Hee-Sang Lee and Seung-Woo Lee

1. National Institute of Meteorological Research 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

2. Background • 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. • Assessment of WRF model performance for very-short range forecasting of precipitation is demanded by forecasters.

3. MM5-30 vs WRF-10 km

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

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) Heavy rainfall event : 09LST 4 July 2007 SFC (2007/07/04 09LST) Mungyung 148.5 mm/12h 60 min. acc. 15 min. acc. IR (2007/07/04 06LST) CAPPI (2007/07/04 06LST) Anyang 104 mm/12h • At early morning 4th 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.

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

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

10. Verification area Model domains and configurations KWRF 3.3 km MM5-30 & KWRF-10 km

11. Experimental design 00 06 12 18 24 30 36 UTC Global (T426) 10 days forecast 3 days forecast 10 days forecast 60-h forecast 6-h forecast CYCLE run 60-h forecast Nestdown to WRF 3.3 km COLD run 60-h forecast Nestdown to WRF 3.3 km 3DVAR data assimilation

12. Observations for impact studies

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

14. 100 km T+12 acc rainfall OBS PRF ACS SAT KAL SFC SYN AWS • 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.

15. 100 km Sensitivity to boundary condition from global model CTRL (operational) OBS ANAL 64 76 104 43 148.5 FCST(C24H) ANAL_IOP 98 64 • 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.

16. 100 km Sensitivity to the cycle with WRF-10 OBS CTRL COLD 11 76 104 148.5 C12H C24H 29 64 • The cycle plays an important role in the spin-up in precipitation process.

17. 100 km Sensitivity to microphysics (WRF 10km) with ANAL_BCs OBS WSM3 WSM6 74 96 104 148.5 CTRL (WSM6) WSM5 ETA_NEW (Ferrier) 94 42 76 • 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. 100 km Sensitivity to microphysics (WRF 3.3km) OBS WSM3 WSM6 93 97 104 128 148.5 WSM5 ETA_NEW (Ferrier) 109 41 • In higher resolution experiment, the magnitude of maximum rainfall is larger than that in lower resolution but no difference in phase.

19. Start Initialization Fitness Evaluation Selection Crossover Mutation Fitness Evaluation Terminal condition NO YES End Genetic Algorithm to optimize WRF-10 model 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.

20. Selection of Chromosomes • Variance and length scale of background error (x1, x2, x3, x4, x5, l1, l2, l3, l4, l5) • Asymptotic mixing length in PBL(m1) Clear air turbulence : 10 – 30 m Cyclogenesis in upper troposphere : < 100m • Closure assumption of KF (m2) 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].

21. Fitness function 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

22. Evolution of chromosomes

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

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. • 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. • Cloud microphysics plays an important role in the simulation of the heavy rainfall area in this case.