Ingredients to improve rainfall forecastin very short-range:

Diabatic initialization and microphysics

Eunha Lim1, Yong-Hee Lee2, and Jong-Chul Ha2

1 Korea Meteorological Administration, Seoul, Korea2 National Institute of Meteorological Research, Seoul, Korea

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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 rep-resent the initial cloud fields in DI

KMA optimized these parameters by adapting the Genetic Algorithm (GA) in 2009

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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

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Cloud analysis and Diabatic initialization

lightning

cloud detection and shap-ing

radars

satellite

metarmetar

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Selection of chromosomes

  - for Cu types (0.5)

- for Sc types (0.05)

- for St (0.01)

0.101.0 1 x

001010 2 .x.

100.0005.0 3 x

Wmax = depth * / dx for CuWmax = depth * / dx for ScWmax = for St

1x2x3x

vertical velocity in a cloud

Radar reflectivity bounding a cloud (15.0 dBz)0.300.1 4 x

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- depth: cloud depth- dx: grid size (5km)

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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 = ,

i

iETS 50,,2,1 i

RFMHRHETS

where i is the precipitation threshold in mm. Here, the ETS is defined as:

H : hitsR : the expected number of hits in a random forecast (R=(H+M)(H+F)/N)F : false alarmsM : misses

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Numerical model - WRF

Physical processesHorizontal Res. 5km

Dimensions 235 X 283 (with 40 vertical levels)

Time step 20 secCP None/KF(15km)

Microphysics WDM6PBL YSU PBL scheme

Radiation RRTM / Dudhia scheme

Surface-Land Noah LSMForecast 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

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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 rainfall09 ~ 15UTC 17 June 2008

117mm

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The best fitness is stable as generation goes The mean fitness rapidly increases earlier generation and merges to the best fitness at later generation

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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

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420 points = 20 mem. * 21 genera-tion

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Evolution of parameters, x1 and x4

Generation:

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Members have different values of x1, and x4 at earlier generation and gradually converges to certain values

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Sensitivity of ETS to parameter, x1

ETS BIAS

0.610.4 :0.410.2 :0.21 :

xxx

0.1010.8 :0.810.6 :

xx

BEST :CTL :

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

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Impact of parameters, x1 and x4

ETS BIAS: CTL: BEST

: BEST except for x1(ctl): BEST except for x4(ctl)

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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

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Difference of wind field at initial timeWind diff. (BEST – CTL) at 850hPa09 UTC 17 June 2008MTSAT satellite image

RADAR

It clearly shows there are convergences in/around cloudy areas

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Rainfall forecast (6hr accum.) at 15UTC 17 June 2008

RADAR (3hr accum.)

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117mm

Observation(AWS)

06/17 12UTC

CTL BEST06/17 15UTC

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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, al-though it is not as significant as one case

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Verification of rainfall forecast by WDM6

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1mm/3hr 10 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

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Compare ETS between KLAPS and KWRF

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KWRF is the regional model: 10km resolution, and 6hr cycling with 3dVarPeriod : June 2008 – Aug. 2008 (8times / day, 736 cases)Influence by both DI and WDM6

2007 2008 2009 2010 201110

15

20

25

30

ETS

(%)

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

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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 ap-

ply 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 observa-

tion (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

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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 observa-

tion 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.

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