Campus Units

Statistics, Center for Survey Statistics and Methodology (CSSM)

Document Type

Conference Proceeding

Conference

6th International Workshop on Climate Informatics: CI 2016

Publication Version

Published Version

Publication Date

9-30-2016

Journal or Book Title

Proceedings of the 6th International Workshop on Climate Informatics: CI 2016

First Page

117

Last Page

120

Conference Title

6th International Workshop on Climate Informatics: CI 2016

Conference Date

September 22-23, 2016

City

Boulder, CO

Abstract

The identification of precipitation regimes is important for many purposes such as agricultural planning, water resource management, and return period estimation. Since precipitation and other related meteorological data typically exhibit spatial dependency and different characteristics at different time scales, clustering such data presents unique challenges. In this short paper, we develop a flexible model-based approach to identify precipitation regimes in China by clustering spatial functional data. Though the focus of this study is on precipitation data, this methodology is generally applicable to other environmental data with similar structure.

Comments

This proceeding is published as Zhang, Haozhe, Zhengyuan Zhu, and Shuiqing Yin. "Identifying precipitation regimes in China using model-based clustering of spatial functional data." In: Banerjee, A., Ding, W., Dy, J., Lyubchich, V., & Rhines, A. (Eds.). Proceedings of the 6th International Workshop on Climate Informatics: CI 2016. (2016): 117-120. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

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