30 August 2017 Lidar observations of long range dust transport over Mauna Loa Observatory
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Abstract
A bistatic CCD camera lidar (CLidar) was used at the National Oceanic and Atmospheric Administration’s Mauna Loa Observatory (MLO) to map aerosol light scattering. Laser light from a 532 nm, Nd:YAG laser was vertically transmitted into the atmosphere and the scatter off clouds, aerosols and air molecules was detected using a CCD camera with wide angle optics and a laser line filter. The intensity of each CCD camera pixel imaging the beam was normalized to a molecular scattering model in an aerosol free region for subtraction of molecular scattering. Aerosol extinction was derived using a column average aerosol phase function derived from AERONET sun photometer measurements at MLO. The CLidar design allows measurements of aerosol scattering all the way to the ground without an overlap correction. MLO, at 3397 m.a.s.l., typically receives free tropospheric air. During spring months, prevailing winds can occasionally transport dust from Asian sources with high dust activity over MLO. Aerosol scattering measurements were taken by the CLidar during spring months at MLO and revealed extinction peaks at mid-range altitudes. Back trajectories of air parcels from MLO at the altitudes of these peaks were conducted using NOAA’s Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and it was found that they passed over regions of Eastern Asia known as sources of high dust activity. Relative humidity data from radiosondes and the NOAA stratospheric lidar’s water vapor channel were examined to differentiate aerosol scattering from tenuous cloud scattering. This paper presents aerosol extinction data with observations of Asian dust as measured by the CLidar during spring months at MLO.
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Jalal Butt, Jalal Butt, Nimmi C. P. Sharma, Nimmi C. P. Sharma, John E. Barnes, John E. Barnes, } "Lidar observations of long range dust transport over Mauna Loa Observatory", Proc. SPIE 10406, Lidar Remote Sensing for Environmental Monitoring 2017, 104060M (30 August 2017); doi: 10.1117/12.2275723; https://doi.org/10.1117/12.2275723
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