A New Distribution Mapping Technique for Climate Model Bias Correction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Seth McGinnis , Doug Nychka , Linda O. Mearns

ABSTRACT

We evaluate the performance of different distribution mapping techniques for bias correction of climate model output by operating on synthetic data and comparing the results to an “oracle” correction based on perfect knowledge of the generating distributions. We find results consistent across six different metrics of performance. Techniques based on fitting a distribution perform best on data from normal and gamma distributions, but are at a significant disadvantage when the data does not come from a known parametric distribution. The technique with the best overall performance is a novel nonparametric technique, kernel density distribution mapping (KDDM). More... »

PAGES

91-99

Book

TITLE

Machine Learning and Data Mining Approaches to Climate Science

ISBN

978-3-319-17219-4
978-3-319-17220-0

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-17220-0_9

DOI

http://dx.doi.org/10.1007/978-3-319-17220-0_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1007527659


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