Validation of the CloudSat precipitation occurrence algorithm using the Canadian C band radar network

Abstract

[1] The ability of CloudSat to detect precipitation in cold season cloud systems is examined using data from the Environment Canada C band weather radar at King City, Ontario. The factors complicating the comparison are the time mismatch, the differences in sensitivity, and the changes to the geometry of cross section with range from the ground radar, W band radar attenuation, and the effect of ground clutter. A total of 40 overpasses with precipitation were observed over the King City radar from September 2006 to April 2007. In about 14% of the precipitation profiles, time mismatches were diagnosed. When these cases were removed, the skill scores of the CloudSat precipitation occurrence product were excellent. The most frequent cause of a false detection was an incorrect precipitation threshold in the algorithm. The most frequent cause of a miss in detection was ground clutter removal of valid echoes by the algorithm. Overall, the CloudSat algorithm handled the effect of attenuation very well. Improvement to the algorithm would arise from a better tuning of the precipitation threshold, a threshold of À10 dBZ instead of À18 dBZ being more appropriate for winter storms in the Great Lakes area, and more effective ground clutter filtering in the lowest four range bins of the CloudSat data. The methodology employed here and the 1456 verified precipitation profiles from CloudSat can serve as a framework for a test bed to evaluate precipitation products from CloudSat.

Utilizing Spaceborne Radars to Retrieve Dry Snowfall

“…Despite its relatively brief existence, CloudSat's CPR has already demonstrated an ability to effectively detect and retrieve snowfall properties, as indicated by the unique census of global snowfall distribution and intensity highlighted by Liu (2008a). Additionally, Matrosov et al (2008b) and Hudak et al (2008) both demonstrate the potential utility of CPR data by comparing them to ground-based radar observations of snowfall cases.…”

TRMM 2A12 Land Precipitation Product - Status and Future Plans

“…In January 2007, GPM GV participated in the snowfall-measurement component of the Canadian CloudSat/Calipso Validation Project (C3VP) (Hudak et al 2006;Petersen et al 2007). GPM's participation was aimed at improving satellite-based snowfall detection and retrieval algorithms.…”

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

“…(1) allow the determination of snow water content and snowfall rate for each radar range bin. For the CloudSat algorithm, the required a priori microphysical and scattering properties were determined from analyses of snow observations from field campaigns focused on cold-season clouds and precipitation, principally the Canadian CloudSat-CALIPSO Validation Project (Hudak et al, 2006). Snowflake particle models were constructed based upon observed mass and horizontally projected area as a function of particle size .…”

“…For example, Wood et al (2015; but see also Wood, 2011) developed snowflake models for the CloudSat snowfall retrieval algorithm based upon field campaigns focused on coldseason clouds and precipitation, principally the Canadian CloudSat-CALIPSO Validation Project (C3VP, Hudak et al, 2006). They used this a priori knowledge of snowfall microphysics to refine expected snowfall-radar reflectivity relationships for the optimal-estimation-based (Rodgers, 2000) CloudSat snowfall retrieval scheme used for the CloudSat 2C-SNOW-PROFILE (Wood et al, 2013) product.…”