TY - JOUR
T1 - Sub-Cloud Turbulence Explains Cloud-Base Updrafts for Shallow Cumulus Ensembles
T2 - First Observational Evidence
AU - Zheng, Youtong
AU - Rosenfeld, Daniel
AU - Li, Zhanqing
N1 - Publisher Copyright:
© 2021. American Geophysical Union. All Rights Reserved.
PY - 2021/3/28
Y1 - 2021/3/28
N2 - Sub-cloud turbulent kinetic energy has been used to parameterize the cloud-base updraft velocity (wb) in cumulus parameterizations. The validity of this idea has never been proved in observations. Instead, it was challenged by recent Doppler Lidar observations showing a poor correlation between the two. We argue that the low correlation is likely caused by the difficulty of a fixed-point Lidar to measure ensemble properties of cumulus fields. Taking advantage of the stationarity and ergodicity of early afternoon convection, we developed a Lidar sampling methodology to measure wb of a shallow cumulus (ShCu) ensemble (not a single ShCu). By analyzing 128 ShCu ensembles over the Southern Great Plains, we show that the ensemble properties of sub-cloud turbulence explain nearly half of the variability in ensemble-mean wb, demonstrating the ability of sub-cloud turbulence to dictate wb. The derived empirical formulas will be useful for developing cumulus parameterizations and satellite inference of wb.
AB - Sub-cloud turbulent kinetic energy has been used to parameterize the cloud-base updraft velocity (wb) in cumulus parameterizations. The validity of this idea has never been proved in observations. Instead, it was challenged by recent Doppler Lidar observations showing a poor correlation between the two. We argue that the low correlation is likely caused by the difficulty of a fixed-point Lidar to measure ensemble properties of cumulus fields. Taking advantage of the stationarity and ergodicity of early afternoon convection, we developed a Lidar sampling methodology to measure wb of a shallow cumulus (ShCu) ensemble (not a single ShCu). By analyzing 128 ShCu ensembles over the Southern Great Plains, we show that the ensemble properties of sub-cloud turbulence explain nearly half of the variability in ensemble-mean wb, demonstrating the ability of sub-cloud turbulence to dictate wb. The derived empirical formulas will be useful for developing cumulus parameterizations and satellite inference of wb.
KW - cloud-base updrafts
KW - cumulus parameterization
KW - Doppler Lidar
KW - shallow cumulus
KW - southern great plains
KW - sub-cloud turbulence
UR - http://www.scopus.com/inward/record.url?scp=85103159342&partnerID=8YFLogxK
U2 - 10.1029/2020GL091881
DO - 10.1029/2020GL091881
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AN - SCOPUS:85103159342
SN - 0094-8276
VL - 48
JO - Geophysical Research Letters
JF - Geophysical Research Letters
IS - 6
M1 - e2020GL091881
ER -