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Merging total ozone data from different uv-vis satellite sensors: GOME / SCIAMACHY / GOME-2 M. Coldewey-Egbers DLR, D. Loyola DLR, W. Zimmer DLR, C.Lerot.

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Presentation on theme: "Merging total ozone data from different uv-vis satellite sensors: GOME / SCIAMACHY / GOME-2 M. Coldewey-Egbers DLR, D. Loyola DLR, W. Zimmer DLR, C.Lerot."— Presentation transcript:

1 Merging total ozone data from different uv-vis satellite sensors: GOME / SCIAMACHY / GOME-2 M. Coldewey-Egbers DLR, D. Loyola DLR, W. Zimmer DLR, C.Lerot BIRA, M. Van Roozendael BIRA, J.-C. Lambert BIRA, M. Dameris DLR, H. Garny DLR, P. Braesicke UCAM, D. Balis AUTH, and M. Koukouli AUTH WMO, Geneva, January 25 th, 2011

2 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie 2 2 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

3 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie 3 3 Remote Sensing Technology Institute Ozone Long-Term Monitoring with European Sensors S CIA. G OME ~35 years G OME-2 E 39C-A (SCN-B2d), Stenke et al., ACP, 2009 G OME /S CIAMACHY /G OME-2, Loyola et al., IJRS Montreal Protocol special issue, 2009 S5 S4 S5p O MI June 1995

4 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie 4 4 Remote Sensing Technology Institute 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-2SCIAMACHYGOME-2 SatelliteERS-2ENVISATMETOP-A Data Availability06/1995-present (*)08/2002-present01/2007-present Spectral Coverage nm nm nm Spectral Resolution nm0.2 – 1.5 nm0.2 – 0.4 nm Viewing GeometriesNadirNadir, Limb, Occult.Nadir Ground Pixel Size320 x 40 km 2 60 x 30 km 2 40 x 80 km 2 Swath Width960 km 1920 km Equator Crossing10:30 a.m. LT10:00 a.m. LT09:30 a.m. LT Global Coverage3 days6 daysAlmost daily (*) GOME global coverage lost in June 2003 Operational Algorithm: GOME Data Processor 4.x (DOAS fit + iterative AMF/VCD) Independent Geophysical Validation

5 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie 5 5 Remote Sensing Technology Institute Monthly Mean Total Ozone: 60°N – 60°S

6 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie 6 6 Remote Sensing Technology Institute GOME – Total Ozone Long-term Stability Update of Fig.(1) in M. Coldewey-Egbers et al., Applied Optics, Vol. 47(26), 2008

7 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie 7 7 Remote Sensing Technology Institute Intercomparison: zonal means GOME vs. SCIAMACHY ( ) GOME vs. GOME-2 ( )

8 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie 8 8 Remote Sensing Technology Institute Merging Algorithm: SCIAMACHY 1. Latitudinal Correction For each month (Jan. to Dec.), averaged over 2002 to 2009, for latitudes Φ from 90°N to 90°S. 2. Add time dependent offset For each individual month from June 1995 to December 2009, averaged over 60°N to 60°S.

9 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie 9 9 Remote Sensing Technology Institute GOME-type Total Ozone – Essential Climate Variable

10 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Intercomparison: Data Sets Satellite Observations GTO-ECV_v0:GOME-1, SCIAMACHY, and GOME-2, , 1°lat x 1°lon, Loyola et al., IJRS, ( NASA-MOD:TOMS, SBUV(/2), and OMI, , 5°lat x 10°lon, Stolarski and Frith, ( Chemistry Climate Models E39C-A:ECHAM4.L39(DLR)/CHEM/-ATTILA, , 3.75°lat x 3.75°lon, Stenke et al., UMUKCA-UCAM: Unified Model / UK Chemistry and Aerosols Module – University of Cambridge, , 2.5°lat x 3.75°lon, Morgenstern et al., Ground-Data: 32 Brewer and 47 Dobson Stations, , 5°lat x 5°lon, Balis et al., 2007.

11 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Total Ozone Comparison – Zonal Means

12 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute 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. 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

13 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Trends: 1995– °N-60°S 60°N-30°N 30°N-30°S 30°S-60°S GTO-ECVMODE39C-A UMUKCA -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) (±0.67) 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:

14 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Trends: Zonal Means

15 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Trends: GTO-ECV Global Trend 2-sigma error Significance Number of years

16 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Workshop questions is your data set suitable for assessing long-term changes? YES how internally consistent is it? what is the evidence that it is internally consistent? Trends: GOME only Trends: GTO-ECV

17 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Workshop questions (2) how can it be used to evaluate other data sets? GTO_ECV_v0 MOD UMUKCA-UCAM E39C-A

18 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute 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

19 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute 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 DOAS GOME/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 missions GOME-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

20 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute

21 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute 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 O 3 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 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

22 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Trends: GTO-ECV seasonal dependence NH WinterNH Summer

23 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Global mean trend (60°N-60°S):

24 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute

25 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute Trends: Comparison with Ground Data GTO-ECV vs. GROUNDGTO-ECV vs. MOD ρ=0.33ρ=0.74

26 Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum Folie Remote Sensing Technology Institute 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.


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