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 data assimilation 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 (SOA) 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 (SO$_2$, NO$_2$, O$_3$, and CO) are assimilated every 6 h 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 (LWP), and surface PM$_{10}$ (PM$_{2.5}$) concentrations are overestimated by a factor of 10 (3) when wet scavenging processes dominate. On the contrary, aqueous chemistry proves to be helpful in simulating the wet deposition of aerosols, accurately predicting the evolution of surface PM concentrations without such overestimation.

Society’s ability to make wise decisions depends onan accurate understanding of the current state of Earthand on an ability to predict future states.The Data Assimilation Research Testbed (DART) is an example of a suite of toolsdesigned to improve our understanding through the combination of observationswith our theoretical understanding embodied in forecast models.DART’s ensemble based data assimilation provides uncertainty quantification as a function of time, location, and variable.Current research using DART includes: Improving streamflow prediction during intense rainfall events, which lead to flooding, using DART and the Weather Research and Forecasting model and the Noah-MP land model (WRF-Hydro). Building an integrated atmosphere and ocean forecasting system using DART and WRF for the Red Sea Initiative. Understanding air pollution using a global meteorology-aerosol-chemistry prediction system that assimilates aerosol optical depth, carbon monoxide, and weather observations into the Community Atmosphere Model with Chemistry (CAM-Chem). Assimilating observations of the Earth system from satellites into the Model for Prediction Across Scales (MPAS; regional and global) using observation operators from the Joint Effort for Data assimilation Integration (JEDI), bias correction for satellite retrievals from the Gridpoint Statistical Interpolation (GSI), and the assimilation environment of DART. Deciphering the flow dependency of forecast errors in the tropics and the relative importance of wind and mass information for tropical analyses. Connecting the U.S. Department of Energy’s E3SM atmospheric model with a broad spectrum of observations to perform short ensemble hindcast simulations for model development and evaluation. Generating atmospheric reanalysis data sets from CAM, which enables efficient data assimilation in other components of the Earth system; ocean, land, cryosphere, … Improving DART by giving users more control over how observations are assimilated, and supporting the assimilation of additional observations, such as radiances through the use of the RTTOV software.