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
Wonbae Bang
BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric REmote sensing (CARE), Kyungpook National University, Daegu, Republic of Korea
BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Center for Atmospheric REmote sensing (CARE), Kyungpook National University, Daegu, Republic of Korea
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Total article views: 4,536 (including HTML, PDF, and XML)
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Total article views: 3,445 (including HTML, PDF, and XML)
Thereof 3,431 with geography defined
and 14 with unknown origin.
Total article views: 1,091 (including HTML, PDF, and XML)
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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.
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been...
