Ozone Data Assimilation K. Wargan S. Pawson M. Olsen A. Douglass P.K. Bhartia J. Witte Global...
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Transcript of Ozone Data Assimilation K. Wargan S. Pawson M. Olsen A. Douglass P.K. Bhartia J. Witte Global...
Ozone Data AssimilationK. Wargan S. Pawson
M. OlsenA. DouglassP.K. BhartiaJ. Witte
Global Modeling and Assimilation Office
2
Motivation and challenges Ozone is important
Controls temperature in the stratosphere – long range forecasts should start from realistic ozone
Pollution in the troposphere, greenhouse gas Modern reanalyses have ozone
Not easy to assimilate Complex, variable structure Limited data coverage Limited vertical resolution
What we do to make it work Fine tuning of background errors More data sources (MLS, radiances)
3
Plan of talk
• Description of the ozone data and the GEOS-5 data assimilation system
• The impact of assimilating averaging kernels• Background error covariance modeling• Assimilation of the Microwave Limb Sounder
(MLS) ozone• Towards direct assimilation of MLS radiances
4
Ozone Data
• Solar Backscatter Ultraviolet Radiometer 2 (SBUV/2)
• Ozone Monitoring Instrument on EOS Aura (OMI)
• Microwave Limb Sounder on EOS Aura (MLS); currently only in research analyses
5
Solar Backscatter Ultraviolet Radiometer 2 (SBUV/2)
SBUV measures backscattered UV radiation.Ozone is retrieved in 21 layers, ~3 km thick, compare to 72 GEOS-5 layers.Footprint: 170 × km 340 kmVery good agreement with independent data.Long data record (1970s to present)Small structures are not resolved – poor resolution below the ozone maximum.
NOAA 17, 18, 19
700
100
10
1
0.1
90S 60S 30S EQ 30N 60N
[DU]
Partial columns / Dobson Units
12 Z
6
Ozone Monitoring Instrument (OMI)
On EOS Aura Nadir viewing geometry Measures radiance in visible and ultraviolet Footprint 13 km × 24 km Retrieved species: ozone, NO2, SO2, BrO, OClO, aerosols
Observation locations and ozone total column, 10/08/2012 12 Z
7
All ozone data
Data coverage, 12Z
OMI Total ozone column
SBUV – vertical information
SBUV and OMI observe sunlit atmosphere
8
All ozone data
Radiance data - meteorology
All observations within a 6h window
~2,500,000
Ozone observations
~20,000
Data coverage, 12Z
OMI Total ozone column
SBUV – vertical information
SBUV and OMI observe sunlit atmosphere
9
The GEOS-5 Data Assimilation System• Atmospheric General Circulation Model:
• Horizontal resolution: flexible - 2.5° to ¼°• 72 layers from the surface to 0.01 hPa• Parameterized ozone chemistry (stratospheric P&L; dry deposition)
• 3D-Var analysis: • Gridpoint Statistical Interpolation• developed in collaboration with NCEP
• Observations: • Conventional (surface, sondes, radar, aircraft, MODIS-derived winds,…)• Satellite radiance data (TOVS/ATOVS, AIRS, IASI, SSM/I, GOES, GPS)• Ozone data
10
A priori profile
efficiencyfactor
[DU]
1000
10
100
Pres
sure
[hPa
]1
1000
10
100
1
8.4S, 130.5W
0 20 40 60 80 0 1.0 2.0 3.0
OMI – efficiency factors (averaging kernels)
Reduced sensitivity near the surface
The retrieved ozone column is a combination of the true signal and a priori climatology.The a priori is removed in the assimilation
11
ERRORxxFO
xxxxh
ERRORxxxy
forecasttruei
ii
priorforecasti
ii
priorforecast
priortruei
ii
priorobs
)(
)()(
)(
11
1
11
1
11
1
OMI – efficiency factors (averaging kernels)
The retrieved ozone column is a combination of the true signal and a priori climatology.The a priori is removed in the assimilation
12
Generally reduced sensitivity near the surface. Efficiency factors can take values >1 due to multiple scattering
13
Analysis without efficiency factorsAnalysis with efficiency factors
90S 60S 30S EQ 30N 60N 90N
90S 60S 30S EQ 30N 60N 90N
-0.2
0.0
0.2
0.4
0.6
0.8
0.0
-0.5
0.5
[ppb
v/da
y][p
pbv/
day]
Tendencies introduced by analysis
Reduced impact of ozone data near the surface
The system’s response to efficiency factorsZonal mean analysis tendency near 900 hPa
Zonal mean analysis tendency near 1000 hPa
14
Tropospheric column relative difference: With minus without efficiency factors February 2007
[%]
OMI efficiency factors – the impact on assimilated tropospheric column
15
The model – ozone chemistry
• Parameterized ozone production and loss rates in the stratosphere
• No chemistry is implemented below the tropopause – observations are the only source of information on ozone distribution in the free troposphere
• Dry deposition mechanism removes surface ozone
Accurate representation of ozone sources near the surface (e.g. anthropogenic ozone precursors) is needed to compensate for limited sensitivity of observations
16
SOME RESULTS
17
2010
2011
Springtime maximum (2009)
Springtime maximum (2011)
Antarctic ozone hole (2010)
The annual cycle of total ozone
northsouth[DU]
Springtime maximum (2010)
Antarctic ozone hole (2009)
2009
18
y=0.94x+4.44R = 0.98RMS diff = 12.78 %Bias = 0.5 %
0
50
100
150
200
250
Assi
mila
tion
[DU
]
0 50 100 150 200 150Ozone sondes [DU]
Ozone integrated between the tropopause and 50 hPa
Comparisons with ozone sondes
Very good agreement with ozone sonde data in the lower stratosphere.
Important for climate forcing by ozone and stratosphere – troposphere exchange
19
Transport using assimilated winds leads to realistic ozone profile structure in the UTLS
Assimilation of SBUV in GEOS-5 does not show this vertical structure: smoothing from the assimilation process
[ppmv]
Pres
sure
[hPa
]Pr
essu
re [h
Pa]
PV contours (white) and ozone (shaded) from the GEOS-5-driven CTM
HIRDLS retrievalsCTMSBUV analysis
Pres
sure
[hPa
]
Ozone [mPa]
Assimilated ozone from GEOS-5 using SBUV/2 data
20
Background errorsGEOS-5/MERRA• NMC Method• 2-D lookup table of variances
and correlation length scales• High ozone gradients in the
UTLS are not resolved• Statistics-based; real-time
dynamics not accounted for
BackgroundMean O3
tropopause
Ozone mixing ratioAl
titud
e
21
Background errorsGEOS-5/MERRA• NMC Method• 2-D lookup table of variances
and correlation length scales• High ozone gradients in the
UTLS are not resolved• Statistics-based; real-time
dynamics not accounted for
ObservedMean O3
BackgroundMean O3
tropopause
Ozone mixing ratioAl
titud
e
22
Background errorsGEOS-5/MERRA• NMC Method• 2-D lookup table of variances
and correlation length scales• High ozone gradients in the
UTLS are not resolved• Statistics-based; real-time
dynamics not accounted for
ObservedMean O3
BackgroundMean O3
tropopause
Ozone mixing ratioAl
titud
e
Background error correlation, vertical extent
23
Background errorsGEOS-5/MERRA• NMC Method• 2-D lookup table of variances
and correlation length scales• High ozone gradients in the
UTLS are not resolved• Statistics-based; real-time
dynamics not accounted for
ObservedMean O3
BackgroundMean O3
tropopause
Ozone mixing ratioAl
titud
e
After assimilation: the analysis increment “leaks” through the tropopause, vertical gradient is reduced
Background error correlation, vertical extent
How can we take advantage of data without damaging transport-
induced small-scale structures?
So...
25
Background errorsGEOS-5/MERRA• NMC Method• 2-D lookup table of variances
and correlation length scales• High ozone gradients in the
UTLS are not resolved• Statistics-based; real-time
dynamics not accounted for
Alternate approach• Proportional method• A possible candidate for σ2:
]/[32 ][ molmolO
[O3] – background ozoneα – specified parameter
• Process-based
26
2007 May 9th, SBUV/2 ozone assimilated
Sonde profileNMC errorsNew errors
Stony Plain53.5N, 114W
0 5 10 15 20 25Ozone [mPa]
1000
10
100
Pres
sure
[hPa
]
Sonde ozoneAnalysis, new errors
0 5 10 15 20 25Ozone [mPa]
New vs. old background errors
Bratt’s Lake51N, 104W
27
Latitude
Thet
a [K
]Th
eta
[K]
Thet
a [K
]Ozone field in the Upper Troposphere – Lower Stratosphere. Fine structures
The High Resolution Dynamics Limb Sounder (HIRDLS) detects small scale structures in the ozone field near the tropopause.
These structures are often absent in SBUV/2 & OMI analysis but do get captured with the new background error model
28
Interpolate to theta (only above 260 hPa)
Average profiles in 2° latitude bands
Determine lamina bottom and top
Apply thickness and magnitude criteria
Lamina must be coherent across 3 mean profiles
Lamina Identification
29
Number of profiles with Laminae April 2007
Total # 1131
Counting the laminae
Latitude
Thet
a [K
]
HIRDLS data
SBUV assim, NMC errors SBUV assim., New errors
Total # 12 Total # 283
Thet
a [K
]
Latitude Latitude
The new background errors lead to large improvements in the representation of the UTLS ozone
30
Conclusions so far
SBUV and OMI based ozone analyses produce ozone fields which are in a reasonable agreement with ozone sonde data in terms of vertically integrated partial columns
Small vertical features near the tropopause are not always represented
Replacing NMC, static background error variances with a process dependent error model leads to a better representation of these features (as compared with independent data)
31
Microwave Limb Sounder on EOS Aura
The EOS Aura satellite was launched on July 15th 2004
32
Microwave Limb Sounder on EOS Aura
MLS measures atmospheric limb emissions in five spectral regions centered around 118 GHz , 190 GHz, 240 GHz, 640 GHz, and 2.5 THz
Designed to measure atmospheric temperature and composition including ozone, moisture, CO, ClO, SO2, cloud ice, ...
Ozone profiles are retrieved at relatively high vertical resolution (varies with version) between ~260 hPa and top of the atmosphere
Why assimilate MLS ozone
• Assimilation of limb sounder data improves the representation of fine scale vertical structures
• Global day and night coverage• Possibility to do it in NRT if
either NRT MLS retrievals or MLS radiances are used
• A template to assimilate other point measurements (e.g. NPP OMPS-Limb Profiler)
33
Legionowo, 52.4N, 21E Apr 6th 2005
Sonde profileSBUV analysisMLS analysis
0 5 10 15 20 25Ozone [mPa]
10
100
1000
Pres
sure
[hPa
]
We can assimilate: Retrieved data – already implemented in GSI Radiance data
34Latitude
Thet
a [K
]Th
eta
[K]
Thet
a [K
]
High resolution data combined with state-dependent background error covariance model reproduces the structure of the UTLS ozone field
Vertical structure near the tropopause
35
HIRDLS data
SBUV assim., New errors
Number Profiles with Laminae April 2007
• MLS assimilation has the most faithful representation of ozone structure
Total # 1131 Total # 821
Total # 283
UTLS ozone laminae in GEOS-5 – comparison with High Resolution Dynamics Limb Sounder
Latitude Latitude
Thet
a [K
]Th
eta
[K]
MLS assim. old errors
MLS assim. New errors
Total # 934
36
MERRA (SBUV/2)MLS+SBUV, GEOS-5.6.1 Station data
400
300
200
100
0
Tota
l ozo
ne c
olum
n [D
U]
Sep Oct Nov 2010
The 2009 Antarctic ozone hole
Oct 18thMLS+ SBUV analysis agrees well with data from the South Pole balloon sondes.
SBUV analysis (without MLS) overestimates ozone over the South Pole by almost 100 DU in September. Good agreement in October and November
37
MLS+SBUV analysis Total Ozone, Oct 15th, 6Z And SBUV coverage
MLS+SBUV analysis Total Ozone, Sep 3rd, 6Z And SBUV coverage
SBUV analysis Total Ozone, Sep 3rd, 6Z
SBUV analysis Total Ozone, Oct 15th, 6Z
The polar night region is not constrained by SBUV observations until October. The polar ozone is overestimated in September
MLS provides near global coverage
38
ASSIMILATION OF MLS RADIANCES
39
Why radiances?
Retrieved product is always affected by priorsIn case of MLS temperature the priors come
from GEOS-5 analysis – self-contamination of the system if these data are assimilated
By assimilating radiances we avoid additional errors resulting from the retrieval process
40
The MLS viewing geometry
125 vertical scans per 25 s in the direction of motionEach scan records emissions at a number of distinct frequenciesBand 7 radiances, centered at 240 GHz are sensitive to ozone, 25 channels
41
Information from MLS radiances• Contrast: ozone concentration• Breadth: tangent pressure• Position: baseline/extinction
We need all three pieces. In the current implementation
only the contrast is assimilated. We use previously retrieved
tangent pressure data.Implementation of online baseline
retrieval is underway
Observed minus simulated radiances
MLS profile v3.3, 82SRadiance assimilation
Some results
42
Comparison with MLS V3.3 retrieved ozone. A single profile
Ozone [ppmv]
Pres
sure
[hPa
]
Ozone [ppmv]Pr
essu
re [h
Pa]
Reasonable agreement in mid- and upper stratosphere – sanity check passed
Mean MLS, 60N-90NRadiance assimilation
43
Equator60S60N30S30N
Relative RMS difference [%]
Relative RMS difference: Assimilation of MLS v3.3 minus radiance assimilation
Agreement within 5% in mid to upper stratosphere except southern high latitudes.The lower stratosphere is expected to improve once the extinction retrieval is implemented
44Ozone [mPa]
MLS v3.3 analysisRadiance analysis
The MLS v3.3 analysis (assimilation of retrieved MLS ozone) exhibits oscillations in the tropical lower stratosphere – even in the zonal mean.
These are not seen in radiance assimilation
Lower stratospheric zonal mean profile at the equator
45
Summary
New process-based background error covariance model leads to significantly improved representation of fine scale structures in assimilated ozone
High vertical resolution ozone data (MLS) brings further improvements to the assimilated product – the structure of the ozone field in the Upper Troposphere – Lower Stratosphere layer are well represented
The impact of assimilating OMI efficiency factor has yet to be fully assessed
Direct assimilation of MLS radiance data (in ozone and temperature bands) is underway.