Carlos F. Gaitan


My research involves the use of machine learning/statistical methods (e.g. neural networks, kernel methods) to downscale (statistically refine) the coarse-resolution reanalysis and global climate model outputs (GCM) to finer, local scales. I am also interested in supervised and unsupervised classification methods, like self-organized maps, and their application to extract features of big datasets. In general, I am passionate about nonlinear time series analyses and enjoy using novel methods to improve our predictive skill of different phenomena.

Applications include climate change projections of maximum and minimum temperatures, precipitation occurrences and amounts, wind speeds, and extreme weather projections for different climate change scenarios. Most recently my co-authors and I used Bayesian Neural Networks to predict maize yield in China.

Research Fields

Atmospheric Sciences

Editorial Board Memberships

Carlos is not currently contributing as an editor for any journal or publisher.

Pre Publication Reviews

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Reviews (last 12 months)


Reviews (average per year)






Review to Publication ratio


Journal Impact Factors of journals reviewed for

The distribution of the Journal Impact Factors of journals Carlos F. Gaitan has reviewed for.

Carlos F. Gaitan

All fields reviewers

Total reviews over time

A cumulative record of Carlos F. Gaitan's total number of reviews.

Reviews per month

The total number of reviews performed by Carlos F. Gaitan each month.

Average review length

The average number of words per review compared to the average of All fields reviewers and the average of reviewers at affiliated institutions.

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Weekly review punchcard

The distribution of days that reviews were performed on, compared to All fields reviewers and reviewers at affiliated institutions.