1. Impact of ProbeX-IOP (KEOP) observations on the predictive skill of heavy rainfall in the middle part of Korea Hee-Sang Lee and Seung-Woo Lee Forecast Research Laboratory / National Institute of Meteorological Research, KMA National Institute of Meteorological Research

2. Background KMA has been using the NCAR/PSU MM5 as a regional model for over 10 years. KMA has been using the NCAR/PSU MM5 as a regional model for over 10 years. KMA considers the WRF model as a candidate of the operational regional model. KMA considers the WRF model as a candidate of the operational regional model. Assessment of WRF model performance for very-short range forecasting of precipitation is demanded by forecasters. Assessment of WRF model performance for very-short range forecasting of precipitation is demanded by forecasters.

3. MM5-30 vs WRF-10 km

4. MM5- 30 km MM5- 10 km MM5- 5 km WRF 10 km AWS observed rainfall WRF 3.3 km [2007. 7.3. 21 KST ~ 7. 4. 12 KST] [2007. 7. 4. 00 KST ~ 12 KST] Predicted rainfall from two different regional models

5. Observations : 4 July 2007 No warning by this time in the routine forecasting.

6. Observations : 4 July 2007

7. 12-h rainfall amount (2007/07/04 00LST ~12LST) IR (2007/07/04 06LST) SFC (2007/07/04 09LST) Heavy rainfall event : 09LST 4 July 2007 Mungyung 148.5 mm/12h Anyang 104 mm/12h 60 min. acc. 15 min. acc. At early morning 4 th July, a convective system associated with the Changma front that produced heavy rainfall over the southern part of Korea moved eastward, then local heavy rainfall occurred over the middle part of Korea. Operational models did not capture this signals over this area. CAPPI (2007/07/04 06LST)

8. Observations : 9-12UTC 3 July 2007 111 ASOS 111 ASOS 773 AWS data 773 AWS data 19 Radiosondes 19 Radiosondes 240 AMDAR 240 AMDAR 451 AMDAR from Korean Airlines (KAL) 451 AMDAR from Korean Airlines (KAL) 10 Wind profiler 10 Wind profiler 5 SATEM 5 SATEM

9. Special observations for impact studies Haenam Haenam Sokcho Sokcho Munsan Munsan Pohang Pohang Baengnyeongdo Ieodo Ieodo Gosan Gosan Osan Osan Huksando Conventional KEOP Air Force Gwangju Gwangju ProbeX-2007 IOP ProbeX-2007 IOP - Observing period : 2007/06/15 ~ 2007/07/15 - Observing period : 2007/06/15 ~ 2007/07/15 - Increasing time resolution : 4 times/day (Baengnyeongdo, Sokcho, Huksando, Pohang, Gosan) - Increasing time resolution : 4 times/day (Baengnyeongdo, Sokcho, Huksando, Pohang, Gosan) - Increase space resolution : Additional enhanced observation (Munsan, Haenam, Ieodo) - Increase space resolution : Additional enhanced observation (Munsan, Haenam, Ieodo) Probex (PRedictability and OBservation Experiment in Korea) in Korea)

10. MM5-30 & KWRF-10 km KWRF 3.3 km Physical processes Domain 1 (10 km) Domain 2 (3.3 km) Remarks Dimensions 574 X 514 (with 30 vertical levels) 334 X 364 (with 30 vertical levels) Run time on CRAY-X1E (1024CPUs / 18.4TFLOPS) Domain 1 : 14 min. with 126 CPUs with 126 CPUs Domain 2 : 50 min. with 64 CPUs with 64 CPUs Time interval (Δt) 60 sec 20 sec Cumulus Parameterization Kain-Fritsch (new Eta) scheme None Microphysics WSM6 / WSM5 / WSM3 / new Eta WSM 6-class scheme PBL YSU scheme Radiation RRTM / Dudhia scheme Surface-Land Noah LSM Initial and Boundary data GDAPST426 hybrid-sigma(0.28125 o ) WRF 10 km Model domains and configurations Verification area

11. 0006121824 UTC Global (T426) 10 days forecast CYCLE run COLD run 3 days forecast 10 days forecast 60-h forecast 6-h forecast 60-h forecast Nestdown to WRF 3.3 km Experimental design 3DVAR data assimilation Nestdown to WRF 3.3 km 3036

12. Experiment IDAssimilated observation data Remarks ( Number of assimilated obs. ) CTLAll available observations without KEOP soundingsOperational, 1247 ALLAll available observations including KEOP soundings1249 OPRConventional TEMP soundings17 TMPConventional TEMP + KEOP soundings19 KOPKEOP soundings only2 PRFWind profiler data10 ACSAMDAR data from FSL240 KALAMDAR data including KAL reports451 KONKAL reports only211 SFCSYNOP, SHIP, BUOY, AWS data884 SYNSYNOP, SHIP, BUOY111 AWS 773 SATSATEM, SATOB, QSCAT5 Observations for impact studies

13. OBSCTLALL OPRTMPIOP 103 T+12 acc rainfall Munsan The location of rainfall was slightly shifted toward observation when the IOP sounding (even in one sounding at Munsan station) data was included. The location of rainfall was slightly shifted toward observation when the IOP sounding (even in one sounding at Munsan station) data was included. 98 104 148.5 100 km

14. OBSPRFACS KALSFCSYN AWS SAT T+12 acc rainfall Sounding data shows positive impact on the improvement of rainfall than the surface observation data. Sounding data shows positive impact on the improvement of rainfall than the surface observation data. The aircraft data from KAL shows most skillful forecasting of precipitation. The aircraft data from KAL shows most skillful forecasting of precipitation. 100 km

15. Sensitivity to boundary condition from global model FCST(C24H) ANAL_IOP OBSANAL CTRL (operational) 64 43 98 148.5 104 Since the BCs of WRF-0 are provided by the GDAPS, perfect BCs from global analyses lead to an improvement of locations of heavy rainfall. Since the BCs of WRF-0 are provided by the GDAPS, perfect BCs from global analyses lead to an improvement of locations of heavy rainfall. 100 km 76 64

16. CTRLCOLD C12HC24H Sensitivity to the cycle with WRF-10 The cycle plays an important role in the spin-up in precipitation process. The cycle plays an important role in the spin-up in precipitation process. OBS 100 km 76 11 29 64 148.5 104

17. Sensitivity to microphysics (WRF 10km) with ANAL_BCs WSM6 OBS WSM3 WSM5 CTRL (WSM6) 96 74 42 76 148.5 104 100 km ETA_NEW (Ferrier) 94 Although the simulated rainfall amount was much smaller than the observed one, ETA_NEW microphysics does better job in location of main rainfall area over the middle part of Korea. Although the simulated rainfall amount was much smaller than the observed one, ETA_NEW microphysics does better job in location of main rainfall area over the middle part of Korea.

18. Sensitivity to microphysics (WRF 3.3km) OBS 41 148.5 104 ETA_NEW (Ferrier) In higher resolution experiment, the magnitude of maximum rainfall is larger than that in lower resolution but no difference in phase. In higher resolution experiment, the magnitude of maximum rainfall is larger than that in lower resolution but no difference in phase. WSM6 97 100 km WSM3 128 WSM5 109 93

19. The 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 – see Goldberg [1989] for a detailed description. Genetic Algorithm to optimize WRF-10 model Start Initialization Fitness Evaluation Selection Crossover Mutation Fitness Evaluation Terminal condition End NO YES

20. Variance and length scale of background error (x 1, x 2, x 3, x 4, x 5, l 1, l 2, l 3, l 4, l 5 ) Variance and length scale of background error (x 1, x 2, x 3, x 4, x 5, l 1, l 2, l 3, l 4, l 5 ) Asymptotic mixing length in PBL(m 1 ) Asymptotic mixing length in PBL(m 1 ) Clear air turbulence : 10 – 30 m Clear air turbulence : 10 – 30 m Cyclogenesis in upper troposphere : < 100m Cyclogenesis in upper troposphere : < 100m Closure assumption of KF (m 2 ) Closure assumption of KF (m 2 ) In the Kain-Fritsch scheme the closure assumption is that convection consumes at least 90% of the environmental convective available potential energy (CAPE) over an advective time period ( 30 min ~ 1 hour) [Kain et al. 2003]. In the Kain-Fritsch scheme the closure assumption is that convection consumes at least 90% of the environmental convective available potential energy (CAPE) over an advective time period ( 30 min ~ 1 hour) [Kain et al. 2003]. Selection of Chromosomes

21. The function to be optimized (i.e., Fitness) is defined by using a QPF skill score, the equitable treat score (ETS) [Schaefer, 1990], Fitness =, where i is the precipitation threshold in mm. Here, the ETS is defined as: H : hit R : the expected number of hits in a random forecast F : rain forecast O : rain observation Fitness function

22. Variance of control variables var_scaling1 (x1, Ψ)var_scaling2 (x2,χ)var_scaling3 (x3,T u )var_scaling4 (x4, q RH )var_scaling5 (x5,Pa) 1.322.681.340.962.36 Horizontal length scales len_scaling1 (l1)len_scaling2 (l2)len_scaling3 (l3)len_scaling4 (l4)len_scaling5 (l5) 0.922.452.501.090.79 Physical parameters asymptotic mixing length (m1) (30)reduction rate (m2) (0.95) 132.50.36 Evolution of chromosomes

23. CTRL GA Preliminary results w/ and w/o GA in WRF-10 Overall the tuned WRF by GA works for locations of heavy rainfall. Overall the tuned WRF by GA works for locations of heavy rainfall. OBS 100 km 148.5 104 50.9 64.5 54.1

24. Summary The assimilation of the intensive observations (KEOP-2007) with the high resolution WRF model (3.3 km) and 3DVAR show a positive impact on the very-short range forecasting of heavy rainfall over Korea. The assimilation of the intensive observations (KEOP-2007) with the high resolution WRF model (3.3 km) and 3DVAR show a positive impact on the very-short range forecasting of heavy rainfall over Korea. Cycling processes to provide the background in 3DVAR play a crucial role in spin-up of precipitation. Cycling processes to provide the background in 3DVAR play a crucial role in spin-up of precipitation. Improvement in boundary conditions from global model may lead to improvement in the forecast of heavy rainfall. Improvement in boundary conditions from global model may lead to improvement in the forecast of heavy rainfall. Cloud microphysics plays an important role in the simulation of the heavy rainfall area in this case. Cloud microphysics plays an important role in the simulation of the heavy rainfall area in this case.