Cloud computing and virtualization within the regional climate model and evaluation system

Mattmann, Chris A.; Waliser, Duane E.; Kim, Jinwon; Goodale, C. E.; Hart, Andrew F.; Ramírez, Paul; Crichton, Daniel J.; Zimdars, Paul; Boustani, Maziyar; Lee, Kyo; Loikith, Paul C.; Whitehall, Kim; Jack, Christopher

doi:10.1007/s12145-013-0126-2

Cited by 14 publications

(9 citation statements)

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“…RCMES provides basic metrics, such as bias calculation, Taylor diagram, and comparison of time series. Earlier RCMES publications (Kim et al, 2013(Kim et al, , 2014 show how to use the basic metrics in multi-model evaluation as illustrated in Figure 4. The metrics module also provides more advanced metrics.…”

Section: Metrics and Plottermentioning

confidence: 99%

“…In CFiles, users can define the evaluation domain, the time period of evaluation, regridding options, and performance metrics to calculate. Released RCMES packages include example CFiles to 5 reproduce the plots/diagrams in the four selected peer-reviewed journal articles including (Kim et al, 2013(Kim et al, , 2014. Kim et al (2013) and Kim et al (2014) evaluate RCM simulations over the North America and Africa respectively.…”

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confidence: 99%

“…Released RCMES packages include example CFiles to 5 reproduce the plots/diagrams in the four selected peer-reviewed journal articles including (Kim et al, 2013(Kim et al, , 2014. Kim et al (2013) and Kim et al (2014) evaluate RCM simulations over the North America and Africa respectively. The tutorials on the RCMES websites (JPL, 2018b) provide step-by-step instructions to reproduce all of the figures included in the two published articles.…”

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confidence: 99%

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Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

Lee¹,

Goodman²,

McGibbney³

et al. 2018

Preprint

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Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics andSpace Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main 5 objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance 10 metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish 15 infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

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Section: Metrics and Plottermentioning

confidence: 99%

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confidence: 99%

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Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

Lee¹,

Goodman²,

McGibbney³

et al. 2018

Preprint

Self Cite

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“…I have led several studies since 2010 to investigate: (1) cloud computing as a platform for data movement, and storage [16]; (2) cloud computing as a platform for scientific processing [17]; and (3) a hybrid combination of public and private cloud resources for storage, processing and for platform virtualization [18] The contributions from these studies involved the identification of when, and where to leverage cloud in a software system's architecture; a comparison model for cloud versus local storage and processing resources, and a set of insights for delivering cloud-based virtual machines with data system software to the Earth science community. These and other contributions were disseminated at the 2011 International Conference on Software Engineering SECLOUD (Software Engineering for Cloud Computing) workshop that I chaired [19].…”

Section: Harnessing the Cloudmentioning

confidence: 99%

Cultivating a research agenda for data science

Mattmann

2014

Journal of Big Data

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I describe a research agenda for data science based on a decade of research and operational work in data-intensive systems at NASA, the University of Southern California, and in the context of open source work at the Apache Software Foundation. My vision is predicated on understanding the architecture for grid computing; on flexible and automated approaches for selecting data movement technologies and on their use in data systems; on the recent emergence of cloud computing for processing and storage, and on the unobtrusive and automated integration of scientific algorithms into data systems. Advancements in each of these areas are a core need, and they will fundamentally improve our understanding of data science, and big data. This paper identifies and highlights my own personal experience and opinion growing into a data scientist.

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“…The work of Mattmann et al [16] present the efforts to infuse cloud computing and virtualization in the Regional Climate Model Evaluation System (RCMES). The authors describe the utilization of cloud computing for elastic data ingestion and querying in data warehousing, and the use of virtual machines as a delivery mechanism for the complex software layer of data analysis toolkit.…”

Section: Related Workmentioning

confidence: 99%

Improving OLAM with Cloud Elasticity

Galante

Bona

Schepke

2014

Computational Science and Its Applications – ICCSA 2014

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Abstract. Elasticity is considered one of the fundamental properties of cloud computing, and can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. This feature is suitable for dynamic applications, whose resources requirements cannot be determined exactly in advance, either due to changes in runtime requirements or in application structure. A good candidate for using cloud elasticity is the Ocean-Land-Atmosphere Model (OLAM), since it presents a significant load variation during its execution and due to online mesh refinement (OMR), that causes load unbalancing problems. In this paper, we present our efforts to adapt OLAM to use the elasticity offered in cloud environments to dynamic allocate resources according to the demands of each execution phase, and to minimize the load unbalancing caused by OMR. The results show that elasticity was successfully used to provide these features, improving the OLAM performance and providing a better use of resources.

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Cloud computing and virtualization within the regional climate model and evaluation system

Cited by 14 publications

References 10 publications

Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

Cultivating a research agenda for data science

Improving OLAM with Cloud Elasticity

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