Changepoint Detection in Daily Precipitation Data

• Activity Number: 138
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Michael Robbins*+ and Colin Gallagher and Robert Lund
• Companies: University of Missouri-Columbia and Clemson University and Clemson University
• Keywords: At Most One Changepoint ; Brownian Bridge ; Changepoints ; CUSUM ; Markov Chain ; Precipitation

Abstract

In this talk, a method to identify an undocumented changepoint time in a daily precipitation series is introduced. 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 non-equatorial 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 daily precipitation series are made.