The authors are grateful to three anonymous reviewers for many constructive comments. This investigation was supported by NOAA/Office of Oceanic and Atmospheric Research under NOAA–University of Oklahoma Cooperative Agreements NA17RJ1227 and NA08OAR4320904, the U.S. Department of Commerce, ONR Grants N00014-11-10439 and N00014-11-1-0518, the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office (CPO) Climate Prediction Program for the Americas/Earth System Science Program (CPPA/ESS) Grant NA10OAR4310160, and the Department of Energy Office of Science Grant DE-SC0006736. The computing for this project was performed at the OU Supercomputing Center for Education and Research (OSCER) at the University of Oklahoma.
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By “dynamically balanced” we mean a microphysical dataset that is produced across a realistic distribution of thermodynamical parameters: the latter can be generated in a 3D CRM or LES simulation.
The “bulk” microphysics parameterization is based on the prediction of moments of the drop size distribution function, which represent bulk cloud characteristics such as cloud/rain liquid water content and cloud/rain drop concentration.
Horizontal cross section is defined from liquid water path field using a threshold of 40 g m−2.
Note that the dataset included output from 13 time levels from the 12–24-h simulation.
These remaining errors are due to the use of rather coarse resolution (only 15 × 15 bins) and limiting the PDF-normalized variables’ range to 3 times the mean value. Discretizing the PDF in greater detail (higher resolution and a wider range) would reduce the errors, but at the expense of a greater number of PDF bins to be integrated over. The selected number of bins (15) is a good compromise between accuracy and complexity of the discretized PDF.