The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is modified to include a volatility basis set (VBS) treatment of secondary organic aerosol formation. The VBS approach, coupled with SAPRC-99 gas-phase chemistry mechanism, is used to model gas-particle partitioning and multiple generations of gas-phase oxidation of organic vapors. In addition to the detailed 9-species VBS, a simplified mechanism using 2 volatility species (2-species VBS) is developed and tested for similarity to the 9-species VBS in terms of both mass and oxygen-to-carbon ratios of organic aerosols in the atmosphere. WRF-Chem results are evaluated against field measurements of organic aerosols collected during the MILAGRO 2006 campaign in the vicinity of Mexico City. The simplified 2-species mechanism reduces the computational cost by a factor of 2 as compared to 9-species VBS. Both ground site and aircraft measurements suggest that the 9-species and 2-species VBS predictions of total organic aerosol mass as well as individual organic aerosol components including primary, secondary, and biomass burning are comparable in magnitude. In addition, oxygen-to-carbon ratio predictions from both approaches agree within 25 %, providing evidence that the 2-species VBS is well suited to represent the complex evolution of organic aerosols. Model sensitivity to amount of anthropogenic semi-volatile and intermediate volatility (S/IVOC) precursor emissions is also examined by doubling the default emissions. Both the emission cases significantly under-predict primary organic aerosols in the city center and along aircraft flight transects. Secondary organic aerosols are predicted reasonably well along flight tracks surrounding the city, but are consistently over-predicted downwind of the city. Also, oxygen-to-carbon ratio predictions are significantly improved compared to prior studies by adding 15 % oxygen mass per generation of oxidation; however, all modeling cases still under-predict these ratios downwind as compared to measurements, suggesting a need to further improve chemistry parameterizations of secondary organic aerosol formation

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models

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Abstract. The online coupled Weather Research and Forecasting-Chemistry (WRF-Chem) model was applied to simulate a haze event that happened in January 2010 in the North China Plain (NCP), and was validated against various types of measurements. The evaluations indicate that WRF-Chem provides reliable simulations for the 2010 haze event in the NCP. This haze event was mainly caused by high emissions of air pollutants in the NCP and stable weather conditions in winter. Secondary inorganic aerosols also played an important role and cloud chemistry had important contributions. Air pollutants outside Beijing contributed about 64.5 % to the PM2.5 levels in Beijing during this haze event, and most of them are from south Hebei, Tianjin city, Shandong and Henan provinces. In addition, aerosol feedback has important impacts on surface temperature, relative humidity (RH) and wind speeds, and these meteorological variables affect aerosol distribution and formation in turn. In Shijiazhuang, Planetary Boundary Layer (PBL) decreased about 278.2 m and PM2.5 increased more than 20 µg m−3 due to aerosol feedback. It was also shown that black carbon (BC) absorption has significant impacts on meteorology and air quality changes, indicating more attention should be paid to BC from both air pollution control and climate change perspectives.

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We evaluate a regional-scale simulation with the WRF-Chem model for the VAMOS (Variability of the American Monsoon Systems) Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx), which sampled the Southeast Pacific's persistent stratocumulus deck. Evaluation of VOCALS-REx ship-based and three aircraft observations focuses on analyzing how aerosol loading affects marine boundary layer (MBL) dynamics and cloud microphysics. We compare local time series and campaign-averaged longitudinal gradients, and highlight differences in model simulations with (W) and without (NW) wet deposition processes. The higher aerosol loadings in the NW case produce considerable changes in MBL dynamics and cloud microphysics, in accordance with the established conceptual model of aerosol indirect effects. These include increase in cloud albedo, increase in MBL and cloud heights, drizzle suppression, increase in liquid water content, and increase in cloud lifetime. Moreover, better statistical representation of aerosol mass and number concentration improves model fidelity in reproducing observed spatial and temporal variability in cloud properties, including top and base height, droplet concentration, water content, rain rate, optical depth (COD) and liquid water path (LWP). Together, these help to quantify confidence in WRF-Chem's modeled aerosol-cloud interactions, especially in the activation parameterization, while identifying structural and parametric uncertainties including: irreversibility in rain wet removal; overestimation of marine DMS and sea salt emissions, and accelerated aqueous sulfate conversion. Our findings suggest that WRF-Chem simulates marine cloud-aerosol interactions at a level sufficient for applications in forecasting weather and air quality and studying aerosol climate forcing, and may do so with the reliability required for policy analysis

We couple airborne, ground‐based, and satellite observations; conduct regional simulations; and develop and apply an inversion technique to constrain hourly smoke emissions from the Rim Fire, the third largest observed in California, USA. Emissions constrained with multiplatform data show notable nocturnal enhancements (sometimes over a factor of 20), correlate better with daily burned area data, and are a factor of 2–4 higher than a priori estimates, highlighting the need for improved characterization of diurnal profiles and day‐to‐day variability when modeling extreme fires. Constraining only with satellite data results in smaller enhancements mainly due to missing retrievals near the emissions source, suggesting that top‐down emission estimates for these events could be underestimated and a multiplatform approach is required to resolve them. Predictions driven by emissions constrained with multiplatform data present significant variations in downwind air quality and in aerosol feedback on meteorology, emphasizing the need for improved emissions estimates during exceptional events.

Abstract. An aerosol optical depth (AOD) three-dimensional variational data assimilation technique is developed for the Gridpoint Statistical Interpolation (GSI) system for which WRF-Chem forecasts are performed with a detailed sectional model, the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). Within GSI, forward AOD and adjoint sensitivities are performed using Mie computations from the WRF-Chem optical properties module, providing consistency with the forecast. GSI tools such as recursive filters and weak constraints are used to provide correlation within aerosol size bins and upper and lower bounds for the optimization. The system is used to perform assimilation experiments with fine vertical structure and no data thinning or re-gridding on a 12 km horizontal grid over the region of California, USA, where improvements on analyses and forecasts is demonstrated. A first set of simulations was performed, comparing the assimilation impacts of using the operational MODIS (Moderate Resolution Imaging Spectroradiometer) dark target retrievals to those using observationally constrained ones, i.e., calibrated with AERONET (Aerosol RObotic NETwork) data. It was found that using the observationally constrained retrievals produced the best results when evaluated against ground based monitors, with the error in PM 2.5 predictions reduced at over 90 % of the stations and AOD errors reduced at 100 % of the monitors, along with larger overall error reductions when grouping all sites. A second set of experiments reveals that the use of fine mode fraction AOD and ocean multi-wavelength retrievals can improve the representation of the aerosol size distribution, while assimilating only 550 nm AOD retrievals produces no or at times degraded impact. While assimilation of multi-wavelength AOD shows positive impacts on all analyses performed, future work is needed to generate observationally constrained multi-wavelength retrievals, which when assimilated will generate size distributions more consistent with AERONET data and will provide better aerosol estimates.