Advanced Statistical Techniques in Oceans and Climate Research
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: closed (30 May 2024) | Viewed by 8444
Special Issue Editors
Interests: big data; fractals; climate change; environmental evolution
Special Issues, Collections and Topics in MDPI journals
Interests: statistical and fractal models; climate change; marine ecosystem
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Earth’s climate and oceans are complex, multidimensional, multiscale stochastic processes in which different physical processes interact on different temporal and spatial scales. Statisticians are working to extract meaningful information from huge amounts of Earth observational and simulation data using various statistical techniques (e.g., EOF, DEA, EVA, CCA, SSA, PCA, MCMC, DFA, IPTA, Bayesian decisions, downscaling analyses, data assimilation, wavelet/framelet, spectral analyses, information entropy, and stochastic networks). Rapid advances in statistical techniques have reached all aspects of atmospheric, oceanic, and climate sciences. At present, the ensemble statistical learning techniques are being developed to deal with the emerging big Earth datasets, which cannot be analyzed deeply using classic statistical techniques due to the size, variety, and dynamic nature of big data. In this Special Issue, we aim to collect recent results on developing and applying advanced statistical techniques to reveal trends and patterns of climate/ocean evolution, determine the statistical correlation between climate/ocean systems and ecosystems, and evaluate the uncertainty of climate/ocean models, as well as estimate anthropogenic carbon emissions and land/marine carbon sinks.
Prof. Dr. Zhihua Zhang
Prof. Dr. M. James C. Crabbe
Guest Editors
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Keywords
- interdisciplinary statistical methods
- climatic time series analysis
- oceanic time series analysis
- hydrological time series analysis
- statistical learning/machine learning
- data assimilation and downscaling
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