We develop a new climatology of the macrophysical properties of single-layer shallow cumuli (ShCu), such as cloud amount and cloud base/top heights, observed during 19 summers (2000-2018) at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) Central Facility in northern Oklahoma, USA. Similar to the established datasets, the climatology incorporates the well-known advantages of the narrow field-of-view (FOV) lidar-radar measurements to resolve the vertical structure of clouds along the wind direction. In contrast to these datasets, the climatology combines the well-known advantages of the wide-FOV sky images to describe the horizontal changes of cloud amount across the wind direction. The recent update includes (1) a new tool for visualization of these across-wind changes with user-selected spatial and temporal resolutions, (2) an additional macrophysical property, the so-called cloud equivalent diameter (CED), estimated over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) and (3) environmental parameters. Our development of the extended climatology is aimed to enhance understanding of the environmental impact on the diurnal evolution of the cloud macrophysical properties and thus to improve performance of ShCu parameterizations.
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.
Cloud amount is an essential and extensively used macrophysical parameter of cumulus clouds. It is commonly defined as a cloud fraction (CF) from zenith-pointing ground-based active and passive remote sensing. However, conventional retrievals of CF from the remote sensing data with very narrow field-of-view (FOV) may not be representative of the surrounding area. Here we assess its representativeness using an integrated dataset collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site in Oklahoma, USA. For our assessment with focus on selected days with single-layer cumulus clouds (2005-2016), we include the narrow-FOV ARM Active Remotely Sensed Clouds Locations (ARSCL) and large-FOV Total Sky Imager (TSI) cloud products, the 915-MHz Radar Wind Profiler (RWP) measurements of wind speed and direction, and also high-resolution satellite images from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that the root-mean-square difference (RMSD) between the 15-min averaged ARSCL cloud fraction (CF) and the 15-min averaged TSI fractional sky cover (FSC) is large (up to 0.3). We also discuss how the horizontal distribution of clouds can modify the obtained large RMSD using a new uniformity metric. The latter utilizes the spatial distribution of the FSC over the 100° FOV TSI images obtained with high temporal resolution (30 sec sampling). We demonstrate that cases with more uniform spatial distribution of FSC show better agreement between the narrow-FOV CF and large-FOV FSC, reducing the RMSD by up to a factor of 2.
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