Radar based Quantitative Precipitation Estimation in WRC Jae-Kyoung Lee

HOW MUCH?? HOW LONG??

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

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

Radar-AWS Rainrate (RAR) Calculation System

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

 Summer season Month (2012~13)AccuracyBiasRMSE Correlation coefficient December January February Average  Winter season Month (2012)AccuracyBiasRMSE Correlation coefficient June July August Average Accuracy of the RAR System

Improvement of the RAR System Part 1

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

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

Results of the LGC Method Application  Summer season Month (2012) RMSE (mm/hr) Improvement ratio (%, RMSE) Before the LGCAfter the LGC June July August Average % △  Winter season Month (2012~13) RMSE (mm/hr) Improvement ratio (%, RMSE) Before the LGCAfter the LGC Average % △ The accuracy of RAR system with the LGC method was improved

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

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

Reflectivity Bias Correction  Application period Summer season: 3 cases in June-August 2012 Winter season: 3 cases in Dec ~ Feb  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

Application Results Case Before the Z-bias correction (mm/hr)After the Z-bias correction (mm/hr) AccBiasRMSECorr 1-hr AccBiasRMSECorr 1-hr RMSECorrRMSECorr ~1900 KST ~2200 KST ~ KST ~2350 KST ~2350 KST ~2350 KST The accuracy of RAR system with Z-bias correction was improved over all

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

Improvement of the RAR System Part 2

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)

The Rainfall Accuracy for Each QC Method Method Rainfall (mm/10-min)Rainfall (mm/hr) AccuracyRMSECorrelation BiasRMSE ORPG QC Fuzzy QC The accuracy of rainfall estimation with the Fuzzy QC was improved

Comparison of the QC Images Case: 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)

Radar-based Multi-sensors QPE (RMQ)

Objective  Verification of the RMQ system accuracy using historical cases

Comparison of the RAR and RMQ Results Case RARRMQImprovement ratio (%) RMSE (mm/hr) RMSECorrelation ~ KST ~ KST ~ KST ~ KST ~ KST Ave △ △ In all cases, the accuracy of RMQ system was superior to the RAR system

Comparison of the RAR and RMQ Images Case3: 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

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

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

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

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 )= ×(Z H ) (Marshall and Palmer) R(Z H, Z DR )= ×(Z H 0.93 )×(10 (0.1×-3.43×ZDR) ) (Bringi and Chandraseker, 2001)

Case Analysis Results  Deagu AWS station CaseZ-R relationship Bias (mm/5-min) RMSE (mm/5-min) Correlation coefficient Case1 ( KST) R(Z H )BC * R(Z H, Z DR )ORG ** R(Z H, Z DR )BC Case2 ( KST) R(Z H )BC R(Z H, Z DR )ORG R(Z H, Z DR )BC Case3 ( KST) R(Z H )BC R(Z H, Z DR )ORG R(Z H, Z DR )BC Case4 ( KST) R(Z H )BC R(Z H, Z DR )ORG R(Z H, Z DR )BC

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

Comparison of the Outcomes for Each Z-R Relation

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

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)= ×Z (Marshall-Palmer) ② R(Z, Zdr)= ×(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 → ×(Z 0.93 )×10 (0.1×-3.43×Zdr) if (Kdp<0.3 or Z<38) and Zdr<0.5 → ×(Z )

Test Operation for the Rainfall Estimation  Description Z-R relation: ⑤ R(Z, Zdr, Kdp)_NSSL R(Z)= 0.017×Z 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)= ｜ Zdr-1 ｜ 1.3 ; f 2 (Zdr)= ｜ Zdr-1 ｜ 1.7

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

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

Hybrid Scan Reflectivity (HSR)

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

Application Data  Description Radar site: KWK, GDK, KSN single-pol radar Case: Case1: ~ KST (Jangma) Case2: ~ KST (Low pressure) Case3: ~ KST (Typhoon) Rainfall based radar: HSR rainfall, RAR rainfall, M-P rainfall Observation data: within radar observation range (100 km) SiteTotalBeam-blockNon-block KWK (54%)93 (46%) GDK (37%)107 (63%) KSN 9154 (59%)37 (41%)

Concept of the HSR Technique

Flowchart of the HSR Technique

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

The Accuracy of the HSR Technique CaseMethodMAE (mm/hr)F-MAERMSE (mm/hr)F-RMSE CASE I HSR RAR M-P(Z=200R 1.6 ) CASE II HSR RAR M-P(Z=200R 1.6 ) CASE III HSR RAR M-P(Z=200R 1.6 ) Total HSR RAR M-P(Z=200R 1.6 )