TY - JOUR
T1 - Modeling and Regionalization of China's PM2.5 Using Spatial-Functional Mixture Models
JF - Journal of the American Statistical Association
Y1 - 2020
A1 - Liang,Decai
A1 - Zhang,Haozhe
A1 - Chang,Xiaohui
A1 - Huang,Hui
KW - Business Analytics
VL - 116
CP - 553
U2 - a
U4 - 176735037440
ID - 176735037440
ER -
TY - JOUR
T1 - Additive Dynamic Models for Correcting Numerical Model Outputs
JF - Computational Statistics and Data Analysis
Y1 - 0
A1 - Chang,Xiaohui
A1 - Chen,Yewen
A1 - Huang,Hui
A1 - Luo,Fangzhi
KW - Business Analytics
AB -
Numerical air quality models are pivotal for the prediction and assessment of 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 other 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 approximate is proposed to capture the different small-scale variations of biases, and a discretized stochastic
integro-differential 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. For statistical inference, a conditional simulation technique is applied to quantify the uncertainty of parameter estimates and bias correction results. 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. Our method improves the root mean squared error and continuous rank probability score by 43.70% and 34.76%, respectively. Compared to other statistical methods under different metrics, our model has advantages in both correction accuracy and computational efficiency.
U2 - a
U4 - 236109015040
ID - 236109015040
ER -