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Ingredients to improve rainfall forecast in very short-range:

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. Introduction.

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Ingredients to improve rainfall forecast in very short-range:

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  1. Ingredients to improve rainfall forecast in very short-range: Diabatic initialization and microphysics Eunha Lim1, Yong-Hee Lee2, and Jong-Chul Ha2 1 Korea Meteorological Administration, Seoul, Korea 2 National Institute of Meteorological Research, Seoul, Korea

  2. 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

  3. 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” atKorea 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

  4. metar metar Cloud analysis and Diabatic initialization cloud detection and shaping satellite lightning radars

  5. Wmax= depth */ dxfor Cu Wmax= depth */ dxfor Sc • Wmax=for St Selection of chromosomes • vertical velocity in a cloud - depth: cloud depth - dx: grid size (5km)   - for Cutypes (0.5) - for Sctypes (0.05) - for St (0.01) • Radar reflectivity bounding a cloud (15.0 dBz)

  6. 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 iis 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 : falsealarms M : misses

  7. Numerical model - WRF Configuration Model Domain • It produces 12hrs forecast field at 25 minute at every hour • It takes 6 min. to forecast with 256 CPUs

  8. Evolution of fitness function 6-hour accumulated rainfall 09 ~ 15UTC 17 June 2008 117mm BEST : the maximum fitness among 20 members in each generation MEAN : the average fitness of 20 members in each generation • 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. Fitness for each parameter 420 points = 20 mem. * 21 generation 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

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

  11. Sensitivity of ETS to parameter, x1 ETS BIAS

  12. Impact of parameters, x1 and x4 ETS BIAS : CTL : BEST : BEST except for x1(ctl) : BEST except for x4(ctl) • 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. 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

  14. Rainfall forecast (6hr accum.) at 15UTC 17 June 2008 Observation(AWS) RADAR (3hraccum.) 117mm 06/17 12UTC 06/17 15UTC CTL BEST

  15. Verification of rainfall forecast • Period : 1 June 2008 – 15 June 2008 (8times / day, 120 cases) 1mm/3hr 10 mm/3hr Tuned parameters increase ETS for the light and the heavy rainfalls, although it is not as significant as one case

  16. Verification of rainfall forecast by WDM6 • Period : June 2008 – Aug. 2008 (8times / day, 736 cases) 1mm/3hr 10 mm/3hr • ETS is improved by WDM6 for both the light and the heavy rainfall within 12 hours • Bias does not show significant difference

  17. CompareETS between KLAPS and KWRF • 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 Annual change of ETS (KLAPS) 12.5mm/6hr (~Sept) • ETS has gradually increased since 2007 • ETS is higher than that of KWRF, especially the heavy rainfall • The positive impact lasts for about 12 hours

  18. 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

  19. 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), groundGPS(21) • We are applying the preliminary version of SSEF to “trade fair” at Yusu in Korea in 2012.

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