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| Titel |
A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction |
| VerfasserIn |
Z. Li, Z. Zang, Q. B. Li, Y. Chao, D. Chen, Z. Ye, Y. Liu, K. N. Liou |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 13, no. 8 ; Nr. 13, no. 8 (2013-04-25), S.4265-4278 |
| Datensatznummer |
250018606
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| Publikation (Nr.) |
 copernicus.org/acp-13-4265-2013.pdf |
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| Zusammenfassung |
| A three-dimensional variational data assimilation (3-DVAR) algorithm for
aerosols in a WRF/Chem model is presented. The WRF/Chem model uses the
MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) scheme,
which explicitly treats eight major species (elemental/black carbon, organic
carbon, nitrate, sulfate, chloride, ammonium, sodium and the sum of other
inorganic, inert mineral and metal species) and represents size
distributions using a sectional method with four size bins. The 3-DVAR
scheme is formulated to take advantage of the MOSAIC scheme in providing
comprehensive analyses of species concentrations and size distributions. To
treat the large number of state variables associated with the MOSAIC scheme,
this 3-DVAR algorithm first determines the analysis increments of the total
mass concentrations of the eight species, defined as the sum of the mass
concentrations across all size bins, and then distributes the analysis
increments over four size bins according to the background error variances.
The number concentrations for each size bin are adjusted based on the ratios
between the mass and number concentrations of the background state.
Additional flexibility is incorporated to further lump the eight mass
concentrations, and five lumped species are used in the application
presented. The system is evaluated using the analysis and prediction of
PM2.5 in the Los Angeles basin during the CalNex 2010 field experiment, with
assimilation of surface PM2.5 and speciated concentration observations. The
results demonstrate that the data assimilation significantly reduces the
errors in comparison with a simulation without data assimilation and
improved forecasts of the concentrations of PM2.5 as well as individual
species for up to 24 h. Some implementation difficulties and limitations of
the system are discussed. |
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