摘要(英) |
Geographically, Taiwan is located at subtropics, snowfall rarely occurred on the low elevation except the mountains. However, January 23 to 24, 2016 northern Taiwan was affected by strong continental cold high pressure system, many low altitude areas observed solid hydrometeor particles.
In order to analyze and validate this snow event, National Central University C-POL dual-polarization radar and CWB laser-optical Particle Size Velocity (PARSIVEL) at Anbu station were employed to classify the type of solid hydrometeor particles.
Dual-Polarization radar provides both high spatial and temporal resolution. An obvious layer with high differential reflectivity ZDR and low cross-correlation coefficient ρHV at very low altitude was observed in vertical cross section as the precipitation type switched from stratiform rain to snow. Simultaneously, the terminal velocity of particles turned smaller and the numbers of aggregrate increased at Anbu parsivel station. The radar data analysis matched parsivel data in the course of melting level gradually approached ground. According to Atlas and Ulbrich (1977) present Z-R relationship and Locatelli and Hobbs (1974) found the best-fit curves between diameter and terminal velocity of particles, summarize a range of suitable snowfall formulas in Taiwan.
Through the comparison between preliminary data analysis and the documented statistical classification results of ice particles, according to Locatelli and Hobbs(1974), the particles was classified as Densely rimed dendrites type. Further, the NCAR fuzzy logic bulk-hydrometeor particle identification algorithm (PID) was applied to the radar data to get the three-dimensional distribution of particle types. Wet snow, light rain and graupel were detected as a result of PID at lowest level, these results conformed with the in situ observation at NCU and Anbu station. |
參考文獻 |
參考文獻
劉妍利,2007:梅雨降水系統的雙偏極化雷達資料分析與WRF模式模擬研究,國立中央大學大氣物理所碩士論文。1-26
林育邦,2015:應用雙偏極化氣象雷達反演過冷水之個案分析。6-7
楊錫豐,2013:颱風雨帶水象粒子分布特徵研究。13-16
Crystal J., Lawrence D., Walter A., Petersen, Mariana, William. ,2011:Development and Testing of Operational Dual-polarimetric Radar Based Lightning Initiation Forecast Techniques. NOAA Tech. Memo. 7p
Edward A., Brandes, Kyoko Ikeda, Gregory Thompsonmichael Schonhuber,2008:Notes and Correspondence Aggregate Terminal Velocity/Temperature Relations. J. Atmos. tec. 2729-2736
Elizabeth J., Steven A., Brenda ,2014:A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation. J. Atmos. tec.,1457-1481
Keenan,2002: Hydrometeor classification with a C-band polarimetric radar. Aust. Met. Mag,52, 23-31
Locatelli J.D., Hobbs P.V.,1974:Fall Speeds and Masses of Solid Precipitation Particles. Journal Geophysical .185-2197
Marzano, Scaranari D., Celano M., Alberoni P., Vulpiani1 G., Montopoli M., 2005: Hydrometeor Classification From Dual-Polarized Weather Radar:Extending Fuzzy Logic From S-band To C-band Data. Advances in Geosciences, 7, 109–114, 2006
Takeharu,K., Hiroshi,U.Tadayasu,O.,Mariko,O.,2015:A Hydrometeor Classification Method for X-Band Polarimetric Radar:Construction and Validation Focusing on Solid Hydrometeors under Moist Environments. J. Atmos. ocean,2052-2074
Vivekanandan, J., Zrnic, Ellis, Oye R.., Ryzhkov , Straka, 1999:Cloud Microphysics Retrieval Using S-Band Dual- Polarization Radar Measurements. Bull.Amer. Meteor.Soc., 80, 381-388. |