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GV for the Evaluation of High Resolution Precipitation Products using WPMM in Korea

The 2 nd GPM-GV Workshop, 27-30, September 2005, Taiwan. GV for the Evaluation of High Resolution Precipitation Products using WPMM in Korea. J.C. Nam 1 , K.Y. Nam 1 , G.H. Ryu 2 , and B.J. Sohn 2 1 Korea Meteorological Administration (KMA) 2 Seoul National University (SNU). Contents.

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GV for the Evaluation of High Resolution Precipitation Products using WPMM in Korea

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  1. The 2nd GPM-GV Workshop, 27-30, September 2005, Taiwan GV for the Evaluation of High Resolution Precipitation Products using WPMM in Korea J.C. Nam1, K.Y. Nam1, G.H. Ryu2, and B.J. Sohn2 1 Korea Meteorological Administration(KMA) 2 Seoul National University(SNU)

  2. Contents • Ground Observation Networks of KMA - Automatic Weather Station(AWS) Network - Radar Network - Haenam Supersite • Window Probability Matching Method (WPMM) • Evaluation Precipitation Products using WPMM • Comparison precipitation products in space and time • Concluding Remarks

  3. Schematic Diagram of Korea GPM(K-GPM) Potential Applications GV • I. Basic Rainfall Validation •  Raingauges(536 AWS) •  Radar(10) •  Radiometer(2) GPM Satellite Data Calibration Confidence GPM Product Rainfall Retrieval Calibration II. KEOP Supersites (HaeNam)  Micro rain radar  Autosonde  Wind profiler  Optical rainguage  Meteorological tower(30m) Improve Retrieval Algorithms Basic Science Modeling Physics, etc. III. Data Assimilation

  4. Ground Observation Network Automatic Weather Station Conventional Station Observation Network

  5. Ground Observation Network • Automatic Weather Station(AWS) Network - ASOS(Automated Surface Observing System) : 42 sites • - Manned AWS(Automatic Weather System) : 35 sites • - Unmanned AWS(Automatic Weather System) : 459 sites ASOS AWS MountainAWS

  6. Ground Observation Network • Spatial resolution • ASOS + AWS network : 13 km • Unmanned AWS network : 14 km • Temporal resolution : 1 min. • Data Collection • - DSU Modem leased line(9,600 bps) • - DSU Modem + Microwave comm. • - ORBCOMM Satellite comm.

  7. Real Time Data Collection Network Telecommunication Network in KMA

  8. Radar Network of KMA Research radar Muan Operational radar • 5 C-band radars • Baekryungdo • Kunsan • Donghae • Cheju • Chungsong • 4 S-band radars • Gwangduksan • Jindo • Gwannaksan • Pusan • 1 C-band(Airport) • Incheon Research radar • 1 X-band radar • Muan

  9. Ground Observation Supersite C-band radar(ROKAF) X-band radar • Haenam Special observation site • autosonde for continuous upper air obs. • boundary layer wind profiler • micro rain radar for vertical structure of rain • optical rain gauge for continuous accurate • rain rate observation • conventional synoptic weather observation S-band radar Aerosonde(from Australia)

  10. Ground Observation Supersite Produce high resolution temporal and spatial data for the monitoring, analysis and prediction of severe weather phenomena(typhoon, fronts…) Autosonde Continuous upper air observation Boundary Layer Radar Producing one-minute profile of vertical and horizontal winds Intensive Observation Optical Rain Gauge Continuous accurate rain rate observation. Micro Rain Radar Producing vertical profiles of rain rate, LWC and drop size distribution Flux Tower Producing sensible, latent, and radiative fluses over land surface Understanding of the land- surface hydrological and cloud-precipitation processes in cloud physics and numerical model. Heanam Super sites

  11. Ground Observation Network

  12. Window Probability Matching Method (WPMM) Rain-gauge Space resol. = 1 km Time resol. = 1 min. 1x1 km Radar • Radar-Raingauge data processing - Special resolution: 1 km (Reflectivity, using Radar Software Library) - Temporal resolution: 1 min.(Rain rate, using TRMM/GSP algorithm) • Z-R Relationship - Set the minimum radar reflectivity corresponding with rain gauge (10 dBZ) - Estimation of Z-R relationship from Z-R pairs in real-time

  13. Data Procedure for WPMM Raingauge Data Raw data check TRMM/GSP Rain rate(mm/h) Data Radar Data NCAR/SPRINT NCAR/CEDRIC 2-D Reflectivity data Calculated the Z-R relationship using WPMM each radar Convert Rain Intensities using the real-time Z-R relationship Composite of all of Radar Intensity (Overlapping Maximum value select)

  14. Radar Scan strategy and characteristics • Jindo, Gwangduksan, Kwanaksan, Pusan (4 S-band Radar) • Baekyeongdo, Donghae, Kunsan, Cheju, Myeonbongsan, Youngjongdo (6 C-band) • 0.0 – 7.0° (C-band: 8 elevations , interval 10 minutes) • 0.0 – 19.0 ° ( S-band: 10 elevations, interval 10 minutes) • Melting layer level is about 3.5 – 5.5 km from June to August BrightBand Ground and sea clutter

  15. Radar Data Processing Research radar 4 3 Height (km) 2 Muan 1 0 0 100 150 170 190 210 240 Range (km) • Beam blocking area • Range attenuation error • Bright-band contamination • C-band(5), S-band(4) Rdar Data NCAR/SPRIINT -Resolution : 1x 1 x 0.5 km(Cartesian) -Height 1.5 – 4.0 km (interval: 0.5 km) • NCAR/CEDRIC • Standard deviation check • Ground clutter check • Beam filling • 2-Dimensional Reflectivity Data • Effective reflectivity height eliminated • the ground and bright band

  16. Raingauge Data Processing mm B C A t • TRMM/GSP algorithm Precipitation events check(B, rainevent) : if the time interval between Tips less than 30 minutes, B is rain event TRMM/GSP Half tip (C) : if the time interval between tips is within 20-30 minutes,insert the half tip in the middle • Calculate the 1-minute rain rate(mm/h) using Cubic Spline Interpolationeach rain event • Calculate the bias of measured rainfall and interpolated rainfall • Bias = rainfall(measured) / rainfall(interpolationed) Calculated rain-rate = bias * {rain(1st sec. of min.) – rain(2nd sec. of min.} *60 Rain-rate greater than 1000 mm/hr single tip or isolated tips (A) -> Gaussian interpolation is applied( R=R0exp(-x2/100) )

  17. Raingauge Data Processing y j+1 Rain-rate (mm/hr) y Accumulated Rainfall (mm) y j -15 0 15 y j-1 Time (min.) x j-1 x j x j+1 x Time (min.) • Gaussian Interpolation(Not TRMM/GSP algorithm) - tip interval greater than 30 minutes => Singletip - single tip consider as small convective precipitation • Cubic Spline Interpolation - tip interval within 30 minutes, effective data > 3 point - the slope of accumulated precipitation convert to rain-rate

  18. TRMM/GSP & ORG • Single tip does not accord to the rain-rate of ORG (Optical Raingauge) • Rain event accords to the rain-rate of ORG • One tip of AWS raingauge is 0.5 mm (rain-rate=30 mm/h)

  19. Rain Rate Product Data reading : (1) 1-minute rain rate of each raingauge using TRMM-GSP (2) 10-minute reflectivity of each radar using RSL WPMM (Window Probability Matching Method) Computation of Z-R pair every 10 minutes during 1 hour : (1) Rain rates of raingauges (- 9 ~ 0 minutes) (2) Reflectivites of radar grids(3×3) around raingauge (horizontal res.: 1 km) (3) Threshold : 10 ~ 60 dBZ, 0.5 mm/h (4) Rain-rate calculates using M – P relationship (if Reiteration Num. = 1) Yes No Z-R pairs > Threshold Num. Z-R Fitting : - Median Fitting ( Under and Over the 30 dBZ ) Z-R Fitting : - Z-R relationship of former time Reiteration Num. > 1 No Yes Computation of rain rates applied Z-R relationship every 10 min. (No precipitation under threshold dBZ) Produce Rain Rate from each radar-raingauges rain rate

  20. Z-R fitting • Y=AX +B Linear equation • Intersection of Y-axis : 10 loga • Slope : 10 b • Fitting Method : Median Fit Y X Fitting example [Gwangduksan]1720 LST July 7, 2004.

  21. Sensitivity test for Z-R relationship 7 July 2004 S-Band C-Band 18 August 2004 S-band Radar Operation stop (reinstalling)

  22. Precipitation Product Comparison 19-20 June 2004 TRMM/GSP WPMM Nowon(407) Corr. = 0.94, RMSE=3.04 mm/h Dongdaemun (408) Corr. = 0.95, RMSE=3.05 mm/h Jungrang(409) Corr. = 0.90, RMSE=3.12 mm/h Dongjak(410) Corr. = 0.97, RMSE=4.4 mm/h 19 Jun. 20 Jun. 19 Jun. 20 Jun.

  23. Comparison Total Rainfall 19 Jun. 20 Jun.

  24. Comparison BAIS 19 Jun. 20 Jun.

  25. Precipitation Product Comparison 18 August 2004 [ Time: 1200 – 1400 KST ] TRMM/GSP WPMM

  26. Verification in Space Radar – AWS difference Radar rain intensity object analysis (AWS grid point) AWS rain intensity object analysis (AWS grid poing)

  27. Verification in Time WPMM rain intensity Z=200R1.6 rain intensity AWS rain intensity • Verification Area • Manned AWS site • Seoul-Gyungki • Gwangwon • Chungcheong • Jeonla • Gyungsang • Cheju

  28. Real time Web Service http://wpmm.metri.re.kr • Products • Rain intensity Composite • Each radar rain intensity • Each radar reflectivity • Verification in space • Verification in time series

  29. Concluding Remarks • KMA’s operational Automatic Weather Station Network (13km*13km, one minute) and Weather radar(10 stations) can be used for GPM calibration and validation. • High resolution(1km x 1km) precipitation intensity were estimated from radar reflectivity with the various Z-R relationship obtained by WPMM using raingauges data of AWS operated by Korea Meteorological Administration (KMA). • Rain intensity produced by WPMM has a good agreement with ground rainfall data measured by raingauge and Optical Rain Gauge (ORG). • Rain intensities of S-band and C-band radars obtained by WPMM were more accurate than Z-R relationship (Z=200R1.6) and S-band radars were more accurate than C-band radars.

  30. Thank you for your attention !

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