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Characterizating Stratiform Clouds Variability from Millimeter-Wave Radar Data

Kogan, Z.N.(a), Mechem, D.B.(b), and Kogan, Y.L.(c), Cooperative Institute for Mesoscale Meteorological Studies (a), University of Oklahoma (b)
Fourteenth Atmospheric Radiation Measurement (ARM) Science Team Meeting

We analyzed the variability of low stratiform clouds over the Atmospheric Radiation Program (ARM) Southern Great Plains Cloud and Radiation Testbed using winter season observations from the millimeter wave cloud radar (MMCR) for a total of 3 years. The objective aims to use statistical moments of reflectivity to characterize cloud system variability that may be used for parameterizations of sub-grid cloud variability in meso- and large-scale numerical models. This information is currently neglected in models microphysical process rate calculations. Nearly 1000 hours of observations of overcast low stratiform clouds were analyzed for two cloud types: boundary layer stratocumulus and low altitude stratiform clouds. Our emphasis was also on the role of precipitation and how it affects cloud variability. For this purpose the cloud radar was used to discriminate clouds into precipitating and non-precipitating categories. We demonstrate that cloud variability strongly depends not only on cloud type and the presence of precipitation but also on cloud mean reflectivity and scale size. As a result different parameterizations are needed for different cloud types, for non-precipitating and precipitating clouds, and for different model grid-sizes. Based on observations of low clouds we suggest new formulations of sub-grid cloud variability for boundary layer and low altitude clouds developed for grid sizes characteristic of mesoscale (10 km) and large-scale (30 km) numerical models. We demonstrate that the ARM MMCR as an instrument can be successfully used to discriminate characteristics of non- and precipitating low clouds. Most importantly, the suggested formulation expands the measurement capabilities of the ARM MMCR by developing a method that obtains not only the mean value of a parameter, but also its variability.

Note: This is the poster abstract presented at the meeting; an extended version was not provided by the author(s).