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Nowcasting and Very Short-range Forecasts of the Convective System: The Korean Perspective

Pre-CAS TECO, 16-17 Nov 2009, Incheon, Korea. Nowcasting and Very Short-range Forecasts of the Convective System: The Korean Perspective. Dong-Eon Chang, Y. H. Lee, J.-C. Ha, H. C. Lee, Y.-H Kim Forecast Research Lab National Institute of Meteorological Research. Background.

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Nowcasting and Very Short-range Forecasts of the Convective System: The Korean Perspective

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  1. Pre-CAS TECO, 16-17 Nov 2009, Incheon, Korea Nowcasting and Very Short-range Forecasts of the Convective System: The Korean Perspective Dong-Eon Chang, Y. H. Lee, J.-C. Ha, H. C. Lee, Y.-H Kim Forecast Research Lab National Institute of Meteorological Research

  2. Background • In Korea 45 % of Casualties by natural disaster is caused by Heavy rainfall events (NEMA, 2006) • 4 Typical heavy rainfalltypes Isolated thunderstorm Squall line Convection band Cloud cluster • Heavy rainfall events (2000-2006) Lee and Kim (2007)

  3. Explicit model 3-8 h Forecast Skill • Nowcasting : 0~2hr • Very short-range forecast : ~12h • By WMO Tech Note No. 1024 Best Extrapolation Forecast Skill NWP Forecast Length By J. Wilson (NCAR)

  4. 3-8 h Forecast Skill – KMA Approach MAPLE (McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation) Best KLAPS (Korea Local Analysis and Prediction System) Forecast Skill KMA Operational Model - KWRF, UM Forecast Length

  5. MAPLE MAPLE Algorithms • Rainfall QPF algorithm • Variational Echo Tracking • Semi-Lagrangian Advection • Scale dependence of predictability • wavelet filtering • Life time for each scale • Probabilistic nowcast • Conditional ranked probability score • Collaborative work with McGill University (2007-2009) [ Lagrangian persistence ] [ Advection scheme ] [ Scale dependence ] [ Variational echo tracking ] [ Predictability of PDF ]

  6. KMA Operational Radar Network

  7. MAPLE - Verifications • High level of forecast skill has been shown up to about 2hr 30min according to the verification of 2008 summertime. • There are overestimation or underestimation due to the missing of initiation and dissipation process. But more likely overestimate. Radar OBS MAPLE Hit overestimates underestimates 0100 KST 23 May, 2008 (~6hr fcst)

  8. KLAPS WRF model Weather Research & Forecasting model KLAPS : Korea Local Analysis and Prediction System pig,prg,sag pig,prg,sag pig,prg,sag pig,prg,sag pig,prg,sag pig,prg,sag pig,prg,sag pig,prg,sag pig,prg,sag pig,prg,sag lso,lgb,lwm lso,lgb,lwm lso,lgb,lwm lso,lgb,lwm lso,lgb,lwm lso,lgb,lwm lso,lgb,lwm lso,lgb,lwm lso,lgb,lwm lso,lgb,lwm (pig),lwm,lw3 (pig),lwm,lw3 (pig),lwm,lw3 (pig),lwm,lw3 (pig),lwm,lw3 (pig),lwm,lw3 (pig),lwm,lw3 (pig),lwm,lw3 (pig),lwm,lw3 (pig),lwm,lw3 lga,snd,pin lga,snd,pin lga,snd,pin lga,snd,pin lga,snd,pin lga,snd,pin lga,snd,pin lga,snd,pin lga,snd,pin lga,snd,pin LSX (Surface) LSX (Surface) LSX (Surface) LSX (Surface) LSX (Surface) LSX (Surface) LSX (Surface) LSX (Surface) LSX (Surface) LSX (Surface) lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga lso,cdw,pin,snd,lga tmg tmg tmg tmg tmg tmg tmg tmg tmg tmg LW3 (3D Wind) LW3 (3D Wind) LW3 (3D Wind) LW3 (3D Wind) LW3 (3D Wind) LW3 (3D Wind) LW3 (3D Wind) LW3 (3D Wind) LW3 (3D Wind) LW3 (3D Wind) LT1 (3D Temp.) LT1 (3D Temp.) LT1 (3D Temp.) LT1 (3D Temp.) LT1 (3D Temp.) LT1 (3D Temp.) LT1 (3D Temp.) LT1 (3D Temp.) LT1 (3D Temp.) LT1 (3D Temp.) lm1,lm2 lm1,lm2 lm1,lm2 lm1,lm2 lm1,lm2 lm1,lm2 lm1,lm2 lm1,lm2 lm1,lm2 lm1,lm2 lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga lso,vrc,lvd,pin,lm2,lga Analysis lt1 (temp./height) lt1 (temp./height) lt1 (temp./height) lt1 (temp./height) lt1 (temp./height) lt1 (temp./height) lt1 (temp./height) lt1 (temp./height) lt1 (temp./height) lt1 (temp./height) lwm (wind.anal) lwm (wind.anal) lwm (wind.anal) lwm (wind.anal) lwm (wind.anal) lwm (wind.anal) lwm (wind.anal) lwm (wind.anal) lwm (wind.anal) lwm (wind.anal) LC3 (3D Cloud) LC3 (3D Cloud) LC3 (3D Cloud) LC3 (3D Cloud) LC3 (3D Cloud) LC3 (3D Cloud) LC3 (3D Cloud) LC3 (3D Cloud) LC3 (3D Cloud) LC3 (3D Cloud) LSM (Soil) LSM (Soil) LSM (Soil) LSM (Soil) LSM (Soil) LSM (Soil) LSM (Soil) LSM (Soil) LSM (Soil) LSM (Soil) lps,lcb,lcv lps,lcb,lcv lps,lcb,lcv lps,lcb,lcv lps,lcb,lcv lps,lcb,lcv lps,lcb,lcv lps,lcb,lcv lps,lcb,lcv lps,lcb,lcv lsx (sfc.anal) lsx (sfc.anal) lsx (sfc.anal) lsx (sfc.anal) lsx (sfc.anal) lsx (sfc.anal) lsx (sfc.anal) lsx (sfc.anal) lsx (sfc.anal) lsx (sfc.anal) lc3 (3D cld) lc3 (3D cld) lc3 (3D cld) lc3 (3D cld) lc3 (3D cld) lc3 (3D cld) lc3 (3D cld) lc3 (3D cld) lc3 (3D cld) lc3 (3D cld) lh3 (rel.humidity) lh3 (rel.humidity) lh3 (rel.humidity) lh3 (rel.humidity) lh3 (rel.humidity) lh3 (rel.humidity) lh3 (rel.humidity) lh3 (rel.humidity) lh3 (rel.humidity) lh3 (rel.humidity) L1S (Accu.) L1S (Accu.) L1S (Accu.) L1S (Accu.) L1S (Accu.) L1S (Accu.) L1S (Accu.) L1S (Accu.) L1S (Accu.) L1S (Accu.) LH3 (Humidity) LH3 (Humidity) LH3 (Humidity) LH3 (Humidity) LH3 (Humidity) LH3 (Humidity) LH3 (Humidity) LH3 (Humidity) LH3 (Humidity) LH3 (Humidity) lga,snd,lvd lga,snd,lvd lga,snd,lvd lga,snd,lvd lga,snd,lvd lga,snd,lvd lga,snd,lvd lga,snd,lvd lga,snd,lvd lga,snd,lvd LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) LC3’ (Cloud-Driven) l1s l1s l1s l1s l1s l1s l1s l1s l1s l1s lq3,lh4 lq3,lh4 lq3,lh4 lq3,lh4 lq3,lh4 lq3,lh4 lq3,lh4 lq3,lh4 lq3,lh4 lq3,lh4 vrc vrc vrc vrc vrc vrc vrc vrc vrc vrc Prediction lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lps,lcv,lso,lw3, lwm,vrc lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 lcp,lty,lwc,lil,lct,lmd,lmt,lco, lrp,lst,(lwm),lhe,liw,lmr,lf1 • Horizontal resolution : 5km, Forecast length : ~12h

  9. KLAPS Data Ingest

  10. KLAPS Data Ingest : Lightning Build deep convective cloud Lightning Network IMPACT (IMProved Accuracy from Combined Technology) - Sensor : IMPACT ESP, LDAR II - Method : MDF + TOA and TOA, Detect CG and CC - Period : Since March 2001

  11. KLAPS Data Ingest : Radar reflectivity

  12. Operational Features Forecasts (every 3 hour) 3D Analysis (every hour) • 3D analysis is produced within 10 min each hour • Forecasts(~12h) guidance ready by 42 min from initial time

  13. Diabatic Initialization • Diabatic initialization is unique technique of the KLAPS for the improvement of precipitation forecast in the early integration time. • Variational adjustment process is applied to produce dynamically balanced wind fields

  14. Effect of Diabatic Initialization Verification score(3 months average)

  15. Recent Improvement • Optimization of initialization • Seeking optimal cloud updraft • Tuning of radar reflectivity threshold • Ingest of VAD wind • Adapting WDM microphysics scheme • Wind Profiler • VAD

  16. Optimization of Cloud updraft velocity Wmax= depth */ dxfor Cu Wmax= depth */ dxfor Sc W =for St   - W to height ratio Cu types (0.5) - W to height ratio Sc types (0.05) - W for St (0.01)

  17. Genetic Algorithm Start Initialization Fitness Evaluation Selection Crossover Mutation Fitness Evaluation Terminal condition NO YES End • The Genetic Algorithm (GA) is a global optimization approach based on the Darwinian principles of natural selection. • This method, developed from the concept of Holland [1975], aims to efficiently seek the extrema of complex function . The PIKAIA seeks to maximize a function f(X) in a bounded n-dimensional space, • Each generation has 20 chromosomes. The crossover probability is set to 0.85, implying that 85% of the chromosomes in a generation are allowed to crossover in an average sense. The maximum and minimum mutation probability is set to 0.05 and 0.005, respectively.

  18. Parameter estimation • GA shows quick convergence. The parameter X1 converged within 5~6th generation. • Optimal value • X1 = 3.95 • X2 = 0.22 • X3 = 0.035 animation

  19. Optimization Results 6h rainfall CTRL Optimized Exp AWS 50~80 mm RADAR

  20. Performance - examples

  21. Performance - examples

  22. KLAPS vs Regional Model • Precipitation verification score (ETS) for Jun – Aug 2009

  23. Summary and Conclusion • MAPLE with KMA operational radar observation provided useful guidance up to 2~3hr. • Diabatic initialization of KLAPS showed promising results in the very short-range precipitation forecasts, and optimization of some parameters using GA was quite successful and efficient. • In the future, blending of MAPLE and KLAPS precipitation forecast will be tested.

  24. Thank you

  25. Sensitivity to parameter X1 ■ ETS ■ BIAS

  26. Verification Scores threshold:1mm/3hr threshold:10mm/3hr • ETS • BIAS

  27. WSM vs WDM • A CASE (INIT: 2008. 6. 18. 00UTC) • Verification (Jun-Aug, 2008) threshold: 1mm/3hr AWS WSM threshold: 10mm/3hr WDM F03H F06H F09H

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