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NCAR
Mesoscale and
Microscale Meteorology
Assimilation of Doppler Radar Observations
Using WRF/MM5 3D-Var System
and Its Impacts on Short-range QPF
Qingnong Xiao
National Center for Atmospheric Research, Boulder, Colorado
Collaborators:
Y.-H. Kuo, Jenny Sun, W.-C. Lee, D. M. Barker, Andrew Crook (NCAR, USA)
Jianfeng Gu (Shanghai Weather Forecasting Center, Shanghai, China)
Eunha Lim (Korean Meteorological Administration, Seoul, Korea)
Soichiro Sugimoto (Central Research Institute of Electric Power Industry, Japan)
09 - 06 - 2005
NCAR
Mesoscale and
Microscale Meteorology
Outline:
• Assimilation of Radial Velocity in the WRF/MM5 3D-Var System
• Vertical velocity increments
• Observation operator for radial velocity
• Assimilation of Reflectivity in the WRF/MM5 3D-Var System
• Cloud water and rainwater increments
• Background error statistics using qt
• Observation operator for reflectivity
• Impacts on QPF
• An IHOP squall line case
• A frontal precipitation case
• A typhoon case
• Real-time verifications
• Summary and Discussion
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NCAR
Mesoscale and
Microscale Meteorology
WRF/MM5 3D-Var
• Minimization is performed in preconditioned
control variable v space
x'(i, j, k) Uv UpUvUhv(i, j, m)
• Horizontal transform Uh: Using
Isotropic/homogeneous recursive filter
• Vertical transform Uv: From eigenvectors of the
vertical component of background error
• Physical transform Up: Convert control variables
(y, c, pu and q’) to model variables (u’, v’, T’, p’,
q’)
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NCAR
Mesoscale and
Microscale Meteorology
Special needs for Doppler radar
data assimilation
• Radial velocity data
3D-Var needs vertical velocity increments
to get the radial winds assimilated
• Reflectivity data
3D-Var needs at least rainwater increments,
it is better to have increments of other
hydrometeor variables in 3D-Var analysis
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NCAR
Mesoscale and
Microscale Meteorology
W Increments in WRF/MM5 3DVAR
• Richardson’s Equation (y,c,Fuu’,v’,T’,p’w’)
r r
w'
w
r
p
p'
p v 'h p' vh vhp'
z
z
r
r
r
v 'p g ( v 'h )dz g ( ' vh )dz
z
z
• It is a combination of continuity equation,
thermodynamic equation and hydrostatic relation
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Mesoscale and
Microscale Meteorology
Assimilation of Doppler Reflectivities
• Control variable qt (qt=qv+qc+qr)
• Background error statistics (using qt
instead of qv)
• A moisture physics scheme to partition
the water vapor and hydrometeor
increments (qv’, qc’, qr’ T’, p’)
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NCAR
Mesoscale and
Microscale Meteorology
Partitioning of hydrometeor increments
PRF
qi
PRA
PRC
PID
PII
PCON
PRA
PRC
qv
PRE
qs
qc
PRE
PRM
PMF
qr
PCON: condensation/evaporation; PRA: accretion; PRC: conversion; PID: deposition onto
ice; PRE: evaporation/deposition; PMF: melting/freezing; PII: initiation of ice; PRM: snow
melting due to fall
Only the PCON, PRA, PRC and PRE processes are included in the partition
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NCAR
Observation operators
• Radial velocity
x xi
y yi
z zi
Vr u
v
(w vT )
ri
ri
ri
vT 5.40a qr 0.125
a ( p0 p)0.4
• Reflectivity
dbZ 43.1 17.5 log qr
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Mesoscale and
Microscale Meteorology
NCAR
Mesoscale and
Microscale Meteorology
Experiments on the IHOP (2002)
Squall Line Case
• The squall line occurred on 12-13 June
2002
• We collected Conventional observations,
Doppler radar data from 11 radar stations
in Kansans and Oklahoma, and the rainfall
observation for the case
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Mesoscale and
Microscale Meteorology
Radar Mosaic (00-12 UTC 13 June)
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Microscale Meteorology
00-03 UTC
03-06 UTC
06-09 UTC
09-12 UTC
09 - 06 - 2005
3-hr Rainfall
Observations
(NCEP 4 km
resolution,
stage IV
precipitation)
NCAR
Mesoscale and
Microscale Meteorology
Experimental Design
21UTC 12
00UTC 13
WRF Forecast
12UTC 13
Doppler radar data DA
at 21 UTC 12 and 00 UTC 13
WRF SI
• Domain of 400X400X28 grids, with grid-spacing of 4km
• WRF SI using eta data at 21 UTC 12 June 2002, WRF 3D-Var
Doppler radar data assimilations at 21 UTC 12 and 00 UTC 13 (3
hour cycling), 12-hr WRF forecast from the 3D-Var analysis at 00
UTC 13 to 12 UTC 13 June 2002.
• 7 experiments are carried out
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Mesoscale and
Microscale Meteorology
7 Experiments
• GTS: WRF 3D-Var reanalysis at 21 UTC 12 and 00 UTC 13 using
•
•
•
•
•
•
only conventional data
RV_ALL: Same as GTS, but using GTS data plus Doppler radial
velocity from all 11 radar stations
RF_ALL: Same as GTS, but using GTS data plus Doppler
reflectivity from all 11 radar stations
RVRF_ALL: Same as GTS, but using GTS data, plus both radial
velocity and reflectivity from all 11 radar stations
RV_VNX: Same as GTS, but using GTS data plus Doppler radial
velocity from KVNX radar station
RF_VNX: Same as GTS, but using GTS data plus Doppler
reflectivity from KVNX radar station
RVRF_VNX: Same as GTS, but using GTS data, plus both radial
velocity and reflectivity from KVNX radar station
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NCAR
Mesoscale and
Microscale Meteorology
3-hr Rainfall Verifications
TS Score with Threshold=10mm
0.3
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
GTS
RV_ALL
RF_ALL
RVRF_ALL
RV_VNX
RF_VNX
GTS
0.25
THREAT SCORE
THREAT SCORE
TS Score with Threshold=1mm
RVRF_VNX
RV_ALL
0.2
RF_ALL
0.15
RVRF_ALL
RV_VNX
0.1
RF_VNX
0.05
RVRF_VNX
0
3
6
9
3
12
9
12
FORECAST TIME (HR)
FORECASE TIME (HR)
TS Score with Threshold=1mm
TS Score with Threshold=10mm
0.45
0.3
0.4
0.25
0.35
GTS
0.3
RVRF_ALL
0.25
RVRF_VNX
0.2
0.15
0.1
THREAT SCORE
THREAT SCORE
6
0.2
GTS
0.15
RVRF_ALL
RVRF_VNX
0.1
0.05
0
3
6
9
FORECAST TIME (HR)
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12
3
6
9
FORECAST TIME (HR)
12
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Mesoscale and
Microscale Meteorology
3hr rainfall at 03 UTC 13 June
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OBS
GTS
RVRF_ALL
RVRF_VNX
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Mesoscale and
Microscale Meteorology
DBZ at 01 UTC 13 June
GTS
RVRF_ALL
OBS
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Mesoscale and
Microscale Meteorology
Experiments on a frontal
precipitation case
• The case occurred in South Korea on 10 June
2002
• We collected conventional GTS observations,
Doppler radar data from Korean Jindo radar
station, and Korean AWS surface observations
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NCAR
3-hr rainfall at 15 UTC June
10
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Microscale Meteorology
3-hr rainfall at 18 UTC June
10
NCAR
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Mesoscale and
Microscale Meteorology
NCAR
Rainfall
Verification
TS scores
is higher with
radar data
The more cycling,
the higher TS
score
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Microscale Meteorology
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Mesoscale and
Microscale Meteorology
Experiments on landfalling typhoon
Rusa (2002) case
• Typhoon Rusa (2002) made landfall on the
South Korea coast on 31 August 2002, and
dumped very heavy rainfall
• We collected conventional GTS observations,
Doppler radar data from Korean Jindo radar
station, and Korean AWS surface observations
09 - 06 - 2005
NCAR
09 - 06 - 2005
Mesoscale and
Microscale Meteorology
NCAR
Doppler reflectivity (dBZ)
OBS
Without Radar
With Radar
00 UTC 31 AUG
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03 UTC 31 AUG
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Microscale Meteorology
NCAR
3-hr rainfall
verification
(Vr and dBz DA)
Mesoscale and
Microscale Meteorology
Without radar
With RF
With RV
With both
a. TS score is higher in the
experiments with Doppler
radar data assimilation.
Assimilation of both Vr
abd dBz obtained the
highest score.
Without radar
b. Assimilation of radial
velocity obtained higher
score and longer positive
impact than assimilation of
reflectivity.
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With RF
With RV
With both
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Mesoscale and
Microscale Meteorology
Real-time Verification
•
•
Period: 2004. 8. 26. 00UTC ~ 2004. 9. 28. 12UTC (3hr-accumulated rainfall)
Operation without radar data/experiment with radar data (blue/red)
2004082600 ~ 2004092812
2004082600 ~ 2004092812
Threshold = 0.1mm
Threshold = 5.0mm
1.0
2.00
1.0
2.00
1.75
1.75
0.8
0.8
1.50
1.50
CSI
1.00
0.4
0.75
BIAS
1.25
0.6
1.25
0.6
1.00
0.4
0.75
0.50
0.2
0.50
0.2
0.25
0.0
0.00
3
6
9
12
15
TIME
18
21
24
0.25
0.0
0.00
3
6
9
12
15
18
21
24
TIME
0.1mm threshold (left) : CSI (TS) is increased, but BIAS is also increased for
12-18 hour forecasts
5.0 mm threshold (right) : CSI is increased except for 6 hour forecast, BIAS is
increased for 12 and 15 hour forecasts
09 - 06 - 2005
NCAR
Mesoscale and
Microscale Meteorology
Summary and Conclusions
• The WRF/MM5 3D-Var system has developed capabilities to
assimilate Doppler radar observations. In additional to conventional
analysis, vertical velocity as well as cloud water and rainwater can be
analyzed with the assimilation of radial velocity and reflectivity data.
• With the Doppler radar data assimilation, the skill of short-range QPF
is improved. The QPF improvements are verified for squall-line
convective case, frontal precipitation case and tropical cyclone case.
One-month verifications prove that Doppler radar data assimilation
added positive impact on the QPF.
• Numerical experiments indicate that 3D-Var with more Doppler radar
data cycles (in temporal distribution), and/or multiple-radar
observations (in spatial distribution) will improve rainfall forecast
skill.
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NCAR
Mesoscale and
Microscale Meteorology
• The Doppler radar data assimilation code has been released. The
system is called WRF-Var now. It can be downloaded at
http://www.mmm.ucar.edu/wrf/users/download/get_source2.html
• WRF-Var includes WRF 3D-var and 4D-Var. We are going to
include 4D-Var capability soon.
• You are welcome to try the system. If you have questions about its
Doppler radar data assimilation, please send me email at
[email protected].
• Published references
Xiao, Q., et al., 2005: Assimilation of Doppler radar observations with a regional
3D-Var system: Impact of Doppler velocities on forecasts of a heavy
rainfall case. J. Appl. Meteor., 44, 768-788.
Xiao, Q., et al., 2004: Assimilation of Doppler radar observations and its impacts
on forecasting of the landfalling typhoon Rusa (2002), ERAD
Publication Series Vol. 2,178-182.
09 - 06 - 2005