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Preliminary Ozone Profile and Tropospheric Ozone Retrievals From OMI

Xiong Liu1,2, Kelly Chance2, Lin Zhang3, Thomas P. Kurosu2, John R. Worden4, Kevin W. Bowman4, Pawan K. Bhartia5, Daniel J.

Jacob3

1 GEST/UMBC 2 Harvard-Smithsonian Center for Astrophysics

3 Harvard University4 Jet Propulsion Laboratory

5 NASA Goddard Space Flight Center

OMI International Science Team MeetingJune 5, 2007

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Outline

Algorithm Description

Derivation of Soft Calibration

Preliminary Results (Collection-2)

Cross-Evaluation with TES and GEOS-CHEM

Comparison between Collection 2 and OPF40

Summary and Future Outlook

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Algorithm Description Spectral fitting+Optimal estimation+LIDORT [Liu et al., 2005]

Fitting Windows: 270-310 nm (UV-1), 310-330 nm (UV-2) Ozone State Vector: 24 layers (each layer is ~2.5 km) from surface to ~60 km, with the NCEP tropopause as stratospheric/tropospheric boundary, 4-6 tropospheric layers A Priori: ozone profile climatology by McPeters et al. [2007] Measurement error: OMI random-noise error

Slit function: Assume Gaussian shape and derive slit widths

Undersampling correction (UV-1)

Fit wavelength shifts among radiance, irradiance, and ozone cross section (3-order)

2 2

2

2 2

{ ( - ) -[ F( )]} ( - ) a

1 1- -2 2

y i i+1 i i i+1 aS K X X Y - X S X X

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Algorithm Description LIDORT (pseudo-spherical) with additional corrections

Polarization correction

Ring effect: directly model the 1st-oder RRS of the direct beam

Clouds: Mixed LER, V8 TOMS/OMI CTP, fc from 368-372 nm

Aerosols: SAGE stratospheric and GOCART tropospheric aerosols

Surface albedo: varying with , partly taking residual aerosol and calibrations into account

NCEP surface, tropopause pressure, and temperature

Directly fit VCDs of other interfering trace gases: SO2, NO2, BrO, HCHO

NO2: PRATMO (stratosphere) + GEOS-CHEM (troposphere)

BrO: PRATMO (stratosphere) + well mixed in the troposphere

SO2/HCHO: no stratospheric + GEOS-CHEM (troposphere)

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(a)

Example of Retrievals

May 8, 2006Overpass US

Partial Column

Ozone (DU)

(a) Retrieval(b) A priori

(b)

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Soft Calibration: Motivation OMI retrievals without additional calibration (Collection-2):

Across-track position dependent biases Negative biases for edge pixels and positive biases for most positions

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Soft Calibration: Motivation

Across-track stripes: ~30-40 DU in TCO ~10 DU in SCO ~20-30 in Total Ozone

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Soft Calibration: Methodology Simulate OMI radiances with McPeters et al. (2007) and Logan (1999)

tropospheric ozone climatology. Parameters other than ozone were fitted in the retrievals. Derive correction vs. and from mean differences (1 day) Assumption: Climatology represents ozone fields on global average

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Soft Calibration: Methodology Applying soft calibration removes most stripes except for edge pixels. Edge pixels: Neglect the sphericity in the line of sight. Remove remaining systematic stripes based on one day’s retrieval Retrieval artifacts due to absorbing aerosols and clouds.

(c)

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OMI TCO (North Pacific on May 05-10, 2006)

OMI tropospheric column ozone

fc < 0.3Gridded to 2.5°×2°

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OMI TCO (May 04-15, 2006)

OMI tropospheric column ozone ( fc < 0.3)

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OMI/TES/GEOS-CHEM Comparison: Methodology OMI/TES retrievals: Different retrieval grid and a priori

Relatively coarser vertical resolution (vs. ozonesonde) TES: More tropospheric ozone info OMI: More stratospheric ozone info, sensitive to ozone through the

troposphere Clear-sky Averaging

Kernels (AKs)

(a) TES (67 levels)

(b) OMI (24 layers)

15°N 40°N 60°N

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Comparison Methodology TES: V 2.0

Compares well with ozonesonde and LIDAR observations: generally biased higher by ~10% [Ray et al., 2007, Richard et al., 2007].

GEOS-Chem simulation: V7-04-09 with GEOS-4 Lightning NOx: 6 Tg/yr, rescaled with OTD/LIS climatology Increase Chinese NOx emission by ~70% (2006)

Use GEOS-Chem as an intermediate, also evaluate GEOS-Chem: Interpolate GEOS-Chem/TES to OMI grid (coarsest) Append GEOS-Chem with TES stratospheric ozone Compare GEOS-Chem with TES (TES AKs + OMI a priori) Compare GEOS-Chem with OMI (OMI AKs + OMI a priori)

Present the comparison on May 08, 2006 (similar on other days) Remove poor retrievals (i.e., TES master flag, emission layer flag,

OMI fitting residuals) and cloudy pixels (OMI fc > 0.3) 550 coincidences

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OMI/TES/GEOS-Chem TCO on May 8, 2006

AK: Averaging Kernels

IG: A Priori

Generally consistent spatial distribution despite systematic biases

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OMI/TES/GEOS-Chem TCO on May 8, 2006

MB = -8% MB = -6%

MB = -7% MB = 4%

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OMI/TES/GEOS-Chem Comparison

(a) Difference due to OMI/TES AKs can be up to 10-15% especially in UT(b) Large negative (10°N-20°N, high sun) and positive (40°S-25°S, low sun)

biases may be caused by non-linearity of the OMI calibration.

(a)

(b)

Mainly systematic OMI/TES differences

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Collection 2 vs. OPF40 (~Collection 3)

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Collection 2 vs. OPF40 (~Collection 3)

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Collection 2 vs. OPF40 (~Collection 3)

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Summary and Future Outlook Apply our GOME ozone profile retrieval algorithm to OMI. Soft calibration: and dependent correction of up to ±8%. OMI TCO seems to be able to capture large spatiotemporal

variability on the daily basis. The spatial distribution of OMI, TES, and GEOS-Chem

tropospheric ozone is similar on the global scale. OMI shows a negative bias of ~15% relative to TES except for

10°N-20°N (~ -30%) and 45°S-25°S (~20%), which may be related to the non-linearity calibration of OMI.

Improve soft calibration with VLIDORT+MLS+Clouds Implement new correction for neglecting polarization and

spherical geometry in the line of sight (with VLIDORT). Use MLS retrievals to reduce the stratospheric influence Improve aerosols, cloud, and surface albedo treatments

AcknowledgementsOMI and TES science team, GEOS-Chem communityNASA and Smithsonian Institution