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Myoung-Hwan Ahn, Jae-Cheol Nam Meteorological Research Institute

KMA Plans for the GPM. Myoung-Hwan Ahn, Jae-Cheol Nam Meteorological Research Institute Korea Meteorological Administration B.J. Sohn Seoul National University 27 August 2003, APAN 16 th Meeting Busan. Contents. Background Objectives Future Plans Concluding remarks.

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Myoung-Hwan Ahn, Jae-Cheol Nam Meteorological Research Institute

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  1. KMA Plans for the GPM Myoung-Hwan Ahn, Jae-Cheol Nam Meteorological Research Institute Korea Meteorological Administration B.J. Sohn Seoul National University 27 August 2003, APAN 16th Meeting Busan

  2. Contents • Background • Objectives • Future Plans • Concluding remarks

  3. Flash flood causes the most damaging natural disaster in Korea Causes of meteorological disasters (average of 1983 to 1992) Human death 142 67 Damages caused by heavy rain fall and flash flood, due to the typhoon RUSA in 2002 58 3 Heavy rain Tropical cyclone Severe storm Hailstorm Human loss: 246 Disaster relief expenditure about 6 billion USD Heavy snowfall Severe drought Backgrounds

  4. Backgrounds The monitoring and prediction of disaster inducing phenomena including tropical cyclone and severe storms are critically important for the KMA’s mission • Tropical cyclone monitoring is mainly done by IR and Radar observation. There is clear advantages of MW data compared to IR. • Assimilation of precipitation data shows a promising improvement in the performance of numerical weather prediction model. Use of highly accurate rainfall information in the global circulation model could increase the accuracy of seasonal outlooks of floods and drought conditions.

  5. Core Satellite • TRMM-Like S/C, NASA • H2-A Launch, NASDA • Non-Sun Synchronous Orbit • ~ 65° Inclination • ~450 km Altitude • Dual Frequency Radar, NASDA • Ku & Ka Bands • ~ 4 km Horizontal Resolution • ~250 m Vertical Resolution • Multifrequency Radiometer, NASA • 10.7, 19, 22, 37, 85, 150 GHz V&H OBJECTIVES ∑ Understand Horizontal & Vertical Structure of Rainfall, its Microphysical Nature, & Associated Latent Heating ∑ Train & Calibrate Algorithms for Constellation Radiometers OBJECTIVES ∑ Provide Sufficient Global Sampling to Reduce Uncertainty in Short-Term Rainfall Accumulations ∑ Extend Scientific and Societal Applications • Global Precipitation Processing Center • Produces Global Precipitation Data Product Streams Defined by GPM Partners • Precipitation Validation Sites • Selected & Globally Distributed Ground- Based Supersites (Multiparameter radar, up looking radiometer/radar/profiler, raingages, & disdrometers) • Dense Regional Raingage Networks Backgrounds NASA/GSFC

  6. Backgrounds:GPM Program • Extend thespatial (mid to high latitudes) and temporal (about 3 hours) coverage, and data record (more than 10 years) of high quality rainfall measurement. • Improve accuracy and reduce uncertainty in rainfall measurements from better radar microphysics capability. • Observe broader spectrum of precipitation (e.g., light/warm rain, & snow). • Expand applications to climate change simulations, weather forecasts, and so on. Well matched with KMA’s future improvement direction.

  7. KMA’s Objectives The KMA objectives through the GPM program in Korea could be categorized into four folds; • The Calibration and Validation with ground observation data(AWS, Radar, Microwave radiometer…). • The Assimilation of GPM data into the Numerical Weather Prediction(NWP) Models. • Understanding of the Severe Weather System(Rain structure, energy cycle,…..) as well as the climate system. • Monitoring of tropical cyclone and severe storms with higher spatial and temporal resolution in real time.

  8. Cal/Val.: Potential Validation Site Canada England Germany NASA Land Spain Italy South Korea ARM/UOK Japan NASA KSC Taiwan France (Niger & Benin) India Brazil NASA Regional Raingage Site Supersite Supersite & Regional Raingage Site

  9. No. of daily observations No. of stations Surface 74 8 - 24 Upper-air 3 2-4 Aeronautical 9 24 - 48 No. of stations No. of daily observations Observing elements AWS 460 continuous Surface temp., wind, preci., etc. 5 Ocean Buoy 24 Temp., wind wave on seas, etc. Ca./Val.:Ground Observation Network Observation Network Conventional Station Automatic station

  10. No. of daily observation Observing elements No. of station Weather radar 6(3) Every 10 min. Cloud, preci., wind, etc. Lightning 10 Every 10 min. Position. movement, etc. Cal./Val.: Radar Network in Korea Weather radar network Composite radar image Lightning

  11. Cal./Val.: Intensive Observation Site 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)

  12. Cal./Val.: Intensive Observation Site 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 Period 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

  13. Cal./Val.: Example-1 Comparison between TRMM/PR and ground based AWS rain fall data for two different rain cloud structure.

  14. Rain Rate Cal./Val.: Example-1 Heavy rainfall by MCC (31 July, 1998) Heavy rainfall by typhoon Yanni (30 September, 1998) 220km

  15. Corr. = 0.87 Corr. = 0.71 latitude latitude 105.4 mm/hr 91.0 mm/hr 302.6 mm/hr 258.1 mm/hr RR_max RR_max longitude longitude longitude longitude Cal./Val.: Example-1 • Regardless of rain type, space based TRMM/PR and ground based AWS shows a good agreement in spatial distribution • Correlation between PR and AWS rain rate is usually better for strong convective system compared to the rain associated with other system such as typhoon or frontal system.

  16. Averaging Time Grid Size [Deg] Cal./Val.: Example-2 • Comparison between rain rate derived from IR and measured by AWS. • Mean bias and rms difference decrease with increasing grid size and averaging period • Correlation coefficient of 0.7 can be achieved either by increasing spatial and/or temporal sampling. • One min. data could be used for many val. Applications. 0.7 Sohn et al. (2002)

  17. Initial field (without Satellite) Model Outputs AWS rainfall distribution Initial field (with Satellite) Model Outputs AWS rainfall distribution Assimilation Heavy Rainfall at Mt. Jiri on 31 June 1998. (Fail to forecast) Heavy Rainfall at Kyung-Gi Pro. on 31 June 1999. (Success to forecast)

  18. 5-Day Storm Track Forecast from 08/20/98 @ 12:00 UTC QPF Threat Scores at Forecast Day 3 GSFC-DAO verified against TRMM observations blue: forecast-control; red: forecast-precip Surface Precipitation at Forecast Day 3 red: best track (NOAA HRD) green: forecast from analysis without precip data blue: forecast from analysis with precip data forecast-control forecast-precip contours show verification rainrates derived from TMI Hou et al., 2002: NASA/GSFC Assimilation

  19. Horizontal & Vertical Winds in Tropical Cyclone Bonnie ECMWF J.-F. Mahfouf Assimilation

  20. Precipitation structure With a parameterized convection Weak convection Without a parameterized convection The portion of convective rain due to the cumulus parameterization scheme averaged for 1979-2001. Areas of precipitation intensity less than 100 mm/month are omitted. Hong(2003) The Korean region is characterized by a smaller portion of convective rainfall. This is a reason why the parameterized convection plays a minor role in the simulation of heavy rainfall over Korea.

  21. TC 11S (ELINEA/LEON) TRMM 85h IR Monitoring Comparison between IR and MW Imagary for the initial stage of the tropical cyclone R.T. Edson(2002)

  22. Monitoring Comparison between IR and MW Imagary for the decaying stage of the tropical cyclone TMI/85GHz GMS-5/IR Ahn et al.(2002)

  23. Composition of N-16/AMSU, F-13,14,and 15/SSMI, and TRMM/TMI data

  24. Future Plans/issues • Enhancement of current observation network. • Based on the KMA’s long-term plan number of AWS and instrumentation will be expanded to have 13 km spatial resolution • Two more doppler radar will be added by 2003 and one more by end of 2005 making total of 10 radar sites. • Research level intensive observation site like the Haenam Observation Site will be added around the middle of Peninsula • Improvement of quality control procedure. • The accuracy of radar rainfall data will be improved by using of the collocated ground observation and multi-radar composition. • More comprehensive quality control procedure for the AWS data will be developed.

  25. Future Plans/issues • Development of cal./val. Procedure • From simple scatter diagram to complete physical validation procedure, there is large areas of room to be improved. • A regular ground based drop size distribution and vertical profiles of rain cloud will be implemented for this purpose. • Acquisition of DATA. • All ground based observation data will be stored at “National Data Center” and can be provided to user community in near-real time. • The means of GPM data exchange among the producer and to the user community seems not clear. • Possible responsibility of data processing and distribution. - There is possibility of Korea’s contribution to the constellation satellite.

  26. Concluding remarks • Automatic Weather Station Network(15km*15km, every minute) and weather radar(9 stations) in Korea could be used in calibration and validation of GPM data. • Improvement of the forecast skill of regional/global NWP model through data assimilation of GPM is expected. • Provide a significant contribution to the monitoring and understanding of flash flood producing severe storm such as tropical cyclone. • Plenty of data to be used further understanding of climate and weather related process including the rain cloud structure. • Successful utilization of the GPM data will be highly dependent on the reliable, fast, and efficient data communication among producers and users.

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