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 2007]. Aerosol layers can also impact and Kim, cloud dynamics and microphysics properties differently depending on their height as shown in [2012] where aerosols in the lower troposphere were important in altering the cloud Yin et al. 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 2012]. CALIPSO derived et al., 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 observations gathered during the 2006 in situ 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
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
PART1-07102013-arn-JGR-Part1WRF.docx