NNumerical simulation with radar data umerical simulation with radar data
assimilation over the Korean Peninsulaassimilation over the Korean Peninsula
Seoul National University
Ji-Hyun Ha, Gyu-Ho Lim and Dong-Kyou Lee
Introduction
� The forecast skill associated with warm season rainfall is relatively low, both by
absolute standards and relative to predictions of winter season weather systems
with strong baroclinicity (Olson et al., 1995; Fritsch et al. 1998).
� However, with the improving performance of numerical prediction models and
increasing computational resources, there is a renwed interest in the
predictability of the daily weather, especially at the mesosale (Ehrendorfer 1997;
Errico et al. 2002).
� Several studies have suggested that data assimilation is needed to improve the � Several studies have suggested that data assimilation is needed to improve the
heavy rainfall prediction and more experimental studies on the assimilation
should be conducted (Wee, 1999; Lee and Lee, 2003;Liu et al., 2005; Yu, 2007).
� Radar data assimilation is a key scientific issue in numerical weather prediction
of convective systems for short-range forecasting (Wilson et al., 1998). In recent
years considerable progress has been made in the assimilation of radar
observations into convective-scale numerical models for heavy rainfall prediction.
� The objective of this study is to investigate short-range forecasting of the WRF
model through the 3DVAR data assimilation of radar data and impact of radar
data assimilation for improving the accuracy of heavy rainfall forecast.
Description for radar observations
- Radars, which provide observations of radial velocity and reflectivity of
hydrometeors, are about 120 km apart on averaged with the observable range
for each exceeding 100 km � cover the entire the southern Korean Peninsula
- A few km spatial resolution and 6-10 min time interval
• Process of radar data for analysis and data assimilation
(b)
(a)
KAF (5) KMA (12) USAF (2)
Preprocessing
High resolution radial velocity and reflectivity
Preprocessing
: noise filtering and dealiasing radial velocity
UF (Universal Format) output
Interpolation into XYZ by SPRINT
-Extract the radial velocity and reflectivity for data assimilation
-Synthesis of reflectivity and wind retrieval for analysis
(from Park and Lee, 2009)
Heavy rainfall case
• On 11-12 July 2006, a heavy rainfall event associated with MCSs occurred over
the Korean Peninsula. One of the reasons for studying this case is that
operational forecasts failed to predict the amount of precipitation.
2100 UTC 11 2200 UTC 11 2300 UTC 11
MTSAT Enhanced IR satellite image
-35 -45 -55
An isolated storm moved eastward while developing quickly from 2200-2300
UTC. The size of the most intensive convective system at 2300 UTC was
approximately 2000 km², which corresponded to the meso-ß scale.
• 12 h accumulated rainfall amount • Synoptic environment (2006071118)
1000 hPa divergence 850 hPa
- 12 h accumulated precipitation at Goyang : 335.0 mm
- 1 hour maximum rainfall amount : 77.5 mm/h at 2300 UTC 11 July 2006
0
10
20
30
40
50
60
70
80
90
1121 1123 1201 1203 1205 1207 1209
Goyang
11-12 July 2006 (UTC)
Hourly Precipitation (mm)
* Geopotential height (solid line)
Equivalent potential temperature (shaded)
Wind speed greater than 12.5 m/s (dahsed line)
2150 UTC
M 2200 UTC M1M2 2210 UTC M1M2M3 2220 UTC
M4 M1 2230 UTC
M2+M3 =
M4
BA
- Evolution of convective cell Reflectivity Divergence (shaded) and vertical
velocity (line)
2150 UTC
2200 UTC
2210 UTC
2220 UTC
M1M2
M1M2M3
M4 M1
2150 UTC
2200 UTC
2210 UTC
2220 UTC
Reflectivity (shaded) and convergence (line)
2230 UTC
M4 M1
M4
2240 UTC
M4 2250 UTC M4 2300 UTC
2230 UTC
2240 UTC
M4 M1
2250 UTC
M4 M1
M4
A
2230 UTC
2240 UTC
2250 UTC
B
� The propagation of the convective system shows the development of back-building
MCS, such as stagnation of the entire convective system oriented in the east-west
direction.
Numerical simulations and results
description Domain 1 (D01) Domain 2 (D02)
Horizontal resolution 18 km 6 km
Horizontal grid number 170 × 150 211 × 211
Vertical layers / Model
top
31 sigma layers / 50 hPa
Explicit moisture WSM6
� Model domain and configuration
Cumulus
parameterization
scheme
Kain-Fritsch
scheme
NO
Boundary layer YSU scheme
Long-wave radiation RRTM radiation
Short-wave radiation Dudhia scheme
Surface physics Thermal diffusion scheme
Model initial and boundary data:
FNL 1°ⅹ1° data
Radar observations
1km level 1.5km level
3 km level
Radar name Wavelength (cm)
USAF (2) RKJK, RKSG S band (10 cm)
KAF (5) RKWJ, RSCN, RTAG, RWNJ, RYCN C band (5 cm)
KMA (12) RGDK, RKWK, RJNI, RKSN, RGSN, RSSP S band (10 cm)
RBRI, RIIA, RPSN, RMYN, RDNH, RCJU C band (5 cm)
Information at the low level is limited due to complex
topography. Thus, we assimilate the surface data for
information at the low level.
� Experiment design
11 JUL12 UTC 15 18 21
D01
D02
Forecast
03 06
ForecastGTS data assimilation
AWS and/or RADAR data assimilation
09 1212 JUL00
Data assimilation using WRF 3DVAR
Experiment name Reference
CNTL Without data assimilation
RADAR+AWS Radial velocity + Reflectivity + Surface data
RADAR Radial velocity + Reflectivity
AWS Surface data
RV Radial velocity
RF Reflectivity
Radar3km 3km horizontal interval
Radar1.5km 1.5km horizontal interval
AWS and/or RADAR data assimilation
Incremental Analysis Update (IAU)� Linear balance in the variational system are often insufficient to prevent the
development of spurious energy on the fastest time scale of numerical forecast
(Polavarapu et al. 2004).
� A separate filtering procedure is required to remove spurious high-frequency gravity
wave noise, which can have a detrimental effect on the first few hours of the forecast,
and on the data assimilation cycle as a whole. Thus, we apply the incremental
analysis update (IAU) method for data assimilation of the WRF model.
� By gradually incorporating analysis increments, the IAU method removes high
frequencies (Lee et al., 2006). Increments generated by WRF 3DVAR are
transformed into tendencies of the model variables (u, v, t and q).
∑∂p1
( )( ) ( )a b
dX tF X W X X
dt= + −
Analysis increment
Original model forcing
Model variables
IAU forcing
without IAUwith IAU assimilation cycle
∑∂
∂≡
N spt
p
NNoiseLevel
1
- The inclusion of data cause a fluctuating curve without IAU method. However, the noise
is removed by IAU method.
OBS RADARCNTL
AWS RADAR+AWS
12 h accumulated rainfall
� Experiments with data assimilation produce a better precipitation forecast
than the experiment without data assimilation.
� RADAR+AWS has captured well the concentration of the heavy rainfall.
� Radar data contributes to the pattern of the precipitation, while, surface data
improves the intensity of precipitation.
Impact of radar data assimilation for heavy rainfall
forecast
RADAR+AWS
OBS
CNTL
RADAR
AWS
� Time series of the precipitation at the grid point of
maximum accumulated 12-h rainfall
� Even though there exists phase error, the simulated rainfall in RADAR
begins and ends in the early hours of the forecast, but in AWS it begins
in the late hours of the forecast and continues up until the final hours. �
Radar data assimilation contributes to storm development in the early
hours of the forecast.
RADAR+AWS
RADAR
AWS
RADAR+AWS
RADAR
� 2300 UTC 11 July (reflectivity (shaded) and wind speed (lines))
� 0100 UTC 12 July
AWS
(a) RADAR+AWS - AWS (b) RADAR - AWS
Rainwater mixing ratio difference (2300UTC)
� Strong reflectivity occurs along the northern edge of the LLJ in RADAR+AWS, RADAR
and AWS� Interactions exist between the MCS and LLJ.
� Strong reflectivity in the east-west direction over 40 dBZ near the west coast of the central
Korean Peninsula is simulated by RADAR+AWS and RADAR, but spread out by AWS �
Radar data, rather than the surface data, contributes to the development of the convective
cells in the model.
� The rainwater mixing ratio shows a positive difference over the west coast of the
central Korean Peninsula, which is consistent with the area of strong reflectivity.
� These positive differences in rainwater mixing ratio seem to cause highly
concentrated convection over the west coast of the central Korean Peninsula, and
contribute to the development of the convection.
Impact of horizontal resolution of radar data
• 12-h accumulated rainfall
RADAR RADAR3km
RADAR1.5kmOBS
0.4
0.6
0.8
1 Threat scores
� The experiments with high-density radar data improve the 12-h accumulated rainfall
amount and distribution compared with the experiment using the low-density (5km in
this study).
� The experiment with 1.5km horizontal interval shows better agreement with the
observations in rainfall amount even though the rainfall distribution of RADAR1.5km
is slightly shifted northward compared with the observed rainfall.
0
0.2
0.4
10 30 50 70
RADAR RADAR3km RADAR1.5km
Summary and conclusion
• An active MCS produces heavy rainfall over the Korean Peninsula on 11-12
July 2006.
• In order to predict the heavy rainfall, WRF 3DVAR data assimilation and
WRF model are adopted to generate optimal initial and subsequent
numerical simulations. In data assimilation, the WRF 3DVAR cycling model
with incremental analysis increment is used to remove high-frequency
gravity wave.
• The assimilation of radar data shows better agreement with the
observations than without data assimilation in terms of rainfall distribution
and amount. The simulation using radar data contributes to the
development of convective storms in the early hours of the forecast.
• In the sensitivity test, radial velocity from the radar data shows larger impact
in simulating the heavy rainfall than reflectivity. The experiments with high-
density radar data improve the accumulated rainfall amount and distribution
compared with the experiment with low-density radar data.
