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Radar rainfall estimate experience in Taiwan

Learn about the rainfall estimation techniques in Taiwan using radar, disdrometer, and rain gauge networks. Explore the challenges and advantages of these methods and the potential of polarimetric radar for improved rainfall estimates.

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Radar rainfall estimate experience in Taiwan

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  1. Radar rainfall estimate experience in Taiwan TaiChi Chen Wang ,National Central University Goals: Precipitation mechanisms in Taiwan area. Rainfall estimate from radar, disdrometer and raingauge network. Ground truth validation.

  2. Base data advantages • CWB High density rain gauge network • CWB S-Band Doppler radar network (since 2001) • Difficulties • Less rain gauge sites in mountain area • Few gauges over ocean • Terrain blocking of radar beams • Heavy rain events often occur in mountain area • Z-R relations variability from QPESUMS

  3. QPESUMS : (2005) • CWB’s solution to real time rainfall estimate • QC to radar and gauge data • Compensation for partial beam • Using gauge data to calibrate radar estimate • Rain map every 6-8 minutes • Good results in dense gauges area.

  4. TRMM/SCSMEX experience (1998) Instruments: C-Pol radar, ARG Data processing: Attenuation effect in ZHH and ZDR ZDR bias KDP calculation Rainfall estimate with polarimetric variables.

  5. NCU Typhoon Nari (2001)

  6. log10 R vs log10Z + : Do < 0.125 cm : 0.125< Do < 0.150 cm . : 0.150 <Do< 0.175 cm X : Do> 0.175 cm log10Z • 同樣的降雨率若對應到較大的 ,則會因為有分布偏向大雨滴的粒徑分布 而有較大的回波強度 。

  7. Radar reflectivity underestimate

  8. 九月十六日 : 1600 ~ 1700 ( UTC ) Z (+3 dBZ ) with modified Z-R x Radar

  9. Drop size distribution and Z-R variability from two years (2001-2003) 2D-video disrometer data

  10. The narrow Do distribution for heavy rainfall rate (> 65mm/hr) happened in typhoon and Mei-Yu cases.

  11. Disdrometer network (2004) Radar reflectivity under estimate due to partial beam

  12. What do we learn from the disdrometer and radar observation? The classical DSD and Z-R relation variability. Heavy rain in typhoon has unique DSD. The under estimate reflectivity from terrain blocking. Careful calibration with disdrometer and raingauges can provide more correct radar rainfall estimate. Will Polarimetric radar help to provide better rainfall estimate?

  13. National Central University, Central Weather Bureau, Water Resources Agency and National Science Council help to upgrade the NCU Doppler radar to a dual Polarimetric system. (2004)

  14. Beam pattern calibration

  15. In northern Taiwan, two Doppler radars, one polarimetric radar , a few disdrometers and raingauge network.

  16. A rainband under the influence of typhoon Nanmadol (2004, Dec.)

  17. ZDR μ 10logKDP/No μ

  18. 資料來源: 2004/12/04 01: 19 to 11:49 Nangang Nan-Gang J-W disdrometer Shi-Ding rain gauge Si-Du raingauge ShiDing Si-Du NCU radar

  19. 51.0mm/hr 南港雨滴譜觀測,12月4日01:19 52.2mm/hr 由KDP及ZDR反演雨滴粒徑分佈

  20. Nan-Gang Rainfall usingsingle Z-R relation Rainfall using DSD retrieved from polarimetric variables ZDR and KDP

  21. Shi-Du terrain blocking Rainfall usingsingle Z-R relation Rainfall using DSD retrieved from polarimetric variables ZDR and KDP

  22. Three hour rainfall accumulation retrieved from dual polarization radar algorithm

  23. Quality control of polarimetric variables Clutter removal by ρ hv < 0.9 threshold 將HV小於0.9的資料去除。 偏極化雷達可藉由低ρHV值,將非氣象資料去除,地形雜波與海面回波可十分容易地濾除。

  24. Attenuation correction

  25. Mei-Yu case 2005 May 12

  26. Matsa typhoon 2005 Aug. 5

  27. Summary • Steady progress including : instrument hardware, • data quality and • rainfall algorithm. • 2. Integration of radar ,disdrometer and raingauge data • to provide the optimum rainfall. • Outlook • 1. Characteristic of polarimetric variables in different weather systems. • 2. Precipitation mechanisms through the kinematic and thermodynamic • retrieval from the Doppler radar data together with the microphysics revealed • from the polarimetric variables. • 3. Radar data assimilation.

  28. 2005年雨滴譜觀測 翡翠水庫 南港中研院 石門水庫 霞雲 中央大學

  29. (A)偏極化雷達估計降水方法 1. 偏極化雷達配合雨滴譜儀推導雷達參數—雨量公式。

  30. (A)偏極化雷達估計降水方法 Gamma雨滴粒徑分佈: N(D) = N0Dμe-ΛD (1) Brandes (2003) (2) (3) 降雨率 , V(D): 3.8D0.67 m/s 雨滴終端落速

  31. 本研究

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