PNNL

Summary
In large‐scale weather prediction and climate models, the vertical structure of clouds is represented in terms of overlapping statistical distributions of various water types in the atmosphere. A previous study found that fast-falling cloud particles, or hydrometeors, were more likely to line up vertically and could be represented in models statistically. Non-precipitating clouds have significantly larger variability layer by layer and, therefore, a more random overlap. These phenomena are important to represent in the model to get the best depiction of the cloud. The study suggested that to improve the representation of cloud overlap, the correlations between cloud particles at different levels had to depend on particle properties, such as fall speeds, in addition to an existing dependency on convection strength.

Building on that work, researchers showed that the vertical correlation varied widely for different hydrometeor types, such as cloud liquid and ice, rain, snow, and graupel (soft hail). Results indicated corresponding fall speed as the primary factor controlling the degree of vertical alignment of these hydrometeor types, with vertical shear of the horizontal wind playing a smaller role. Researchers derived relationships between vertical correlation and fall speed by analyzing high-resolution simulations of cloud systems over DOE Atmospheric Radiation Measurement (ARM) user facility sites in Oklahoma and Australia, as well as radar observations. Further examinations using simple conceptual models linked horizontal and vertical cloud variability and provided insights into the roles of corresponding fall speed and wind shear. From this analysis, researchers proposed a parameterization that captured the observed range of vertical correlations. When based on a physical property of hydrometeors, such as fall speed, rather than artificially defined and model‐specific hydrometeor types, the parameterization can be applied to a wide range of treatments for microphysics, radiation, and instrument simulator calculations. This, in turn, will lead to improved regional and global models.
 
PI Contact
Jiwen Fan, Pacific Northwest National Laboratory, jiwen.fan@pnnl.gov
 
Funding
This research was supported by the Climate Model Development and Validation activity funded by the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science, with V. Larson’s portion supplied by grant DE-SC0016287. Forcing data are available from the ARM archive, sponsored by the DOE Office of Science. Computing resources for the simulations were provided by the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science user facility.