Clouds in the Cloud: Weather Forecasts and Applications within Cloud Computing Environments

Molthan, Andrew; Case, Jonathan L.; Venner, Jason; Schroeder, Richard.; Checchi, Milton R.; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

doi:10.1175/bams-d-14-00013.1

Cited by 26 publications

(22 citation statements)

References 17 publications

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“…Deployment was on the Terradue Developer Cloud Sandbox and the results provided better access to datasets by centralizing their location and getting them out from behind firewalls and a sandbox for access to and processing of these datasets (Combal and Caumont, 2016) 9 . Molthan et al (2015) used a NASA private cloud sandbox to explore deploying the WRF weather model and to test various system configurations before re-deploying on a commercial cloud for full scale testing. The UK Met Office Visualization Lab does all their work in the cloud and can be thought of as an all-encompassing sandbox.…”

Section: Sandboxes In the Cloud For Modeling And Developmentmentioning

confidence: 99%

From the Oceans to the Cloud: Opportunities and Challenges for Data, Models, Computation and Workflows

et al. 2019

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Intent: Explores integrating the use of cloud-based data and how scientists can access large volumes of diverse, current and authoritative data, addresses the problem of locating and using large amounts of scientific data. The Section "Architectures for Real-Time Data Management and Services for Observations" describes streaming data and an architecture for making it easy to gather data. Also see Johanson et al. (2016). Cloud-Based Management of Scientific Data-Storing Data in the Cloud Intent: Explores storing and managing data in the cloud. Addresses the problem of ever increasing data quantities with decreasing budgets for data management. Explores the ways scientific projects can meet data access and dissemination requirements such as the U.S. Public Access to Research Results (PARR) mandate (Holdren, 2013). The section on the NOAA Big Data Project and open data and archiving are examples of this pattern. Also see Meisinger et al. (2009). Computing Infrastructure for Scientific Research Intent: Explores the ways in which cloud computing, in the form of PaaS or IaaS could be used as part of a research program and for teaching. It addresses the need for larger computational capabilities, especially under constrained budgets. The modeling efforts described in later sections are examples of this pattern.

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Section: Sandboxes In the Cloud For Modeling And Developmentmentioning

confidence: 99%

From the Oceans to the Cloud: Opportunities and Challenges for Data, Models, Computation and Workflows

et al. 2019

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“…In research related to Numerical Weather Prediction, researchers at NASA's Marshall Space Flight Center and Ames Research Center collaborated on the use of the WRF model by making use of public and private cloud resources [9]. STRC-EMC was a case study deployed on AWS EC2 and discussed the opportunities enabled through the usage of cloud resources, especially for those who have no access to large scale HPC infrastructure.…”

Section: Environmental Models and Cloud Computingmentioning

confidence: 99%

SE in ES

Simm

Samreen

Bassett

et al. 2018

Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Society

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New and emergent computing architectures and software engineering practices provide an opportunity for environmental models to be deployed more efficiently and democratically. In this paper we aim to capture the software engineering practices of environmental scientists, highlight opportunities for software engineering and work towards developing a domain specific language for the configuration and deployment of environmental models. We hold a series of interviews with environmental scientists involved in developing and deploying computer based environmental models about the approach taken in engineering models, and describe a case study in deploying an environmental model (WRF: Weather Research Forecasting) on a cloud architecture. From these studies we find a number of opportunities for A) software engineering methods and tools such as Domain Specific Languages to play a role in abstracting from underlying computing complexity, and for B) new architectures to increase efficiency and availability of deployment. Together, we propose they will allow scientists to concentrate on fundamental science rather than specifics of the underlying computing. CCS CONCEPTS • Applied computing → Environmental sciences; • Software and its engineering → Abstraction, modeling and modularity;

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“…In terms of the above examples, it enables finer resolution and larger ensembles. This potential has spurred interest in porting (operational) NWP to modern computer architectures, in particular, graphical processing units/GPUs (Michalakes & Vachharajani, 2008;Shimokawabe et al, 2014;Vanderbauwhede & Takemi, 2016;Deconinck et al, 2017), but there are also attempts toward using cloud computing (Molthan et al, 2015;Siuta et al, 2016;Blaylock et al, 2017;Chen et al, 2017). Associated challenges comprise scalability (the model should run on thousands of cores), fault awareness and tolerance (the model should tolerate failure of a thread or core), advanced data compression techniques and I/O, or also concepts for portability (computer architectures may differ among consortia members) and composability (relevant physics may differ among users, e. g., mountain snow pack or storm surges) or modularity (Bauer et al, 2015;Palmer, 2015;Düben & Dawson, 2017).…”

Section: Model Developmentmentioning

confidence: 99%

Climate, weather, space weather: model development in an operational context

Folini

2018

J. Space Weather Space Clim.

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-Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of "operational stability" versus "dynamic development" of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between "pure research" and "operational forecast" people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the operational code remains with the forecast provider. The pace of model development reflects operational lead times. The points are illustrated with selected examples, many of which reflect the author's background and personal contacts, notably with the Swiss Weather Service and the Max Planck Institute for Meteorology, Hamburg, Germany. In view of current and future challenges, large collaborations covering a range of expertise are a must À within and across climate, weather, and space weather. To profit from and cope with the rapid progress of computer architectures, supercompute centers must form part of the team.

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Clouds in the Cloud: Weather Forecasts and Applications within Cloud Computing Environments

Cited by 26 publications

References 17 publications

From the Oceans to the Cloud: Opportunities and Challenges for Data, Models, Computation and Workflows

From the Oceans to the Cloud: Opportunities and Challenges for Data, Models, Computation and Workflows

SE in ES

Climate, weather, space weather: model development in an operational context

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