MERRAMax: A machine learning approach to stochastic convergence with a multi-variate dataset

Using a combination of high end computing and machine learning algorithms we developed a system to interrogate climate reanalysis data in a species distribution model. The results show that this system can be used as a tool to identify key variables of interest relevant to a species and to generate...

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Bibliographic Details
Main Authors: Carroll, M.L., Schnase, J.L., Gill, R.L., Tamkin, G.S., Li, J., Maxwell, T.P., Strong, S.L., Aronne, M.
Format: Conference Proceeding
Language:English
Subjects:
Atmospheric modeling
Computational modeling
Data models
Entropy
Land surface
Meteorology
Predictive models
Online Access:Request full text
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Summary:Using a combination of high end computing and machine learning algorithms we developed a system to interrogate climate reanalysis data in a species distribution model. The results show that this system can be used as a tool to identify key variables of interest relevant to a species and to generate a probability map of the distribution of a species of interest. This opens new avenues for statistical inference in regions with sparse observational data.
ISSN:2153-7003
DOI:10.1109/IGARSS39084.2020.9324185