Assimilation of ATOVS level1C radiance and MODIS polar winds at JMA

1
Assimilation of ATOVS level1C radiance and MODIS
polar winds at JMA

• Masahiro Kazumori
• Japan Meteorological Agency / EMC Visiting
  scientist

2
Introduction of JMA NWP models
3
Satellite data in JMA global data assimilation
system
Now using operationally
Now testing

• 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)

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

Planning
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Progress on global data assimilation in 2004-2005
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.

• 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
4D-Var
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Assimilation of ATOVS level 1C radiance
Masahiro Kazumori Hiromi Owada Kazuyo
Fukuda Numerical Prediction Division Japan
Meteorological Agency
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ATOVS level 1C radiance data
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

• 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

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AAPP MSC direct read out data
AAPP(ATOVS and AVHRR Processing Package) Direct
broadcast data are received at JMA Meteorological
Satellite Center.
JMA
Global level 1B data from NESDIS
Level 1C radiance
AAPP
Direct read out data (HRPT) at MSC
Level 1D radiance
decode
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.
The limit of data receiving.
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Difference of ATOVS data between level 1C and
level 1D
Level 1D data
Level 1D data No high latitude data. Thinned and
removed by NESDIS NESDIS provide some quality
flag for observation
Level 1C data
BlueNOAA15 RedNOAA16
Level 1C data All observation data are
available. thinning and quality check are needed.
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Difference of ATOVS data between level 1C and
level 1D
Level 1D data
Level 1D data No high latitude data. Thinned and
removed by NESDIS
Level 1C data
Level 1C data All observation data are
available. Original data thinning and quality
check are needed.
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Mapping (computing sounder data to another
sounder grid)
FOV
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.
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Assimilation scheme of ATOVS 1C radiance

• Data thinning
• 240km for AMSU-A, 180km for AMSU-B
• Land sea decision 0.250.25degree 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
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Land Sea Mask based on FOV
Land sea mask are modified based on FOV size for
each sensor.
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Results of cloud, rain detection
Level 1C
Clear ?Thin cloud
For level 1C, HIRS can not be used for cloud
detection (no mapping)
Used Retrieval Algorithm for QC CLW from AMSU-A
(English et al. 1997) gt 100g/m2
rejected RAIN (Scattering index method) AMSU-A
(over ocean) SI ETB15 TB15 gt10 rejected
AMSU-B ( over ocean) SI TB1-TB2gt3 and Median
filter QC for CH1
Level 1D

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

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Mean departure for each scan position
Level1C (AMSU-A)
Level1D (AMSU-A)
Stop the use of Ch14
RedW Bias correction BlueW/O Bias correction
Level 1D data has an effect of mapping to HIRS
field of view
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Difference between level 1C and level 1D

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

Level 1C
Level 1C data All observation data are
available. Data thinning and quality check should
be done by ourselves.
Level 1D
Level 1C data No high latitude data. Thinned and
removed by NESDIS (NESDIS pre-processed data)
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Monthly mean O-B distribution (AMSU-A)
level1C
level1D
The bias of level1C seems smaller than 1D It
means the QC of level 1C works better.
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Assimilation experiment of ATOVS 1C

• Period
• SummerFrom 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
• AnalysisGlobal 3D-Var, ForecastT213L40 GSM0407
  NAPEX R111
• CNTL same with operational(For summer, with
  MODIS polar winds in the S.H.)
• TESTCNTL 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

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CNTL GUESS
TEST-CNTL
TEST GUESS
TEST ANAL
CNTL ANAL
TEST-CNTL
ANAL-GUESS
ANAL-GUESS
Summer Z500
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Impact on analysis
Zonal mean difference of Temperature for August
2004
TEST-CNTL

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

Change at the high latitudes in the troposphere
And large change in the stratosphere.
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Impacts on forecast
Z500RMSE
level 1C level 1D
Improvement of RMSE of 500hPa Geopotential height
Operational use of level 1C data started on 2
Dec. 2004
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Where can I find the improvements?
Difference of RMSE between TEST and CNTL (blue
color means improvement)
summer
winter
1day
S.H. and the polar region were improved.
3day
5day
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Comparison of typhoon track prediction

• Target
• The 11 typhoon in the summer test period (
  maximum sample number 62 )
• redTEST, greenCNTL

T0409
T0410
T0411
T0412
T0413
T0414
T0415
T0418
T0416
T0417
There are some difference between TEST and
CNTL. But, statistically, the impact was almost
neutral.
T0419
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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.

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Plan for 2005 on ATOVS at JMA
KMA and JMA are going to exchange the direct
received ATOVS data.
Red JMA(Tokyo) BlueKMA(Seoul)
For Early analysis, These data make the data
coverage expand.
Data will be available within 50 min. after the
observation
At present, data impact study is being conducted.
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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.
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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.

ReferenceCGMS/WMO REGIONAL ATOVS RE-TRANSMISSION
SYSTEM(RARS) WORKSHOP REPORT
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Data coverage
EARS(EUMETSAT ATOVS Retransmission Service )
RARS(Eastern Asia)
http//www.eumetsat.int/en/dps/atovs/images/covera
ge.gif
If we use both, we will get much data in Early
analysis.
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Next
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Assimilation of MODIS polar winds at JMA
Masahiro Kazumori Yoshiyuki Nakamura Numerical
Prediction division Japan Meteorological Agency
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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.

Data distribution of AMVs from Satellite
Orange Terra Green Aqua
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.
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Difference between CIMSS and NESDIS

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

CIMSS
NESDIS
O-B
Period From 27 December 2003 To 9 February
2004
Difference in Quality And Coverage
Number
JMA use CIMSS MODIS winds.
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Quality of MODIS Polar Winds

• BIAS and RMSE of Wind Speed against first guess

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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 CNTLTerra/MODISAqua/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.

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Impacts on Analysis

• Mean field
• TemperatureRise in the lower troposphere and
  fall in the upper troposphere ( about 0.5 degree
  in zonal mean)
• 500hPa heightIncrease 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.

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Zonal mean difference of Temperature
Monthly mean for July 2003
Monthly mean for January 2004
hPa

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

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Impacts on Analysis

• Mean field
• TemperatureRise in the lower troposphere and
  fall in the upper troposphere ( about 0.5 degree
  in zonal mean)
• 500hPa heightIncrease 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.

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Change of 500hPa Z

• Monthly mean error for July 2003

Analysis (TEST-CNTL)
(m)
(m)
(m)
(m)
First guess (TEST-CNTL)
Analysis and first guess in the TEST became close
to RAOB, especially over Siberia.
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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.

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Anomaly Correlation of 500hPa height
MODIS polar winds assimilation in the Arctic
TESTWith MODIS CNTLWithout MODIS
Large improvements were found for the forecast
score.
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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.
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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.

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

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Mean positional error of typhoon track predictions

• 22 events in July 2003

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

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Summary

• The MODIS polar winds assimilation experiments
  were performed at JMA
• PeriodJuly 2003, January 2004
• Used dataAqua/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
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The change of Quality of MODIS polar winds
Wind speed comparison between CIMSS and NESDIS
After June 2004, both data became similar. Same
algorithm for retrieval, and same first guess for
height assignment.
CIMSS changed the first guess for height
assignment from NAVY model to GFS in June 2004.
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Next
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Improvement of operational forecast score
12-month average
Forecast score of JMA global model is rapidly
improving.
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RMSE of 500hPa height against initial
5day forecast
1day forecast
N.H.
N.H.
5day forecast
S.H.
1day forecast
S.H.
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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)
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Thank you.