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) 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) The 2 nd GPM-GV Workshop, 27-30, September 2005, Taiwan

Remote Sensing Research Lab.  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 Contents

Remote Sensing Research Lab. Confidence GPM Product II. KEOP Supersites (Hae Nam)  Micro rain radar  Autosonde  Wind profiler  Optical rainguage  Meteorological tower(30m) I. Basic Rainfall Validation   Raingauges(536 AWS)   Radar(10)   Radiometer(2) Rainfall Retrieva l GPM Satellite Data Potential Applications Weather prediction Water management Severe weather monitoring Flood prediction Cloud microphysics Cloud-radiation modeling Climate research Understanding weather phenomena Agriculture III. Data Assimilation GV Basic Science Modeling Physics, etc. Calibration Improve Retrieval Algorithms Schematic Diagram of Korea GPM(K-GPM)

Remote Sensing Research Lab. Observation Network Conventional Station Automatic Weather Station No. Obs. No. of station No. of daily observation Surface Upper-air32-4 Aeronautical No. Obs. No. of station No. of daily observation Measured elements AWS536 Every min. (Continuous) Temp., Wind, Preci., etc Ocean Buoy 524 Temp., Wind, Wave, SST, etc. Ground Observation Network

Remote Sensing Research Lab.  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 ASOSAWSMountain AWS Ground Observation Network

Remote Sensing Research Lab. 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. Ground Observation Network

Remote Sensing Research Lab. Real Time Data Collection Network Telecommunication Network in KMA

Remote Sensing Research Lab. Radar Network of KMA No. of stationTime resolutionObservation eliments Operational Weather Radar 10(1)Every 10 min. Reflectivity, Radial Velocity, Spectral Width 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

Remote Sensing Research Lab. C-band radar(ROKAF) S-band radar Aerosonde(from Australia) 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 Ground Observation Supersite

Remote Sensing Research Lab. Heanam Super sites Understanding of the land- surface hydrological and cloud-precipitation processes in cloud physics and numerical model. Intensive 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 Optical Rain Gauge Continuous accurate rain rate observation. Autosonde Continuous upper air observation Boundary Layer Radar Producing one-minute profile of vertical and horizontal winds Produce high resolution temporal and spatial data for the monitoring, analysis and prediction of severe weather phenomena(typhoon, fronts…) Ground Observation Supersite

Remote Sensing Research Lab. Ground Observation Network FacilityAgencyEquipmentStatusAdditional Info NationalKMA 536 Rain Gauges, 13 x 13 km 1 minute readout Operational.1 and.5 mm tipping bucket 10 Radars (C-, S-band)Operational 10 Wind Profilers Planned 2004 (2), 2005-’08 (8) Haenam KEOP Supersite METRI (KMA) X-band Doppler RadarOperational Operating as CEOP Supersite AutosondeOperational Boundary Layer Wind Profiler Operational Micro Rain RadarOperational Flux Tower (10 m)Operational Optical Rain GaugeOperational RadiometerOperational

Remote Sensing Research Lab. 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 Window Probability Matching Method (WPMM) Space resol. = 1 km Time resol. = 1 min. 1x1 km

Remote Sensing Research Lab. Radar Data NCAR/SPRINTNCAR/CEDRIC 2-D Reflectivity data Raingauge Data Raw data check TRMM/GSP Rain rate(mm/h) 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) Data Procedure for WPMM

Remote Sensing Research Lab. Radar Scan strategy and characteristics Bright Band Ground and sea clutter  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

Remote Sensing Research Lab.  Beam blocking area  Range attenuation error  Bright-band contamination  C-band(5), S-band(4) 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 Rdar Data Height (km) Range (km) 0 0 Radar Data Processing Research radar Muan

Remote Sensing Research Lab. Precipitation events check ( B, rain event ) : if the time interval between Tips less than 30 minutes, B is rain event Half tip ( C ) : if the time interval between tips is within minutes, insert the half tip in the middle Calculate the 1-minute rain rate(mm/h) using Cubic Spline Interpolation each rain event Calculate the bias of measured rainfall and interpolated rainfall Bias = rainfall(measured) / rainfall(interpolationed) Rain-rate greater than 1000 mm/hr single tip or isolated tips ( A ) -> Gaussian interpolation is applied( R=R 0 exp(-x 2 /100) ) Calculated rain-rate = bias * {rain(1 st sec. of min.) – rain(2 nd sec. of min.} *60 TRMM/GSP t mm A B C  TRMM/GSP algorithm Raingauge Data Processing

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

Remote Sensing Research Lab.  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) TRMM/GSP & ORG

Remote Sensing Research Lab. 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) Computation of rain rates applied Z-R relationship every 10 min. (No precipitation under threshold dBZ) Z-R Fitting : - Median Fitting ( Under and Over the 30 dBZ ) Z-R pairs > Threshold Num. Z-R Fitting : - Z-R relationship of former time Yes No Data reading : (1) 1-minute rain rate of each raingauge using TRMM-GSP (2) 10-minute reflectivity of each radar using RSL Produce Rain Rate from each radar-raingauges rain rate WPMM (Window Probability Matching Method) Reiteration Num. > 1 Yes No Rain Rate Product

Remote Sensing Research Lab. YX  Y=AX +B Linear equation  Intersection of Y-axis : 10 loga  Slope : 10 b  Fitting Method : Median Fit Fitting example [Gwangduksan] 1720 LST July 7, Z-R fitting

Remote Sensing Research Lab. 7 July August 2004 S-band Radar Operation stop (reinstalling) Sensitivity test for Z-R relationship S-Band C-Band

Remote Sensing Research Lab. 19 Jun.20 Jun. 19 Jun.20 Jun. 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 Precipitation Product Comparison June 2004 TRMM/GSP WPMM

Remote Sensing Research Lab. 19 Jun.20 Jun. Comparison Total Rainfall

Remote Sensing Research Lab. 19 Jun.20 Jun. Comparison BAIS

Remote Sensing Research Lab. 18 August 2004 [ Time: 1200 – 1400 KST ] Precipitation Product Comparison TRMM/GSP WPMM

Remote Sensing Research Lab. Radar – AWS difference Radar rain intensity object analysis (AWS grid point) AWS rain intensity object analysis (AWS grid poing) Verification in Space

Remote Sensing Research Lab. WPMM rain intensity AWS rain intensity Z=200R 1.6 rain intensity Verification Area Manned AWS site -Seoul-Gyungki -Gwangwon -Chungcheong -Jeonla -Gyungsang -Cheju Verification in Time

Remote Sensing Research Lab. Products Rain intensity Composite Each radar rain intensity Each radar reflectivity Verification in space Verification in time series Products Rain intensity Composite Each radar rain intensity Each radar reflectivity Verification in space Verification in time series wpmm.metri.re.kr Real time Web Service

Remote Sensing Research Lab.  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=200R 1.6 ) and S-band radars were more accurate than C-band radars. Concluding Remarks

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