PI Product : Combined Retrieval, Microphysical Retrievals & Heating Rates
[ research data - ASR funded ]
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.
|Developed by||Zhe Feng|
PO Box 999 MSIN: K9-24
Richland, WA 99352
|Data Usage||There are several sets of files provided and are explained below: 1) Merged KAZR/S-Pol/MPL moments files, containing basic radar moments, cloud mask, precipitation, etc.: gan2kazrspolcombineM1 2) CombRet cloud microphysics files, containing water content, effective particle size, cloud phase, etc.: gan2combret7fengM1 3) CombRet radiative heating rate files, containing shortwave/longwave broadband radiative heating rate profiles, surface/TOA fluxes, etc.: gan2combret7feng_hr1M1 4) 1-hourly averaged radiative heating rate files on constant pressure grid for entire AMIE period, containing all relevant variables in 3): gan2combret7feng_hr_20111010_20120208.nc 5) Quick look images for each day: combrethr_quicklooks|
|Content Time Range||2011.10.10 — 2012.02.08|
|Instruments||Active Remote Sensing of CLouds (ARSCL) product using Ka-band ARM Zenith Radars
NCAR S-PolKa dual-polarimetric radar (10 cm)
Surface Meteorological Instrumentation
|Attribute Accuracy||No formal attribute accuracy tests were conducted|
|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.|
|Factor Affecting the Research||N/A|
|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: http://dx.doi.org/10.1175/JTECH-D-13-00132.1
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.