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).
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Acknowledgments
Thanks to Anne Stoner for providing a very helpful review of our implementation of the ARRM algorithm that corrected some errors. Thanks also to Dorit Hammerling and Ian Scott-Fleming for helpful comments on drafts of the paper. This research was supported by the NSF Earth Systems Models (EaSM) Program award number 1049208, and by the DoD Strategic Environmental Research and Development Program (SERDP) under project RC-2204.
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McGinnis, S., Nychka, D., Mearns, L.O. (2015). A New Distribution Mapping Technique for Climate Model Bias Correction. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_9
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DOI: https://doi.org/10.1007/978-3-319-17220-0_9
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