1. National S&T Center for Disaster Reduction Rainfall estimation by BMRC C-Pol radar ICMCS-V 2006.11.03 Lei FengBen Jong-Dao Jou 1 Lei Feng and 1,2 Ben Jong-Dao Jou ( 鳳雷 ) ( 周仲島 ). 1 National S&T Center for Disaster Reduction 2 National Taiwan University

2. National S&T Center for Disaster Reduction Objectives To illustrate the ability of rainfall estimation using Areal R(Φ DP ) and R(K DP ) by BMRC C- Pol radar. Radar-Raingauge comparisons in three different sizes of area : –Multi-beam –Multi-beam (Area ~100 km 2 ) Areal R(Φ DP ) –Single-beam –Single-beam (Area ~ 25 km 2 ) Areal R(Φ DP ) –Point –Point (Area ~ 2 km 2 ) R(K DP ) wind drift effectTry to correct the wind drift effect when comparing with single raingauge.

3. National S&T Center for Disaster Reduction NSSL Ryzhkov and Zrnic(1998) CSU Bringi (2001) Two Areal Rainfall schemes Notice the difference: Gate area ↑ with range ↑ but NSSL scheme without area weighting Keep the area weighting, but need Φ DP information at each range gate 1

4. National S&T Center for Disaster Reduction C-Pol radar at (0,0) In Darwin 18 rain gauges in the 10 x 10 km 2 area BMRC C-Pol rain gauge network 5 single-beams, the area of each beam is ~ 25 km 2 1 multi-beam, the area of each beam is ~ 100 km 2 18 raingauges, the radar coverage of each gauge is ~ 2 km 2 (radius 0.8 km) RD-69

5. National S&T Center for Disaster Reduction Case A - 15 Jan 1999 Case A, Time series plot (100 km 2 )

6. National S&T Center for Disaster Reduction Case B - 01 Mar 1999 Case B, Time series plot (100 km 2 )

7. National S&T Center for Disaster Reduction Case C - 17 Mar 1999 Case C, Time series plot (100 km 2 )

8. National S&T Center for Disaster Reduction Multi-beam results Area size: ~100 km 2 Very high correlation coefficient: 0.97 Small standard deviation: 1.99 mm/hr Little underestimate Sample number: 108 case (A+B+C)

9. National S&T Center for Disaster Reduction Single-beam results Area size: ~25 km 2 High correlation coefficient: 0.94 Small standard deviation: 3.43 mm/hr Little underestimate Sample number: 590 case (A+B+C)

10. National S&T Center for Disaster Reduction Point results Area size: ~2 km 2 Low correlation coefficient: 0.86 Large standard deviation: 6.38 mm/hr Under estimation Sample number: 1091 case (A+B+C) The result is getting worse as the verification area getting smaller. Why ?

11. National S&T Center for Disaster Reduction Point Comparison Problems Inherence difference of the measurements: Rain gauge accumulates continuously rainfall on a point while radar samples almost instantaneously a volume averaged rainfall rate. Zawadzki (1975) already described Radar-Gauge comparison problems: Wind drift effect Time lag effect

12. National S&T Center for Disaster Reduction Can we correct the wind drift effect ? from DLOC Strong horizontal wind Overestimate or Underestimate ? How about the wind drift effect ?

13. National S&T Center for Disaster Reduction Optimaloffsetvector Optimal offset vector An area of radar data which covering all surface rain gauges is moved around the original point in a square window (8km x 8km) with 200 m interval in X and Y direction. The cross-correlation coefficient is calculated between the time lagged (1.5 minute) surface rain rates of the gauges and the space shifted radar rain rates. A two-dimensional correlation field is produced. The distance from the point of the maximum correlation to the original point was defined as “optimal offset” of the horizontal displacement.

14. National S&T Center for Disaster Reduction Optimaloffset vector Optimal offset vector

15. National S&T Center for Disaster Reduction Only 39/89 volumes can be easily found out the offset vectors, most of them are convective type rain. Case B Case A Case C Is these two reasonable ? 2 km

16. National S&T Center for Disaster Reduction Checking the optimal vector far from system moving velocity case

17. National S&T Center for Disaster Reduction No wind drift correction After wind drift correction Case A Case B

18. National S&T Center for Disaster Reduction If the coefficient of R(K DP ) estimator increase 50%, it look better. Can we do this change for this case ? No wind drift correction after wind drift correction But significant underestimation

19. National S&T Center for Disaster Reduction Rainfall with smaller raindrops need to use higher coefficient in R(K DP ) estimator Adopted from L. D. Carey ATMO 689 big Zdr ~ big Do small Zdr ~ small Do

20. National S&T Center for Disaster Reduction Averaged Volume median diameter using Zdr(D 0 ) CASE gauge rain rateradar rain rate > 5 mm/h>10 mm/h> 20 mm/h> 5 mm/h> 10 mm/h >20 mm/h A1.381.441.541.381.461.55 B1.421.461.531.441.461.51 C1.091.101.141.111.20-- In case C, D 0 is significant lower than case A and B

21. National S&T Center for Disaster Reduction Storm motion

22. National S&T Center for Disaster Reduction Disdrometer observation Radar estimation Volume median diameter D 0 estimation Note: the comparison here is not the same case, but are similar squall line type precipitation in Darwin.

23. National S&T Center for Disaster Reduction No wind drift correction After wind drift correction

24. National S&T Center for Disaster Reduction summary (1) It’s very important to consider the wind drift effect when doing single point radar-gauge comparison. In this study, the normalized error has 17% improvement. rainrate Arainrate Brainrate C Rainrate ABC Do No Wind Drift correction 0.780.850.740.810.74 Wind drift correction 0.930.960.870.930.93 Correlation Coefficient of radar-raingauge comparison

25. National S&T Center for Disaster Reduction summary (2) Use the BMRC C-Pol radar phase base estimator to estimate rain rate is very accurate, especially on convective rainfall. For accurate rain rate estimation, it needs to consider the DSD variability such as stratiform rainfall, orographic rainfall, shallow convective warm rain and so on when using R(K DP ) estimator.

26. National S&T Center for Disaster Reduction Lag 3 minLag 4 min Optimal vector Finding, 06:40 15-Jan-1999 (C-Pol at Darwin) Lag 0 minLag 1 minLag 2 min Lag 5 min