Transcript 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.

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
National Institute of Meteorological Research
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.
MM5-30 vs WRF-10 km
12h Precip. ETS for 24h fcst
(June, 2007)
12h Precip. ETS for 12h fcst
(June, 2007)
0.4
0.4
0.35
0.35
0.3
0.3
0.25
0.25
MM5-30
WRF-10
0.2
0.15
MM5-30
WRF-10
0.2
0.15
0.1
0.1
0.05
0.05
0
0
0.1mm
5mm
15mm
25mm
0.1mm
50mm
5mm
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
MM5-30
WRF-10
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
MM5-30
WRF-10
0.1mm
15mm
Threshold Value
50mm
12h Precip. Bias Score for 24h fcst
(June, 2007)
12h Precip. Bias Score for 12h fcst
(June, 2007)
5mm
25mm
Threshold Value
Threshold Value
0.1mm
15mm
25mm
50mm
5mm
15mm
Threshold Value
25mm
50mm
Predicted rainfall from two different regional models
AWS observed rainfall
[2007. 7. 4. 00 KST ~ 12 KST]
WRF 3.3 km
MM5- 30 km
WRF 10 km
[2007. 7.3. 21 KST ~ 7. 4. 12 KST]
MM5- 10 km
MM5- 5 km
Observations : 4 July 2007
No warning by this time in
the routine forecasting.
Observations : 4 July 2007
12-h rainfall amount
Heavy rainfall event : 09LST 4 July 2007
(2007/07/04 00LST ~12LST)
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.
Observations : 9-12UTC 3 July 2007
 111 ASOS
 19 Radiosondes
 773 AWS data
 240 AMDAR
 451 AMDAR from Korean Airlines (KAL)
 10 Wind profiler
 5 SATEM
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)
Baengnyeongdo
Sokcho
Osan
Munsan
Pohang
Huksando
Gwangju
Haenam
Conventional
Probex (PRedictability and
OBservation Experiment
in Korea)
KEOP
Gosan
Air Force
Ieodo
Model domains and configurations
KWRF 3.3 km
MM5-30 & KWRF-10 km
Verification area
Physical processes
Domain 1 (10 km)
Domain 2 (3.3 km)
Dimensions
574 X 514 (with 30 vertical levels)
334 X 364 (with 30 vertical levels)
Time interval (Δt)
60 sec
20 sec
Cumulus Parameterization
Kain-Fritsch (new Eta) scheme
None
Microphysics
WSM6 / WSM5 / WSM3 / new Eta
WSM 6-class scheme
PBL
YSU scheme
YSU scheme
Radiation
RRTM / Dudhia scheme
RRTM / Dudhia scheme
Surface-Land
Noah LSM
Noah LSM
Initial and Boundary data
GDAPST426
hybrid-sigma(0.28125o)
WRF 10 km
Remarks
Run time on CRAY-X1E
(1024CPUs / 18.4TFLOPS)
Domain 1 : 14 min.
with 126 CPUs
Domain 2 : 50 min.
with 64 CPUs
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
60-h forecast
COLD run
Nestdown to WRF 3.3 km
3DVAR data assimilation
Observations for impact studies
Experiment ID
Assimilated observation data
Remarks
( Number of assimilated obs. )
CTL
All available observations without KEOP soundings
Operational, 1247
ALL
All available observations including KEOP soundings
OPR
Conventional TEMP soundings
17
TMP
Conventional TEMP + KEOP soundings
19
KOP
KEOP soundings only
2
PRF
Wind profiler data
10
ACS
AMDAR data from FSL
240
KAL
AMDAR data including KAL reports
451
KON
KAL reports only
211
SFC
SYNOP, SHIP, BUOY, AWS data
884
SYN
SYNOP, SHIP, BUOY
111
AWS
AWS
773
SAT
SATEM, SATOB, QSCAT
1249
5
T+12 acc rainfall
OBS
CTL
ALL
Munsan
98
103
104
148.5
OPR
TMP
Munsan
100 km
IOP
Munsan
 The location of rainfall was slightly shifted toward observation when the IOP sounding (even in
one sounding at Munsan station) data was included.
T+12 acc rainfall
OBS
PRF
KAL
SFC
100 km
ACS
SAT
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.
Sensitivity to boundary condition from global model
CTRL (operational)
OBS
ANAL
64
76
104
43
148.5
FCST(C24H)
64
100 km
ANAL_IOP
98
 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.
Sensitivity to the cycle with WRF-10
OBS
CTRL
COLD
11
76
104
148.5
100 km
C12H
C24H
29
64
 The cycle plays an important role in the spin-up in precipitation process.
Sensitivity to microphysics (WRF 10km) with ANAL_BCs
OBS
WSM3
WSM6
74
96
104
148.5
CTRL (WSM6)
76
100 km
WSM5
94
ETA_NEW (Ferrier)
42
 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.
Sensitivity to microphysics (WRF 3.3km)
OBS
WSM3
WSM6
93
97
104
128
148.5
100 km
WSM5
109
ETA_NEW (Ferrier)
41
 In higher resolution experiment, the magnitude of maximum rainfall is larger than that in
lower resolution but no difference in phase.
Genetic Algorithm to optimize WRF-10 model
Start
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.
Initialization
Fitness Evaluation
Selection
Crossover
Mutation
Fitness Evaluation
NO
Terminal condition
YES
End
Selection of Chromosomes
 Variance and length scale of background error (x1, x2, x3,
x4, x5, l1, l2, l3, l4, l5)
0.5  xn , ln  3.0
 Asymptotic mixing length in PBL(m1)
Clear air turbulence : 10 – 30 m
Cyclogenesis in upper troposphere : < 100m
 Closure assumption of KF (m2)
10  m1  200
0.02  m2  0.96
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].
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 =
 ETS
i
,
i  1,2,,100
i
where i is the precipitation threshold in mm. Here, the ETS is defined as:
ETS 
H R
F O H R
H : hit
R : the expected number of hits in a random
forecast
F : rain forecast
O : rain observation
R  FO / N
Evolution of chromosomes
Variance of
control variables
var_scaling1 (x1, Ψ)
var_scaling2 (x2,χ)
var_scaling3 (x3,Tu)
var_scaling4 (x4, qRH)
var_scaling5 (x5,Pa)
1.32
2.68
1.34
0.96
2.36
Horizontal length
scales
len_scaling1 (l1)
len_scaling2 (l2)
len_scaling3 (l3)
len_scaling4 (l4)
len_scaling5 (l5)
0.92
2.45
2.50
1.09
0.79
Physical
parameters
asymptotic mixing length (m1) (30)
reduction rate (m2) (0.95)
132.5
0.36
Preliminary results w/ and w/o GA in WRF-10
OBS
CTRL
104
50.9
148.5
100 km
GA
54.1
64.5
 Overall the tuned WRF by GA works for locations of heavy rainfall.
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.