Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales

Dear Colleagues,

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.) which may help to overcome different types of problems that still constitute key challenges for the climate science community (e.g., diagnosis, classification, forecasting, downscaling, etc.).

Dr. Rodrigo Manzanas
Guest Editor

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