Adaptive Modelling of the Daily Behavior of the Boundary Layer Ozone in Macau

Abstract

The present study aims to develop an efficient dynamic statistical model to describe the daily behavior of boundary layer ozone in Macau. Four types of Kalman-filter-based models were proposed and applied to model the daily maximum of the 8 hr averaged ozone concentrations within a decade (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009). First, the boundary layer ozone was modelled with the timevarying autoregressive model of order p, TVAR(p), which is a pure time series model hindcasting the ozone concentration by a weighted sum of the ozone histories of the previous p days. Then, it was modelled with the time-varying autoregressive model with linear exogenous input, TVAREX-Lin, which combines the TVAR model and the exogenous input of key meteorological variables in a linear fashion. Next, the nonlinear TVAREX model (TVAREX-NLin) which assumes the nonlinear influence of individual meteorological variable on ozone was adopted. Finally, a semiempirical TVAREX model (TVAREX-O 3 ) was proposed to address the coastal nature of Macau and the interaction between the input variables. It was found that the proposed TVAREX-O 3 model was the most efficient one among the model candidates in terms of the general modelling performance and the capability of modelling the episode situation.

Development of an Adaptive Empirical Model for Storm Tide Simulation

“…Therefore, the objective of this study is to introduce an adaptive statistical model for improvement. The model adaptiveness is fulfilled by the time varying model coefficients estimated by the Kalman filter algorithm (Kalman, 1960;Kalman and Bucy, 1961) that has board application in the field of environmental science and engineering (Choi et al, 2002;Chao et al, 2012 andHoi et al, 2013). The proposed methodology is tested by simulating the storm tide of forty tropical cyclone cases in Macau.…”