Chemical Data Assimilation—An Overview

Adrian Sandu,Tianfeng Chai

doi:10.3390/atmos2030426

Abstract

Chemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere. Leveraging advances in algorithms and increases in the available computational power, the integration of numerical predictions and observations has started to play an important role in air quality modeling. This paper gives an overview of several methodologies used in chemical data assimilation. We discuss the Bayesian framework for developing data assimilation systems, the suboptimal and the ensemble Kalman filter approaches, the optimal interpolation (OI), and the three and four dimensional variational methods. Examples of assimilation real observations with CMAQ model are presented.

Highlights

Chemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere
In the following vertical ozone error statistics estimation and ozone optimal interpolation (OI) data assimilation test runs, the CMAQ model is from the released version 4.6 with the Carbon Bond IV (CBIV) gas-phase chemical mechanism and aerosol module version 4 (AERO-4) [163,164]
Chemical data assimilation has begun to play an essential role in air quality assessments for environmental management

Summary

A Comparison of Various Data Assimilation Approaches

Insightful comparisons of the relative merits of EnKF and 4D-Var [132,133,134], and of EnKF and. In the following vertical ozone error statistics estimation and ozone OI data assimilation test runs, the CMAQ model is from the released version 4.6 with the Carbon Bond IV (CBIV) gas-phase chemical mechanism and aerosol module version 4 (AERO-4) [163,164]. Without fully accounting for the background error covariance, the 4D-Var case still generates the best results during the first day in terms of the model biases and root-mean-square errors (RMSEs) against the AIRNow observations. The extinction coefficients calculated from two visibility methods, Mie theory approximation and mass reconstruction method [171], are quite similar and we chose to use the results from the mass reconstruction method Both Terra and Aqua fine mode AOD data are used during the assimilation time period (August 14–20, 2009).

Conclusions and Future

58. Protocol Monitoring for the GMES Service Element