Evaluation of raindrop size distribution. retrievals based on the Doppler spectra. using three beams. Christine Unal

Transcription

1 Evaluation of raindrop size distribution retrievals based on the Doppler spectra using three beams Christine Unal Remote-sensing of the environment (RE)

2 In this talk: Differential reflectivity Z dr cannot be used (near-vertical profiling or light rain) Doppler spectra first evaluation of rain Drop ize Distribution (DD) comparing retrievals from the same radar resolution volume using two different polarizations second evaluation comparing DD retrievals in different directions during stratiform light rain R radar Influence of the radial wind on the DD estimates 2 echnology Remote-sensing of the environment (RE)

3 F-CW Doppler-polarimetric -band R radar wind measurement available echnology Remote-sensing of the environment (RE) 3

4 Doppler spectra model Based on oisseev, Chandrasekar, Unal and Russchenberg, 2006: Dualpolarization spectral analysis for retrieval of effective raindrop shapes Input - Drop ize Distribution (DD) : utput median volume diameter D 0 intercept parameter N w shape parameter µ - Radial wind (v 0 ) - pectral broadening (σ 0 ) 4 echnology Remote-sensing of the environment (RE)

5 Retrieval technique Based on oisseev, Chandrasekar, Unal and Russchenberg, 2006: Dualpolarization spectral analysis for retrieval of effective raindrop shapes Non-linear optimization of D 0, µ, σ 0 + estimation of N w, v 0 + = 5 echnology Remote-sensing of the environment (RE)

6 ensitivity analysis imulation results on averaged Doppler spectra (10-55 dbz reflectivity, average:30) Parameter D 0 N w µ σ 0 v 0 Region mm mm -1 m m s m s -1 RD 0.12 mm 1350 mm -1 m m s m s -1 CV(RD) 17% 54% 8.4% 28% Parameter Z LWC N t RD 0.30 db 0.13 g m m -3 CV(RD) 0.91% 22% 7.4% 6 echnology Remote-sensing of the environment (RE)

7 ulti-beam retrieval example (non-averaged Doppler spectra) Vertical 75 deg 75 deg beam 69 deg echnology 7 Remote-sensing of the environment (RE)

8 ulti-beam retrieval profile example edian volume diameter D 0 profile in 3 different looking directions Retrieval procedure + height-smoothing Vertical beam 8 echnology Remote-sensing of the environment (RE)

9 Retrievals comparison (light rain) HH and VV non averaged Doppler spectra D 0 N w µ v 0 σ 0 Z N t LWC 9 echnology Remote-sensing of the environment (RE)

10 Dynamical retrieval v 0 Comparison between retrieval of radial wind (-v 0 ) and radial component of mean horizontal wind measured by R echnology 10 Remote-sensing of the environment (RE)

11 Influence of the error on v 0 on the DD retrieval Doppler spectra are shifted using R mean horizontal wind measurement before retrieval 11 echnology Remote-sensing of the environment (RE)

12 Influence of the error on v 0 on the median volume diameter (D 0 ) 1600 m ean horizontal wind correction before retrieval 1400 m 800 m 400 m 12 echnology Remote-sensing of the environment (RE)

13 Conclusions development of an automatic rain retrieval technique based on the complete Doppler spectrum (from drizzle to heavy rain) a tool for study cases consistency of the retrievals (D 0, µ, v 0, σ 0, LWC) representing the same radar resolution volume when Z dr is too small (light precipitation, near-vertical profiling), it looks necessary to input the radial component of the mean horizontal wind to reduce the errors on (D 0, N w, µ) adding two other looking directions (beams) to estimate the contribution of the radial wind may solve this problem 13 echnology Remote-sensing of the environment (RE)

14 oisseev, Chandrasekar, Unal and Russchenberg, 2006: Dual-polarization spectral analysis for retrieval of effective raindrop shapes echnology 14 Remote-sensing of the environment (RE)

15 1 Iterative selection procedure 2 estimation procedure Do + σ 0 + µ Nw + V 0 Do min L σ o min L µ min L v Do max max meas mod L( D ) = sz ( v) sz ( v, D ) 0 HH, db HH, db 0 v= vmin ( σ ) v σ o max max meas mod HH db( ) HH db(, σ ) D0 = min L = sz v sz v 0,, 0 v v v Variable projection method (Rust, 2003) Estimation from Do, σ 0 and µ ax cross-correlation µ max σ 0, D0 v= vmin max meas mod + L( µ ) = sz HH, db( v) sz HH, db ( v, µ ) 2 2 Do σ 0 µ odel spectrum 4 ptimization procedure from cost function study (L) 5 Final outcome 15 echnology 3 utput of the Remote-sensing model of the environment (RE) 2 Fitting measured spectrum Error calculation Non linear least-square algorithm

16 Retrieval outputs: dsd, v 0 and σ 0 median volume diameter D 0 µ bounded [-1 5] spectral broadening σ 0 bounded [0 1] m s -1 intercept parameter N w free radial wind v 0 free crucial to get the actual D 0 help Polarimetry with Z dr Wind estimates with v 0 Reflectivity 16 echnology Remote-sensing of the environment (RE)

17 ulti-beam raindrop size comparison (Z-D 0 ) 1600 m ean horizontal wind correction before retrieval 1400 m 800 m 400 m 17 echnology Remote-sensing of the environment (RE)

18 ulti-beam raindrop size comparison (N w ) 1600 m ean horizontal wind correction before retrieval 1400 m 800 m 400 m 18 echnology Remote-sensing of the environment (RE)

19 ulti-beam raindrop size comparison (µ) 1600 m ean horizontal wind correction before retrieval 1400 m 800 m 400 m 19 echnology Remote-sensing of the environment (RE)