Dear Colleagues,

The expansion and scope of geophysical and climate data, with associated analytical tools and approaches, are changing rapidly day after day as we move into the future. The rise of “Big Data” sets deepens our understanding of such complex structured and unstructured data. In the relatively recent past of the last few decades, these datasets were relatively small and frequently disregarded by statisticians as biased data. With the availability of large geophysical and climate datasets, i.e., “Big Data”, the scientific community (statisticians, computer scientists, geophysicists, etc.) was forced to develop analytically rigorous methods in order to deal with such vast datasets. To evaluate data patterns, variable relationships, and prediction, various methods have been created, followed by emerging appropriate novel statistical theories.  

This topic seeks to offer a forum for in-depth discussion and contributions that can have either an applied or theoretical perspective and highlight different problems, with special emphasis on data analytics and methods. Manuscripts summarizing the most recent state of the art on these topics are welcome. The topics to be covered include but are not limited to the following: cutting-edge statistical learning tools for the analysis of geophysical and climate data; cutting-edge machine learning tools for the analysis of geophysical and climate data; innovative applications of existing statistical learning and/or machine learning tools for geophysical and climate data.

Prof. Dr. Vladimir Sreckovic
Dr. Zoran Mijic
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data in geo and climate and related fields
• analysis and prediction
• new applications
• algorithms
• machine learning
• visualization