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Special Issue "Data Mining and Machine Learning Techniques for Seasonal Forecasting and Climate Change"
Deadline for manuscript submissions: 17 April 2020.
Traditionally, standard statistical methods have been used to solve many of the problems that arise in climate research. Nevertheless, the enormous volume of data that have been made available during the last decade (in situ and/or satellite records, reanalysis, ESM simulations, etc.), and the rapid development of powerful computing resources have motivated the adaptation and use of more complex and sophisticated tools, namely, data mining and machine learning techniques, which allow to extract useful knowledge by directly operating on the data.
This Special Issue of Atmosphere focuses on the application of data mining and machine learning techniques (association rules, classification/regression trees, random forests, Gaussian mixture models, artificial neural networks, support vector machines, Bayesian networks, etc.) in the context of seasonal forecasting and climate change projections, with interest in a number of problems of different nature that constitute key challenges for the climate science community (e.g., diagnosis, classification, forecasting, downscaling). Topics of interest include, but are not limited to:
(i) Representation of clouds and other small-scale aspects of the atmosphere and the ocean that can help to better predict global and regional climate’s response to rising greenhouse gas concentrations
(ii) Identification of relevant processes and their associated responses (i.e., atmospheric teleconnections)
(iii) Proper model weighting to improve ensemble forecasts
(iv) Automatic predictor selection for statistical downscaling which helps to reduce the uncertainty in local/regional predictions
Dr. Rodrigo Manzanas
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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. Atmosphere 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 1500 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.
- Statistical forecasts
- Climate change projections
- Data mining
- Machine learning
- Neural networks
- Small-scale processes representation
- Prediction uncertainty
- Model weighting
- Statistical downscaling