1. Radar based Quantitative Precipitation Estimation in WRC Jae-Kyoung Lee 2014. 06. 14

2. HOW MUCH?? HOW LONG??

3. Contents Radar-AWS Rainrate Calculation System Radar-based Multi-sensors QPE System 1 2 NCAR-based QPE Algorithm of S-band Dual-pol Radar 3  Improvement of the RAR system: Part I  Improvement of the RAR system: Part II Hybrid Scan Reflectivity 4

4. Quantitative Precipitation Estimation Model in Weather Radar Center Single-polDual-pol RAR system RMQ system Test operation for the QPE

5. Radar-AWS Rainrate (RAR) Calculation System

6. Concept of the RAR System Real-time parameter estimation Radar data AWS data (OBS)

7.  Summer season Month (2012~13)AccuracyBiasRMSE Correlation coefficient December 0.904.383.650.80 January 0.876.921.980.81 February 0.838.031.720.95 Average 0.876.442.450.85  Winter season Month (2012)AccuracyBiasRMSE Correlation coefficient June 0.873.103.930.87 July 0.802.048.640.86 August 0.792.806.600.87 Average 0.822.656.390.87 Accuracy of the RAR System

8. Improvement of the RAR System Part 1

9. Objectives and Methods  Objective Improve the RAR system using the bias correction methods  Methods Model bias correction: Local Gauge Correction (LGC) Observation bias correction: Reflectivity(Z) bias correction

10. Concept of the Local Gauge Correction R e : radar error, w: weight of error, e: error between radar and observation rainfall, i: number of obs. station α: impact factor, D: radar obs. range, d: distance between AWS and radar pixel α≥1: # of AWS are enough α<1: # of AWS are sparse  Ware (2005) Post-processing

11. Results of the LGC Method Application  Summer season Month (2012) RMSE (mm/hr) Improvement ratio (%, RMSE) Before the LGCAfter the LGC June 4.594.237.82 July 11.0910.089.11 August 10.379.736.13 Average 8.688.01 7.69% △  Winter season Month (2012~13) RMSE (mm/hr) Improvement ratio (%, RMSE) Before the LGCAfter the LGC 12 2.502.298.32 1 2.911.8835.18 2 2.341.8321.74 Average 2.582.00 22.45% △ The accuracy of RAR system with the LGC method was improved

12. Image Results of the LGC Application Case: 20120713 0530 KST in summer season Before the LGC methodAfter the LGC method Improvement of outcomes

13. Case: 20130201 1000 KST in winter season Image Results of the LGC Application Before the LGC methodAfter the LGC method Improvement of outcomes

14. Reflectivity Bias Correction  Application period Summer season: 3 cases in June-August 2012 Winter season: 3 cases in Dec. 2012 ~ Feb. 2013  Description Standard radar : Bislsan S-band dual-pol radar Bias correction value for each radar SiteZ bias (dBZ)SiteZ bias (dBZ) BRI-7.87JNI-1.16 GDK-4.29KSN-4.87 GSN-3.99KWK-5.15 GNG-4.77MYN-5.63 IIA-5.19PSN-2.28 SSP-4.5

15. Application Results Case Before the Z-bias correction (mm/hr)After the Z-bias correction (mm/hr) AccBiasRMSECorr 1-hr AccBiasRMSECorr 1-hr RMSECorrRMSECorr 20120608 0600~1900 KST 0.8722.4512.9820.5063.8700.7790.8823.1170.7450.6403.5710.848 20120810 0300~2200 KST 0.7595.6813.9580.5609.8430.1980.7837.9070.9260.79010.2070.618 20120829 1500~ 20120830 2300 KST 0.7503.6663.7020.5466.6320.8960.7523.8080.8300.5376.7210.892 20130121 0000~2350 KST 0.7521.4791.1490.4581.4420.9360.7161.8750.8130.4881.3990.930 20130201 0000~2350 KST 0.8081.1182.0500.5753.1910.7130.7211.4590.9500.3213.2380.981 20130205 0000~2350 KST 0.6606.9860.6170.4161.5790.8830.66811.0770.8030.8191.3790.880 The accuracy of RAR system with Z-bias correction was improved over all

16. BeforeAfter Before Image Results of the Z-bias Correction Case: 20120810 1500 & 1530 KST in summer season

17. Improvement of the RAR System Part 2

18. Objectives and Methods  Objective Improve the accuracy of rainfall estimation using the Quality Control (QC) methods  Methods Existing method: ORPG(Open Radar Product Generator) QC New method: Fuzzy QC (2013 version)

19. The Rainfall Accuracy for Each QC Method Method Rainfall (mm/10-min)Rainfall (mm/hr) AccuracyRMSECorrelation BiasRMSE ORPG QC0.8200.8260.5800.950-4.7157.221 Fuzzy QC0.8130.8470.5850.945-2.9315.982 The accuracy of rainfall estimation with the Fuzzy QC was improved

20. Comparison of the QC Images Case: 20120608 1000 KST in summer season Rainfall areas of all methods were similar to the AWS The Fuzzy QC had the larger rainfall area than the ORPG(brown circle) The Fuzzy QC didn’t handle the AP area(Purple circles)

21. Radar-based Multi-sensors QPE (RMQ)

22. Objective  Verification of the RMQ system accuracy using historical cases

23. Comparison of the RAR and RMQ Results Case RARRMQImprovement ratio (%) RMSE (mm/hr) RMSECorrelation 20120608 0600 ~ 0608 0800 KST 6.361.2979.671.78 20120615 0500 ~ 0616 0400 KST 2.750.6277.377.38 20120714 0800 ~ 0715 1500 KST 8.593.5858.3021.41 02120814 1700 ~ 0816 2300 KST 13.765.0363.4747.71 20120829 1500 ~ 0830 2300 KST 10.191.2887.4449.54 Ave 8.332.3673.25 △ 25.56 △ In all cases, the accuracy of RMQ system was superior to the RAR system

24. Comparison of the RAR and RMQ Images Case3: 20120715 1300 KST AWS RAR RMQ Rainfall areas of the RAR and RMQ system were similar to the AWS The RAR system displayed rainfall image excessively(red circles) The RMQ image looked more similar to the AWS

25. NCAR based QPE Algorithm of S-band Dual-pol Radar

26. Objectives and Research Contents  Objective Improve the NCAR based QPE algorithm for S-band dual-pol radar Carry out the verification case analysis  Research contents Improve the NCAR QPE algorithm of a X-band dual-pol radar from the NIMR to the QPE of a S-band dual-pol radar Carry out the case analysis and verify the accuracy of the rainfall amount estimation

27. Improvements in 2013 Bias correction: Z, Zdr QPE Generate QPE outcome files Attenuation, unfolding, filtering Bias correction: Z, Zdr QPE: R(Z, Zdr) Generate QPE outcomes files Remove the attenuation Use the proper Z & Zdr-bias Use of simple Z-R relationship Data transformation X-band dual-pol radar S-band dual-pol radar - improvement Radar data transformation Input: Radar UF data Output: TXT file (ASCII type) Input: Use of radar UF file directly (remove the data transformation) Output: DAT file (ASCII type) Output: Binary & NetCDF type QPE algorithm unfolding, filtering etc

28. Case Analysis  Description Radar site: Bislsan (BSL) S-band dual-polarization radar Observation: Deagu, Jinju AWS station Case: 4 cases in summer season 2012 Accuracy measures: Bias, RMSE, Correlation coefficient Z-R relationship: R(Z H )= 0.0365×(Z H 0.625 ) (Marshall and Palmer) R(Z H, Z DR )= 0.0067×(Z H 0.93 )×(10 (0.1×-3.43×ZDR) ) (Bringi and Chandraseker, 2001)

29. Case Analysis Results  Deagu AWS station CaseZ-R relationship Bias (mm/5-min) RMSE (mm/5-min) Correlation coefficient Case1 (20120630 KST) R(Z H )BC * -0.0050.2310.597 R(Z H, Z DR )ORG ** -0.0430.2300.618 R(Z H, Z DR )BC-0.0140.2270.618 Case2 (20120713 KST) R(Z H )BC-0.1340.8630.936 R(Z H, Z DR )ORG-0.0130.4340.967 R(Z H, Z DR )BC-0.0100.4320.967 Case3 (20120823 KST) R(Z H )BC-0.1920.6320.803 R(Z H, Z DR )ORG-0.2040.6200.786 R(Z H, Z DR )BC-0.0860.6200.786 Case4 (20120828 KST) R(Z H )BC-0.0010.2800.239 R(Z H, Z DR )ORG-0.0320.2750.224 R(Z H, Z DR )BC0.0030.3100.225

30. Comparison of the Images for Each Z-R Relation R(Z H,Z DR )BC R(Z H,Z DR )ORG R(Z H )BC

31. Comparison of the Outcomes for Each Z-R Relation

32. Improved unfolding Pdp Improved FIR filtering Improved the K DP calculation - Remove the non-meteorological echo - Median filtering Completion Improvements in 2014 Bias correction: Z, Zdr QPE: R(Z, Zdr) Generate QPE outcomes files Input: Use of radar UF file directly (remove the data transformation) Output: Binary & NetCDF type unfolding, filtering etc S-band dual-pol radar In progress

33. Test Operation for the Rainfall Estimation  Description Radar site: BRI S-band dual-pol radar Case: 2 cases in 2014 Z-R relation: ① R(Z)= 0.0365×Z 0.625 (Marshall-Palmer) ② R(Z, Zdr)= 0.0067×(Z 0.93 )×10 (0.1×-3.43×Zdr) (Bringi and Chandraseker, 2001) ③ R(Kdp, Zdr)= 90.3×(Kdp 0.93 )×10 (0.1×-1.69×Zdr) (Gorgucci and Scarchilli, 1997) ④ R(Z, Zdr, Kdp)_CSU (liquid part) if Kdp≥0.3 and Z≥38 and Zdr≥0.5 → R(Kdp, Zdr)= 90.3×(Kdp 0.93 )×10 (0.1×-1.69×Zdr) if Kdp≥0.3 and Z≥38 and Zdr<0.5 → R(Kdp)= 40.5×(Kdp 0.85 ) if (Kdp<0.3 or Z<38) and Zdr≥0.5 → 0.0067×(Z 0.93 )×10 (0.1×-3.43×Zdr) if (Kdp<0.3 or Z<38) and Zdr<0.5 → 0.0170×(Z 0.7143 )

34. Test Operation for the Rainfall Estimation  Description Z-R relation: ⑤ R(Z, Zdr, Kdp)_NSSL R(Z)= 0.017×Z 0.714 R(Kdp)= 44.0× ｜ Kdp 0.93 ｜ sign(Kdp) if R(Z)<6mm/hr : R=R(Z)/f 1 (Zdr) if 6≤R(Z)<50mm/hr : R=R(Kdp)/f 2 (Zdr) ; (under condition: Kdp≥0.3) if R(Z)≥50mm/hr : R=R(Kdp) ; (under condition: Kdp≥0.3) where, f 1 (Zdr)=0.4+5.0 ｜ Zdr-1 ｜ 1.3 ; f 2 (Zdr)=0.4+3.5 ｜ Zdr-1 ｜ 1.7

35. Test Operation for the Rainfall Estimation  Results: 20140427 0700 KST AWS R(Z) R(Z,Zdr) R_CSU R(Kdp)R_NSSL

36. Test Operation for the Rainfall Estimation  Real-time rainfall estimation for the test operation Z-R relation: if Zdr≥0.5 → 0.0067×(Z 0.93 )×10 (0.1×-3.43×Zdr) if Zdr<0.5 → 0.0170×(Z 0.7143 ) R(Z)_existing R(Z,Zdr)_test

37. Hybrid Scan Reflectivity (HSR)

38.  Objective Estimate rainfall in order to improve the bias of rainfall estimation due to the radar beam-blocking Objective

39. Application Data  Description Radar site: KWK, GDK, KSN single-pol radar Case: Case1: 20120705 0400 ~ 20120707 0200 KST (Jangma) Case2: 20120814 1700 ~ 20120816 2300 KST (Low pressure) Case3: 20120829 1500 ~ 20120830 2300 KST (Typhoon) Rainfall based radar: HSR rainfall, RAR rainfall, M-P rainfall Observation data: within radar observation range (100 km) SiteTotalBeam-blockNon-block KWK 203110 (54%)93 (46%) GDK 17164 (37%)107 (63%) KSN 9154 (59%)37 (41%)

40. Concept of the HSR Technique

41. Flowchart of the HSR Technique

42. Comparison of the Images for Each Method  Case: 201208301200 LST (Typhoon) Beam-block map HSR Rain AWS Rain GDK PPI Rain 0 degree elevation RAR_GDK Rain

43. The Accuracy of the HSR Technique CaseMethodMAE (mm/hr)F-MAERMSE (mm/hr)F-RMSE CASE I HSR 3.000.756.241.56 RAR 3.610.907.831.95 M-P(Z=200R 1.6 ) 3.090.776.301.58 CASE II HSR 4.730.709.401.40 RAR 5.680.8511.271.69 M-P(Z=200R 1.6 ) 4.910.739.511.41 CASE III HSR 2.560.564.541.05 RAR 2.890.695.561.40 M-P(Z=200R 1.6 ) 2.610.594.831.15 Total HSR 3.430.676.731.34 RAR 4.060.818.221.68 M-P(Z=200R 1.6 ) 3.540.696.881.38