merging total ozone data from different uv-vis satellite sensors: gome / sciamachy / gome-2

Merging total ozone data from different uv-vis satellite sensors:

GOME / SCIAMACHY / GOME-2

M. Coldewey-EgbersDLR, D. LoyolaDLR, W. ZimmerDLR, C.LerotBIRA, M. Van RoozendaelBIRA, J.-C. LambertBIRA,

M. DamerisDLR, H. GarnyDLR, P. BraesickeUCAM, D. BalisAUTH, and M. KoukouliAUTH

WMO, Geneva, January 25th, 2011

Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum

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Remote Sensing Technology Institute

Outline

Motivation

GOME / SCIAMACHY / GOME-2: Total ozone retrieval & intercomparison

Merging algorithm

Intercomparison with other datasets: Total ozone and ozone trends

Workshop questions

Summary and outlook


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Ozone Long-Term Monitoring with European Sensors

SCIA.

GOME

~35 years

GOME-2

GOME-2

GOME-2

E39C-A (SCN-B2d), Stenke et al., ACP, 2009

GOME/SCIAMACHY/GOME-2, Loyola et al., IJRS Montreal Protocol special issue, 2009

S5S4S5p

OMI

June 1995


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Instrument Overview: GOME, SCIAMACHY, and GOME-2

passive remote sensing spectrophotometers

satellites fly in sun-synchronous and near polar orbit at a height of ~790km

GOME / ERS-2 SCIAMACHY GOME-2Satellite ERS-2 ENVISAT METOP-AData Availability 06/1995-present (*) 08/2002-present 01/2007-presentSpectral Coverage 240-790 nm 240-2380 nm 240-790 nmSpectral Resolution 0.2 - 0.4 nm 0.2 – 1.5 nm 0.2 – 0.4 nmViewing Geometries Nadir Nadir, Limb, Occult. NadirGround Pixel Size 320 x 40 km2 60 x 30 km2 40 x 80 km2

Swath Width 960 km 960 km 1920 kmEquator Crossing 10:30 a.m. LT 10:00 a.m. LT 09:30 a.m. LTGlobal Coverage 3 days 6 days Almost daily

(*) GOME global coverage lost in June 2003

Operational Algorithm: GOME Data Processor 4.x (DOAS fit + iterative AMF/VCD)

Independent Geophysical Validation


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Monthly Mean Total Ozone: 60°N – 60°S


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GOME – Total Ozone Long-term Stability

Update of Fig.(1) inM. Coldewey-Egbers et al., Applied Optics,

Vol. 47(26), 2008


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Intercomparison: zonal means

GOME vs. SCIAMACHY (2002-2009)

GOME vs. GOME-2 (2007-2009)


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Merging Algorithm: SCIAMACHY 1. Latitudinal CorrectionFor each month (Jan. to Dec.), averaged over 2002 to 2009, for latitudes Φ from 90°N to 90°S.

2. Add time dependent offsetFor each individual month from June 1995 to December 2009, averaged over 60°N to 60°S.


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GOME-type Total Ozone – Essential Climate Variable


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Intercomparison: Data Sets Satellite Observations

GTO-ECV_v0: GOME-1, SCIAMACHY, and GOME-2, 1995-2009, 1°lat x 1°lon, Loyola et al., IJRS, 2009. (http://atmos.caf.dlr.de/gome/gto_ecv.html)

NASA-MOD: TOMS, SBUV(/2), and OMI, 1978-2009, 5°lat x 10°lon, Stolarski and Frith, 2006. (http://acdb-ext.gsfc.nasa.gov/Data_services/merged/mod_data.public.html)

Chemistry Climate Models

E39C-A: ECHAM4.L39(DLR)/CHEM/-ATTILA, 1960-2050, 3.75°lat x 3.75°lon, Stenke et al., 2008.

UMUKCA-UCAM: Unified Model / UK Chemistry and Aerosols Module – University of Cambridge, 1960-2100, 2.5°lat x 3.75°lon, Morgenstern et al., 2009.

Ground-Data: 32 Brewer and 47 Dobson Stations, 1995-2008, 5°lat x 5°lon, Balis et al., 2007.


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Total Ozone Comparison – Zonal Means


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Trends: Statistical Model

Monthly mean total ozone time series model: Vyushin et al., JGR 112, 2007“Impact of long-range correlations on trend detection in total ozone”.

)()()()()()( 0 mmTmSmQmAam

Residual

Linear Trend (1)

Solar Flux 10.7cm (3)

QBO at 30 and 50hPa (2x3)

Seasonal Cycle (8)

Overall Mean (1)

Total Ozone at Month m (June 1995 to December 2009)

sin() and cos() terms

for seasonal dependence


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Trends: 1995–2009

60°N-60°S

60°N-30°N

30°N-30°S

30°S-60°S

GTO-ECV MOD E39C-AUMUKCA-UCAM

60°N-60°S

(global)

0.57 (±0.41)

1.07 (±0.40)

0.39 (±0.37)

1.05 (±0.87)

60°N-30°N

(NH mid lat)

0.79 (±1.03)

1.58 (±0.97)

-0.01 (±0.67)

1.57 (±1.34)

30°N-30°S

(tropics)

0.72 (±0.59)

0.89 (±0.70)

0.15 (±0.46)

0.94 (±1.01)

30°S-60°S

(SH mid lat)

0.05 (±1.19)

0.84 (±1.45)

1.23 (±0.85)

0.73 (±2.33)

Trend [%/decade (±2σ)]

Anomalies: 1980-2040


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Trends: Zonal Means 1995-2009


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Trends: GTO-ECV Global 1995-2009

Trend

2-sigma error

Significance

Number

of years


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Workshop questions

is your data set suitable for assessing long-term changes? YEShow internally consistent is it?what is the evidence that it is internally consistent?

Trends: GOME only Trends: GTO-ECV


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Workshop questions (2)

how can it be used to evaluate other data sets?

GTO_ECV_v0

MOD

UMUKCA-UCAM

E39C-A

1995-2029


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Workshop questions (3)

can it be used in conjunction with other data sets to provide a long (20-30 year) record?

Past and future missions can be added (see outlook)

what has been learnt that is relevant in assessing other data sets?

Provide not only data but also associated errors

Internal consistency

Validation with ground based data

Comparison with similar data sets

Comparison of trends


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Outlook

The generation of a long-term total ozone ECV data record from combined European missions will be continued in the framework of the ESA Climate Change Initiative

Newest retrieval algorithm GDP 5: GODFIT instead of DOASGOME/ERS-2 data reprocessed with GDP5 to be released in 2Q/2011

Optimised version of the GDP5 algorithm will be applied to all 3 european sensors

Refined merging algorithm including error calculation

Add future missionsGOME-2/MetOp-B (2012), GOME-2/MetOp-C

Sentinel Series (S5p, S4, S5)

Add past missions (optional work in cooperation with USA)Merge MOD and GTO-ECV


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http://atmos.caf.dlr.de/gome/gto-ecv.html


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Two steps GDOAS approach (M. van Roozendael et al., JGR 2006) DOAS fit for ozone slant column and effective temperature

Iterative AMF/VCD computation using a single wavelength

Improved O3 Retrieval

Molecular Ring Correction parameterised On-the-fly RTM simulations using LIDORT v3.3 (R. Spurr, 2003) Cloud Correction: OCRA&ROCINN v2.0 (D. Loyola et al., TGRS 2007)

Independent Geophysical Validation (D. Balis et al., JGR 2007)

Milestones: 2004 GDP 4.0 operationally with GOME 2006 GDP 4.0 operationally with SCIAMACHY (C. Lerot et al., AMT 2009) 2007 GDP 4.1 operationally with GOME-2 2010 GDP 4.4 GOME-2 reprocessed (D. Loyola et al., accepted, JGR 2011)

Intra-cloud ozone, sun-glint and scan angle dependency corrections

Daily composites (0.33° x 0.33°) and monthly averages (1° x 1°)

GDP 4.x – Algorithm Summary and Milestones


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Trends: GTO-ECV 1995-2009 seasonal dependence

NH Winter NH Summer


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Global mean trend (60°N-60°S): 1995-2040


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Trends: Comparison with Ground Data

GTO-ECV vs. GROUND GTO-ECV vs. MOD

ρ=0.33 ρ=0.74


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Summary

GOME-type Total Ozone - Essential Climate Variable(GTO-ECV) available since 2009:Monthly-mean total ozone data record (06/1995 to 12/2009) generated by merging GDP 4.x data from GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/MetOp-A.

Global Total Ozone Trend Analysis: Significant positive trends for the global mean ozone and in some regions of the northern hemisphere.

merging total ozone data from different uv-vis satellite sensors: gome / sciamachy / gome-2

Documents

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outlookinstitut fr methodik

sinstitut fr methodik

gome data processor

gome global coverage

total ozone data

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