TY - JOUR
T1 - A dual-frequency radar retrieval of two parameters of the snowfall particle size distribution using a neural network
AU - Chase, Randy J.
AU - Nesbitt, Stephen W.
AU - McFarquhar, Greg M.
N1 - Funding Information:
Acknowledgments. Funding for this research was provided to the University of Illinois by NASA Precipitation Measurement Missions Grant 80NSSC19K0713 and NASA Earth System Science Fellowship 80NSSC17K0439. We thank all of the participants of the field campaigns used here for their tireless effort in collecting the data used in this study. Furthermore, we also thank Google Colaboratory for their open-source free computing platform that allowed for the simple implementation of the neural network presented here. We also thank the three anonymous reviewers for the insightful comments that enhanced this paper.
Publisher Copyright:
© 2021 American Meteorological Society.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - Aircraft observations
KW - Ice particles
KW - Neural networks
KW - Radars/Radar observations
KW - Remote sensing
KW - Snowfall
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U2 - 10.1175/JAMC-D-20-0177.1
DO - 10.1175/JAMC-D-20-0177.1
M3 - Article
AN - SCOPUS:85103825362
SN - 1558-8424
VL - 60
SP - 341
EP - 359
JO - Journal of Applied Meteorology and Climatology
JF - Journal of Applied Meteorology and Climatology
IS - 3
ER -