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Clarifying Remotely Retrieved Precipitation of Shallow Marine Clouds from the NSF/NCAR Gulfstream V

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  • 1 a Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida
  • | 2 b Argonne National Laboratory, Lemont, Illinois
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Abstract

Precipitation is a key process within the shallow cloud life cycle. The Cloud System Evolution in the Trades (CSET) campaign included the first deployment of a 94-GHz Doppler radar and 532-nm lidar. Despite a larger sampling volume, initial mean radar/lidar-retrieved rain rates based on the upward-pointing remote sensor datasets are systematically less than those measured by in situ precipitation probes in the cumulus regime. Subsequent retrieval improvements produce rain rates that compare better to in situ values but still underestimate them. Retrieved shallow cumulus drop sizes can remain too small and too few, with an overestimated shape parameter narrowing the raindrop size distribution too much. Three potential causes for the discrepancy are explored: the gamma functional fit to the drop size distribution, attenuation by rain and cloud water, and an underaccounting of Mie dampening of the reflectivity. A truncated exponential fit may represent the drop sizes below a showering cumulus cloud more realistically, although further work would be needed to fully evaluate the impact of a different drop size representation upon the retrieval. The rain attenuation is within the measurement uncertainty of the radar. Mie dampening of the reflectivity is shown to be significant, in contrast to previous stratocumulus campaigns with lighter rain rates, and may be difficult to constrain well with the remote measurements. An alternative approach combines an a priori determination of the drop size distribution width based on the in situ data with the mean radar Doppler velocity and reflectivity. This can produce realistic retrievals, although a more comprehensive assessment is needed to better characterize the retrieval errors.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mampi Sarkar, mampi.sarkar@rsmas.miami.edu

This article is included in the Cloud System Evolution over the Trades (CSET) special collection.

Abstract

Precipitation is a key process within the shallow cloud life cycle. The Cloud System Evolution in the Trades (CSET) campaign included the first deployment of a 94-GHz Doppler radar and 532-nm lidar. Despite a larger sampling volume, initial mean radar/lidar-retrieved rain rates based on the upward-pointing remote sensor datasets are systematically less than those measured by in situ precipitation probes in the cumulus regime. Subsequent retrieval improvements produce rain rates that compare better to in situ values but still underestimate them. Retrieved shallow cumulus drop sizes can remain too small and too few, with an overestimated shape parameter narrowing the raindrop size distribution too much. Three potential causes for the discrepancy are explored: the gamma functional fit to the drop size distribution, attenuation by rain and cloud water, and an underaccounting of Mie dampening of the reflectivity. A truncated exponential fit may represent the drop sizes below a showering cumulus cloud more realistically, although further work would be needed to fully evaluate the impact of a different drop size representation upon the retrieval. The rain attenuation is within the measurement uncertainty of the radar. Mie dampening of the reflectivity is shown to be significant, in contrast to previous stratocumulus campaigns with lighter rain rates, and may be difficult to constrain well with the remote measurements. An alternative approach combines an a priori determination of the drop size distribution width based on the in situ data with the mean radar Doppler velocity and reflectivity. This can produce realistic retrievals, although a more comprehensive assessment is needed to better characterize the retrieval errors.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mampi Sarkar, mampi.sarkar@rsmas.miami.edu

This article is included in the Cloud System Evolution over the Trades (CSET) special collection.

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