Jae Kim 1 , S. M. Kim 1 , and Mike Newchurch 2 Pusan National University, Korea

Jae Kim1, S. M. Kim1, and Mike Newchurch2

1.Pusan National University, Korea2.University of Alabama in Huntsville, USA

The analyses and intercomparison of

satellite-derived HCHO measurements

with statistical approaches

AURA Science Workshop, 14- 18 September, Leiden, Netherland

Global climate change is currently the biggest issue.

Palmer et al., 2003; 2006Because global temperature has been

increased, isoprene from biogenic activity must be increased. Expect to see an increasing tendency in HCHO trend.

Motivation

.

Data

Satellite HCHO data

Period

GOMEApril 1996 - June

2003

SCIAMACHYJan 2003-Dec

2007

OMIOct 2004-Dec

2008

HCHO trend on tropical rain forests

Amazon

Africa rain forest

Western Pacific

GOME overPacific withall time

GOME overPacific tillYear 2001

6.9%/year

1.2%/year

GOME P2

SCIAMACHY

OMI

Africarainforest

0.7%/year

0.5%/year

7.5%/year

GOME P2

SCIAMACHY

OMI

Amazon

-2.3%/year

1.7%/year

7.9%/year

GOME P2

SCIAMACHY

OMI

Western Pacific

-1.0%/year

1.0%/year

10.1%/year

GOME P2

SCIAMACHY

OMI

Central Pacific1.2%/year

0.0%/year

7.0%/year

/yearPerio

d

Amazon[75W-50W,

10S-5N]

Africa[15W-25E

,5S-10N]

WesternPacific[90E-150E,

10S-10N]

Central Pacific[180W-160W ,

10S-10N]

GOME P2Apr96-Dec01

-2.3% 0.7% -1.0% 1.2%

SCIAMACHY

Jan03-Dec07

1.7% 0.5% 1.00.0%

OMIOct04-Dec08

7.9% 7.5% 10.1% 7.0%

HCHO Trend analyses

Satellite data have an intrinsic problem, ill-posed problem, that comes from the fact that a number of various physical parameters can have a similar effect on measured radiance.

Most of the previous evaluations of satellite performance have relied on point-by-point comparisons with limited spatial and temporal coverage of in-situ measurements

The levels of agreement from these comparisons vary according to location and season, so there is not a clear superior method for various satellite tropospheric gas products.

Difficulty in satellite measurement validation comes from large uncertainties, especially HCHO vertical columns, whose error typically range from 40-105% [Palmer, et al., 2006; Kurosu, et al., 2008]. Inter-comparison between satellites HCHO measurements are challenging

Validation of satellite HCHO

.

Our approach is to validate the satellite measurements by analyzing spatial and temporal coherence between individual satellite products and a known source data set

MOPITT CO

A promising statistical tools for identifying these coupled relationships with spatial-temporal patterns are

individual parameters is Empirical Orthogonal Function (EOF)

combinations of two parameters, Singular Value Decomposition (SVD)

Power Spectrum analyses for cycle of the data sets

Statistical tools for validation

Tropical areas with biomass burning and biogenic activity in rain forests

South America

Africa

Western Pacific

EOF and SVD analyses of GOME, SCIAMACHY, and OMI HCHO measurements in conjunction with MOPITT CO.

Data periods

Data

Sensor Period

GOMEApril 1996 - June

2003

SCIAMACHYJan 2003-Dec

2007

OMIOct 2004-Dec

2008

MOPITT COMarch 2000-Dec

2008

EOFMode1

GOME P1HCHO

SCIAMACHYHCHO

OMIHCHO

MOPITTCO

sudden increasing tendency

January

September

Red: +Blue: -

AfricaAfrica

GOME HCHO

SCIAMACHYHCHO

OMIHCHO

MOPITTCO

Power Spectrum analysis

GOME P1HCHO

SCIAMACHYHCHO

OMIHCHO

MOPITTCOMay

September

GOME HCHO

SCIAMACHYHCHO

OMIHCHO

MOPITTCO

AmazonAmazon

GOME P1 HCHO

SCIAMACHYHCHO

OMIHCHO

MOPITTCO

March

October

GOME HCHO

SCIAMACHYHCHO

OMIHCHO

MOPITTCO

Central PacificCentral Pacific

western Pacificwestern Pacific

HCHO-CO SVD analysis

data Period

SCIAMACHY HCHO

Jan 2003-Dec 2007

OMI HCHOOct 2004-Dec

2008

MOPITT COMarch 2000-Dec

2008

SVD 1st mode of MOPITT CO and SCIAMACHY HCHO

August

February

SVD 1st mode of MOPITT CO and OMI HCHO

August

January

SVD 1st mode of MOPITT CO and SCIAMACHY HCHO

September

February

SVD 1st mode of MOPITT CO and OMI HCHO

September

January

SVD 1st mode of MOPITT CO and SCIAMACHY HCHO

SVD 1st mode of MOPITT CO and OMI HCHO

1. EOF analyses shows spatial and temporal distribution of GOME, SCIAMACHY HCHO, MOPITT CO match each other. However, OMI HCHO shows different spatial and temporal pattern compared with others.

2. SVD analyses shows GOME HCHO-MOPITT CO, SCIAMACHY HCHO – MOPITTCO shows consistent spatial and temporal coherence

3. However, OMI HCHO – MOPITT CO shows relatively low correlation.

Relationship between GOME (SCIAMACHY) HCHO and CO shows that biomass burning is most likely the major source of HCHO over Africa and South America.

However, relationship between OMI HCHO and CO suggests biomass burning is not as significant source as of HCHO.

4. GOME and SCIAMACHY HCHO trend is marginal, but OMI HCHO trend is as high as 10% climate change can not explain the big increase. It could be due to OMI instrument or calibration error.

5. EOF and SVD analyses can be another useful method for satellite data validation.

Conclusions