This study introduces a new chemistry option in the Weather Research and Forecasting model data assimilation (WRFDA) system, coupled with the WRF‐Chem model (Version 4.4.1), to incorporate aqueous chemistry (AQCHEM) in the assimilation of ground‐level chemical measurements. The new DA capability includes the integration of aqueous‐phase aerosols from the Regional Atmospheric Chemistry Mechanism (RACM) gas chemistry, the Modal Aerosol Dynamics Model for Europe (MADE) aerosol chemistry, and the Volatility Basis Set (VBS) for secondary organic aerosol production. The RACM‐MADE‐VBS‐AQCHEM scheme facilitates aerosol‐cloud‐precipitation interactions by activating aerosol particles in cloud water during the model simulation. With the goal of enhancing air quality forecasting in cloudy conditions, this new implementation is demonstrated in the weakly coupled three‐dimensional variational data assimilation (3D‐Var) system through regional air quality cycling over East Asia. Surface particulate matter (PM) concentrations and four gas species (SO2, NO2, O3, and CO) are assimilated every 6 hr for the month of March 2019. The results show that including aqueous‐phase aerosols in both the analysis and forecast can represent aerosol wet removal processes associated with cloud development and rainfall production. During a pollution event with high cloud cover, simulations without aerosols defined in cloud water exhibit significantly higher values for liquid water path, and surface PM10(PM2.5) concentrations are overestimated by a factor of 10 (3) when wet scavenging processes dominate. On the contrary, AQCHEM proves to be helpful in simulating the wet deposition of aerosols, accurately predicting the evolution of surface PM concentrations without such overestimation.
- Award ID(s):
- 1847493
- NSF-PAR ID:
- 10316077
- Date Published:
- Journal Name:
- Science advances
- Volume:
- 8
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/sciadv.abj9156
