Research - Aerosol Modeling

Aerosol modeling, data assimilation using mesoscale models - Simulation of dust and biomass burning smoke.
Although satellite remote sensing data sets are widely used to map the geographical distribution of aerosols at high spatial and temporal resolutions and to explore the effects of atmospheric aerosols on the earth’s radiation budget, numerical models are the preferred tool for studying the role of tropospheric aerosols in modulating several important atmospheric processes such as surface energetics and atmospheric heating rates. Currently, satellite derived aerosol information is not commonly used in numerical models, especially regional models. Our research is focused upon

1. Assimilating satellite derived aerosol optical thickness to study the role of dust aerosols over the oceans. We demonstrate a case study of a dust event observed during the Puerto Rico Dust Experiment (PRIDE) and explore the utility of assimilating satellite derived aerosol information into numerical models, to examine aerosol radiative effects.
2. Use satellite derived fire products to study the role of smoke aerosols on the earth-atmosphere system.
3. Use mesoscale models to forecast particulate matter air quality.

We use the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) as a start and use a modified four-stream radiative transfer model for aerosol radiative effects along with new dry and wet deposition modules for smoke aerosols. We also geostationary satellites for characterizing diurnal fires and smoke emissions. 
Through assimilation of geostationary satellite-derived aerosol optical thickness (AOT) into the RAMS, spatial and temporal aerosol distribution is optimally characterized, facilitating accurate estimation of aerosol radiative effects. Radiative effect of dust aerosols is then estimated using different types of radiative transfer and aerosol transport schemes and comparisons against observations show that a direct online consideration of aerosol radiative effects produces the best results. 
Qualitatively, 30-day simulation of smoke spatial distribution as well as the timing and location of the smoke fronts are consistent with those identified from PM2.5 network, local air quality report, and the measurements of aerosol optical thickness and aerosol profile in ARM SGP site in Oklahoma. Quantitatively, the simulated daily-averaged dry smoke mass near the surface correlates well with the PM2.5 mass over 36 locations in Texas, the carbon mass and non-soil potassium (KNON) mass over 4 IMPROVE sites along the smoke pathway (with linear correlation coefficients R = 0.7, 0.76 and 0.67, respectively). The “top-down” sensitivity analysis indicated that total smoke particle emission during the one-month study period is about 1.3?0.2Tg.

For further information 
Wang, J., U. Nair, and S.A. Christopher (2004), GOES-8 Aerosol Optical Thickness Assimilation in a Mesoscale Model: Online Integration of Aerosol Radiative Effects, J. Geophysical Research-Atmospheres, Vol. 109, No. D23, D23203 04 (pdf file).
Wang, J., U.S.Nair, S. A Christopher, R.T. McNider, J.E. Reid, E. M. Prins, and J. Sykzman, An Integrated System for Studying the effect of Central American smoke aerosols on air quality and climate over the Southeastern United States, 13th Conference on Satellite Meteorology and Oceanography, 20-24 September 2004, Norfolk, Virginia.