State of Mato Grosso - MT has the second largest area with degraded forest among the states of the Brazilian Legal Amazon. Land use and land cover change processes that occur in this region cause the loss of forest biomass, releasing greenhouse gases that contribute to the increase of temperature on earth. These degraded forest areas lose biomass according to the intensity and magnitude of the degradation type. The estimate of forest biomass, commonly performed by forest inventory through sample plots, shows high variance in degraded forest areas. Due to this variance and complexity of tropical forests, the aim of this work was to estimate forest biomass using LiDAR point clouds in three distinct forest areas: one degraded by fire, another by selective logging and one area of intact forest. The approach applied in these areas was the Individual Tree Detection (ITD). To isolate the trees, we generated Canopy Height Models (CHM) images, which are obtained by subtracting the Digital Elevation Model (MDE) and the Digital Terrain Model (MDT), created by the cloud of LiDAR points. The trees in the CHM images are isolated by an algorithm provided by the Quantitative Ecology research group at the School of Forestry at Northern Arizona University (SILVA, 2015). With these points, metrics were calculated for some areas, which were used in the model of biomass estimation. The methodology used in this work was expected to reduce the error in biomass estimate in the study area. The cloud points of the most representative trees were analyzed, and thus field data was correlated with the individual trees found by the proposed algorithm. In a pilot study, the proposed methodology was applied generating the individual tree metrics: total height and area of the crown. When correlating 339 isolated trees, an unsatisfactory R² was obtained, as heights found by the algorithm were lower than those obtained in the field, with an average difference of 2.43 m. This shows that the algorithm used to isolate trees in temperate areas did not obtained satisfactory results in the tropical forest of Mato Grosso State. Due to this, in future works two algorithms, one developed by Dalponte et al. (2015) and another by Li et al. (2012) will be used.

Adventures in Private Cloud: Balancing Cost and Capability at the CloudSat Data Processing Center

NASA Astrophysics Data System (ADS)

Partain, P.; Finley, S.; Fluke, J.; Haynes, J. M.; Cronk, H. Q.; Miller, S. D.

2016-12-01

Since the beginning of the CloudSat Mission in 2006, The CloudSat Data Processing Center (DPC) at the Cooperative Institute for Research in the Atmosphere (CIRA) has been ingesting data from the satellite and other A-Train sensors, producing data products, and distributing them to researchers around the world. The computing infrastructure was specifically designed to fulfill the requirements as specified at the beginning of what nominally was a two-year mission. The environment consisted of servers dedicated to specific processing tasks in a rigid workflow to generate the required products. To the benefit of science and with credit to the mission engineers, CloudSat has lasted well beyond its planned lifetime and is still collecting data ten years later. Over that period requirements of the data processing system have greatly expanded and opportunities for providing value-added services have presented themselves. But while demands on the system have increased, the initial design allowed for very little expansion in terms of scalability and flexibility. The design did change to include virtual machine processing nodes and distributed workflows but infrastructure management was still a time consuming task when system modification was required to run new tests or implement new processes. To address the scalability, flexibility, and manageability of the system Cloud computing methods and technologies are now being employed. The use of a public cloud like Amazon Elastic Compute Cloud or Google Compute Engine was considered but, among other issues, data transfer and storage cost becomes a problem especially when demand fluctuates as a result of reprocessing and the introduction of new products and services. Instead, the existing system was converted to an on premises private Cloud using the OpenStack computing platform and Ceph software defined storage to reap the benefits of the Cloud computing paradigm. This work details the decisions that were made, the benefits that have been realized, the difficulties that were encountered and issues that still exist.

Evaluation of the sensitivity of the Amazonian diurnal cycle to convective intensity in reanalyses

NASA Astrophysics Data System (ADS)

Itterly, Kyle F.; Taylor, Patrick C.

2017-02-01

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics—specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Evaluation of the Sensitivity of the Amazonian Diurnal Cycle to Convective Intensity in Reanalyses

NASA Technical Reports Server (NTRS)

Itterly, Kyle F.; Taylor, Patrick C.

2016-01-01

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics-specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Microphysical Characteristics of Tropical Updrafts in Clean Conditions.

NASA Astrophysics Data System (ADS)

Stith, Jeffrey L.; Haggerty, Julie A.; Heymsfield, Andrew; Grainger, Cedric A.

2004-05-01

The distributions of ice particles, precipitation embryos, and supercooled water are examined within updrafts in convective clouds in the Amazon and at Kwajalein, Marshall Islands, based on in situ measurements during two Tropical Rainfall Measuring Mission field campaigns. Composite vertical profiles of liquid water, small particle concentration, and updraft/downdraft magnitudes exhibit similar peak values for the two tropical regions. Updrafts were found to be favored locations for precipitation embryos in the form of liquid or frozen drizzle-sized droplets. Most updrafts glaciated rapidly, removing most of the liquid water between -5° and -17°C. However, occasional encounters with liquid water occurred in much colder updraft regions. The updraft magnitudes where liquid water was observed at cold (e.g., -16° to -19°C) temperatures do not appear to be stronger than updrafts without liquid water at similar temperatures, however. The concentrations of small spherical frozen particles in glaciated regions without liquid water are approximately one-half of the concentrations in regions containing liquid cloud droplets, suggesting that a substantial portion of the cloud droplets may be freezing at relatively warm temperatures. Further evidence for a possible new type of aggregate ice particle, a chain aggregate found at cloud midlevels, is given.

Stewardship and management challenges within a cloud-based open data ecosystem (Invited Paper 211863)

NASA Astrophysics Data System (ADS)

Kearns, E. J.

2017-12-01

NOAA's Big Data Project is conducting an experiment in the collaborative distribution of open government data to non-governmental cloud-based systems. Through Cooperative Research and Development Agreements signed in 2015 between NOAA and Amazon Web Services, Google Cloud Platform, IBM, Microsoft Azure, and the Open Commons Consortium, NOAA is distributing open government data to a wide community of potential users. There are a number of significant advantages related to the use of open data on commercial cloud platforms, but through this experiment NOAA is also discovering significant challenges for those stewarding and maintaining NOAA's data resources in support of users in the wider open data ecosystem. Among the challenges that will be discussed are: the need to provide effective interpretation of the data content to enable their use by data scientists from other expert communities; effective maintenance of Collaborators' open data stores through coordinated publication of new data and new versions of older data; the provenance and verification of open data as authentic NOAA-sourced data across multiple management boundaries and analytical tools; and keeping pace with the accelerating expectations of users with regard to improved quality control, data latency, availability, and discoverability. Suggested strategies to address these challenges will also be described.

A case study for cloud based high throughput analysis of NGS data using the globus genomics system

DOE PAGES

Bhuvaneshwar, Krithika; Sulakhe, Dinanath; Gauba, Robinder; ...

2015-01-01

Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the “Globus Genomics” system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-end NGS analysis requirements. The Globus Genomicsmore » system is built on Amazon's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research.« less

Rapid protein alignment in the cloud: HAMOND combines fast DIAMOND alignments with Hadoop parallelism.

PubMed

Yu, Jia; Blom, Jochen; Sczyrba, Alexander; Goesmann, Alexander

2017-09-10

The introduction of next generation sequencing has caused a steady increase in the amounts of data that have to be processed in modern life science. Sequence alignment plays a key role in the analysis of sequencing data e.g. within whole genome sequencing or metagenome projects. BLAST is a commonly used alignment tool that was the standard approach for more than two decades, but in the last years faster alternatives have been proposed including RapSearch, GHOSTX, and DIAMOND. Here we introduce HAMOND, an application that uses Apache Hadoop to parallelize DIAMOND computation in order to scale-out the calculation of alignments. HAMOND is fault tolerant and scalable by utilizing large cloud computing infrastructures like Amazon Web Services. HAMOND has been tested in comparative genomics analyses and showed promising results both in efficiency and accuracy. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

A case study for cloud based high throughput analysis of NGS data using the globus genomics system

PubMed Central

Bhuvaneshwar, Krithika; Sulakhe, Dinanath; Gauba, Robinder; Rodriguez, Alex; Madduri, Ravi; Dave, Utpal; Lacinski, Lukasz; Foster, Ian; Gusev, Yuriy; Madhavan, Subha

2014-01-01

Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the “Globus Genomics” system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research. PMID:26925205

Falco: a quick and flexible single-cell RNA-seq processing framework on the cloud.

PubMed

Yang, Andrian; Troup, Michael; Lin, Peijie; Ho, Joshua W K

2017-03-01

Single-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellization of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq datasets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6-145.4 times faster using Falco than running on a highly optimized standalone computer. Falco also allows users to utilize low-cost spot instances of Amazon Web Services, providing a ∼65% reduction in cost of analysis. Falco is available via a GNU General Public License at https://github.com/VCCRI/Falco/. j.ho@victorchang.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

An explicit GIS-based river basin framework for aquatic ecosystem conservation in the Amazon

NASA Astrophysics Data System (ADS)

Venticinque, Eduardo; Forsberg, Bruce; Barthem, Ronaldo; Petry, Paulo; Hess, Laura; Mercado, Armando; Cañas, Carlos; Montoya, Mariana; Durigan, Carlos; Goulding, Michael

2016-11-01

Despite large-scale infrastructure development, deforestation, mining and petroleum exploration in the Amazon Basin, relatively little attention has been paid to the management scale required for the protection of wetlands, fisheries and other aspects of aquatic ecosystems. This is due, in part, to the enormous size, multinational composition and interconnected nature of the Amazon River system, as well as to the absence of an adequate spatial model for integrating data across the entire Amazon Basin. In this data article we present a spatially uniform multi-scale GIS framework that was developed especially for the analysis, management and monitoring of various aspects of aquatic systems in the Amazon Basin. The Amazon GIS-Based River Basin Framework is accessible as an ESRI geodatabase at doi:10.5063/F1BG2KX8.

Seasonal variations in the physico-chemical characteristics of aerosols in North Taiwan

NASA Astrophysics Data System (ADS)

Chou, Charles

2014-05-01

From 2007 to 2012, this study investigated the mass concentration and chemical composition of ambient aerosols (i.e. PM10, PM2.5, and PMc = PM10-PM2.5) at Cape Fuguei, Yangminshan, and NTU (National Taiwan University) stations in northern Taiwan. It was found that the concentration and composition of aerosols exhibited significant seasonal variations but without an inter-annual trend during the study period. Moderate correlations (R2 = 0.4-0.6) were observed among the aerosol concentrations at the respective stations, indicating that the aerosol concentrations were dominated by factors on regional scales. During the seasons of northeasterly winter monsoons, long range transport of dust and particulate air pollutants from the Asia Continent had negatively impacted the atmospheric environment in this area. On the other hand, as a highly developed urban area, Taipei has substantial local emissions of air pollutants that should have transported to the surrounding areas of Taipei basin and caused deterioration of air quality and visibility in Cape Fuguei and Yangminshan. The results indicated that the major components of aerosols in Taipei include sulfate, sea salts, dust, and organic matters. In addition, contributions from nitrate, ammonium, and elemental carbon were also significant. In terms of mass concentration, most of the sea salts and dust particles existed in the coarse mode of aerosols, whereas sulfate and EC were confined within PM2.5. This suggests that the dust and sea salts particles were externally mixed with EC and sulfate in the aerosols over Taipei area. Further, it was found that nitrate were closely associated with sea salts in aerosols, suggesting the reaction between nitric acid and sea salt particles. Different seasonality was observed for sea salt and dust: sea salts peaked in fall and dust reached the maximal level in springtime, implying their sources were regulated by independent seasonal factors. Particulate pollutants (i.e. sulfate, nitrate, OM and EC) were consistently reaching their respective maxima in spring, agreeing with dust particle, suggesting the influences of long range transport of air pollutants. This study also found that both the mass fraction of OM in aerosols and OC/EC ratio exhibited peaks in summertime. Secondary organic aerosols (SOA) produced from photochemical reactions and heteo-nucleation were among the major factors controlling the seasonal variations of aerosol concentration in Taipei area. Because the formation of SOA could alter the interactions between aerosols and cloud/fog and, in turn, have potential impacts upon the regional radiation budget, this study suggests conduct an in-depth study upon the relationship between cloud condensation nuclei (CCN) and SOA in this region.

BioVLAB-MMIA-NGS: microRNA-mRNA integrated analysis using high-throughput sequencing data.

PubMed

Chae, Heejoon; Rhee, Sungmin; Nephew, Kenneth P; Kim, Sun

2015-01-15

It is now well established that microRNAs (miRNAs) play a critical role in regulating gene expression in a sequence-specific manner, and genome-wide efforts are underway to predict known and novel miRNA targets. However, the integrated miRNA-mRNA analysis remains a major computational challenge, requiring powerful informatics systems and bioinformatics expertise. The objective of this study was to modify our widely recognized Web server for the integrated mRNA-miRNA analysis (MMIA) and its subsequent deployment on the Amazon cloud (BioVLAB-MMIA) to be compatible with high-throughput platforms, including next-generation sequencing (NGS) data (e.g. RNA-seq). We developed a new version called the BioVLAB-MMIA-NGS, deployed on both Amazon cloud and on a high-performance publicly available server called MAHA. By using NGS data and integrating various bioinformatics tools and databases, BioVLAB-MMIA-NGS offers several advantages. First, sequencing data is more accurate than array-based methods for determining miRNA expression levels. Second, potential novel miRNAs can be detected by using various computational methods for characterizing miRNAs. Third, because miRNA-mediated gene regulation is due to hybridization of an miRNA to its target mRNA, sequencing data can be used to identify many-to-many relationship between miRNAs and target genes with high accuracy. http://epigenomics.snu.ac.kr/biovlab_mmia_ngs/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

GATE Monte Carlo simulation in a cloud computing environment

NASA Astrophysics Data System (ADS)

Rowedder, Blake Austin

The GEANT4-based GATE is a unique and powerful Monte Carlo (MC) platform, which provides a single code library allowing the simulation of specific medical physics applications, e.g. PET, SPECT, CT, radiotherapy, and hadron therapy. However, this rigorous yet flexible platform is used only sparingly in the clinic due to its lengthy calculation time. By accessing the powerful computational resources of a cloud computing environment, GATE's runtime can be significantly reduced to clinically feasible levels without the sizable investment of a local high performance cluster. This study investigated a reliable and efficient execution of GATE MC simulations using a commercial cloud computing services. Amazon's Elastic Compute Cloud was used to launch several nodes equipped with GATE. Job data was initially broken up on the local computer, then uploaded to the worker nodes on the cloud. The results were automatically downloaded and aggregated on the local computer for display and analysis. Five simulations were repeated for every cluster size between 1 and 20 nodes. Ultimately, increasing cluster size resulted in a decrease in calculation time that could be expressed with an inverse power model. Comparing the benchmark results to the published values and error margins indicated that the simulation results were not affected by the cluster size and thus that integrity of a calculation is preserved in a cloud computing environment. The runtime of a 53 minute long simulation was decreased to 3.11 minutes when run on a 20-node cluster. The ability to improve the speed of simulation suggests that fast MC simulations are viable for imaging and radiotherapy applications. With high power computing continuing to lower in price and accessibility, implementing Monte Carlo techniques with cloud computing for clinical applications will continue to become more attractive.

77 FR 40084 - Certain Portable Communication Devices; Determination Not To Review Initial Determinations...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-06

...''); Amazon.com , Inc. of Seattle, Washington (``Amazon''); Nokia Corporation of Espoo, Finland, Nokia Inc. of..., Motorola, Samsung, Sony, Amazon, and Pantech filed a joint motion under Commission Rule 210.21(a)(2) to...

USGS Science Data Life Cycle Tools - Lessons Learned in moving to the Cloud

NASA Astrophysics Data System (ADS)

Frame, M. T.; Mancuso, T.; Hutchison, V.; Zolly, L.; Wheeler, B.; Urbanowski, S.; Devarakonda, R.; Palanisamy, G.

2016-12-01

The U.S Geological Survey (USGS) Core Science Systems has been working for the past year to design, re-architect, and implement several key tools and systems within the USGS Cloud Hosting Service supported by Amazon Web Services (AWS). As a result of emerging USGS data management policies that align with federal Open Data mandates, and as part of a concerted effort to respond to potential increasing user demand due to these policies, the USGS strategically began migrating its core data management tools and services to the AWS environment in hopes of leveraging cloud capabilities (i.e. auto-scaling, replication, etc.). The specific tools included: USGS Online Metadata Editor (OME); USGS Digital Object Identifier (DOI) generation tool; USGS Science Data Catalog (SDC); USGS ScienceBase system; and an integrative tool, the USGS Data Release Workbench, which steps bureau personnel through the process of releasing data. All of these tools existed long before the Cloud was available and presented significant challenges in migrating, re-architecting, securing, and moving to a Cloud based environment. Initially, a `lift and shift' approach, essentially moving as is, was attempted and various lessons learned about that approach will be discussed, along with recommendations that resulted from the development and eventual operational implementation of these tools. The session will discuss lessons learned related to management of these tools in an AWS environment; re-architecture strategies utilized for the tools; time investments through sprint allocations; initial benefits observed from operating within a Cloud based environment; and initial costs to support these data management tools.

Amazon Forests Response to Droughts: A Perspective from the MAIAC Product

NASA Technical Reports Server (NTRS)

Bi, Jian; Myneni, Ranga; Lyapustin, Alexei; Wang, Yujie; Park, Taejin; Chi, Chen; Yan, Kai; Knyazikhin, Yuri

2016-01-01

Amazon forests experienced two severe droughts at the beginning of the 21st century: one in 2005 and the other in 2010. How Amazon forests responded to these droughts is critical for the future of the Earth's climate system. It is only possible to assess Amazon forests' response to the droughts in large areal extent through satellite remote sensing. Here, we used the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) data to assess Amazon forests' response to droughts, and compared the results with those from the standard (Collection 5 and Collection 6) MODIS VI data. Overall, the MAIAC data reveal more realistic Amazon forests inter-annual greenness dynamics than the standard MODIS data. Our results from the MAIAC data suggest that: (1) the droughts decreased the greenness (i.e., photosynthetic activity) of Amazon forests; (2) the Amazon wet season precipitation reduction induced by El Niño events could also lead to reduced photosynthetic activity of Amazon forests; and (3) in the subsequent year after the water stresses, the greenness of Amazon forests recovered from the preceding decreases. However, as previous research shows droughts cause Amazon forests to reduce investment in tissue maintenance and defense, it is not clear whether the photosynthesis of Amazon forests will continue to recover after future water stresses, because of the accumulated damages caused by the droughts.

Aerosol climate change effects on land ecosystem services.

PubMed

Unger, N; Yue, X; Harper, K L

2017-08-24

A coupled global aerosol-carbon-climate model is applied to assess the impacts of aerosol physical climate change on the land ecosystem services gross primary productivity (GPP) and net primary productivity (NPP) in the 1996-2005 period. Aerosol impacts are quantified on an annual mean basis relative to the hypothetical aerosol-free world in 1996-2005, the global climate state in the absence of the historical rise in aerosol pollution. We examine the separate and combined roles of fast feedbacks associated with the land and slow feedbacks associated with the ocean. We consider all fossil fuel, biofuel and biomass burning aerosol emission sources as anthropogenic. The effective radiative forcing for aerosol-radiation interactions is -0.44 W m -2 and aerosol-cloud interactions is -1.64 W m -2 . Aerosols cool and dry the global climate system by -0.8 °C and -0.08 mm per day relative to the aerosol-free world. Without aerosol pollution, human-induced global warming since the preindustrial would have already exceeded the 1.5 °C aspirational limit set in the Paris Agreement by the 1996-2005 decade. Aerosol climate impacts on the global average land ecosystem services are small due to large opposite sign effects in the tropical and boreal biomes. Aerosol slow feedbacks associated with the ocean strongly dominate impacts in the Amazon and North American Boreal. Aerosol cooling of the Amazon by -1.2 °C drives NPP increases of 8% or +0.76 ± 0.61 PgC per year, a 5-10 times larger impact than estimates of diffuse radiation fertilization by biomass burning aerosol in this region. The North American Boreal suffers GPP and NPP decreases of 35% due to aerosol-induced cooling and drying (-1.6 °C, -0.14 mm per day). Aerosol-land feedbacks play a larger role in the eastern US and Central Africa. Our study identifies an eco-climate teleconnection in the polluted earth system: the rise of the northern hemisphere mid-latitude reflective aerosol pollution layer causes long range cooling that protects Amazon NPP by 8% and suppresses boreal NPP by 35%.

Sentinel-1 Interferometry from the Cloud to the Scientist

NASA Astrophysics Data System (ADS)

Garron, J.; Stoner, C.; Johnston, A.; Arko, S. A.

2017-12-01

Big data problems and solutions are growing in the technological and scientific sectors daily. Cloud computing is a vertically and horizontally scalable solution available now for archiving and processing large volumes of data quickly, without significant on-site computing hardware costs. Be that as it may, the conversion of scientific data processors to these powerful platforms requires not only the proof of concept, but the demonstration of credibility in an operational setting. The Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC), in partnership with NASA's Jet Propulsion Laboratory, is exploring the functional architecture of Amazon Web Services cloud computing environment for the processing, distribution and archival of Synthetic Aperture Radar data in preparation for the NASA-ISRO Synthetic Aperture Radar (NISAR) Mission. Leveraging built-in AWS services for logging, monitoring and dashboarding, the GRFN (Getting Ready for NISAR) team has built a scalable processing, distribution and archival system of Sentinel-1 L2 interferograms produced using the ISCE algorithm. This cloud-based functional prototype provides interferograms over selected global land deformation features (volcanoes, land subsidence, seismic zones) and are accessible to scientists via NASA's EarthData Search client and the ASF DAACs primary SAR interface, Vertex, for direct download. The interferograms are produced using nearest-neighbor logic for identifying pairs of granules for interferometric processing, creating deep stacks of BETA products from almost every satellite orbit for scientists to explore. This presentation highlights the functional lessons learned to date from this exercise, including the cost analysis of various data lifecycle policies as implemented through AWS. While demonstrating the architecture choices in support of efficient big science data management, we invite feedback and questions about the process and products from the InSAR community.