assimilation of various observational data using jma meso 4d-var and its impact on precipitation...
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Assimilation of various observational data using JMA meso 4D-VAR and its impact on precipitati
on forecasts
Ko KOIZUMI
Numerical Prediction Division
Japan Meteorological Agency
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JMA Mesoscale Model(input to VSRF system)
• Hydrostatic MSM– Dynamics
• hydrostatic, spectral model– primitive equation
– no acoustic mode
• model top at ~ 0 hPa
– Moisture processes• grid scale condensation
• cumulus parameterization
• Non-hydrostatic MSM(since Sep.2004)
– Dynamics• non-hydrostatic, grid model
– fully compressible, non-hydrostatic equation
– specific treatment for acoustic mode
• model top at ~ 22 km
– Moisture processes• bulk cloud microphysics (3-ice)
• 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
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RSM (20km L40)
MSM (10km L40)
Model Areas
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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.
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Radar-AMeDAS Precipitation Analysis
JMA radarsites in Japan
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Radar-AMeDAS Precipitation Analysis
1. Radar echo intensity is converted to precipitation rate using.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.
6.1200RZ
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Scattering diagram of radar-AMeDAS and independent rain-gauge observation
5808 cases during May to Sep. 1994
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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).
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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 radar-AMeDAS precipitation analysis
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Impacts of Precip. Assimilation(June 2001, 10km resolution)
0.05
0.1
0.15
0.2
3 6 9 12 15 180
0.2
0.4
0.6
0.8
1
1.2
3 6 9 12 15 18
0
0.2
0.4
0.6
0.8
1
1.2
3 6 9 12 15 18
Red: with Precip. Blue: w/o Precip.
(h) (h)
(h) (h)
10mm/3h
Threat score Bias Score
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
3 6 9 12 15 18
30mm/3h
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Statistical property of 3-hour precipitation of first 3 hour forecast [10km](June 2001) w/o precip. assim.
Appearance rate (log.)
3-hour precipitation amount(mm/3 hour)
Red: forecastBlue: observation
3-hour precipitation amount(mm/3 hour obs.)
(mm/3hr forecast)
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Statistical property of 3-hour precipitation of first 3 hour forecast [10km]
(June 2001) with precip. assim.Appearance rate (log.)
3-hour precipitation amount(mm/3 hour)
Red: forecastBlue: observation
3-hour precipitation amount(mm/3 hour obs.)
(mm/3hr forecast)
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Limitation of precipitation Assimilationwith a variational method
• Precipitation processes in NWP have “on-off” 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).
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0-3 h forecastObservation
Precipitation assimilation does not always produce appropriate rain
(Initial Time: 18UTC 23 March 2002)
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TCPW and rain-rate from satellite microwave imagers
SSM/I(DMSP), TMI(TRMM) and AMSR-E(Aqua)
TCPW RR
TCPW estimation:• Takeuchi (1997)• Empirical method• Only over the sea• Using SST, SSW and 850hPa Temp. as external data.
Rain rate estimation:• Takeuchi (1997)• Empirical method• Only over the sea
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0.25
0.270.29
0.310.33
0.350.37
0.390.41
0.430.45
1 2 3 4 5 6
実験ルーチン
a)
0.50.6
0.70.8
0.91
1.11.2
1.31.4
1.5
1 2 3 4 5 6
b)
0.05
0.070.09
0.110.13
0.150.17
0.190.21
0.230.25
3 6 9 12 15 18
実験ルーチン
c)
0.50.6
0.70.8
0.91
1.11.2
1.31.4
1.5
3 6 9 12 15 18
d)
Threat Score
1mm/3h
10mm/3h
w. SSM/I and TMI
w/o SSM/I and TMI
Impact test of PW and rain-rate fromSSM/I and TMI
- 3-16 June 2003- 18 hour forecasts made four times a day
(hour)
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18JST
06JST
12JST
Contribution of AMSR-E• Coverage
– Observation Time (Japan)• AMSR-E … 1:30 / 13:30 JST
• 3 SSM/Is … 6-8 / 18-20 JST
• Data availability– March - June, 2004 ( w/o AMSR-E )
• Very low … 03-06, 15-18UTC
– March - June, 2005 ( with AMSR-E )
• Fill the data gap
01:30JST(16:30UTC)
13:30JST(04:30UTC)
00JST
SSM/I
AMSR-E
18UTC
00UTC
12UTC
06UTC
MWR Obs. (Local Time)
Analysis Time
MWR data utilization rate of each time window [ ]%
0
20
40
60
80
100
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 ]
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• 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)
Impact Study of AMSR-E
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Verification of Precipitation Forecasts
• 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.
---- w. AMSR-E---- w/o AMSR-E
Threat Score Winter 1mm/ 3hour
0.25
0.30
0.35
0.40
0.45
1 2 3 4 5 6
Threat Score Summer 1mm/ 3hour
0.25
0.30
0.35
0.40
0.45
1 2 3 4 5 6
Threat Score Summer 10mm/ 3hour
0.10
0.12
0.14
0.16
0.18
0.20
1 2 3 4 5 6
Threat score Winter 1mm/3hour
Threat Score Summer 1mm/3hourThreat Score Summer 10mm/3hour
3 6 9 12 15 18 3 6 9 12 15 18
3 6 9 12 15 18Y axis : Threat Score
X axis : Forecast Time
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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 sprin
g 2001
RAOB sitesWPR sites (since 2001)WPR sites (since 2003)
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Heavy rain on Matsuyama city on 19th June 2001
w/o WPR with WPR observation
FT=0-3
FT=3-6
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Wind at 850hPa levelwith WPR w/o WPRFT=0 FT=0
FT=0-3 FT=0-3
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• Red line: 4D-Var with wind-profiler
• Blue line: 4D-Var without wind-profiler
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)
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Doppler radars at eight airports
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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
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Radar might observe several model levels at the same time
Beam intensity is assumed as Gaussian function of distancefrom the beam center
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Forecast example (init. 2005/2/1 18UTC) FT=15
3 hour precipitation
Observation with DPR w/o DPR
風の解析 動径風なし動径風使用
850hPa wind
Analysis with DPR w/o DPR
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Statistical verification of precipitation forecasts - Winter experiment: 1-14 February 2004 - Summer experiment: 1-13 September 2004
冬実験の降水スレットスコア10mm/ 3hour
0.05
0.075
0.1
0.125
3 6 9 12 15 18
[hour]予報時間
夏実験の降水スレットスコア10mm/ 3hour
0.125
0.15
0.175
0.2
0.225
3 6 9 12 15 18
[hour]予報時間
February experiment September experiment
Forecast time (hour) Forecast time (hour)
Red: with DPR Blue: w/o DPR
Threat scores
- positive impact on moderate rain- impacts are not clear for weak rain (not shown)
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observation
(init: 2004/7/17 12UTC)
19UTC
20UTC
21UTC
22UTC
23UTC
00UTC
Non-hydrostatic 4DVAR (FT=6-9)
Hydrostatic 4DVAR (FT=6-9)
Non-hydrostatic 4DVAR (FT=9-12) Hydrostatic 4DVAR (FT=9-12)
Ongoing worksdevelopment of non-hydrostatic model-based 4D-Var
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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