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Evaluation of SWER(Ze) Relationships by Precipitation Imaging Package (PIP) during ICE-POP 2018

  • Ali Tokay
  • , Charles N. Helms
  • , Kwonil Kim
  • , Patrick N. Gatlin
  • , David B. Wolff
  • University of Maryland, Baltimore County
  • NASA Goddard Space Flight Center
  • University of Maryland, College Park
  • NASA Marshall Space Flight Center
  • National Aeronautics and Space Administration

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relation-ships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.

Original languageEnglish
Pages (from-to)691-708
Number of pages18
JournalJournal of Hydrometeorology
Volume24
Issue number4
DOIs
StatePublished - 2023

Keywords

  • Cloud microphysics
  • In situ atmospheric observations
  • Snowfall

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