1. Ingredients to improve rainfall forecast in very short-range: Diabatic initialization and microphysics Eunha Lim 1, Yong-Hee Lee 2, and Jong-Chul Ha 2 1 Korea Meteorological Administration, Seoul, Korea 2 National Institute of Meteorological Research, Seoul, Korea

2. 마스터 제목 스타일 편집 Introduction  KMA introduced Korea Local Analysis and Prediction System (KLAPS) in 2006  KLAPS has been developed to enhance the rainfall forecast for the very short-range period (0~6hrs)  KLAPS consists of LAPS and WRF for the analysis and the forecast respectively  One of the characteristics of KLAPS is a diabatic initialization (DI) including analysis of clouds  DI can improve the initiation and the evolution of rainfall (Shaw et al. 2001)  The cloud analysis, the first step for DI, has several parameters to represent the initial cloud fields in DI  KMA optimized these parameters by adapting the Genetic Algorithm (GA) in 2009 2

3. 마스터 제목 스타일 편집 Introduction – cont.  In the forecast model, microphysics affects directly on the forecast of rainfall  KMA introduced the double moment microphysics, WDM6 in 2009  WDM6 predicts CCN, number concentration of clouds, and rain  It requires 8% more computer resources  However it is expected to improve the rainfall forecast  Other ingredients are also improved  Landuse is updated to “mixed forest” at Korea Peninsular. Previously it is mostly savanna.  The observation from domestic airlines are also included in the analysis  Mother domain of KLAPS, 15km resolution, adapted analysis FDDA in 2010 3

4. 마스터 제목 스타일 편집 Cloud analysis and Diabatic initialization lightning cloud detection and shaping radars satellite metar 4

5. 마스터 제목 스타일 편집 Selection of chromosomes - for Cu types (0.5) - for Sc types (0.05) - for St (0.01) W max = depth * / dx for Cu W max = depth * / dx for Sc W max = for St  vertical velocity in a cloud  Radar reflectivity bounding a cloud (15.0 dBz) 5 - depth: cloud depth - dx: grid size (5km)

6. 마스터 제목 스타일 편집 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 : hits R : the expected number of hits in a random forecast ( R=(H+M)(H+F)/N ) F : false alarms M : misses 6

7. 마스터 제목 스타일 편집 Numerical model - WRF Physical processes Horizontal Res.5km Dimensions 235 X 283 (with 40 vertical levels) Time step20 sec CPNone/KF(15km) MicrophysicsWDM6 PBLYSU PBL scheme RadiationRRTM / Dudhia scheme Surface-LandNoah LSM Forecast hrs/freq.12hrs / 24 times Configuration Model Domain  It produces 12hrs forecast field at 25 minute at every hour  It takes 6 min. to forecast with 256 CPUs 7

8. 마스터 제목 스타일 편집 Evolution of fitness function BEST : the maximum fitness among 20 members in each generation MEAN : the average fitness of 20 members in each generation 6-hour accumulated rainfall 09 ~ 15UTC 17 June 2008 117mm 8  The best fitness is stable as generation goes  The mean fitness rapidly increases earlier generation and merges to the best fitness at later generation

9. 마스터 제목 스타일 편집 Fitness for each parameter x1 parameter (Cu)x2 parameter (Sc) X4 parameter (Radar) x3 parameter (St)  x1 and x4 converge at a narrow range of values, especially x1  x2 and x3 are less sensitive to the fitness 9 420 points = 20 mem. * 21 generation

10. 마스터 제목 스타일 편집 Evolution of parameters, x1 and x4 Generation: 10 Members have different values of x1, and x4 at earlier generation and gradually converges to certain values

11. 마스터 제목 스타일 편집 Sensitivity of ETS to parameter, x1 ETSBIAS x1x2x3x4Fitness CTL0.5000.0500.010015.00013.965 BEST7.5530.0190.0810 8.52826.361

12. 마스터 제목 스타일 편집 Impact of parameters, x1 and x4 ETSBIAS : CTL : BEST : BEST except for x1(ctl) : BEST except for x4(ctl) 12  x1 (parameter for Cu) is the most sensitive variable to the rainfall forecast  x4 (parameter for radar) is sensitive to the light rainfall forecast  Though x1 is the most effective parameters to enhance ETS, the best result is achieved by the combination of all parameters

13. 마스터 제목 스타일 편집 Difference of wind field at initial time Wind diff. (BEST – CTL) at 850hPa 09 UTC 17 June 2008 MTSAT satellite image RADAR It clearly shows there are convergences in/around cloudy areas 13

14. 마스터 제목 스타일 편집 Rainfall forecast (6hr accum.) at 15UTC 17 June 2008 RADAR (3hr accum.) 14 117mm Observation(AWS) 06/17 12UTC CTLBEST 06/17 15UTC

15. 마스터 제목 스타일 편집 Verification of rainfall forecast Period : 1 June 2008 – 15 June 2008 (8times / day, 120 cases) 1mm/3hr10 mm/3hr Tuned parameters increase ETS for the light and the heavy rainfalls, although it is not as significant as one case 15

16. 마스터 제목 스타일 편집 Verification of rainfall forecast by WDM6 16 1mm/3hr10 mm/3hr Period : June 2008 – Aug. 2008 (8times / day, 736 cases)  ETS is improved by WDM6 for both the light and the heavy rainfall within 12 hours  Bias does not show significant difference

17. 마스터 제목 스타일 편집 Compare ETS between KLAPS and KWRF 17  KWRF is the regional model: 10km resolution, and 6hr cycling with 3dVar  Period : June 2008 – Aug. 2008 (8times / day, 736 cases)  Influence by both DI and WDM6 12.5mm/6hr Annual change of ETS (KLAPS)  ETS has gradually increased since 2007 (~Sept)  ETS is higher than that of KWRF, especially the heavy rainfall  The positive impact lasts for about 12 hours

18. 마스터 제목 스타일 편집 Summary and future plans  Tuned parameter x1 which determines the vertical velocity of cumulus cloud is the most sensitive to the rainfall forecast  However the parameters are tuned using only one forecast. We will apply GA during one month period(July ‘11) to get stable parameters.  Parameters in QG balance equation are also included in GA  WDM6 outperformed WSM6 for both light and heavy rainfall  The overall performance of rainfall forecast has increased since 2007  It is not only introducing DI and WDM6 but also adding more observation (quality controlled), updating precise surface conditions and model itself, etc.  Two items are preparing to insert into KLAPS  Exploit COMS satellite. It takes less than 15 min. to get data around KP  Improve the radar pre-processing: introduce fuzzy logic for QC, remove hard-coded parameters related with coordinate conversion 18

19. 마스터 제목 스타일 편집 Summary and future plans – cont.  However there are demands for more detailed forecast in both time and space  We are planning storm-scale ensemble forecast(SSEF) at the metropolitan area in 2015  It has1km resolution and provide 3hrs forecast at every 20 minutes  It consists of 16 members by combining physical processes and initial conditions  Integrated network of instruments are deployed to get dense observation in space and time Lightning detection (in-cloud, inter-cloud), celio-meters(21), ground GPS(21)  We are applying the preliminary version of SSEF to “trade fair” at Yusu in Korea in 2012. 19