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

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

RSM (20km L40)

MSM (10km L40)

Model Areas

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 radarsites in Japan

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

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 radar-AMeDAS precipitation analysis

Impacts of Precip. Assimilation(June 2001, 10km resolution)

0.05

0.1

0.15

0.2

3 6 9 12 15 180

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0.4

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0.8

1

1.2

3 6 9 12 15 18

0

0.2

0.4

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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

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3 6 9 12 15 18

30mm/3h

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)

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)

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).

0-3 h forecastObservation

Precipitation assimilation does not always produce appropriate rain

(Initial Time: 18UTC 23 March 2002)

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

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)

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 ]

• 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

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

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0.30

0.35

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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

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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

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)

Heavy rain on Matsuyama city on 19th June 2001

w/o WPR with WPR observation

FT=0-3

FT=3-6

Wind at 850hPa levelwith WPR w/o WPRFT=0 FT=0

FT=0-3 FT=0-3

• 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)

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 distancefrom the beam center

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

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)

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

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