A0909
Title: Additive dynamic models for correcting numerical model outputs
Authors: Xiaohui Chang - Oregon State University (United States) [presenting]
Abstract: Numerical air quality models are pivotal for predicting and assessing air pollution, but numerical model outputs may be systematically biased. An additive dynamic model is proposed to correct large-scale raw model outputs using data from other sources, including readings collected at ground monitoring networks and weather outputs from different numerical models. An additive partially linear model specification is employed for the nonlinear relationships between air pollutants and covariates. In addition, a multi-resolution basis function approximation is proposed to capture the different small-scale variations of biases. A discretized stochastic integrodifferential equation is constructed to characterize the dynamic evolution of the random coefficients at each spatial resolution. An expectation-maximization algorithm is developed for parameter estimation, and a multi-resolution ensemble-based scheme is embedded to accelerate the computation. The proposed approach is used to correct the biased raw outputs of PM2.5 from the Community Multiscale Air Quality (CMAQ) system for China's Beijing-Tianjin-Hebei region. The method improves the root mean squared error and continuous rank probability score by 43.7\% and 34.76\%, respectively. Compared to other statistical methods under different metrics, this model has correction accuracy and computational efficiency advantages.
