4.7 Article

The Precipitation Imaging Package: Phase Partitioning Capabilities

Journal

REMOTE SENSING
Volume 13, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/rs13112183

Keywords

precipitation; mixed-phase precipitation; rain rate; snowfall rate; snow mass retrieval; video disdrometers

Funding

  1. NASA [NNX12AQ76G, 80NSSC18K0701, 80NSSC19K0712, 80NSSC19K0732, 80NSSC20K0982]
  2. NOAA [NA15NES4320001]
  3. Academy of Finland [333901]

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This study demonstrates the capability of the Precipitation Imaging Package (PIP) in distinguishing precipitation phases and providing reliable precipitation accumulations. The comparison with NWS observer records and co-located observations from radars and meteorological measurements supports the accuracy of the PIP in identifying precipitation phases.
Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events.

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