A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; prediction

Li, Z.; Zang, Zengliang; Li, Q. B.; Chao, Yi; Chen, D.; Ye, Z.; Liu, Y.; Liou, Kuo‐Nan

doi:10.5194/acp-13-4265-2013

Cited by 78 publications

(65 citation statements)

References 43 publications

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“…The four bins are located between 0.039-0.1, 0.1-1.0, 1.0-2.5, and 2.5-10 µm, and the total mass of the first three bins are PM 2.5 . A 3DVAR system for the MOSAIC (4-bin) scheme has been developed by Li et al (2013). For comparisons, we employ this 3DVAR system with the same model configurations as employed by Li et al (2013).…”

Section: ×10mentioning

confidence: 99%

“…A 3DVAR system for the MOSAIC (4-bin) scheme has been developed by Li et al (2013). For comparisons, we employ this 3DVAR system with the same model configurations as employed by Li et al (2013). The data assimilation and forecasting experiments are performed with a focus on assessing the impact of cross-correlations of the BEC on analyses and forecasts.…”

Section: ×10mentioning

confidence: 99%

“…Recently, several studies have suggested that the BEC with balanced cross-correlation should be introduced into aerosol variational data assimilation (Kahnert, 2008;Liu et al, 2011;Li et al, 2013;Saide et al, 2013). Kahnert (2008) exhibited cross-correlations of the 17 aerosol variables from the Multiple-scale Atmospheric Transport and Chemistry (MATCH) model.…”

Section: ×10mentioning

confidence: 99%

“…The number of variables must be decreased prior to assimilation. Li et al (2013) lumped these variables into five species as control variables in the 3DVAR system. The five species consist of EC, OC, NO 3 , SO 4 and OTR (other species).…”

Section: Data Assimilation System and Becmentioning

confidence: 99%

“…Schwartz et al (2012) increased the number of the species to 15 based on the study of Liu et al (2011). Li et al (2013) estimated the BEC for five species derived from the Model for Simulation Aerosol Interactions and Chemistry (MOSAIC) scheme.…”

Section: ×10mentioning

confidence: 99%

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Background error covariance with balance constraints for aerosol species and applications in variational data assimilation

Zang

Hao

et al. 2016

Geosci. Model Dev.

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Abstract. Balance constraints are important for background error covariance (BEC) in data assimilation to spread information between different variables and produce balance analysis fields. Using statistical regression, we develop a balance constraint for the BEC of aerosol variables and apply it to a three-dimensional variational data assimilation system in the WRF/Chem model; 1-month forecasts from the WRF/Chem model are employed for BEC statistics. The cross-correlations between the different species are generally high. The largest correlation occurs between elemental carbon and organic carbon with as large as 0.9. After using the balance constraints, the correlations between the unbalanced variables reduce to less than 0.2. A set of data assimilation and forecasting experiments is performed. In these experiments, surface PM 2.5 concentrations and speciated concentrations along aircraft flight tracks are assimilated. The analysis increments with the balance constraints show spatial distributions more complex than those without the balance constraints, which is a consequence of the spreading of observation information across variables due to the balance constraints. The forecast skills with the balance constraints show substantial and durable improvements from the 2nd hour to the 16th hour compared with the forecast skills without the balance constraints. The results suggest that the developed balance constraints are important for the aerosol assimilation and forecasting.

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Section: ×10mentioning

confidence: 99%

Section: ×10mentioning

confidence: 99%

Section: ×10mentioning

confidence: 99%

Section: Data Assimilation System and Becmentioning

confidence: 99%

Section: ×10mentioning

confidence: 99%

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Background error covariance with balance constraints for aerosol species and applications in variational data assimilation

Zang

Hao

et al. 2016

Geosci. Model Dev.

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Assimilating aerosol observations with a “hybrid” variational‐ensemble data assimilation system

et al. 2014

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Total 550 nm aerosol optical depth, surface fine particulate matter (PM 2.5 ), and meteorological observations were assimilated with continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and hybrid variational-ensemble data assimilation systems. The hybrid system's background error covariances (BECs) were a blend of those in 3DVAR and produced by the cycling EnSRF system, and the 3DVAR, EnSRF, and hybrid systems differed almost exclusively by their BECs. New analyses were produced every 6 h between 0000 UTC 1 June and 1800 UTC 14 July 2010 over a domain encompassing the contiguous United States (CONUS) and adjacent areas. Additionally, a control experiment that only assimilated meteorological observations was performed. Each 1800 UTC analysis initialized a 48 h Weather Research and Forecasting with Chemistry model forecast. These forecasts were evaluated with a focus on air quality prediction. The ensemble aerosol spread was generally insufficient, particularly over the western CONUS. However, despite the suboptimal ensemble spread, the hybrid system performed quite well and usually produced the best aerosol forecasts. Additionally, both the 3DVAR-and EnSRF-initialized forecasts typically outperformed the control. These results are encouraging and suggest the resiliency of the hybrid method. Improved aerosol ensembles should translate into even better future hybrid forecasts.

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Evaluating the impact of multisensor data assimilation on a global aerosol particle transport model

Zhang

Campbell²,

Hyer³

et al. 2014

JGR Atmospheres

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A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM<sub>2.5</sub> prediction

Cited by 78 publications

References 43 publications

Background error covariance with balance constraints for aerosol species and applications in variational data assimilation

Background error covariance with balance constraints for aerosol species and applications in variational data assimilation

Assimilating aerosol observations with a “hybrid” variational‐ensemble data assimilation system

Evaluating the impact of multisensor data assimilation on a global aerosol particle transport model

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