Abstract
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 PM2.5 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.
Highlights
Atmospheric aerosols interact with society and the environment in several important ways such as producing acute health impacts, generating visibility issues and creating a substantial climate response (e.g., Ramanathan et al, 2008)
In this study we develop the ability of the Gridpoint Statistical Interpolation (GSI) three dimensional variational (3DVAR) system to simultaneously assimilate various aerosol optical depth (AOD) products to correct Weather Research and Forecasting/Chemistry (WRF-Chem) forecasts when using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) treatment (Zaveri et al, 2008)
We evaluate the impact of assimilating observationally constrained retrievals (i.e., National Aeronautics and Space Administration (NASA) Neural Network Retrieval (NNR) and Naval Research Laboratory (NRL)–University of North Dakota (UND)) and, second, we assess the inclusion of fine mode AOD and multiple-wavelengths to the assimilation
Summary
Atmospheric aerosols interact with society and the environment in several important ways such as producing acute health impacts, generating visibility issues and creating a substantial climate response (e.g., Ramanathan et al, 2008). Filter passes run along size bins in incremental order and are applied locally for each aerosol size distribution, in a similar way to how vertical scales are applied (Wu et al, 2002). The isotropic nature of one-dimensional recursive filters restricts the ability to apply different correlations scales to bins that have smaller and larger sizes than the reference one. Such anisotropic correlation would be preferred for bins located at the edges of fine and coarse distributions. We hypothesize this limitation could be partially overcome when computing the correlation with methods such as the one described
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