Abstract

Big Data frameworks enable rigorous, evidence-based climate pattern analysis through structured data handling, scalable computation, and reproducible methodologies. Climate pattern analysis aims to identify significant climate patterns that affect the behavior of the climate system, estimate their risk, and qualify their spatiotemporal changes. The analysis of big data requires studies to address aspects such as the volume of data generated by sources with high temporal and spatial resolutions, the velocity of processing to produce timely information, the quantitative handling of all available datasets of different origins and natures, and the veracity of results. These data-acquisition aspects including the workflows, the quality-control protocols, the production of metadata following standards, and the management of in-situ stations according to the principles of the Global Climate Observing System are critical for building confidence in pattern analysis. In addition to elaborate methodology for producing big data, Machine learning and Statistical inference can be combined to explore and quantify risk of extreme weather events related to big data patterns. When coupled with fast and efficient simulation models able to go beyond multiple real-world observations of patterns and able to quantify uncertainty, this machine Learning + Climate Science approach represents a true data explosion to develop, monitor and validate future generation warning systems for extreme weather patterns. But even the pioneering research on the ocean-atmosphere interactions patterns responsible for climate variabilities of El Niño, La Niña etc. and the open oceanic couplings of the Mixd Layer and gyres have benefited from diving into the Big Data era of Climate.

Keywords

  • Big Data Climate Analytics
  • Climate Pattern Analysis
  • Spatiotemporal Climate Variability
  • Scalable D

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