Recent activities on AMSR-E data utilization in NWP at JMA

1
Recent activities on AMSR-E data utilization in
NWP at JMA

• Masahiro Kazumori,
• Koichi Yoshimoto, Takumu Egawa
• Numerical Prediction Division
• Japan Meteorological Agency

2-3 June, 2010
AMSR-E Science Team Meeting, Huntsville, AL,
U.S.A.
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Outline

• Status of JMA NWP models and Microwave imager
  data utilization
• Verification of AMSR-E TPW retrieval algorithm
  with global GPS TPW data
• Application to SSMIS TPW retrieval and the
  assimilation experiment in JMA NWP
• Expectations for Microwave imager data
• Observational local time
• Data latency
• Summary

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JMA NWP models
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MW Imager data utilization in JMA
For Global Model Radiance assimilation
Brightness Temperature in clear sky condition
For Meso scale Model Retrieval Assimilation
Total Precipitable Water(TPW) and Rain Rate (RR)
Data thinning 200km grid box QC cloud
screening and bias correction Colored point data
are actually assimilated.
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Recent update in MSM

• Ground based GPS TPW data in Japan

GPS TPW data in Japan was introduced in
operational JMA MSM DA system in Oct. 2009. The
GPS data provide accurate and periodic TPW
information over land. Improvements of rain
prediction were confirmed in heavy rain cases.
Atmospheric moisture information is essential to
produce better rain forecast. Also global GPW
TPW data set are available in JMA for
verifications of NWP models TPW and satellite
TPW products.
With GPS
Analyzed precipitation
Without GPS
Three-hourly accumulated precipitation of 3-hour
forecasts from 20 Jul. 2009 at an initial time of
21 UTC. From the left, analyzed precipitation,
the forecast of Test (with GPS TPW) and that of
Control (without GPS TPW) .
GPS data are delivered from Geospatial
Information Authority of Japan (GSI) and
converted to TPW products in JMA.
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Verification of AMSR-E TPW products with global
GPS TPW data
Locations of collocated GPS Data (35 sites)
ZTD Zenith Tropospheric Delay ZHD Zenith
Hydrostatic Delay ZWD Zenith Wet Delay
AMSR-E and GPS collocation criteria GPS altitude
lt 200m, Spatial diff. lt 20km, Time diff. lt 10
min. Period 20 Jun. 20 Aug. 2009
GPS analysis GPS satellite ephemeris final
ephemeris of International Global Navigation
Satellite System Service (IGS). GPS data (RINEX)
IGS station Software GIPSY/OASIS-II
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Verification of AMSR-E TPW productswith global
GPS TPW data

• Scatter diagram of TPW GPS vs. AMSR-E

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Verification of AMSR-E TPW products by global GPS
TPW data set

• TPWs time sequences for NEW, JAXA-L2, and NSIDC
  products

CHICHIJIMA
Chatham Island
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A case studyAssimilation of SSMIS TPW RR in
MSM
Heavy rain case in Japan July 19 26, 2009
The average year value for Julys one month
rainfall
00UTC Jul. 21, 2009
MTSAT IR image
24hr observed rainfall
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Data coverage of Microwave Imager data in JMA MSM
SSMIS TPW and RR assimilation period July 19 to
26, 2009
MSM analyses were executed in every 3 hour
(00,03,06,09,12,15,18 and 21UTC)
33 hours forecasts were produced from 03,09,15
and 21UTC initial.
Cntl (W/O SSMIS)
15
00
03
06
09
12
18
21
Test (With SSMIS)
00
03
06
09
12
15
18
21
SSMIS data is available in these analysis time
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Impact on moisture analysis in July 20
Analyzed TPW field in Test (with SSMIS)
03UTC
09UTC
15UTC
21UTC
TPW Analysis difference (Test-Cntl)
Generally, assimilation of SSMIS intensify
moisture flow in the analysis.
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Jul. 20 15UTC INITIAL FT0
TPW DIFF (TEST-CNTL)
TEST TPW
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FT1 hour
TPW DIFF (TEST-CNTL)
TEST TPW
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FT2
TPW DIFF (TEST-CNTL)
TEST TPW
15
FT3
TPW DIFF (TEST-CNTL)
TEST TPW
16
FT4
TPW DIFF (TEST-CNTL)
TEST TPW
17
FT5
TPW DIFF (TEST-CNTL)
TEST TPW
18
FT6
TPW DIFF (TEST-CNTL)
TEST TPW
19
FT7
TPW DIFF (TEST-CNTL)
TEST TPW
20
FT8
TPW DIFF (TEST-CNTL)
TEST TPW
21
FT9
TPW DIFF (TEST-CNTL)
TEST TPW
22
FT10
TPW DIFF (TEST-CNTL)
TEST TPW
23
FT11
TPW DIFF (TEST-CNTL)
TEST TPW
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FT12
TPW DIFF (TEST-CNTL)
TEST TPW
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Impact on Rain Forecast
Valid Time Jul. 21 12JST
CNTL(w/o SSMIS)
TEST (with SSMIS)
Radar observation
3hr rain
FT12
FT12
TEST-CNTL TPW DIFF
TEST TPW
FT12
FT12
mm
mm
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Observational Local Time
For the purpose of operational use of satellite
microwave imager data in NWP, observational local
time is a key element. NWP centers use 6hrs
assimilation time window. Continuity of MW
measurements in A-train is indispensable.
Light Blue Aqua/AMSR-E Purple DMSP
F-16/SSMIS Green DMSP F-17/SSMIS Orange
Coriolis/WindSat
00
06
18
1330
12
Dark black points indicate WindSat data in 6-hrs
time window
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Data Latency

• Timely data delivery is also important for the
  use of satellite data in operational NWP.
• Especially, regional analysis demand strict cut
  off time for data receiving. MSM requires 50min
  cut off time after the analysis time for every
  analysis (8 time/day).
• Direct receiving in the frame work of WMO RARS
  and EARS are suitable for the regional data use
  for ATOVS.

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Summary

• TPW data from MW-Imager play important role for
  accurate rain forecasts in MSM.
• TPW retrieval algorithm was verified with ground
  based GPS TPW data. Improvement was found
  compared with current JAXA L2 product, however,
  there is room for further improvement. NSIDC
  products showed better accuracy in GPS TPW
  verification.
• The algorithm was applied for F-16 and F-17
  SSMIS. The retrieved TPW and RR were assimilated
  in JMA MSM for a heavy rain case in Japan.
  Assimilation of new SSMIS TPW data produced
  strong rain band forecast, but the forecasted
  rain band location was not improved.
• Data coverage is a key issue for satellite data
  utilization in operational NWP. Large coverage in
  each analysis is expected with timely data
  delivery. AMSR-E observation in afternoon orbit
  (A-train) is indispensable.

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• Backup slides

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Comparison between RAOB and GPS (Spatial
diff.lt30km, altitude diff. lt 200m)
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GPS Remote Sensing
Vapor
Zenith Tropospheric Delay Zenith
Hydrostatic Delay Zenith Wet Delay
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Other datas coverage in MSM
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Theoretical basis of the algorithm
(1.1)
Microwave Brightness temperature Eq.
Ta is defined as the average of upward Tu and
downward Td Water vapor Ta is equal to cloud
liquid water Ta
(1.2)
Observed brightness temperature
Mean emission temperature
(1.3)
Ocean surface emissivity
Atmospheric Transmittance
(1.4)
Vertical mean temperature of atmosphere and ocean
surface system
Determination of by pre-defined LUT as a
function of frequency, incidence angle, SST and
SSW
Step1
Step2
Initial atmospheric transmittance is set as
exp(-0.2)
Step3
Step4
Calculation of mean emission temperature by
using Eq. (1-4)
Step5
Calculation of Transmittance (V pol. H pol.)
by using Eq. (1-3)
Step6
Calculation of new transmittance
Iteration calculation of Step 3 6 to obtain
optimized Transmittance
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Retrieval of TPW and CLW
Theoretical calculation
TPW
From Eq.(1.2)
TPW can be derived by absorption coefficients of
water vapor kv and cloud liquid water kl by using
two different frequency. However, it is not able
to calculate kv and kl because these depend on
vertical profile of temperature, water vapor and
liquid water.
a function of SST
Determined to be maximize the correlation between
TPW index and RAOB match-up TPW
CLW
A function decreased with TPW
A constant
Theoretically estimated
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Updated TPW algorithm for AMSR-E

• LUT in the algorithm was updated by using 3-yr
  RAOB and AMSR-E collocated dataset (2006-2008).
• Updated LUTs
• T850, Transmittance and Mean atmospheric
  temperature table
• Wind speed correction table and extended to
  strong wind condition beyond 20m/s
• Conversion table PWI (Precipitable water index)
  to TPW
• Correction coefficients on SST , SSW dependency
  of emissivity
• No use of internal Tb conversion from ver.2 to
  ver.1 (JAXA L1B Tb version)

TPW Verification against RAOB (2009.1-5)
Collocation criteria Within 60min. 150km
mm
mm
NEW Num 1349 Min -18.836 Max 19.008
Ave -0.135 Std 3.355 Current Num 1344
Min -18.532 Max 15.366 Ave 0.817 Std 4.071
AMSR-E TPW
AMSR-E TPW
RAOB TPW
RAOB TPW
mm
mm
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V003 vs GPS_PWV (2009?6?20?8?20?)
(mm)
Ver. 003
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Optimized by 3years RAOB TPW data2007 - 2009
(mm)
Ver. 004
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Optimized by 3months GPS TPW dataJun.20 Aug.
20, 2009
(mm)
Ver. 005 (preliminary)