A Dual-Frequency Radar Retrieval of Two Parameters of the Snowfall Particle Size Distribution Using a Neural Network

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  • 1 Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
  • 2 Cooperative institute of Mesoscale Meteorological Studies and School of Meteorology, University of Oklahoma, Norman, OK 73072, USA
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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 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 significant improved ice water content (IWC) retrievals compared 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 and IWC, respectively. An evaluation on three case-studies with co-located radar observations and in-situ microphysical data show 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 compared 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.

Corresponding author: Randy J. Chase, randyjc2@illinois.edu

This article is included in the Global Precipitation Measurement (GPM) special collection.

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 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 significant improved ice water content (IWC) retrievals compared 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 and IWC, respectively. An evaluation on three case-studies with co-located radar observations and in-situ microphysical data show 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 compared 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.

Corresponding author: Randy J. Chase, randyjc2@illinois.edu

This article is included in the Global Precipitation Measurement (GPM) special collection.

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