Understanding the microphysical control and spatial-temporal variability of warm rain probability using CloudSat and MODIS observations

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Geophysical Research Letters - 2022 - Zhang - Understanding the Microphysical Control and Spatial‐Temporal Variability of.pdf (2.86 MB)

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https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022GL098863

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https://doi.org/10.1029/2022GL098863
 http://hdl.handle.net/11603/25038

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UMBC Physics Department
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https://orcid.org/0000-0001-9491-1654

Date

2022-05-13

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journal articles
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Citation of Original Publication

Zhang, Z., Oreopoulos, L., Lebsock, M. D., Mechem, D. B., & Covert, J. (2022). Understanding the microphysical control and spatial-temporal variability of warm rain probability using CloudSat and MODIS observations. Geophysical Research Letters, 49, e2022GL098863.https://doi.org/10.1029/2022GL098863

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract

By combining measurements from MODIS and the CloudSat radar, we develop a parameterization scheme to quantify the combined microphysical controls by liquid water path (LWP) and cloud droplet number concentration (CDNC) of the probability of precipitation (PoP) in marine low cloud over tropical oceans. We demonstrate that the spatial-temporal variation of grid-mean in-cloud <PoP> can be largely explained by the variation of the joint probability density function of LWP and CDNC in the phase space specified by the bivariate PoP (LWP and CDNC) function. Through a series of sensitivity tests guided by this understanding, we find that in the Southeastern Pacific and Atlantic the stratocumulus to cumulus transition of the <PoP> is mainly due to the variation of CDNC while the annual cycle is mainly due to the variation of LWP. The results of this study provide a viable way to diagnose the root cause of warm rain problems in global climate models.