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Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables. Reference: Jung Y., G. Zhang, and M. Xue, 2008: Assimilation of simulated polarimetric
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Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables Reference: Jung Y., G. Zhang, and M. Xue, 2008: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables. Mon. Wea. Rev., 136, 2228–2245. Speaker: Yi-Yun Chien Adviser: Prof. Ming-Jen Yang
OUTLINE • Keywords • Introduction • The model and convective-storm simulations • The observation operators • Applications to convective storms • Summary and conclusions
Keywords(1,2) • OSSEs (Observing System Simulation Experiments)觀測系統模擬實驗 • Radar simulator 雷達模擬器 for Dual-Polarmetric Radar RUN1: Generate a "nature" atmosphere IC: Sounding, etc. Observation operators Simulate observations (Radar simulator) Assess the impact of DA Data assimilation RUN2:Add perturbation
Keywords(3) • Melting ice (snow-hail) model 冰融化模型 Dry snow (Dry hail) Rain-snow mixture (Rain-hail mixture) qs(qh) qrs(qrh) MI model : Melting ice model
Introduction • The advantage of Data Assimilation (DA) • Problem1: Microphysics scheme in convective scale?Doppler radar: Vr and reflectivityPolarimetric Doppler radar: Zdr, Zdp, Kdp • Problem2:Mixed-phase hydrometeor => MI model
The model and convective-storm simulations • Advanced Regional Prediction System (ARPS) Xue et al. (2000; 2001; 2002) • Ice microphysics scheme -> Lin et al. (1983) • Output variables : u, v, w, p, θ, qv, qc, qi, qr, qs, qh, etc. • StormSquall-lineSupercell storm
The observation operators • The shape, orientation, and drop size distribution of hydrometeors • Melting ice (snow-hail) model • Observation operators
a. The shape, orientation, and drop size distribution of hydrometeors D:equivalent diameter(mm) By an equivalent model (Green 1975) Minor axis Major axis r≡ Canting angle:
a. The shape, orientation, and drop size distribution of hydrometeors x : hydrometeor type D: diameter λ : slope (Lin et al. 1983) (From Marshall and Palmer, 1948.)
b. Melting ice (snow-hail) model • Fraction F: fraction of rain-snow mixture Set Fmax = 0.5 Fqr : mixing ratio of rainwater in the mixture form (1 – Fqr) : mixing ratio of rainwater in the pure water form qrs=F(qr+qs) Snow melting fw = 0 fs = 1 fw = 1 fs = 0 • Dielectric constant Maxwell–Garnett mixing formula (Maxwell-Garnett 1904)
c. Observation operators x : hydrometeor type fa , fb : backscattering amplitudes ZVV ZHH (Radar Lab, NCU)
c. Observation operators Hydrometeor reflectivities Total reflectivities
(Zrnic, 1999) ZDR: differential reflectivity Zdp:reflectivity difference KDP :specific differential phase
Applications to convective storms a. Squall-line b. Supercell storm
LI : Linear interpolation model (Jung et al. 2005) MI : Melting ice model Convective precipitation region
qr ZH ZDR qs qh KDP
qr ZH ZDR qs qh KDP
Summary and conclusions • The purpose to develop radar operators:1. To assimilate the corresponding measurements into storm-scale numerical models2. To verify model predictions against radar observations • A new melting model:Assuming a function for water fraction based on known mixing ratios of rainwater, snow, and hail=> Improve microphysics schemes
Summary and conclusions • Convective-storm characteristics are well reproduced • Reflectivity is overestimated due to:1. Neglecting non-Raleigh scattering2. Fixed DSD intercept parameter of hail3. Lack of raindrop breakup
Keywords(1,2) • OSSEs (Observing System Simulation Experiments)觀測系統模擬實驗“將真實觀測到的環境場資訊,ex:sounding等,放到數值模式裡建立虛擬的真實大氣情況與(雷達)觀測資料。再配合實驗的目的修改模式或同化方法,重新預報,最後和虛擬真實大氣做比較,以瞭解各實驗的預報能力。” (黃,2007) • Radar simulator 雷達模擬器 for Dual-Polarmetric Radar
Keywords(1,2) Radar simulator Radar simulator (黃, 2007)
a. The shape, orientation, and drop size distribution of hydrometeors *Fit a polynomial function (Zhang et al. 2001) Minor axis Major axis D:equivalent diameter(mm) By an equivalent model (Green 1975) r≡ 31
c. Observation operators x : hydrometeor type fa , fb : backscattering amplitudes (function of fw)
Rain Minor-axis Major-axis Rain-snow Rain-hail Major-axis Major-axis Minor-axis Minor-axis c. Observation operators
Zdp Zhrain Zhrain+ice
ε: relative permittivity ε0: permittivity in vacuum p.10 Dielectric constant Maxwell–Garnett mixing formula (Maxwell-Garnett 1904)
Microphysics scheme: LFO83 (Lin,1983)
p.21-22 LI(linear interpolation) 100% dry hail 100% dry snow -2.5˚C -5˚C Linear change Linear change 2.5˚C 0˚C 100% wet hail 100% wet snow Function of temperature
Scattering http://hyperphysics.phy-astr.gsu.edu/Hbase/atmos/blusky.html#c4