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The intercomparison of tropical tropospheric compositions measured from satellite

Jae Kim1, Somyoung Kim1, and M. J. Newchurch2

1: Pusan National Univ, Korea

2: University of Alabama in Huntsville, USA

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Contents

1. Cloud-induced OMI total ozone error and its impact on tropospheric ozone

2. Empirical Orthogonal Function (EOF) method for evaluating satellite products

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Cloud-induced OMI total ozone error and its impact on tropospheric ozone

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OMI-MLS tropospheric ozone map March

April September

May

August

October

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OMI reflectivity

OMI total ozone

MODIS cloud fraction

Ozone hole

Marine stratocumulus regions

Convective cloudy regions

Ozone fountain

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OMI total O3 (all) – total O3 (clear)

Jan (2005.01-2007.12) Feb (2005.01-2007.12)

Mar (2005.01-2007.12) April (2005.01-2007.12)

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May (2005.01-2007.12) Jun (2005.01-2007.12)

Jul (2005.01-2007.12) Aug (2005.01-2007.12)

OMI total O3 (all) – total O3 (clear)

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Sep (2005.01-2007.12) Oct (2005.01-2007.12)

Nov (2005.01-2007.12)

Dec (2005.01-2007.12)

OMI total O3 (all) – total O3 (clear)

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OMI-MLS tropospheric ozone before and after correction for cloud

Dec

Jan

April

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OMI-MLS tropospheric ozone before and after correction for cloud

Aug

Sep

Oct

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Tried to evaluate satellite product quantitatively by calculating correlation, STD, etc. with ground-based measurements and other satellite product. However, there is limitation in spatial and temporal coverage for ground-based data, and we don’t know how good the other satellite product is.

Seek a different point of view in evaluating satellite products qualitatively by answering to the question: “Are satellite products self-consistent in chemical and dynamical point of view?

Accomplish this objective by analyzing the spatial-temporal pattern of satellite products with the statistical tool, Empirical Orthogonal Function (EOF).

The EOF method is the method of choice for analyzing the variability of a single filed. The method finds the spatial patterns of variability, their time variation, and gives a measure of the “importance” of each field.

2. Empirical orthogonal function (EOF) method for evaluating satellite products

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EOF for test 1 case

Spatial pattern; Blue: negative, red:positive value

Time series; Principle Component

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Cloud fraction EOF mode 1

ATSR fire EOFMODIS AOD EOF

OMI NO2 EOF

MOPITT CO EOF

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OMI-MLS tropospheric O3 EOF

SAM tropospheric O3 EOF

TES tropospheric O3 EOF

GOME tropospheric O3 EOF

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OMI-MLS tropospheric O3 EOF

SAM tropospheric O3 EOF

TES tropospheric O3 EOF

GOME tropospheric O3 EOF

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SAM is not available in latitude higher than 20º

GOME tropo O3 EOFTES tropos O3 EOF OMI-MLS tropo O3 EOF

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Cloud fraction EOF mode 1 OMI-MLS EOF mode 2

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OMI-MLS over western Pacific Ocean

mode 1 mode 2

OMI-MLS over central Pacific Ocean

mode 1 mode 2

GOME tropo O3 over western Pacific Ocean

mode 1 mode 2

GOME tropo O3 over central Pacific Ocean

mode 1 mode 2

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GOME tropo O3 over W. Pacific mode 3 GOME tropo O3 over C. Pacific mode 3

OMI-MLS tropo O3 over W. Pacific mode 3 OMI-MLS tropo O3 over C. Pacific mode 3

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Camp et al. (2003, JGR)

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Summary

1. OMI total ozone appears to have cloud related error. We found total ozone is overestimated over marine stratocumulus cloud and underestimated over convective cloud. This leads discontinuous tropospheric ozone distribution between ocean and continent.

2. Empirical Orthogonal Function (EOF) method can be effectively used to evaluate satellite products qualitatively.