characterization of both horizontal and vertical distribution of aerosols are important for simulating the dust impact on TC development [Zhang et al., 2007; Min et al., 2009; Wang et al., 2009; AlizadehChoobari et al., 2012]. The Saharan Air Layer (SAL), which is the warm, dry and often dusty Saharan air mass advected over the cooler and humid marine air mass over the Atlantic [Karyampudi and Carlson, 1988], impacts tropical cyclone formation through multiple pathways [Jenkins et al., 2008]. Baroclinicity associated with the Saharan Air Layer (SAL) enhance development of African Easterly waves [Karyampudi and Carlson, 1988]. The SAL also enhances cyclonic vorticity and positive vorticity advection [Karyampudi and Pierce, 2002] and has a positive impact on tropical cyclone formation and development. On the other hand, enhancement of atmospheric stability and wind shear due to SAL negatively impact the formation and development of tropical cyclones [Dunion and Velden, 2004]. Over larger timescales, reduction of sea surface temperature due to dust radiative forcing has a negative impact on tropical cyclone genesis [Lau and Kim, 2007]. Horizontal thermal gradients are tied to all these important dynamical features of the SAL and thus the realistic specification of dust spatial distribution is important. MODIS derived aerosol products provide good constraints on the horizontal spatial distribution and also column dust loading. However, vertical distribution of aerosols is also important as the transport behavior varies drastically depending upon the vertical placement of dust [Karyampudi and Carlson, 1988; Alizadeh Choobari et al., 2012] with aerosols in the free atmosphere being transported long distances. Thus, aerosols in the free atmosphere have longer lasting radiative impact compared to those in the PBL with shorter life time. Furthermore, dust layers in the free atmosphere can have a much greater cooling effect on the surface than low-level dust layers [e.g. Chung and Zhang, 2004] as the atmospheric heating due to the absorbing dust is unlikely to be
transferred to the surface at such heights. Thus, elevated dust over the Atlantic Ocean may lead to significant greater surface cooling than lower level dust, and TC development is highly sensitive to the sea surface temperature [Lau and Kim, 2007]. Aerosol layers can also impact cloud dynamics and microphysics properties differently depending on their height as shown in Yin et al. [2012] where aerosols in the lower troposphere were important in altering the cloud dynamics and microphysics while aerosols at heights above the mid-troposphere led to minimal change. The strong vertical velocity associated with deep convection can effectively transport lower tropospheric aerosols upward in convective clouds which impacts the dynamic and microphysical processes along with the precipitation [Yin et al., 2012]. CALIPSO derived aerosol products provide another constraint for the vertical distribution of dust aerosols. This study uses a combination of Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol products and Goddard Chemistry Aerosol Radiation and Transport (GOCART) model outputs to specify realistic three-dimensional distribution of dust aerosols in the WRF simulations and minimize the errors associated with the parameterized dust emission and transport schemes. In this paper, we discuss the experimental design for numerical model experiments, methodology utilized for constraining WRF-Chem simulations using satellite observations and evaluation of the technique using in situ observations gathered during the 2006 AMMA field experiment. This paper is organized as follows: In section 2, we discuss the model and data used in this study. A description of the model is provided where we discuss the physics, dynamics, and chemistry options chosen in the model. We also introduce the data used as an input into our satellite data constraint technique and the data used for validating the technique. In section 3, we provide in-depth details on the technique. Then, in section 4, we evaluate the
Surface layer MM5 scheme [Obukhov, 1971] Land surface Noah [Chen and Dudhia, 2001] Cumulus cloud scheme Deactivated microphysics Lin [Lin et al., 1983], Morrison [Morrison et al., 2005] Aerosol chemistry 4-bin MOSAIC [Zaveri et al., 2008] Gas phase chemisty CBM-Z [Zaveri and Peters, 1999] Aerosol-radiation interactions Activated Aerosol-cloud interactions Activated Aqueous chemistry and wet scavenging Activated Table 1. Noteworthy WRF-Chem configuration options chosen in this study. Satellite / Model Data Level / Version Horizontal Resolution Vertical Resolution Parameters Used CALIPSO Level 1B Version 3.01 333 m (sfc-8.3 km) 30 m (sfc-8.3 km) 532 nm Attenuated Backscatter CALIPSO Level 2 Version 3.01 - Cloud 333 m Up to 5 layers Layer Top and Base Heights CALIPSO Level 2 Version 5 km Up to 10 layers Layer Top and Base Heights CALIPSO Level 2 Version 3.01 - Aerosol 5 km Up to 8 layers Layer Top and Base Heights MODIS Aqua/Terra Level 3 daily product 1° by 1° NA QA-weighted τ GOCART Version 6 daily product 2.5° by 2.0° NA τ (dust, sulfate, organic and black carbon, sea salt) Table 2. Summary of the data products used as input into the satellite data constraint technique. Bin Diameter Upper Diameter Qext Dm Dvm 1 0.0390625 μm 0.15625 μm 0.09093 0.097656 μm 0.078125 μm 2 0.15625 μm 0.625 μm 3.12023 0.390625 μm 0.3125 μm 3 0.625 μm 2.5 μm 1.5625 μm 1.25 μm