Kalman Filter Analysis in Dendroclimatology

TLDR: In this article, the use of the Kalman filter in the analysis of tree-ring data is summarized, and a method for the selection of predictor variables is proposed, which takes into account the special features of time-dependent regression models.

Abstract: SUMMARY The use of the Kalman filter in the analysis of tree-ring data is summarized. By use of this filter technique, the traditional multiple regression models can be modified to cover linear models with time-dependent coefficients. In that way changes in tree response to weather variations could be detected, possibly indicating anthropogenic influences. The filtering and smoothing operations of the filter are illustrated using simulated tree-ring series. A method for the selection of predictor variables is proposed, which takes into account the special features of time-dependent regression models. Furthermore, the consequences of highly correlated predictors are discussed. An application is given to a ring-width series of a European silver fir from Bad Herrenalb (F.R.G.). The method appears suitable for dealing with both gradual changes and sudden shocks in tree response.