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” 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

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

5. Selection of chromosomes
vertical velocity in a cloud
Wmax = depth * / dx for Cu
Wmax = depth * / dx for Sc
Wmax = for St
- depth: cloud depth
- dx: grid size (5km)
  - for Cu types (0.5)
- for Sc types (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 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

7. 235 X 283 (with 40 vertical levels)
Numerical model - WRF
Configuration
Model Domain
Physical processes
Horizontal Res.
5km
Dimensions
235 X 283 (with 40 vertical levels)
Time step
20 sec CP
None/KF(15km)
Microphysics
WDM6
PBL
YSU PBL scheme
Radiation
RRTM / Dudhia scheme
Surface-Land
Noah LSM
Forecast hrs/freq.
12hrs / 24 times
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)
x3 parameter (St)
X4 parameter (Radar)
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
BIAS x1 x2 x3 x4
Fitness
CTL
0.500
0.050
0.0100
15.000
13.965
BEST
7.553
0.019
0.0810
8.528
26.361

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 (3hr accum.)
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 (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. Compare ETS between KLAPS and KWRF
KWRF is the regional model: 10km resolution, and 6hr cycling with 3dVar
Period : June 2008 – Aug (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. 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), ground GPS(21)
We are applying the preliminary version of SSEF to “trade fair” at Yusu in Korea in 2012.