This interdisciplinary session welcomes contributions on novel conceptual and/or methodological approaches and methods for the analysis and statistical-dynamical modeling of observational as well as model time series from all geoscientific disciplines.
Methods to be discussed include, but are not limited to linear and nonlinear methods of time series analysis. time-frequency methods, statistical inference for nonlinear time series, including empirical inference of causal linkages from multivariate data, nonlinear statistical decomposition and related techniques for multivariate and spatio-temporal data, nonlinear correlation analysis and synchronisation, surrogate data techniques, filtering approaches and nonlinear methods of noise reduction, artificial intelligence and machine learning based analysis and prediction for univariate and multivariate time series.
Contributions on methodological developments and applications to problems across all geoscientific disciplines are equally encouraged. We particularly aim at fostering a transfer of new methodological data analysis and modeling concepts among different fields of the geosciences.
Sub-Session "Mathematical Climatology and Space-time Data Analysis" (Abdel Hannachi, Amro Elfeki, Christian Franzke, Muhammad Latif, Carlos Pires)
The recent progress in mathematical methods to solve various problems in weather & climate nonlinear dynamics and data analysis calls for the need to develop a new session that focus on those methods. Novel and powerful mathematical methods have been developed and used in different subjects of climate. Because those methods are used within specific contexts they go unnoticed most of the time by climate researchers. The proposed new session will provide the opportunity to climate scientists and researchers working on developing mathematical methods for climate to come together and present their findings in a transparent way. This will also be easily accessible to other climate scientists who look for, and are interested in specific methods to solve their problems.
Contributions are encouraged from researchers working on mathematical methods and their application to weather and climate. We particularly welcome contributions on optimization, dimension reduction and data mining, space-time patterns identification, machine learning, statistical prediction modelling, nonlinear methods , Bayesian statistics, and Monte-Carlo Markov Chain (MCMC) methods in stochastic modelling.
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