Abstracts are invited for a special session on Big Data in Oceanography and Meteorology at the annual meeting of the Australian Meteorological & Oceanographic Society (AMOS) and the International Conference on Tropical Meteorology and Oceanography (ICTMO), held in Darwin, NT, from 11-15 June 2019.
AMOS-ICTMO 2019 will bring together experts in meteorology, oceanography, climate, and other related sciences from Australia and around the world as well as government representatives, NGOs, businesses and the media to focus on the latest research.
The following special session on big data will be convened by Moninya Roughan, Shane Keating, and Steefan Contractor. Abstracts for oral and poster presentations are to be submitted via the AMOS-ICTMO 2019 submission site. The deadline for submission is Sunday 18 November 2018.
Big Data in Oceanography and Meteorology: Challenges, Applications, and Data Products
Oceanographers and meteorologists are drowning in a tide of data. Data availability has increased steadily in recent years due to the move towards higher resolution modelling and the increase in observations. Observational density has increased because of an increase in frequency of measurements, introduction of new single and multi-instrument datasets and new remote-sensing platforms. As a result, studies of geophysical fluid dynamics are becoming increasingly data driven. In order to derive valuable insights and knowledge from large volumes of data, new methods and techniques are emerging in the field of big data for visualization, analysis, and data dispensation. Vast numbers of datasets are being published and released freely for analysis and sharing.
We invite talks that are broadly related to the field of big data and data products. We encourage presentations on analysis of data from the syntheses of programmes such as IMOS and CMIP5 analyses, projects that deliver products and insights to end users, or that focus on pattern analysis and identification of complex relationships, predictive modelling using supervised and unsupervised algorithms, tools for handling large datasets and for increasing computational efficiency, novel applications of statistical learning and high volume time series analysis, blending of diverse datasets, reproducibility of analysis, inherent structural uncertainties in data, best practices in big datasets and guidance on recommended use for new and existing datasets.
Mathematics for Planet Earth 