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Reconstructing Subgrid Cloud Variability Guided by CloudSat/CALIPSO ObservationsPredicting realistic cloud subgrid variability remains a challenge for Global Climate Models (GCMs) even though it can play an important role for proper representation of processes pertaining to cloud microphysics, precipitation, and radiation, but also for comparisons with satellite observations which are of much higher resolution than model grids. The diagnostic approach of subgrid cloud variability is commonly handled by subcolumn cloud generators. For a specific GCM, one ideally wants the subgrid variability used for comparisons with satellite observations to be created by the same generator and with the same rules as the one used for model integration. With this in mind, we have embarked in an effort to test and improve cloud
subcolumn generators appropriate for GCMs. For this purpose, we use cloud (hydrometeor) products from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering signi􀂦cant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability.

The main question is: given a profile of cloud condensate mean and variance, can we create a subgrid cloud field that is statistically similar to the observed subgrid cloud field? By “statistically”, we suggest that we do not aspire to reproduce “well” each individual subgrid cloud field of a GCM-scale region, but that our generator performs well for a large ensemble of cases. We simulate radar, passive imager, and radiation flux fields from the observed 2D cloud fields and create one-point statistics; we use the profiles of cloud condensate mean and variance as input to the generator to create subgrid cloud fields; we compare the statistics; we adjust the rules of
the generator to create the best possible agreement between the radar, imager and radiation field statistics. In this process, the active observations have actually a dual role: they provide the actual subgrid cloud field which can be reduced to a profile of mean and variance used by the subcolumn generator, but they also provide the rules needed by the generator, such as measures (e.g, decorrelation length) of the vertical overlap of cloud fraction and of the condensate horizontal variability. This presentation will show our progress using Cloudsat and CALIPSO
products in this dual fashion.
Document ID
20205011564
Acquisition Source
Goddard Space Flight Center
Document Type
Poster
Authors
Nayeong Cho
(Universities Space Research Association Columbia, Maryland, United States)
Dongmin Lee
(Morgan State University Baltimore, Maryland, United States)
Lazaros Oreopoulos
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Zhibo Zhang
(University of Maryland, Baltimore County Baltimore, Maryland, United States)
Matthew D Lebsock
(Jet Propulsion Lab La Cañada Flintridge, California, United States)
Date Acquired
December 15, 2020
Subject Category
Geosciences (General)
Meeting Information
Meeting: AGU Fall Meeting
Location: Virtual
Country: US
Start Date: December 1, 2020
End Date: December 17, 2020
Sponsors: American Geophysical Union
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
CONTRACT_GRANT: NNX15AT34A
CONTRACT_GRANT: 80NM0018D0004P00002
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
Single Expert
Keywords
clouds
CLoudSat
CALIPSO
subgrid
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