This article is published in Environmetrics as a special issue on Advances in Statistical Methods for Climate Analysis, edited by Peter Guttorp, University of Washington, Norwegian Computing Center, Stephan R. Sain, National Center for Atmospheric Research, Christopher K. Wikle, University of Missouri.
Special Issue Paper
Changepoint detection in daily precipitation data†
Article first published online: 19 JUN 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Special Issue: Advances in Statistical Methods for Climate Analysis
Volume 23, Issue 5, pages 407–419, August 2012
How to Cite
Gallagher, C., Lund, R. and Robbins, M. (2012), Changepoint detection in daily precipitation data. Environmetrics, 23: 407–419. doi: 10.1002/env.2146
- Issue published online: 25 JUL 2012
- Article first published online: 19 JUN 2012
- Manuscript Accepted: 17 APR 2012
- Manuscript Revised: 30 JAN 2012
- Manuscript Received: 1 OCT 2011
- National Science Foundation. Grant Number: DP0881391
- at most one changepoint;
- Brownian bridge;
- Markov chain;
This paper introduces a method to identify an undocumented changepoint time in a daily precipitation series. A two-state Markov chain is used to induce dependence in the precipitation amounts; our dynamics allow for seasonality in the daily observations, a structure inherent to many nonequatorial region series. No current precipitation changepoint techniques exist that consider day-to-day dependencies, the zero support set aspect (the fact that most measurements are zero), and the periodic dynamics of the problem. The test statistic is constructed by applying cumulative sum methods to a strategically devised set of one-step-ahead prediction residuals. The methods are robust to distributional assumptions, requiring only seasonal mean and transition probability estimators. Simulations are presented that demonstrate the efficacy of the methods; application to two daily precipitation series is made. Copyright © 2012 John Wiley & Sons, Ltd.