Assimilation of OMI Data Into NCEPs GFS

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Assimilation of OMI Data Into NCEPs GFS

• Craig Long,
• S. Zhou, T. Beck, A.J. Miller
• NOAA/NWS/NCEP/Climate Prediction Center
• L.Flynn
• NOAA/NESDIS/STAR

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Outline

• Background
• Improvements due to OMI coverage
• OMI Issues
• Comparisons between SSI and GSI
• How OMI data is assimilated
• Thinning possibilities
• Summary
• Whats Next

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Aspects of Ozone in NWP

• Three aspects of dealing with ozone in NWP
• Assimilation of ozone observations
• Horizontal and vertical
• Agreement between multiple sources
• Transport of ozone once in the model
• Brewer Dobson Circulation
• Ozone Chemistry
• Homogeneous Production and Loss
• f Latitude, Pressure, Season
• Heterogeneous 'Ozone Hole' type depletion
• Need additional observations

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Background

• Currently NCEP GFS assimilates SBUV/2 total and
  profile ozone measurements from both NOAA-16 and
  17.
• SBUV/2 provides about 90 nadir observations per
  orbit.
• Replacement instrument is the OMPS (Ozone Mapping
  and Profiler Suite)
• Combination of scanning mapper and limb profile
• On NPP and NPOESS
• Will provide higher vertical and horizontal
  resolution
• Current additional sources of ozone data
  available
• Aura OMI, HIRDLS, MLS, TES
  NRT
• MetOp GOME2

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Background cont.

• Why is ozone assimilated?
• LW and SW radiation schemes need realistic ozone.
• Used to extract correct temperature component
  from the ozone sensitive HIRS channels.
• Biggest impacts in terms of temperature and
  dynamics and should occur in the UT/LS.
• Won't improve short term skill (days 1-3)
• But should improve days gt3
• Ozone forecasts used in UV Index forecasts.
• Used for boundary conditions in Air Quality
  forecasts.

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OMI shows finer structure than the GFS, e.g.,
the relatively high ozone off the East coast is
captured by OMI but missed by GFS.
OMI Comparison with GFS using SBUV/2
OMI
GFS
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Adding OMI makes 5 day total ozone forecast
agree more with NASA/TOMS
November 11, 2005
SBUV/2 only Adding OMI
TOMS obs.
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November 12, 2005
SBUV/2 only Adding OMI
TOMS obs.
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November 13, 2005
SBUV/2 only Adding OMI
TOMS obs.
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November 14, 2005
SBUV/2 only Adding OMI
TOMS obs.
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November 15, 2005
SBUV/2 only Adding OMI
TOMS obs.
end
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OMI Issues

• Conflicts with SBUV/2 at high SZA
• Also SBUV/2 is V6 product
• Is V8 much different? Where? When?
• Noise in some channels
• affects TO3 at high SZA
• Cloud climatology may degrade quality of TO3
• Comparisons with DOAS products
• DOAS has striping
• But, better estimate of cloud top heights
• High density of data
• 840 points per single SBUV/2 ob
• Needs thinning
• Comparisons with surface obs

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Comparison between OMTO3 (NASA/TOMS) and OMDOAO3
(KNMI/DOAS)
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OMTO3 vs OMDOAO3 Zonal Mean Total Ozone
Mean may average out to near zero, but
variability is quite high!
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Striping in DOAS Total ozone makes it unusable
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TOMS and SBUV/2 V8 Clim Cloud Tops Results in
Total Ozone being too High
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OMTO3 Cloud Top Pressure Climatology Issue
1000
500
0
1000
500
0
DOAS Cloud Top Pressures
OMTO3 Cloud Top Pressures
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If DOAS Cloud Top Pressures are used, OMTO3 Total
Ozone usually is lower
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258
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OMTO3 using cloud own climatology
OMTO3 using DOAS cloud top heights
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GSI vs SSI
(12Z)
OMI
N-17 SBUV/2
N-16 SBUV/2
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GSI
SSI
SBUV/2 only (N16,N17)
OMI only
SBUV/2 and OMI
Total Ozone increment (DU)
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GSI SSI differences
SBUV/2 only
OMI only
SBUV/2 and OMI
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Data Thinning

• There are many ways to thin massive amounts of
  sat. obs.
• Experimentation is only way to determine best
  density
• Sometimes less is more
• OMI vs SBUV of obs
• 60 OMI obs/scan x 14 scans/SBUV retrieval
• Or 840 points per SBUV retrieval
• 76,000 points per orbit
• Need to restrict OMI to quality data points
• Thin by selection
• Fewer points in flat regions - more points in
  dynamic regions
• Background errors may be adjusted to be more
  sensitive in dynamic regions
• Thin by averaging
• Uniform coverage
• Average out noisy data

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Dynamic regions
Flat region
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Dynamic ozone regions
Flat region
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Dynamic ozone regions
Flat region
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Data thinning method tested

• Method averaging data in 1o x 1o model grid box.
• Selection when there are overlapped data from
  multiple orbits within a 1o x 1o box, select data
  only from one major orbit.
• Reduction total number of data is reduced to
  6.

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(12Z)
OMI
N-17 SBUV/2
N-16 SBUV/2
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1o (lat) x 1o (lon) thinning
12Z
From 76,000 obs per orbit to 4000
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1o (lat) x 2o (lon) thinning
12Z
From 76,000 obs per orbit to 2000
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Ozone difference of thinning and non-thinning
(GSI)SBUV/2 and OMI
DU
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DU
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GSI and SSI TOZ difference (1o x 2o thinning)
DU
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Summary

• OMI adds additional information in horizontal
• OMI data have issues to be rectified
• Are ways to improve it!
• GSI assimilation of OMI data not significantly
  different from SSI

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Whats Next

• Move to Aqua computer when available.
• Continue experimenting with thinning options.
• Quality assessment of data
• Assess impacts in forecast mode.
• Determine resolution dependence
• Impacts to temperatures and dynamics
• Strive for improvement in multi-day forecasts.
• Begin looking at OMI profile products
• Profile total ozone may be better than best
  ozone
• Additional profiles
• Use March 2006 as test month
• Compare profiles with ozonesonde and Lidar data.
• HIRDLS data

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fini
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EOS AURA was launched in July 2004, which has 4
ozone measuring instruments.
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Aura instruments

• OMI (ozone Monitoring Instrument)
• total ozone and ozone profile, high horizontal
  resolution
• HIRDLS (High Resolution Dynamics Limb Sounder)
• ozone profile, high vertical resolution (1.25 km,
  10-80 km)
• MLS (Microwave Limb Sounder)
• ozone profile (3 km, 8-50 km)
• TES (Tropospheric Emission Spectrometer)
• tropospheric ozone (0-34 km)