CHEMICAL WEATHER ANALYSIS OPTIMISATION WITH EMISSION IMPACT ESTIMATION USING NESTED FOUR-DIMENSIONAL VARIATIONAL CHEMISTRY DATA ASSIMILATION

2007 ◽  
pp. 315-325
Author(s):  
HENDRIK ELBERN ◽  
ACHIM STRUNK
Keyword(s):  
Data Assimilation ◽  
Chemistry Data

A review on the use of the adjoint method in four-dimensional atmospheric-chemistry data assimilation

2001 ◽  
Vol 127 (576) ◽  
pp. 2181-2204 ◽  
Author(s):  
K.-Y. Wang ◽  
D. J. Lary ◽  
D. E. Shallcross ◽  
S. M. Hall ◽  
J. A. Pyle
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Adjoint Method ◽  
Chemistry Data

scholarly journals Impact of ozone observations on the structure of a tropical cyclone using coupled atmosphere–chemistry data assimilation

2015 ◽  
Vol 15 (8) ◽  
pp. 11573-11597
Author(s):  
S. Lim ◽  
S. K. Park ◽  
M. Zupanski
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Cost Function ◽  
Positive Impact ◽  
Eastern China ◽  
Chemical Information ◽  
Chemistry Data ◽  
Air Quality Forecast ◽  
The Cost ◽  
The Impact

Abstract. Since the air quality forecast is related to both chemistry and meteorology, the coupled atmosphere–chemistry data assimilation (DA) system is essential to air quality forecasting. Ozone (O3) plays an important role in chemical reactions and is usually assimilated in chemical DA. In tropical cyclones (TCs), O3 usually shows a lower concentration inside the eyewall and an elevated concentration around the eye, impacting atmospheric as well as chemical variables. To identify the impact of O3 observations on TC structure, including atmospheric and chemical information, we employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) with an ensemble-based DA algorithm – the maximum likelihood ensemble filter (MLEF). For a TC case that occurred over the East Asia, our results indicate that the ensemble forecast is reasonable, accompanied with larger background state uncertainty over the TC, and also over eastern China. Similarly, the assimilation of O3 observations impacts atmospheric and chemical variables near the TC and over eastern China. The strongest impact on air quality in the lower troposphere was over China, likely due to the pollution advection. In the vicinity of the TC, however, the strongest impact on chemical variables adjustment was at higher levels. The impact on atmospheric variables was similar in both over China and near the TC. The analysis results are validated using several measures that include the cost function, root-mean-squared error with respect to observations, and degrees of freedom for signal (DFS). All measures indicate a positive impact of DA on the analysis – the cost function and root mean square error have decreased by 16.9 and 8.87%, respectively. In particular, the DFS indicates a strong positive impact of observations in the TC area, with a weaker maximum over northeast China.

scholarly journals Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation

2015 ◽  
Vol 8 (5) ◽  
pp. 1315-1320 ◽  
Author(s):  
S. K. Park ◽  
S. Lim ◽  
M. Zupanski
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Control Variable ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Single Observation ◽  
Chemistry Data ◽  
Error Covariance ◽  
Atmospheric Observations ◽  
Realistic Structure

Abstract. In this study, we examined the structure of an ensemble-based coupled atmosphere–chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere–chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert an impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate that the ensemble-based coupled atmosphere–chemistry data assimilation will respond similarly to assimilation of real observations.

scholarly journals Forecast error covariance structure in coupled atmosphere–chemistry data assimilation

2014 ◽  
Vol 7 (6) ◽  
pp. 8757-8767
Author(s):  
S. K. Park ◽  
S. Lim ◽  
M. Zupanski
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Control Variable ◽  
Wrf Model ◽  
Covariance Structure ◽  
Weather Research And Forecasting ◽  
Single Observation ◽  
Chemistry Data ◽  
Error Covariance ◽  
Atmospheric Observations

Abstract. In this study, we examined the structure of an ensemble-based coupled atmosphere–chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere–chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed, e.g., atmospheric observations can exert impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate the ensemble-based coupled atmosphere–chemistry data assimilation will respond similarly to assimilation of real observations.

scholarly journals Ozone episode analysis by four-dimensional variational chemistry data assimilation

2001 ◽  
Vol 106 (D4) ◽  
pp. 3569-3590 ◽  
Author(s):  
Hendrik Elbern ◽  
Hauke Schmidt
Keyword(s):  
Data Assimilation ◽  
Chemistry Data ◽  
Ozone Episode

Implementation of a parallel 4D‐variational chemistry data‐assimilation scheme

1999 ◽  
Vol 10 (4) ◽  
pp. 236-245 ◽  
Author(s):  
Hendrik Elbern ◽  
Hauke Schmidt ◽  
Adolf Ebel
Keyword(s):  
Data Assimilation ◽  
Chemistry Data ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme
Sign in / Sign up

Export Citation Format

Share Document