9C.4 Global Snowfall as Revealed by IMERG V07

Wednesday, 31 January 2024: 9:15 AM
339 (The Baltimore Convention Center)
Jackson Tan, NASA Goddard Space Flight Center, Greenbelt, MD; University of Maryland Baltimore County, Baltimore, MD; and L. Milani and G. J. Huffman

The Integrated Multi-satellitE Retrievals for GPM (IMERG) product is a NASA precipitation product gridded at 0.1° every half-hour globally from the Global Precipitation Measurement (GPM) mission. On top of estimating surface precipitation rates, IMERG also provides a diagnostic estimate of the probability of precipitation phase, thus enabling a distinction between rainfall and snowfall. With improvements to the precipitation retrievals, the refinement of the phase estimate, and extension to full global coverage, the recent V07 release represents a unique opportunity to study the global snowfall rates at an unprecedented resolution.

This presentation examines the distribution of snowfall in IMERG V07A both globally and regionally. By leveraging IMERG’s high resolution and long record, we investigate the climatology and seasonal variation not just from a snowfall volume point of view but also from peak snowfall intensity and snow event duration perspectives that only high-resolution data can provide. For example, while the climatological map of total snowfall volume is similar to that of the number of snow days, the Great Lakes region stands out with more snowfall volume for its number of snow days, likely due to greater intensity from lake effect snow. We select a few regions of focus, such as California where precipitation phase plays an important role in water resource management. We also compare the IMERG snowfall against global observations from CloudSat and GPM as well as regional ground measurements to assess its reliability and identify its limitations.

With the latest advances in the algorithm, IMERG V07 represents a unique opportunity to study snowfall globally using a combination of fine resolution, complete global coverage, and long record, with the potential of tracking snowfall events globally.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner