![]() ![]() ![]() Other applications of cloud computing in atmospheric and ocean sciences have been compiled and range from data storage and analysis to visualization. This approach is also used by the Met Office . NOAA has partially externalized its data storage and computing (for example, using Amazon AWS Lambda) through partnerships with major vendors in the framework of the Big Data Project . The data cluster of the Science and Technology Facilities Council (STFC, ) Centre for Environmental Data Archival works in part as a piece of the cloud computing infrastructure. Such platforms are designed to run a Hadoop cluster in a hybrid (i.e., local and remote) cloud that shares storage space with the HPC infrastructure, using Python notebooks as the primary interface for the user. For example, the United Kingdom Met Office is developing a distributed data analysis platform that obviates the need for scientists to move data around ( ). ![]() Institutions studying weather and climate have begun to explore the use of cloud platforms. For example, in April 2015, Microsoft launched the Azure4Research Climate Data Award Program in support of the White House Climate Data Initiative, and the European Commission established a plan to develop an European Open Science Cloud by the end of 2016 that continues to be developed. Beyond the private sector, cloud computing is also increasingly popular in research laboratories around the world . In the private sector, the migration to cloud computing from traditional IT infrastructure is increasing and is expected to continue over the next few years . We also discuss problems related to computing security, reliability and scientific reproducibility.Ĭloud computing has emerged in recent years as both a new business model and a sensible technological choice, as it allows users to adapt resources to demand and/or budget relatively easily, reducing the need to manage a computing infrastructure on premises. Finally, we discuss how cloud computing can be used for geoscientific modelling, including issues related to the allocation of resources by funding bodies. The computational performance and cost of each model within this new type of environment are discussed, and an assessment is given in qualitative terms. The adaptations and procedures necessary to run the models in these environments are described. Each was customized in a different way to run in public cloud computing environments (hereafter cloud computing) provided by three different public vendors: Amazon, Google and Microsoft. In this paper, we discuss the use of cloud computing as a tool to improve the range of resources available for climate science, presenting the evaluation of two different climate models. Cloud computing is a mature technology that has already shown benefits for a wide range of academic research domains that, in turn, utilize a wide range of application design models. ![]()
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