학술논문

Big Data Challenges in Climate Science: Improving the next-generation cyberinfrastructure
Document Type
Periodical
Source
IEEE Geoscience and Remote Sensing Magazine IEEE Geosci. Remote Sens. Mag. Geoscience and Remote Sensing Magazine, IEEE. 4(3):10-22 Sep, 2016
Subject
Geoscience
Power, Energy and Industry Applications
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Meteorology
Data models
Big data
Atmospheric modeling
Analytical models
Satellites
Climate change
Language
ISSN
2473-2397
2168-6831
2373-7468
Abstract
The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Some examples of the types of activities that increasingly require an improved cyberinfrastructure for dealing with large amounts of critical scientific data are climate model intercomparison (CMIP) experiments; the integration of observational data and climate reanalysis data with climate model outputs, as seen in the Observations for Model Intercomparison Projects (Obs4MIPs), Analysis for Model Intercomparison Projects (Ana4MIPs), and Collaborative Reanalysis Technical Environment-Intercomparison Project (CREATE-IP) activities; and the collaborative work of the Intergovernmental Panel on Climate Change (IPCC). This article provides an overview of some of climate science's big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), which is the primary cyberinfrastructure currently supporting global climate research activities.