Abstract
Abstract. In this study, we apply the four-dimensional variational (4D-Var) data assimilation to optimize initial ozone state and to improve the predictability of air quality. The numerical modeling systems used for simulations of atmospheric condition and chemical formation are the Weather Research and Forecasting (WRF) model and the Community Multiscale Air Quality (CMAQ) model. The study area covers the capital region of South Korea, where the surface measurement sites are relatively evenly distributed. The 4D-Var code previously developed for the CMAQ model is modified to consider background error in matrix form, and various numerical tests are conducted. The results are evaluated with an idealized covariance function for the appropriateness of the modified codes. The background error is then constructed using the NMC method with long-term modeling results, and the characteristics of the spatial correlation scale related to local circulation are analyzed. The background error is applied in the 4D-Var research, and a surface observational assimilation is conducted to optimize the initial concentration of ozone. The statistical results for the 12 h assimilation periods and the 120 observatory sites show a 49.4 % decrease in the root mean squared error (RMSE), and a 59.9 % increase in the index of agreement (IOA). The temporal variation of spatial distribution of the analysis increments indicates that the optimized initial state of ozone concentration is transported to inland areas by the clockwise-rotating local circulation during the assimilation windows. To investigate the predictability of ozone concentration after the assimilation window, a short-time forecasting is carried out. The ratios of the RMSE (root mean squared error) with assimilation versus that without assimilation are 8 and 13 % for the +24 and +12 h, respectively. Such a significant improvement in the forecast accuracy is obtained solely by using the optimized initial state. The potential improvement in ozone prediction for both the daytime and nighttime with application of data assimilation is also presented.
Highlights
Data assimilation provides a consistent representation of the physical state such as the atmosphere by blending imperfect model predictions and noisy observations
To examine the appropriation of modified code, the Balgovind distribution expressed in Eq (7) is selected for constructing the background error covariance (BEC) that has the components of matrix form
The forward run (FWD) test is conducted without data assimilation, and the other test is performed with data assimilation
Summary
Data assimilation provides a consistent representation of the physical state such as the atmosphere by blending imperfect model predictions and noisy observations. With more chemical observations available in recent years, including the satellite data, data assimilation is expected to make more contributions to weather forecasting and further improve the predictability of air quality. When the data assimilation technique is used in an air quality model, it improves the initial concentration distribution of pollutants, and optimizes the emissions. The four-dimensional variational (4D-Var) data assimilation requires an adjoint model for use in non-linear numerical models. Accurate error covariances for background and observation are important for the quality of data assimilation. Is the defined cost function in CMAQ-ADJ to optimize initial condition, which refers to concentration at the initial time
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