# Presentation: 408868

## Assimilation of ATOVS level1C radiance and MODIS polar winds at JMA

- Masahiro Kazumori
- Japan Meteorological Agency / EMC Visiting scientist

**Notes:**
Hello everyone.
My name is Masahiro Kazumori
I’m visiting scientist from Japan Meteorological Agency.
In this seminar, I’d like to talk on “ Assimilation of ATOVS level1C radiance and MODIS polar winds at JMA”

## Introduction of JMA NWP models

**Notes:**
At first, I show you JMA operational numerical weather prediction models.
JMA has three type of NWP models. Global model(GSM), regional model(RSM), and meso scale model (MSM).
The purpose  of GSM is a medium range forecast, RSM is for short range forecast, MSM provides disaster prevention information.
All analysis system is 4D-Var.

## Satellite data in JMA global data assimilation system

- Radiance
- AMSU-A(NOAA15/16, Aqua)
- AMSU-B(NOAA15/16/17)
- Wind
- GOES(9/10/12),METEOSAT(5/7)
- MODIS(Terra/Aqua)
- QuikSCAT	**	**

- Radiance
- AIRS (Aqua)
- SSMI,TRMM,AMSR-E
- GOES-9,METEOSAT,MTSAT-1R
- Wind
- METEOSAT(8),MTSAT-1R
- SSMIS(DMSP-16)
- HIRS,AMSU-A,HSB(NOAA-18)
- IASI (Metop)
- ASCAT(Metop)**	****	**

- Now using operationally

- Now testing

- Planning

**Notes:**
This slide shows what kind of satellite data are used in the JMA global data assimilation system.
As for radiance data, JMA use AMSU-A and AMSU-B radiance.
As for wind data, JMA use atmospheric motion vectors from geostationary satellite and polar orbit satellite.
And sea surface wind from QuikSCAT are also used.
These data are now using in the operational system.

Rights are now testing data.
Some assimilation experiments are running for these new data in JMA.
JMA has some plan to assimilate SSMIS, NOAA-18, IASI, and ASCAT data, and is requesting to provide them in real time.

## Progress on global data assimilation in 2004-2005

- Dec. 2004 :
- Direct assimilation of ATOVS level-1C data replaced that of level-1D data
- The radiative transfer model for the radiance assimilation was upgraded from RTTOV-6 to RTTOV-7
- Feb. 2005 :
- Introduction of 4D-Var in operational system
- March. 2005:
- Start of Aqua/AMSU-A radiance data assimilation

- ATOVS level 1C radiance

- MODIS polar winds

- May 2004 :
- Assimilation of  polar winds from Terra/MODIS and Aqua/MODIS in the north polar region started.
- Sep. 2004 :
- Assimilation of  polar winds from Terra/MODIS and Aqua/MODIS in the south polar region started.

- 4D-Var

**Notes:**
From 2004 to 2005, there are three big progress.
The one is MODIS polar winds assimilation and the other is ATOVS radiance and 4D-Var.
I’ll show you about MODIS polar winds and ATOVS level 1C radiance.
May 2004,
JMA started MODIS polar winds data assimilation in the north polar region.
And September 2004, JMA start to use the data in the south polar region.
The start time of use are different between N.H. and S.H.
I’ll show you the reason in later slide.

JMA have been using ATOVS radiance in the operational system.
I change the data from level 1d to level1c.
At the same time, I update the radiative transfer model from RTTOV-6 to RTTOV-7.

I have two topics in my talk. The one is on ATOVS radiance and the other is MODIS polar winds.
I talk about ATOVS radiance first, and then I talk about MODIS polar winds.

## Assimilation of ATOVS level 1C radiance 

- Masahiro Kazumori
- Hiromi Owada
- Kazuyo Fukuda
- Numerical Prediction Division
- Japan Meteorological Agency

## ATOVS level 1C radiance data

- Dec. 2004
- ATOVS data was changed from level-1D data(NESDIS pre-processed data) to level-1C data(unmapped instrument data)
- At the same time, RTM upgrade was performed.
- JMA uses RTTOV (Saunders et al. 1998) as RTM
- The update was from RTTOV-6 to RTTOV-7
- RTTOV-7 treats each sensor separately
- RTTOV-7 has an ability to calculate AIRS radiance

- Since May 2003, ATOVS radiance data have been assimilated directly into the global model with JMA 3D-Var system.

- But..., it had been using  ATOVS level 1D radiance data

## AAPP & MSC direct read out data

- AAPP(ATOVS and AVHRR Processing Package)
- Direct broadcast data are received at JMA Meteorological Satellite Center.

- Global  level 1B data from NESDIS

- Direct read out data (HRPT) at MSC

**AAPP**

- Level 1C radiance

- decode

- The limit of data receiving.

- Data coverage of ATOVS level 1c for early analysis.
- Blue points are the global data from NESDIS and red points are direct read out data at MSC at JMA. 12UTC 31 July 2004.

- Level 1D radiance

- JMA

**Notes:**
I use AAPP for ATOVS data conversion from level 1b data to level 1c data.
And I also use the direct read out ATOVS data  received at Meteorological Satellite Center of JMA.
These direct read out ATOVS data are useful for JMA early analysis to expand the data coverage. 
In the right figure, the red data are direct read out data and blue data are NESDIS data.
For early analysis, the data cut off time is early. So the direct read out data is quite useful for JMA early analysis and forecast.

## Difference of ATOVS data between level 1C and level 1D

- Level 1D data
- No high latitude data.
- Thinned and removed by NESDIS
- NESDIS provide some quality flag for observation

- Level 1C data
- All observation data are available.
- thinning and quality check are needed.

- Level 1D data

- Level 1C data

- Blue:NOAA15 Red:NOAA16

## Difference of ATOVS data between level 1C and level 1D

- Level 1D data
- No high latitude data.
- Thinned and removed by NESDIS

- Level 1C data
- All observation data are available.
- Original data thinning and quality check are needed.

- Level 1D data

**Level 1C data**

## Mapping (computing sounder data to another sounder grid)

- HIRS

- AMSU-A

- In level 1D data,
- Mapping of AMSU-A FOV to HIRS FOV are conducted.

- In level 1C data,
- We can assimilative those data in their original observation points.

- FOV

## Assimilation scheme of ATOVS 1C radiance

- Data thinning
- 240km for AMSU-A, 180km for AMSU-B
- Land sea decision: 0.25*0.25degree map and considering the maximum size of FOV for each sensor.
- Quality Control
- Rain detection ( based on scattering index)
- Cloud detection( based on amount of cloud liquid water)
- Observation error
- Remove the adjustment for each  FOV
- Bias correction
- Scan bias correction ( fixed for each channel)
- Air mass correction (fixed for all season)
- Predictor: Calculated brightness temperature of AMSU-A Ch5,7,10, Surface temperature.
- Coefficient was calculated  from collocated data set with RAOB

- discontinued

## Land Sea Mask based on FOV

- Land sea mask are modified based on FOV size for each sensor.

## Results of cloud, rain detection

**Level 1C data**
- There are many rain observation
- (Level 1C have original all observation data)
- Many cloud around high latitude in winter.

- For level 1C,
- HIRS can not be used for cloud detection (no mapping)

**Clear ****→****Thin cloud**

- Level 1D

- Level 1C

- Used Retrieval Algorithm for QC
- CLW from AMSU-A (English et al. 1997)
-    > 100g/m^2    rejected
- RAIN (Scattering index method)
-  _AMSU-A _(over ocean)
-   SI = ETB15 – TB15 >10   rejected
-  _AMSU-B_ ( over ocean)
-   SI = TB1-TB2>3  and Median filter QC for CH1

## Mean departure for each scan position

- Level1C (AMSU-A)

- Level1D (AMSU-A)

-  Level 1D data has an effect of mapping to HIRS field of view

- Red:W Bias correction
- Blue:W/O Bias correction

- Stop the use of
- Ch14

## Difference between level 1C and level 1D

- Example: AMSU-A Channel 7 (2004/10/07/12UTC)

- Level 1D

- Level 1C

- Level 1C data
- No high latitude data.
- Thinned and removed by NESDIS
- (NESDIS pre-processed data)

- Level 1C data
- All observation data are available.
- Data thinning and quality check should be done by ourselves.

**Notes:**
Next, I show you the difference between level 1c data and level 1d data.
Upper figures show the level 1c data and lower figures show the level 1d data characteristics.

Left figures show the available data distribution of these data.
Right figures show the distribution of departure.
Blue is before bias correction one, and red is after bias correction one.

As you can see, level 1c data has better data coverage than that of level 1d data, especially in the high latitudes.
And the trend of bias is quite different between level 1c and level 1d data.
The bias in level 1c is small and it seems simple and easy to correct.

## Monthly mean O-B distribution (AMSU-A)

- level1D

- level1C

- The bias of level1C seems smaller than 1D
- It means the QC of level 1C works better.

## Assimilation experiment of ATOVS 1C

- Period:
- Summer:From July 13, 2004 To September 9, 2004
- Verification Period: From July 26, 2004 to August 31, 2004
- Winter: From December 27 2003 to February 2004
- Verification Period: From January 1,2004 to January 31 2004
- Setting of experiments
- Analysis:Global 3D-Var, Forecast:T213L40 GSM0407 NAPEX R111
-  CNTL: same with operational(For summer, with MODIS polar winds in the S.H.)
-  TEST:CNTL + replace of ATOVS data ( from 1D to 1C)
-      Update of RTM from RTTOV-6 to RTTOV-7
- Used level1C
-  NOAA15,16 AMSU-A,B, NOAA17AMSU-B
- Discontinued level 1D data
-  NOAA15,16AMSU-A,B, NOAA16 HIRS

**TEST GUESS**

**TEST ANAL**

**CNTL ANAL**

**CNTL GUESS**

**TEST****-****CNTL**

**TEST****-****CNTL**

**ANAL****-****GUESS**

**ANAL****-****GUESS**

**Summer Z500**

## Impact on analysis

- TEST:
- ATOVS level 1C data
- RTTOV-7
- CNTL:
- ATOVS level 1D data
- RTTOV-6

- Zonal mean difference of Temperature for August 2004

- Change at the high latitudes in the troposphere
- And large change in the stratosphere.

- TEST-CNTL

**Notes:**
Next, I show you an impact on analysis by ATOVS level 1c data.
TEST means the use of level 1c data and RTTOV-7.
CNTL means the use of level 1d data and RTTOV-6.

This figure shows the zonal mean difference of temperature for August 2004.
I found a change at the high latitudes in the troposphere and large change in the stratosphere for all latitudes.
The change at the high latitudes in the troposphere means the level 1c ATOVS data assimilated in the TEST.
The change in the stratosphere show the effect that ATOVS data are dominant in these level and are assimilated their original observation points without mapping.

## Z500RMSE

- level 1D

- Improvement of RMSE of 500hPa Geopotential height

- Z500RMSE

- Impacts on forecast

-  Operational use of level 1C data  started on 2 Dec. 2004

**Notes:**
Next, I show you the impacts on forecast. These figure shows the Root mean square error at 500 hPa geopotential  height.
Red is level 1c and blue means level 1d.
I found clear and significant improvement on the score, especially in the southern hemisphere.
Based on these findings, operational use of level 1d data started on 2 Dec. 2004.

## Where can I find the improvements?

- 1day

- 3day

- 5day

- summer

- winter

- Difference of RMSE between TEST and CNTL (blue color means improvement)

- S.H. and the polar region were improved.

## Target
   The 11 typhoon in the summer test period ( maximum sample number 62 )
      red:TEST, green:CNTL

-    The 11 typhoon in the summer test period ( maximum sample number 62 )
-       red:TEST, green:CNTL

- Comparison of typhoon track prediction

- T0409

- T0410

- T0411

- T0412

- T0413

- T0414

- T0415

- T0416

- T0417

- T0418

- T0419

- There are some difference between TEST and CNTL.
- But, statistically, the impact was almost neutral.

## Summary

- JMA use ATOVS level-1C radiance since Dec. 2004 operationally.
-    The use of ATOVS level-1D radiance was discontinued.
- Land sea mask are modified for each sensor.
- Cloud, rain detection scheme are introduced.
- Use of direct read out data received at MSC/JMA
- Change of the RTM from RTTOV-6 to RTTOV-7.
- OSE showed the improvement of forecast score on 500hPa height.
** **

**Notes:**
I summarize my presentation.

## Plan for 2005 on ATOVS at JMA

- KMA and JMA are going to exchange the direct received ATOVS data.** **

- For Early analysis,
- These data make the data coverage expand.

- Data will be available within 50 min. after the observation

- Red: JMA(Tokyo)
- Blue:KMA(Seoul)

- At present, data impact study is being conducted.

## Plan for 2005 on ATOVS at JMA

- Japan has a ground station(ice station) in the Antarctic. SHOWA-kichi

- JMA is getting the direct received ATOVS data in the Antarctic through National Institute of Polar Research (NIPR).
- The data is available within 50 min. after observation.
- The data is coming through International Mobile Satellite Organization.
- Data acquisition is going well.

## Exchange of ATOVS Direct Broadcast Data in Eastern Asia

- JMA will contribute to establish a RARS together with the associated inter-regional data exchange mechanisms;
- -	JMA is willing to perform its part of the re-transmission functions through the GTS and/or the Internet, in co-operation with CMA, ABoM and other centres;
- -	The APSDEU forum should be used to co-ordinate the Asian RARS activities;
- -	Responsibility for implementation of functions and operations to be shared between centres.

**Reference:CGMS/WMO REGIONAL ATOVS RE-TRANSMISSION SYSTEM(RARS) WORKSHOP REPORT**

## Data coverage

**EARS****(EUMETSAT ATOVS Retransmission Service )**

**RARS(Eastern Asia)**

**If we use both, we will get much data in Early analysis.**

- http://www.eumetsat.int/en/dps/atovs/images/coverage.gif

## Next...

## Assimilation of MODIS polar winds at JMA

- Masahiro Kazumori
- Yoshiyuki Nakamura
- Numerical Prediction division
- Japan Meteorological Agency

## MODIS polar winds

- AMVs from Geostationary Satellites have been used at JMA
- But, the polar regions have been remained as data poor regions because no winds from geostationary satellite for these regions. RAOB and Aircraft-network are also sparse**. **And ATOVS 1D data had no high latitude data.

- Since July 2002, CIMSS(Cooperative Institute for Meteorological Satellite) at Univ. of Wisconsin have been produced AMVs from MODIS on Terra and Aqua for the polar regions.

- Orange Terra
- Green Aqua

- Data distribution of AMVs from Satellite

-  MODIS polar winds fill the gap of observation and will improve the accuracy of analysis in the polar regions, and bring a better forecast in the mid-latitudes.

**Notes:**
Atmospheric motion vectors  from geostationary satellites have been used in our system.
But the polar region have been remained as data poor regions because Geo-Sat produce no winds for these regions. 
And RAOB and Aircraft network are sparse.

Since July 2002, CIMSS at Univ. Wisconsin have been produced AMVs from MODIS on Terra and Aqua for the polar regions.

## Difference between CIMSS and NESDIS

- Terra/MODIS WV 400hPa N.H. Wind Speed O-B and number

- Period:
- From 27 December 2003
- To       9 February   2004

- Difference in Quality
- And Coverage

- O-B

- Number

**CIMSS**

**NESDIS**

- JMA use CIMSS MODIS winds.

## Quality of MODIS Polar Winds

- BIAS and RMSE of Wind Speed against first guess

**Notes:**
Next is quality of MODIS polar winds data. These are BIAS and RMSE of wind speed against first guess at JMA.
Upper panels are Terra and lower panels are Aqua. Left are BIAS and right are RMSE. Red line is IR and blue line is water vapor. Solid line is the Arctic and dotted line is the Antarctic. Light color lines are data number.
These are ALL data acquired from Wisconsin FTP server via Internet.
(Animation start)
So, the figure is the same but for the quality check passed data.
This figure suggest the quality check are reasonable.

## MODIS polar winds data assimilation Experiments

- Period of the experiments
- From 27 June 2003 to 9 August 2003
- From 27 December 2003 to 9 February 2004
- Configurations
- JMA Global Spectral Model (GSM)+ 3D-Var T213L40
-  CNTL the same as JMA operational run.
-              MODIS polar winds were passively monitored.
-  TEST CNTL+Terra/MODIS+Aqua/MODIS
- Used MODIS polar winds (Only in the Arctic)
-  Over ocean IR: above 700hPa  WV: above 550hPa
-  Over land    IR,WV: above 400hPa.
- Data thinning 150km(horizontal) 100hPa(vertical)
- The data in the Antarctic have large bias. That degraded the forecast scores in another experiment.

**Notes:**
To investigate the impacts on NWP by MODIS polar winds data, assimilation experiments were performed for two periods.
Configurations are follows,
JMA Global Spectral Model and 3D-Var system were used.
Control run was the same as JMA operational run.
MODIS data were passively monitored.
In the test run, MODIS data from Terra and Aqua were assimilated in the Arctic.
Quality check on data is showed here.
These threshold were the same as ECMWF setting.
And the data thinning was 150km.

Because the MODIS data quality in the Antarctic are not good,
Only the data in the Arctic were used.

## Impacts on Analysis

- Mean field
- Temperature:Rise in the lower troposphere and fall in the upper troposphere ( about 0.5 degree in zonal mean)
- 500hPa height:Increase over ocean and decrease over land, especially over Siberia area.
- Comparison with Radiosonde observation(RAOB)
- Analysis and first guess in the TEST became close to RAOB, especially over Siberia
- Improvements on background bring better forecasts.

**Notes:**
Next I show the impacts on analysis.
The results is as follows.
About mean field on the temperature, rise in the lower troposphere and fall in the upper troposphere. The change was about 0.5 degree in zonal mean.But the change was limited in the polar region.
As for 500hPa geopotential height, Increase over ocean and decrease over land were found. Large decrease of height was found over Siberia area.
These feature is conspicuous  in the first guess.
And these features was confirmed by radiosonde observation.

## Zonal mean difference of Temperature

- Rise in the lower troposphere and fall in the upper troposphere
-                                ( about 0.5 degrees in zonal mean)

- Monthly mean for July 2003

- Monthly mean for January 2004

**hPa**

**Notes:**
These panels show zonal mean difference of temperature for July 2003 and January 2004.
The vertical axis is pressure from 1000 hPa to 10 hPa. The horizontal axis is latitudes. The color from yellow to read mean positive difference and blue color mean negative difference.
I found that rise in the lower troposphere and fall in the upper troposphere about 0.5 degrees in zonal mean.

## Impacts on Analysis

- Mean field
- Temperature:Rise in the lower troposphere and fall in the upper troposphere ( about 0.5 degree in zonal mean)
- 500hPa height:Increase over ocean and decrease over land, especially over Siberia area.
- Comparison with Radiosonde observation(RAOB)
- Analysis and first guess in the TEST became close to RAOB, especially over Siberia.
- Improvements on background bring better forecasts.

**Notes:**
As for 500hPa geopotential height, Increase over ocean and decrease over land were found. Decrease of height was found over Siberia area.
These feature is conspicuous  in the first guess.
And these features was confirmed by radiosonde observation.

## Change of 500hPa Z 

- Monthly mean error for July 2003

**Analysis (TEST-CNTL)**

**First guess (TEST-CNTL)**

- Analysis and first guess in the TEST became close to RAOB, especially over Siberia.

**(m)**

**(m)**

**(m)**

**(m)**

**Notes:**
Next I show the change of 500 hPa geopotential height in the analysis and the first guess field.
These panels are monthly mean error for July 2003.

This figure shows the monthly mean difference of TEST and CNTL for analysis field at 500hPa geopotential height.
And this figure shows that of first guess.
Increase over ocean and decrease over land were found. This feature is conspicuous in the first guess.
These left panels show the mean difference first guess and radio sonde observations.
I wish to call your attention particularly on the circle.

## Impacts on Forecast

- Anomaly Correlation and RMSE at 500hPa
- Large improvement for both seasons in the N.H.
- Neutral for the Tropics and the S.H.
-      (no MODIS assimilation in these regions in this test.)
- Change in the Arctic spread to the lower latitudes.
- RMS forecast error of wind vectors were reduced.
- Especially, improvements at 500hPa was remarkable.
-   ( 500hPa was the level with maximum data number)
- Improvements on typhoon track prediction
- Small, but positive impacts were found at the later stage in the forecasts.

**Notes:**
Next, I show impacts on forecast.
Anomaly correlation and root mean square error at 500hPa were  compared with TEST and CNTL.
Large improvement for both season was found in the Northern Hemisphere.
Neutral for the Tropics and the S.H. ( no MODIS assimilation in these regions)
The change in the Arctic spread to the lower latitudes.

## Anomaly Correlation of 500hPa height

- TEST:With MODIS
- CNTL:Without MODIS

- Large improvements were found for the forecast score.

- MODIS polar winds assimilation in the Arctic

**Notes:**
These panels show the anomaly correlation of 500hPa geopotential height.
Left panels are July 2003.
Right panels are January 2004.
Upper panels are the Northern Hemisphere and lower panels are the Southern Hemisphere.
Red line is the TEST and blue line is the CNTL.

As you can see easily, large improvements were found for the Northern Hemisphere. As for the Southern Hemisphere, the  impacts are almost neutral.

## Impacts on Forecast by MODIS polar winds

- RMSE at 500hPa
- Large improvement for both seasons in the polar region
- Change in the polar regions spread to the lower latitudes.

- RMS forecast error difference for 500hPa Z TEST minus CNTL

- Monthly mean difference for July 2003

- 1day forecast

- 3day forecast

- 5day forecast

- Positive Impacts of MODIS (negative difference) spread to mid-latitudes with procession of forecast.

**Notes:**
Next, I show impacts on forecast.
Anomaly correlation and root mean square error at 500hPa were  compared with TEST and CNTL.
Large improvement for both season was found in the Northern Hemisphere.
Neutral for the Tropics and the S.H. ( no MODIS assimilation in these regions)
The change in the Arctic spread to the lower latitudes.

## Impacts on Forecast

- Anomaly Correlation and RMSE at 500hPa
- Large improvement for both seasons by 9-day forecasts
- Neutral for the tropics and the S.H.
-      (no MODIS assimilation)
- Change in the Arctic spread to the lower latitudes.
- RMS forecast error of wind vectors were reduced.
- Especially, improvements at 500hPa were remarkable.
-   ( 500hPa was the level with maximum data number)
- Improvements on typhoon track prediction
- Small, but positive impacts were found at the later stage in the forecasts.

**Notes:**
RMS forecast error of wind vectors were reduced.
Especially, improvement at 500hPa were remarkable. 500hPa was the level with maximum data number.

## Zonal mean of RMSE of Wind Speed at 500hPa(5-day forecasts,January2004)

- Improvement

- Improvement

**Notes:**
Upper panel is zonal mean of root mean square error of wind speed at 500hPa height.
Red line is TEST and blue line is CNTL.
Lower panel is the difference  between TEST and CNTL.
As you can see easily, large improvement was found in the north polar region.

## Impacts on Forecast

- Anomaly Correlation and RMSE at 500hPa
- Large improvement for both seasons by 9-day forecasts
- Neutral for the tropics and the S.H.
-      (no MODIS assimilation)
- Change in the Arctic spread to the lower latitudes.
- RMS forecast error of wind vectors were reduced.
- Especially, improvements at 500hPa was remarkable.
-   ( 500hPa was the level with maximum data number)
- Improvements on typhoon track prediction
- Small, but positive impacts were found at the later stage in the forecasts.

**Notes:**
Impacts on typhoon track prediction were also found.
The impacts was small but positive impacts at the later stage in the forecasts.

## Mean positional error of typhoon track predictions

- 22 events in July 2003

- Neutral or slightly positive at the later stage in the forecast time.

**Notes:**
This figure show a comparison of mean positional error of typhoon track predictions.
Red bar is position error of the TEST and blue bar is that of the CNTL.
Black line is the number of cases. 
At the later stage in the forecast time, mean positional error was reduced about 20km.

## Summary

- The MODIS polar winds assimilation experiments were performed at JMA
- Period:July 2003, January 2004
- Used data:Aqua/MODIS,Terra/MODIS in the Arctic
- QC
- Over land, above 400hPa for IR and WV
- Over ocean, above 700hPa for IR and 550hPa for WV
- Data thinning 150km(horizontal) 100hPa(vertical)
- Results
- Improvements on the analysis and first guess in the Arctic.
- Large positive impacts on forecasts for both seasons.   ( height, temperature, wind fields)
- Improvements on the typhoon track prediction.

- Since 27 May 2004, operational use in the Arctic
- Since 16 Sep 2004, operational use in the Antarctic

**Notes:**
I summarize my presentation.
The MODIS polar winds assimilation experiments were performed at JMA.
The periods were July 2003 and January 2004.
Used data are Aqua MODIS and Terra MODIS in the Arctic.
Quality controls are the same as ECMWF and these threshold was considered as  reliable from the investigation at the CNTL run.

Experimental results.
Improvements on the analysis and first guess in the Arctic were found.
And Large positive impacts on forecasts for both seasons were found. (Geopotential height, temperature and wind fields.)
Moreover, improvements on the typhoon track prediction were found.
As these findings, JMA started the MODIS polar winds in the Arctic operationally at 27 May 2004.

## The change of Quality of MODIS polar winds 

- After June 2004, both data became similar.
- Same algorithm for retrieval, and same first guess for height assignment.

- Wind speed comparison between CIMSS and NESDIS

- CIMSS changed the first guess for height assignment from NAVY model to GFS in June 2004.

## Next...

## Improvement of operational forecast score

- 12-month average

**Forecast score of JMA global model is rapidly improving. **

## RMSE of 500hPa height against initial 

- 1day forecast

- 5day forecast

- N.H.

- N.H.

- 5day forecast

- S.H.

- 1day forecast

- S.H.

## Conclusion

- _ATOVS level 1C radiance data_ are used in JMA global data assimilation system operationally.
- _MODIS polar winds data_ are used in JMA global data assimilation system operationally.
- In virtue of these data and 4D-Var system, operational forecast score of JMA global model is improving rapidly.

- Filling data poor region(space) with satellite data make better analysis and better forecast.

- Next step:
-  Effective data usage for 4D-Var. Intelligent data thinning and Quality control for 4D-Var time slot in the assimilation window.
- Use of new satellite data (AIRS, SSMI, AMSR-E, SSMIS, etc...)

## Thank you.