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Detection and Retrieval of Mineral Dust Aerosols Using AERI Data:Implications for Longwave Surface Forcing

Richard Hansell UCLA
Kuo-Nan Liou UCLA
Szu-cheng Ou University of California, Los Angeles
Si-Chee Tsay NASA Goddard Space Flight Center
Quiang (Jack) Ji UMCP/ESSIC
Jeff Reid

Category: Radiation

A dust detection/retrieval method using ground-based atmospheric emitted radiance interferometer (AERI) brightness temperature spectra has been developed. Taking advantage of the high spectral resolution of AERI, we exploit differences between the spectral absorptive power for dust and cloud in prescribed thermal IR window sub-bands to separate dust from clouds, and to retrieve dust IR optical depths. Dust composition models were prescribed using refractive index datasets for minerals commonly observed around the United Arab Emirates (UAE) region, including quartz, kaolinite and kaolinite mixed with hematite and calcium carbonate. Five dust microphysical models were constructed using in situ data from the United Arab Emirates Unified Aerosol Experiment (UAE2), and the single-scattering properties for oblate spheroids and hexagonal plates, two particle geometries routinely observed in electron microscopy were computed. Sensitivity of the AERI spectra to dust composition, shape, and size and precipitable water vapor was investigated using the Code for High Resolution Accelerated Radiative Transfer with Scattering (CHARTS) radiative transfer program. AERI data for four dust and cirrus cases from the UAE2 field campaign were selected to demonstrate the effectiveness of the detection/retrieval approach. The AERI detection results compare reasonably well with coincident and collocated MPLNET micropulse lidar, where most dust and cloud events were detected. The AERI retrieved optical depths were found to be within 30% of the Aerosol Robotic Network (AERONET) sunphotometer measurements. Using the AERI retrieved optical depths, the longwave surface forcing during the UAE2 was also determined This novel AERI detection/retrieval approach will help detect and track regional dust events, and quantify dust IR radiative forcing surface parameters for both daytime and nighttime conditions.

This poster will be displayed at ARM Science Team Meeting.