cldradheatcombret > Combined Retrieval, Microphysical Retrievals & Heating RatesData Source Type(s) > PI

The PNNL Combined Remote Sensor retrieval algorithm (CombRet) is designed to retrieve cloud and precipitation properties for all sky conditions. The retrieval is based on a combination of several previously published retrievals, with new additions related to the retrieval of cloud microphysical properties when only one instrument is able to detect cloud (i.e. radar only or lidar only). The CombRet has been evaluated against other algorithms in Zhao et al. (2012) and Comstock et al. (2013).


The purpose of the dataset is to provide best estimate total hydrometeor profiles from non-precipitating clouds to precipitating deep convection in a single dataset, to retrieve cloud microphysics and broadband radiative heating rate profiles for all-sky conditions, to help understanding of the cloud radiative impacts in the warm tropical oceanic environment, and to evaluate and improve numerical weather prediction model and climate model simulations.


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Data Details

Developed By Zhe Feng
Contact Zhe Feng
Resource(s) Data Directory
Data format netcdf
Site GAN
Content time range 10 October 2011 - 8 February 2012
Attribute accuracy No formal attribute accuracy tests were conducted
Positional accuracy N/A
Data Consistency and Completeness Yes, dataset may contain some bad values, although some basic Quality Control of removing out-of-range values have been applied. Users are advised to read the provided detail documentation carefully for additional details.
Access Restriction No access constraints are associated with this data.
Use Restriction No use constraints are associated with this data.
File naming convention SiteName + DataSetName + Version + Author + FacilityID: e.g. gan2combret7fengM1
Directory Organization Merged KAZR/S-Pol moment profiles: gan2kazrspolcombineM1; Cloud microphysics retrieval: gan2combret7fengM1; Cloud radiative heating rate retrieval: gan2combret7feng_hr1M1
Citations Feng, Z., S. A. McFarlane, C. Schumacher, S. Ellis, J. Comstock, and N. Bharadwaj, 2014: Constructing a Merged Cloud-Precipitation Radar Dataset for Tropical Convective Clouds during the DYNAMO/AMIE Experiment at Addu Atoll. J. Atmos. Oceanic Technol., 31, 1021-1042. doi:

Comstock, J. M., A. Protat, S. A. McFarlane, J. Delanoe, and M. Deng, (2013): Assessment of Uncertainty in Cloud Radiative Effects and Heating Rates through Retrieval Algorithm Differences: Analysis using 3-years of ARM data at Darwin, Australia. J. Geophys. Res., 118, 4549-4571, doi:10.1002/jgrd.50404.

Zhao, C., et al. (2012), Toward understanding of differences in current cloud retrievals of ARM ground-based measurements, J. Geophys. Res., 117, D10206, doi:10.1029/2011JD016792.


Yu H, R Johnson, P Ciesielski, and H Kuo. 2018. "Observation of Quasi-2-Day Convective Disturbances in the Equatorial Indian Ocean during DYNAMO." Journal of the Atmospheric Sciences, 75(9), 10.1175/JAS-D-17-0351.1.


Thorsen TJ, Q Fu, and JM Comstock. 2013. "Cloud effects on radiative heating rate profiles over Darwin using ARM and A-train radar/lidar observations." Journal of Geophysical Research: Atmospheres, 118(11), 10.1002/jgrd.50476.

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