A dual-frequency radar retrieval of two parameters of the snowfall particle size distribution using a neural network

Randy J. Chase, Stephen W. Nesbitt, Greg M. McFarquhar

Research output: Contribution to journalArticlepeer-review

Abstract

With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter Dml and the liquid equivalent normalized intercept parameter Nwl. Evaluations against a test dataset showed statistically significantly improved ice water content (IWC) retrievals relative to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were-0.7%, +2.6%, and +1% for Dml, Nwl,andIWC, respectively. An evaluation on three case studies with collocated radar observations and in situ microphysical data shows that the NN retrieval has MPE of-13%, +120%, and +10% for Dml, Nwl, and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals relative to the default algorithm, removing the default algorithm’s ray-to-ray instabilities and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations.

Original languageEnglish (US)
Pages (from-to)341-359
Number of pages19
JournalJournal of Applied Meteorology and Climatology
Volume60
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • Aircraft observations
  • Ice particles
  • Neural networks
  • Radars/Radar observations
  • Remote sensing
  • Snowfall

ASJC Scopus subject areas

  • Atmospheric Science

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