Singular Vector Analysis for Atmospheric Chemical Transport Models

Quart J Royal Meteoro Soc

et al. 2007

Self Cite

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air-quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF) and compare it with a state-of-the-art 4D-Var approach. We analyse different aspects that affect the assimilation process, and investigate several ways to avoid filter divergence. Results with a real model and real observations show that EnKF is a promising approach for chemical data assimilation. The results also point to several issues on which further research is necessary.

Section: Introductionmentioning

confidence: 99%

“…Considerable progress has been made recently in the development of variational tools for chemical data assimilation (Liao et al, 2005;Sandu et al, 2003;Sandu et al, 2005a;Sandu, 2006). However, compared with NWP and ocean applications, little work has been done to date on assimilating chemical observations using nonlinear ensemble filters.…”

Section: Introductionmentioning

confidence: 99%

Ensemble‐based chemical data assimilation. I: General approach

Constantinescu

Quart J Royal Meteoro Soc

et al. 2007

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“…The real setting presented several challenges and difficulties: in particular, filter divergence, and limitation of the ensemble spread by the positivity constraints of the model. Additionally, a comparison with 4D-Var (Courtier et al, 1994;Rabier et al, 2000;Liao et al, 2006;Sandu et al, 2005;Chai et al, 2006) was undertaken in order to assess EnKF's effectiveness against a well-proven technique. The results indicated that 4D-Var and EnKF produced results of similar quality.…”

Section: Introductionmentioning

confidence: 99%

Ensemble‐based chemical data assimilation. II: Covariance localization

Constantinescu

Quart J Royal Meteoro Soc

et al. 2007

Self Cite

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air-quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we implement, and assess the performance of, a localized 'perturbed-observations' ensemble Kalman filter (LEnKF). We analyse different settings of the ensemble localization, and investigate the joint assimilation of the state, emissions and boundary conditions. Results with a real model and real observations show that LEnKF is a promising approach for chemical data assimilation. The results also point to several issues on which future research is necessary.

“…An autoregressive model of background errors [74,25] was used, which accounted for spatial correlations, distance decay, and chemical lifetime.…”

Section: Results With Ensemble-based Chemical Data Assimilationmentioning

confidence: 99%

“…In [74] we studied the computation of singular vectors for atmospheric chemical transport models. The study found that singular vectors are shaped by both the meteorology and chemistry.…”

Section: Singular Vectors and Maximal Error Growthmentioning

confidence: 99%

Predicting Air Quality: Current Status and Future Directions

Carmichael

Air Pollution Modeling and Its Application XIX

et al.

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Air quality prediction plays an important role in the management of our environment. Computational power and efficiencies have advanced to the point where chemical transport models can predict pollution in an urban air shed with spatial resolution less than a kilometer, and cover the globe with a horizontal resolution of less than 50 km. Predicting air quality remains a challenge due to the complexity of the governing processes and the strong coupling across scales. While air quality prediction is closely aligned with weather prediction, there are important differences, including the role of pollution emissions and their associated large uncertainties. As more atmospheric chemical observations become available chemical data assimilation is expected to play an essential role in air quality forecasting. In this paper the current status of air quality forecasting is discussed and illustrated by comparison of predictions with observations. The future directions are also discussed, with an emphasis on data assimilation. Applications of the four dimensional variational method (4D-Var) and the ensemble Kalman filter (EnKF) approach are presented and discussed.