Paper
9 October 2018 Shallow cumulus macrophysical properties at midcontinental US site: integrated multiyear active and passive observations
Evgueni Kassianov, Erin A. Riley, Jessica M. Kleiss, Laura Riihimaki, Charles N. Long, Victor Morris, Larry K. Berg
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Abstract
Substantial difference between cloud amounts obtained from active and passive remote sensing has been documented by previous studies. The difference is typically attributed to two main factors: the different field-of-view (FOV) (first factor) and different sensitivity to cloud properties (second factor) of the active and passive ground-based instruments. The relative impact of these two main factors on shallow cumuli cloud amount is demonstrated in this study. The demonstration involves a new multi-year (2000-2017) product, which integrates both the active and passive remote sensing data collected at the mid-continental Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility. Cloud fraction (CF) obtained from the narrow-FOV lidar-radar observations and wide-FOV fractional sky cover (FSC) acquired from sky images are key components of the integrated product. Results of this study indicate that (1) CF tends to overestimate FSC and this overestimation can be large (~40% on average) even at extended temporal scales (several years) and (2) the observed overestimate is primarily due to different sensitivity of the active and passive remote sensing instruments to shallow cumuli, while the limited FOV of active remote sensing instruments plays a minor role in such overestimation.
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Evgueni Kassianov, Erin A. Riley, Jessica M. Kleiss, Laura Riihimaki, Charles N. Long, Victor Morris, and Larry K. Berg "Shallow cumulus macrophysical properties at midcontinental US site: integrated multiyear active and passive observations", Proc. SPIE 10786, Remote Sensing of Clouds and the Atmosphere XXIII, 107860O (9 October 2018); https://doi.org/10.1117/12.2326691
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KEYWORDS
Clouds

Active remote sensing

Passive remote sensing

Radar

Atmospheric sensing

Imaging systems

LIDAR

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