The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall events in the CloudSat snowfall dataset. The remaining 5% of snowfall events are distributed between other categories. Distinct oceanic versus continental trends exist between the two major snowfall categories, as shallow cumuliform snow-producing clouds occur predominantly over the oceans. Regional differences are also noted in the partitioned dataset, with over-ocean regions near Greenland, the far North Atlantic Ocean, the Barents Sea, the western Pacific Ocean, the southern Bering Sea, and the Southern Hemispheric pan-oceanic region containing distinct shallow snowfall occurrence maxima exceeding 60%. Certain Northern Hemispheric continental regions also experience frequent shallow cumuliform snowfall events (e.g., inland Russia), as well as some mountainous regions. CloudSat-generated snowfall rates are also partitioned between the two major snowfall categories to illustrate the importance of shallow snow-producing cloud structures to the average annual snowfall. While shallow cumuliform snowfall produces over 50% of the annual estimated surface snowfall flux regionally, about 18% (82%) of global snowfall is attributed to shallow (nimbostratus) snowfall. This foundational spaceborne snowfall study will be utilized for follow-on evaluative studies with independent model, reanalysis, and ground-based observational datasets to characterize respective dataset biases and to better quantify CloudSat snowfall detection and quantitative snowfall estimate uncertainties.

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Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.