Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3559-2025
https://doi.org/10.5194/gmd-18-3559-2025
Model evaluation paper
 | 
18 Jun 2025
Model evaluation paper |  | 18 Jun 2025

Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data

Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee

Data sets

The processed PARSIVEL, sounding, and AWS dataset Wonbae Bang et al. https://doi.org/10.5281/zenodo.14351937

The model output of SBM (version 1DSBM-19M) Wonbae Bang and GyuWon Lee https://doi.org/10.5281/zenodo.14353025

The new decision algorithm of surface precipitation type for PARSIVEL data and final decision results Wonbae Bang et al. https://doi.org/10.5281/zenodo.14353519

Model code and software

The source code of SBM (version 1DSBM-19M) Jacob Carlin et al. https://doi.org/10.5281/zenodo.14350651

The plotting program for MRR data Wonbae Bang and Kwonil Kim https://doi.org/10.5281/zenodo.14352684

The calculation and plotting program for the 5 diagnosis methods Wonbae Bang et al. https://doi.org/10.5281/zenodo.14354011

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Short summary
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
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