Transcript Assimilation of various observational data using JMA meso

Assimilation of various observational data using
JMA meso 4D-VAR and its impact on
precipitation forecasts
Ko KOIZUMI
Numerical Prediction Division
Japan Meteorological Agency
JMA Mesoscale Model
(input to VSRF system)
• Non-hydrostatic MSM(since Sep.2004)
– Dynamics
• non-hydrostatic, grid model
– fully compressible, nonhydrostatic equation
– specific treatment for acoustic
mode
• model top at ~ 22 km
– Moisture processes
• bulk cloud microphysics (3-ice)
• cumulus parameterization
• Hydrostatic MSM
– Dynamics
• hydrostatic, spectral model
– primitive equation
– no acoustic mode
• model top at ~ 0 hPa
– Moisture processes
• grid scale condensation
• cumulus parameterization
• Common specifications
– domain: 361 x 289 x 40, horizontal resolution 10 km
– initial condition from 4D-VAR, boundary condition from RSM
– forecasts are made within 1.5 hrs from initial time
Model Areas
RSM
(20km L40)
MSM (10km L40)
Operational 4D-Var System
- An incremental approach is taken with an inner loop model
with resolution of 20 km L40.
Inner forward : nonlinear full-physics model
Inner backward : reduced-physics adjoint model
(grid-scale condensation, moist convective adjustment,
simplified vertical diffusion, simplified longwave radiation)
- Consecutive 3-hour assimilation windows are adopted.
- Minimization is limited up to 15 minutes of running time.
- 40 nodes of Hitachi-SR8000E1 (80 nodes) are used.
Radar-AMeDAS Precipitation Analysis
JMA radar
sites in Japan
Radar-AMeDAS Precipitation Analysis
1. Radar echo intensity is converted to precipitation
rate using. Z  200 R1.6
2. Eight precipitation rates observed during one-hour
are averaged to make estimation of one-hour
precipitation amount.
3. The estimated precipitation amount is calibrated
using rain-gauges and neighboring radar data.
Scattering diagram of radar-AMeDAS
and independent rain-gauge observation
5808 cases during May to Sep. 1994
Radar-AMeDAS Precipitation Analysis
(as input to the data assimilation system)
• Hourly precipitation amount data, provided with
2.5km resolution, are up-scaled to 20km resolution
(inner-model resolution) and assimilated to MSM
by the meso 4D-Var.
• The same data are also used for verification of
precipitation forecasts, after up-scaled to the model
resolution (10km).
Impact test of precipitation assimilation
• 18-hour forecasts were made from 0,6,12 and 18UTC
during 1-30 JUNE 2001.
• Consecutive 3-hour forecast-analysis cycle was
employed with 3-hour assimilation window.
• Observational data : SYNOP, SHIP, buoys, aircraft data,
radiosondes, AMVs, wind-profiler radars and
temperature retrieved from TOVS by NESDIS
• 3-hour precipitation forecasts are verified against radarAMeDAS precipitation analysis
Impacts of Precip. Assimilation
(June 2001, 10km resolution)
Threat score
0.2
Bias Score
1.2
1
0.15
10mm
/3h
0.8
0.6
0.1
0.4
0.2
0.05
0
3
30mm
/3h
6
9
12
15
18
3
(h)
0.07
1.2
0.06
1
0.05
6
9
12
15
18
(h)
0.8
0.04
0.6
Red: with Precip.
Blue: w/o Precip.
0.03
0.4
0.02
0.2
0.01
0
0
3
6
9
12
15
18(h)
3
6
9
12
15
18
(h)
Statistical property of 3-hour precipitation
of first 3 hour forecast [10km]
(June 2001) w/o precip. assim.
Appearance rate (log.)
Red: forecast
Blue: observation
3-hour precipitation amount
(mm/3 hour)
(mm/3hr forecast)
3-hour precipitation amount
(mm/3 hour obs.)
Statistical property of 3-hour precipitation
of first 3 hour forecast [10km]
(June 2001) with precip. assim.
Appearance rate (log.)
(mm/3hr forecast)
Red: forecast
Blue: observation
3-hour precipitation amount
(mm/3 hour)
3-hour precipitation amount
(mm/3 hour obs.)
Limitation of precipitation Assimilation
with a variational method
• Precipitation processes in NWP have “onoff” switches and it cannot be “turned on”
by iterative calculation of 4D-Var if it
started from “turned off” state (e.g. it is very
dry in the first guess field).
• For the successful precipitation assimilation,
the background moisture field needs to be
sufficiently accurate (e.g. moisture data
seems to be more important).
Precipitation assimilation does not
always produce appropriate rain
(Initial Time: 18UTC 23 March 2002)
Observation
0-3 h forecast
TCPW and rain-rate from satellite microwave imagers
SSM/I(DMSP), TMI(TRMM) and AMSR-E(Aqua)
Rain rate estimation:
TCPW estimation:
• Takeuchi (1997)
• Empirical method
• Only over the sea
• Takeuchi (1997)
• Empirical method
• Only over the sea
• Using SST, SSW and 850hPa
Temp. as external data.
RR
TCPW
Threat Score
Impact test of PW and rain-rate from
SSM/I and TMI
- 3-16 June 2003
- 18 hour forecasts made four times a day
0.45
0.43
0.41
0.39
0.37
0.35
0.33
0.31
0.29
0.27
0.25
0.25
0.23
0.21
0.19
0.17
0.15
0.13
0.11
0.09
0.07
0.05
a)
1
1.5
1.4
1.3
1.2
1.1
1
0.9
0.8
0.7
0.6
0.5
1mm/3h
2
3
4
5
6
c) 10mm/3h
3
6
9
12
15
18
(hour)
w. SSM/I and TMI
w/o SSM/I and TMI
1.5
1.4
1.3
1.2
1.1
1
0.9
0.8
0.7
0.6
0.5
b)
実験
ルーチン
1
2
d)
実験
ルーチン
3
6
MWR Obs. (Local Time)
Contribution of AMSR-E
00JST
12UTC
• Coverage
– Observation Time (Japan)
• AMSR-E … 1:30 / 13:30 JST
• 3 SSM/Is … 6-8 / 18-20 JST
18
JST
01:30JST
(16:30UTC)
18UTC
SSM/I
06
JST
AMSR-E
• Data availability
– March - June, 2004 ( w/o AMSR-E )
• Very low … 03-06, 15-18UTC
– March - June, 2005 ( with AMSR-E )
00UTC
06UTC
13:30JST 12JST
(04:30UTC)
• Fill the data gap
Analysis Time
MWR data utilization rate of each time window [％]
100
80
60
40
20
0
00-03 03-06 06-09 09-12 12-15 15-18 18-21 21-00
w/o AMSR-E （2004）
with AMSR-E （2005）
[ UT ]
Impact Study of AMSR-E
• Cycle Experiments
– CNTL (without AMSR-E) … Operational MSM
– TEST (with AMSR-E) … CNTL + AMSR-E
• Data … TCPW and RR ( retrieved from AMSR-E)
• Period
– Summer … 15 samples ( July – August, 2004 )
– Winter … 15 samples ( January, 2004 )
• Case Study
– Fukui Heavy Rain (2004)
• “Assimilation of the Aqua/AMSR-E data
to Numerical Weather Predictions”,
Tauchi et, al., IGARSS04 Poster
• Rainfall Verification
– Threat Score
• Summer
– Heavy Rain (10mm/3hour) & Weak Rain (1mm/3hour)
• Winter
– Weak Rain (1mm/3hour)
---- w. AMSR-E
---- w/o AMSR-E
Verification of Precipitation Forecasts
0.20
Threat Score Summer 10mm/3hour
Threat
Score Summer 10mm/3hour
0.18
0.45
Threat Score Summer 1mm/3hour
Threat
Score Summer 1mm/3hour
0.40
0.16
0.35
0.14
0.30
0.12
0.10
0.25
31
62
93
4
12
155
6
18
• Threat score of heavy rain
(summer) improved at almost
all forecast time.
• The score of weak rain was
good or neutral for both
summer and winter
experiments.
Y axis : Threat Score
X axis : Forecast Time
31
62
93
Threat Score Winter
0.45
4
12
155
6
18
1mm/3hour
Threat score Winter 1mm/3hour
0.40
0.35
0.30
0.25
31
62
93
4
12
155
6
18
JMA wind-profiler network
• 31 stations with about
100km distance
• 1.3GHz wind-profiler
radar observing up to
about 5km every 10
min.
• assimilated hourly
• operational since
spring 2001
RAOB sites
WPR sites (since 2001)
WPR sites (since 2003)
Heavy rain on Matsuyama city on 19th June 2001
w/o WPR
FT=0-3
FT=3-6
with WPR
observation
Wind at 850hPa level
with WPR
FT=0
FT=0-3
w/o WPR
FT=0
FT=0-3
Impact test on precipitation forecasts
- 26 initials during 13 June and 7 July 2001
- forecast-analysis cycle was not employed
- 25 WPR stations are used
Threat scores
Forecast time (hour)
•
•
Forecast time (hour)
Red line: 4D-Var with wind-profiler
Blue line: 4D-Var without wind-profiler
Doppler radars at eight airports
Data selection policy of DPR radial wind
- based on Seko et al. (2004) • Data within 10km from radar are not used
• Data of elevation angle > 5.9 degree are not
used
• Radar beam width is considered in the
observation operator
• Data thinning is made with about 20km
distance
Radar might observe several model levels at the same time
Beam intensity is assumed as Gaussian function of distance
from the beam center
Forecast example (init. 2005/2/1 18UTC) FT=15
3 hour precipitation
Observation
with DPR
w/o DPR
850hPa wind
風の解析
Analysis
動径風使用
with
DPR
動径風なし
w/o
DPR
Statistical verification of precipitation forecasts
- Winter experiment: 1-14 February 2004
- Summer experiment: 1-13 September 2004
Threat scores
夏実験の降水スレットスコア
September experiment
10mm/3hour
February experiment
冬実験の降水スレットスコア
10mm/3hour
0.225
0.125
0.2
0.1
0.175
0.075
0.15
0.05
0.125
3
6
9
12
15
予報時間[hour]
Forecast
time (hour)
18
3
6
9
12
15
予報時間[hour]
Forecast
time (hour)
Red: with DPR Blue: w/o DPR
- positive impact on moderate rain
- impacts are not clear for weak rain (not shown)
18
observation
19UTC
Ongoing works
development of non-hydrostatic model-based 4D-Var
20UTC
Non-hydrostatic 4DVAR (FT=6-9)
Hydrostatic 4DVAR (FT=6-9)
Non-hydrostatic 4DVAR (FT=9-12)
Hydrostatic 4DVAR (FT=9-12)
21UTC
22UTC
23UTC
00UTC
(init: 2004/7/17 12UTC)
Summary
• Assimilation of precipitation data improve precipitation
forecasts, especially for the first few hours
• Use of satellite microwave imager data (as TCPW and
rain-rate) further improve the precipitation forecasts
• Dense and frequent wind observation (WPR and DPR)
have positive impact on moderate to heavy rain
• Modification of assimilation method (hydrostatic based
4D-Var to non-hydrostatic based 4D-Var) could improve
the forecasts even with the same observational data