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ERS Exploitation

Invitation to Tender (ITT) AO/1-4235/02/I-LG

GOME Direct Fitting (GODFIT)

GDOAS Delta-validation report

___________________________________________________________________________ prepared by M. Van Roozendael, J.-C. Lambert, C. Fayt and R.J.D. Spurr

reference

issue 1

revision 0

date of issue January 28, 2004

status final version

Document type Delta-validation report

BIRA-IASB | SAO

ESA ITT AO/1-4235/02/I-LG Delta-validation report 28-Jan-04 I – of 42

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GDOAS DELTA-VALIDATION REPORT


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Foreword This document describes results from the delta validation of the GOME total ozone DOAS product (GDOAS) generated as part of the GOme Direct FITting (GODFIT) project. GDOAS uses a classical DOAS approach to total ozone retrieval closely inspired from the algorithms developed in the successive versions of the GOME Data Processor (GDP): a slant column fit is performed in the 325-335 nm wavelength interval, which is followed by conversion to vertical column using a classical single wavelength AMF calculation. GDOAS essentially differs from the latest version of GDP (3.0) by the wavelength used for the AMF computation (325.5 nm instead of 325 nm), the application of an improved correction for the Ring effect, the use of a different cloud product (FRESCO instead of ICFA), and the use of the TOMS version 8 ozone profiles climatology (instead of version 7 of the same climatology). In contrast to GDP also, AMFs are calculated on the fly for each GOME pixel and not interpolated from pre-calculated look-up tables.

Although the settings used in GDOAS are similar to those now implemented in version 3.4 of the GDP (the main exception being the cloud correction, which is still based on the ICFA algorithm in GDP 3.4), it must be stressed that the GDOAS and GDP codes are entirely independent. GDOAS was build as an option of the more general and totally new direct fitting code (GODFIT). Due to the early stage of development of the algorithm (and the lack of time to apply systematic checking procedures) possible implementation errors in GDOAS cannot be fully excluded, which means that care must be taken in translating present validation results to GDP 3.4.


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TABLE OF CONTENTS

ACRONYMS............................................................................................................................5

1. INTRODUCTION AND ESA QUESTIONNAIRE ......................................................6

1.1 INTRODUCTION ...........................................................................................................6 1.2 BRIEF DESCRIPTION OF GDOAS .................................................................................6 1.3 GDOAS ERROR BUDGET.............................................................................................6 1.4 RESPONSE TO ESA QUESTIONNAIRE ...........................................................................8

2. INTERCOMPARISON TO TOMS V 8.......................................................................11

3. INTERCOMPARISON TO GDP VERSION 3.0........................................................13

4. INTERCOMPARISON TO TOGOMI AND GOTOCORD......................................15

4.1 INTERCOMPARISON TO TOGOMI .............................................................................15 4.2 INTERCOMPARISON TO GOTOCORD .......................................................................17

5. INTERCOMPARISON TO GROUND-BASED MEASUREMENTS......................19

5.1 GROUND-BASED CORRELATIVE DATA SET .................................................................19 5.2 GOME ORBITS..........................................................................................................19 5.3 ANALYSIS OF CYCLIC SIGNATURES (MID- AND LOW LATITUDE STATIONS) ................20 5.4 SOLAR ZENITH ANGLE DEPENDENCE (HIGH LATITUDE STATIONS) .............................26 5.5 TOTAL OZONE COLUMN DEPENDENCE (HIGH LATITUDE STATIONS) ...........................31 5.6 MONTHLY MEAN DIFFERENCES (ALL STATIONS) .......................................................36

6. MEAN DIFFERENCES AND SEASONAL WAVE AMPLITUDES.......................39

7. CONCLUSION ..............................................................................................................41


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ACRONYMS

AMF Air Mass Factor

ATBD Algorithm Theoretical Basis Document

BIRA-IASB Belgian Institute for Space Aeronomy

DFD German Remote Sensing Data Center

DLR German Aerospace Center

DOAS Differential Optical Absorption Spectroscopy

DU Dobson Unit

ERS-2 European Remote Sensing Satellite-2

ESA European Space Agency

GAW Global Atmospheric Watch

GDP GOME Data Processor

GOME Global Ozone Monitoring Experiment

IPA Independent Pixel Approximation

NDSC Network for the Detection of Stratospheric Change

RRS Rotational Raman Scattering

RT Radiative Transfer

TOA Top of Atmosphere

TOMS Total Ozone Mapping Spectrometer

WMO World Meteorological Office


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1. INTRODUCTION AND ESA QUESTIONNAIRE

1.1 Introduction

This document presents results from the delta validation of the GOME total ozone DOAS product (GDOAS) generated as part of the GOme Direct FITting (GODFIT) project. Its format is compliant with the guidelines defined by ESA. The accuracy of the GDOAS retrievals is assessed through:

- comparisons with the latest version of TOMS (version 8),

- comparisons with the GOME total ozone retrievals performed at KNMI (TOGOMI) and University of Bremen (GOTOCORD)

- comparisons with ground-based total ozone measurements from 11 stations of the GAW/WMO/NDSC networks.

1.2 Brief description of GDOAS

The GDOAS code has been build as an option of the more general and new direct fitting code (GODFIT). GDOAS was set up with the aim to provide an appropriate reference for comparison with direct-fitting retrievals (see GODFIT ATBD and validation report). It uses a classical DOAS approach to total ozone retrieval as developed in successive versions of the GOME Data Processor (GDP). A slant column fit where O3, NO2, Ring and undersampling cross-sections are taken into account [see GODFIT ATBD] is first performed in the 325-335 nm wavelength interval. This is followed by conversion to O3 vertical columns using an air mass factor (AMF) calculation performed at one wavelength taken as representative of the mean O3 absorption in the fitting interval. In contrast to GDP, GDOAS AMFs are calculated on the fly for each GOME pixel without any need for interpolation through pre-calculated look-up tables. An iterative scheme allows for adjustment of the O3 AMFs based on the use of a column/latitude/season-resolved O3 profile climatology. The baseline for processing is the TOMS version 8 O3 profile climatology [Barthia, 2003]. However the TOMS version 7 climatology [Wellemeyer et al., 1997] and the Dynamical Ozone Climatology [M. Weber et al., private communication] have both been implemented as additional options.

GDOAS essentially differs from the latest version of GDP (3.0) by the wavelength used for the AMF computation (325.5 nm instead of 325 nm), by the application of an improved correction for the Ring effect, and by the use of a different cloud product (FRESCO instead of ICFA). As in GDP, clouds are treated in the independent pixel approximation (IPA) with ghost columns derived from the same O3 climatology used for the AMF calculation.

1.3 GDOAS error budget

In line with the two-step DOAS approach adopted for GDOAS retrievals, the sources of uncertainties to be considered in the error budget can be separated in two groups: errors affecting the retrieval of slant columns (DOAS-related errors) and errors affecting the conversion of slant columns into vertical columns (AMF-related errors). Tentative estimates


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of the main error sources of GDOAS total ozone retrievals are presented in Table 1. The DOAS-related (slant column) uncertainties quoted in the table are, for a large part, extracted from the study performed as part of the GDP 3.0 Delta-validation exercise [Van Roozendael et al., 2002]. Errors due to Ring effect are derived from closed-loop retrieval tests presented in the GODFIT main validation report. Uncertainties related to cloud correction and O3 AMFs are estimated from error propagation of the uncertainties on the FRESCO cloud parameters [Koelemeijer and Stammes, 2001] and from sensitivity tests using different settings for the O3 AMF calculations (e.g. different O3 profile climatologies). Several error sources being significantly enhanced at large solar zenith angles (typical of polar spring and autumn observations), the error budget is given separately for low (<80°) and large (>80°) values of the SZA.

Table 1. Estimation of the error sources of the GDOAS total ozone retrievals (single pixel retrieval).

Percent error Error source SZA < 80° SZA > 80°

Ozone slant column O3 absorption cross-sections <2 <2 Atmospheric (effective) temperature determination <1.5 <3 Instrument signal-to-noise 0.5 <2 Instrument spectral stability (wavelength registration) 0.5 0.5 Solar I0-effect 0.2 0.2 Ring and molecular Ring effect <2 <2 Ozone Air Mass Factor Single wavelength calculation (325.5 nm) <1 <2 O3 profile <1 <4 Surface albedo 0.3 0.3 Cloud fraction 0.8 0.8 Cloud top pressure 1 1 Ghost column <2 <3 Tropospheric aerosols 0.2 0.2 Ozone vertical column (accuracy) Clear <3.6 <6.4 Cloudy <4.3 <7.2 Ozone vertical column (precision) Clear <2.4 <4.9 Cloudy <3.3 <5.9


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1.4 Response to ESA questionnaire

1.4.1 How does the choice of temperature impact trend monitoring? In the two-step DOAS approach used here, the largest impact of atmospheric temperature is through the temperature-dependence of the ozone absorption cross-sections. This dependence is accounted for in the DOAS algorithm by fitting two ozone spectra at two different temperatures. This procedure, which was first suggested by Andreas Richter (Uni. Bremen), allows for linear adjustment of the slant column retrieval to the actual O3 profile weighted mean atmospheric temperature [see Van Roozendael et al., 2002]. The accuracy of this approach (see Lambert et al., 2002 for validation results) is limited towards large SZA due to the breakdown of the optically thin approximation, towards extreme stratospheric temperatures (due to non-linearities in the temperature dependence of the ozone cross-sections), and by the accuracy of the laboratory cross-sections themselves. Although not tested in this work, it is possible that instrument degradation impacts on the accuracy of the effective temperature determination. Results from GDOAS overpass processing over Hohenpeissenberg and Lauder, extending from 1996 until 2003 with no particular effort to address known GOME degradation problems, suggests that the algorithm is stable and not strongly influenced by the degradation of the instrument (see Figure 15 and Figure 18 of this report).

1.4.2 How does the choice of the spectral wavelength range impact trend monitoring (window starting at 325 nm instead of window starting at 331 nm)? GDOAS retrievals have not been tested over a different wavelength interval than 325-335 nm (the standard GDP setting). We expect that ozone slant column retrievals performed in a smaller interval shifted towards the visible (e.g. 331-336 nm) will be more accurate at large solar zenith angles because of the reduced ozone optical depth. On the other hand, we expect larger noise at small solar zenith angles in tropical regions. Noise might be an issue for accurate long-term trend monitoring, hence our preference goes to the well-established 325-335 nm interval.

1.4.3 How does the ozone profile information impact the total ozone? Appropriate knowledge concerning the ozone profile shape and its total content are key-parameters controlling the accuracy of the total ozone retrieval, especially in regions of high-latitudes where the ozone profile-shape sensitivity of the AMFs is enhanced by the extreme variations in the ozone field (e.g. ozone hole) combined with large solar zenith angles. GDOAS uses the so-far most advanced ozone profile climatology (TOMS version 8), which is resolved in total column, latitude and season.


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1.4.4 How does the choice of different ozone climatology (e.g. TOMS V7/V8) impact the total ozone column? The GDOAS code has been tested using both TOMS version 7 and version 8 ozone profiles climatologies. The resulting changes in the retrieved total ozone columns are displayed in Figure 1. Largest differences are found in polar regions (especially in the Southern hemisphere) close to the terminator where GOME solar zenith angles are at their maximum. A surprisingly large sensitivity is also found in the Northern tropics during summer when the GOME SZA is at minimum.

Figure 1. Relative differences in GOME total ozone retrieved using two different versions of GDOAS, respectively set up with the TOMS version 7 and the TOMS version 8 ozone profile climatologies. Differences are mostly significant in polar regions, close to the terminator, as well as in Northern tropical regions around the place of minimum GOME solar zenith angle.

1.4.5 Are there systematic differences between the 3 GOME ground-pixels (is there a scan angle dependence)? Tests performed using the 1997 data set do not show any significant scan angle dependency.


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1.4.6 How does aerosol in the stratosphere impact the algorithm? Stratospheric aerosols, if emitted in large quantities after a major volcanic eruption (like e.g. the Mt. Pinatubo eruption in 1991), might very likely be a source of significant error in the GOME total ozone retrieval, since the scattering properties of the atmosphere will be strongly altered at stratospheric altitudes. No effort was done during this project to quantify this potential problem and eventually elaborate a strategy to account for it in the retrieval. We believe that this aspect is important and would deserve to be further studied.


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2. INTERCOMPARISON TO TOMS V 8

GDOAS total ozone retrievals have been compared to the TOMS version 8 data product. Although not yet publicly available, the TOMS total ozone data values have been kindly provided to us by Jim Gleason (NASA/Langley). The comparison results displayed below are based on the processing of 465 GOME orbits from 1997, extracted from the GDP 3.0 delta-validation orbits data set [Lambert et al., 2002]. All GOME pixel types (up to 88 degrees of solar zenith angle) have been considered for comparison with TOMS.

Figure 2. Relative difference between GDOAS and TOMS v8, as a function of latitude and month. The analysis is based on a subset of 465 GOME orbit files from 1997 extracted from the GOME data set used during the GDP 3.0 delta-validation exercise [Lambert et al., 2002].


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Figure 3. Absolute difference between GDOAS and TOMS v8 total ozone, as function of latitude and month in 1997.

Figure 4. Solar zenith angle dependence of relative differences between GDOAS and TOMS v8, for Northern and Southern hemisphere separately. Mean differences and standard deviations for 5° intervals are shown.


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3. INTERCOMPARISON TO GDP VERSION 3.0

GDOAS total ozone retrievals have been compared to the GDP 3.0 data product, as distributed to GOME users by DLR-DFD. The comparison results displayed below are based on the processing of 465 GOME orbits from 1997, extracted from the GDP 3.0 delta-validation orbits data set [Lambert et al., 2002]. All GOME pixel types (up to 88 degrees of solar zenith angle) have been considered for comparison with GDP 3.0.

Figure 5. Relative difference between GDOAS and GDP 3.0, as a function of latitude and month. The analysis is based on a subset of 465 GOME orbit files from 1997 extracted from the GOME data set used during the GDP 3.0 delta-validation exercise [Lambert et al., 2002].


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Figure 6. Absolute difference between GDOAS and GDP 3.0 total ozone, as function of latitude and month in 1997.

Figure 7. Solar zenith angle dependence of relative differences between GDOAS and GDP 3.0, for Northern and Southern hemisphere separately. Mean differences and standard deviations for 5° intervals are shown.


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4. INTERCOMPARISON TO TOGOMI AND GOTOCORD

4.1 Intercomparison to TOGOMI

GDOAS total ozone retrievals have been compared to the TOGOMI data product, as provided by the KNMI for the present exercise. The comparison results displayed below are based on the processing of 465 GOME orbits from 1997, extracted from the GDP 3.0 delta-validation orbits data set [Lambert et al., 2002]. All GOME pixel types excluding large swath pixels (not provided by KNMI) have been considered for comparison with TOGOMI.

Figure 8. Relative difference between GDOAS and TOGOMI, as a function of latitude and month. The analysis is based on a subset of 465 GOME orbit files from 1997 extracted from the GOME data set used during the GDP 3.0 delta-validation exercise [Lambert et al., 2002].


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Figure 9. Absolute difference between GDOAS and TOGOMI total ozone, as function of latitude and month in 1997.

Figure 10. Solar zenith angle dependence of relative differences between GDOAS and TOGOMI, for Northern and Southern hemisphere separately. Mean differences and standard deviations for 5° intervals are shown.


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4.2 Intercomparison to GOTOCORD/WDOAS

GDOAS total ozone retrievals have been compared to the WDOAS data product, as provided by the University of Bremen for the present exercise. The comparison results displayed below are based on the processing of 465 GOME orbits from 1997, extracted from the GDP 3.0 delta-validation orbits data set [Lambert et al., 2002]. All GOME pixel types excluding large swath pixels (not provided by the University of Bremen) have been considered for comparison with WDOAS.

Figure 11. Relative difference between GDOAS and GOTOCORD (WDOAS), as a function of latitude and month. The analysis is based on a subset of 465 GOME orbit files from 1997 extracted from the GOME data set used during the GDP 3.0 delta-validation exercise [Lambert et al., 2002].


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Figure 12. Absolute difference between GDOAS and GOTOCORD (WDOAS) total ozone, as function of latitude and month in 1997.

Figure 13. Solar zenith angle dependence of relative differences between GDOAS and GOTOCORD (WDOAS), for Northern and Southern hemisphere separately. Mean differences and standard deviations for 5° intervals are shown.


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5. INTERCOMPARISON TO GROUND-BASED MEASUREMENTS

5.1 Ground-based correlative data set

The ground-based correlative data sets to be used for comparison with GDOAS total ozone evaluations have been defined by C. Zehner after consultation with project partners. Stations selected for use in the delta-validation exercise are listed in Table 2.

Table 2. Characteristics of the ground-based total ozone correlative instruments used for the delta-validation exercise: station name, geographical location, coordinates, and type of instrument.

Station Location Lat. Long. Instrument Barrow Alaska 71°N 203°E Dobson Sodankylä Finland 67°N 27°E Brewer Uccle Belgium 51°N 4°E Brewer, Dobson Hohenpeißenberg Germany 48°N 11°E Brewer, Dobson Singapore Indonesia 1°N 104°E Dobson Bauru Brazil 22°S 49°W SAOZ Lauder New Zealand 45°S 170°E Dobson Faraday Antarctica 65°S 64°W Dobson Rothera Antarctic Peninsula 68°S 68°W SAOZ Syowa Antarctica 69°S 40°E Dobson Halley-Bay Antarctica 76°S 27°W Dobson

5.2 GOME orbits

For all stations except Barrow, Hohenpeissenberg, Lauder and Syowa, the GOME orbits selected for processing are those defined for the GDP 3.0 delta-validation exercise [Lambert et al., 2002]. The original list of 399 orbits was used for the delta validation of GDP version 2.7 [Lambert et al., 1999b], and for GDP 3.0, this was supplemented with 1858 additional orbits selected to optimise validation studies relying on NDSC data, and to allow long-term verification. The resulting list of 2257 validation orbits represents a good compromise between minimum processing time and maximum coverage. In the case of Barrow, Hohenpeissenberg, Lauder and Syowa, coincidences have been searched based on the complete GOME orbit data set.


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5.3 Analysis of cyclic signatures (mid- and low latitude stations)

The approach used to characterise the cyclic signatures in the GOME versus ground-based ozone comparisons consists in fitting time-series of individual comparison data points to a simple cosine function:

[ ]( )Φ−⋅+= tBAR π2cos where:

− R is the percentage relative difference between GDOAS (or GDP) and correlative total ozone measurements

− A is the offset parameter, representing the mean total ozone relative difference − B is the amplitude of the annual wave variation − t is the time in decimal year − Φ is the phase of the cosine function

In the series of Figures presented below (Figures 14 to 18), results from cosine analysis performed at different stations from mid- and low latitudes are presented. In each case, the relative agreement between GDOAS and ground-based total ozone values is displayed and contrasted to results from identical analyses applied to both GDP 3.0 (top figures) and TOMS v.8 (bottom figures). Seasonal variation of the GOME versus ground station discrepancy has been used as an indicator of the quality of the GDP data products during past validation exercises. In comparison with previous GDP versions, cyclic signatures in GDP 3.0 were significantly reduced but not fully eliminated, especially in northern polar and mid-latitude stations. Results from the present comparison show a further reduction in the cyclic signatures, by a factor of two in average. The mean agreement with ground-based measurements is also improved (see section 6 for a more quantitative analysis of these results). Since the most significant change in the GDOAS algorithm in comparison to GDP 3.0 is the introduction of an improved molecular Ring effect correction, we conclude that the reduced seasonality of the GDOAS versus ground-based comparisons is mainly due to the better Ring correction.

Comparing GDOAS and TOMS v.8, residual seasonalities in the satellite to ground-based total ozone differences are generally equivalent in size (but not in phase) for the mid-latitude stations. In tropical regions, GDOAS results tend to match better ground-based data than TOMS v.8.


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Figure 14-a. Time series of total ozone relative difference between GDOAS (black dots) or GDP 3.0 (red dots) and Brewer measurements at Uccle, Belgium. Black (GDOAS) and red (GDP 3.0) lines are one-year period cosine functions fitted to the percent differences.

Figure 14-b. Time series of total ozone relative difference between GDOAS (black dots) or TOMS v8 (green dots) and Brewer measurements at Uccle, Belgium. Black (GDOAS) and green (TOMS v8) lines are one-year period cosine functions fitted to the percent differences.


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Figure 15-a. Time series of total ozone relative difference between GDOAS (black dots) or GDP 3.0 (red dots) and Brewer measurements at Hohenpeissenberg, Germany. Black (GDOAS) and red (GDP 3.0) lines are one-year period cosine functions fitted to the percent differences.

Figure 15-b. Time series of total ozone relative difference between GDOAS (black dots) or TOMS v8 (green dots) and Brewer measurements at Hohenpeissenberg, Germany. Black (GDOAS) and green (TOMS v8) lines are one-year period cosine functions fitted to the percent differences.


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Figure 16-a. Time series of total ozone relative difference between GDOAS (black dots) or GDP 3.0 (red dots) and Dobson measurements at Singapore. Black (GDOAS) and red (GDP 3.0) lines are one-year period cosine functions fitted to the percent differences.

Figure 16-b. Time series of total ozone relative difference between GDOAS (black dots) or TOMS v8 (green dots) and Dobson measurements at Singapore. Black (GDOAS) and green (TOMS v8) lines are one-year period cosine functions fitted to the percent differences.


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Figure 17-a. Time series of total ozone relative difference between GDOAS (black dots) or GDP 3.0 (red dots) and SAOZ measurements at Bauru, Brazil. Black (GDOAS) and red (GDP 3.0) lines are one-year period cosine functions fitted to the percent differences.

Figure 17-b. Time series of total ozone relative difference between GDOAS (black dots) or TOMS v8 (green dots) and SAOZ measurements at Bauru, Brazil. Black (GDOAS) and green (TOMS v8) lines are one-year period cosine functions fitted to the percent differences.


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Figure 18-a. Time series of total ozone relative difference between GDOAS (black dots) or GDP 3.0 (red dots) and Dobson measurements at Lauder, New Zeland. Black (GDOAS) and red (GDP 3.0) lines are one-year period cosine functions fitted to the percent differences.

Figure 18-b. Time series of total ozone relative difference between GDOAS (black dots) or TOMS v8 (green dots) and Dobson measurements at Lauder, New Zeland. Black (GDOAS) and green (TOMS v8) lines are one-year period cosine functions fitted to the percent differences.


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5.4 Solar zenith angle dependence (high latitude stations)

Figure 19-a. Relative difference between GOME and Dobson total ozone at Barrow for GDOAS (black) and GDP 3.0 (red), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.

Figure 19-b. Relative difference between GOME/TOMS and Dobson total ozone at Barrow, Alaska, for GDOAS (black) and TOMS v8 (green), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.


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Figure 20-a. Relative difference between GOME and Brewer total ozone at Sodankyla, Finland, for GDOAS (black) and GDP 3.0 (red), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.

Figure 20-b. Relative difference between GOME/TOMS and Brewer total ozone at Sodankyla, Finland, for GDOAS (black) and TOMS v8 (green), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.


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Figure 21-a. Relative difference between GOME and Dobson total ozone at Faraday, Antarctica, for GDOAS (black) and GDP 3.0 (red), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.

Figure 21-b. Relative difference between GOME/TOMS and Dobson total ozone at Faraday, Antarctica, for GDOAS (black) and TOMS v8 (green), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.


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Figure 22-a. Relative difference between GOME and Dobson total ozone at Syowa, Antarctica, for GDOAS (black) and GDP 3.0 (red), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.

Figure 22-b. Relative difference between GOME/TOMS and Dobson total ozone at Syowa, Antarctica, for GDOAS (black) and TOMS v8 (green), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.


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Figure 23-a. Relative difference between GOME and Dobson total ozone at Halley-Bay, Antarctica, for GDOAS (black) and GDP 3.0 (red), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.

Figure 23-b. Relative difference between GOME/TOMS and Dobson total ozone at Halley-Bay, Antarctica, for GDOAS (black) and TOMS v8 (green), as a function of GOME SZA. Lines show the same data averaged in bins of 5°.


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5.5 Total ozone column dependence (high latitude stations)

Figure 24-a. Relative difference between GOME and Dobson total ozone at Barrow for GDOAS (black) and GDP 3.0 (red), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.

Figure 24-b. Relative difference between GOME/TOMS and Dobson total ozone at Barrow, Alaska, for GDOAS (black) and TOMS v8 (green), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.


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Figure 25-a. Relative difference between GOME and Brewer total ozone at Sodankyla, Finland, for GDOAS (black) and GDP 3.0 (red), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.

Figure 25-b. Relative difference between GOME/TOMS and Brewer total ozone at Sodankyla, Finland, for GDOAS (black) and TOMS v8 (green), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.


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Figure 26-a. Relative difference between GOME and Dobson total ozone at Faraday, Antarctica, for GDOAS (black) and GDP 3.0 (red), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.

Figure 26-b. Relative difference between GOME/TOMS and Dobson total ozone at Faraday, Antarctica, for GDOAS (black) and TOMS v8 (green), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.


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Figure 27-a. Relative difference between GOME and Dobson total ozone at Syowa, Antarctica, for GDOAS (black) and GDP 3.0 (red), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.

Figure 27-b. Relative difference between GOME/TOMS and Dobson total ozone at Syowa, Antarctica, for GDOAS (black) and TOMS v8 (green), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.


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Figure 28-a. Relative difference between GOME and Dobson total ozone at Halley-Bay, Antarctica, for GDOAS (black) and GDP 3.0 (red), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.

Figure 28-b. Relative difference between GOME/TOMS and Dobson total ozone at Halley-Bay, Antarctica, for GDOAS (black) and TOMS v8 (green), as a function of total ozone. Lines show the same data averaged in bins of 20 DU.


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5.6 Monthly mean differences (all stations)

Figure 29. Monthly averaged percentage differences in total ozone between GDOAS retrievals (blue squares) or GDP 3.0 (red triangles) and correlative measurements at 11 stations. Results from the period 1996-2003 are binned and plotted as a function of the month.


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Figure 30. Same as Figure 29, with differences to TOMS v8 added (green circles)


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Figure 31. Monthly averaged total ozone as measured by correlative instruments at the 11 stations. Results from the period 1996-2003 are binned and plotted as a function of the month.


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6. MEAN DIFFERENCES AND SEASONAL WAVE AMPLITUDES

As already shown graphically in section 5.3, time-series of individual comparison data points have been fitted to the following equation:

[ ]( )Φ−⋅+= tBAR π2cos where:

− R is the percentage relative difference between GDOAS (or GDP) and correlative total ozone measurements

− A is the offset parameter, representing the mean total ozone relative difference − B is the amplitude of the annual wave variation − t is the time in decimal year − Φ is the phase of the cosine function

Results for the fitted A and B parameters (i.e. mean relative difference and wave amplitude) are given in Table 3 for all ground-based stations included in the present exercise. Results obtained with GDOAS have been systematically compared with results from the same analysis applied to GDP 3.0. To minimise risks of artefact due to possible differences in the data sampling, we carefully checked that the same GOME pixels were used for both analysis.

Table 3. Total ozone mean percentage differences and seasonal wave amplitudes determined from cosine function fits to time-series of relative differences between GDOAS (or GDP 3.0) and correlative ozone measurements at 11 ground-based stations (see text for explanation).

GDOAS GDP 3.0 Latitude Longitude Mean diff.

[%] Annual wave

amplitude [%]

Mean diff. [%]

Annual wave amplitude

[%] 71°N 203°E 1.4 0.8 0.3 4.3 67°N 27°E -1.1 1.2 -3.0 4.5 51°N 4°E 0.5 1.0 -0.6 2.4 48°N 11°E 0.7 1.0 -0.9 1.4 1.3°N 104°E -0.3 0.3 -1.9 0.6 22°S 49°W -0.2 0.4 -0.8 0.6 45°S 170°E 0.6 0.3 -1.4 1.9 65°S 64°W 0.2 1.1 -2.0 1.1 67°S 292°E 4.6 5.1 2.4 4.7 69°S 40°E 1.8 1.1 0.4 2.3 76°S 27°W 3.1 4.4 1.8 3.3

In order to synthesize results of Table 3, mean relative differences and wave amplitudes have been globally averaged and sorted by latitudinal bands representative of high latitudes, mid-


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latitudes and tropics (Table 4). In this final analysis, all stations have been considered except Rothera (a station obviously inconsistent with others at similar latitudes).

From these results, we conclude that GDOAS total ozone retrievals are in average 1.5% larger than those from GDP 3.0, in better agreement with ground-based measurements especially in tropical regions. The most significant feature is the large reduction of the cyclic variations. In comparison to GDP 3.0, annual wave amplitudes are reduced by a factor of two in average. The best improvement is obtained at Northern high latitudes. In contrast, no obvious improvement is found over Antarctic stations. Possible reasons (which remain to be further explored) for the remaining discrepancies observed at Southern high latitudes are:

− Systematic errors in the GDOAS algorithm, specific to Antarctic regions

− Co-location problems between satellite and ground-based data, that may possibly introduce bias in the comparisons in case of strong gradients in the O3 field

− Systematic errors in ground-based data (related to the difficulty to maintain the accuracy of the total ozone spectrometers in the remote Southern polar regions)

Table 4. Same as Table 3, but averaged according to latitudinal bands representative of tropics, mid- and high latitude regions. Results at the Rothera station (red-shaded values in Table 3), obviously inconsistent with those at other stations in the same latitude area, are not included in this analysis.

GDOAS GDP 3.0 Latitude band

Mean diff. [%]

Annual wave amplitude [%]

Mean diff. [%]

Annual wave amplitude [%]

> 60°N 0.15 1.0 -1.4 4.4 30°N – 60°N 0.6 1.0 -0.8 2.0

Tropics -0.2 0.3 -1.1 0.6 30°S – 50°S 0.6 0.3 -1.4 1.9

> 60°S 1.7 2.2 0.1 2.2 All latitudes 0.6 1.0 -1.0 2.2


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7. CONCLUSION

This report has considered the delta-validation of the GDOAS total ozone algorithm (developed as a side activity of the GODFIT project), and its comparison with the existing baseline GOME GDP 3.0, as well as with the new EP-TOMS version 8. GDOAS sample total ozone data products have been generated for a pre-defined set of GOME pixels coincident with correlative measurements at 11 ground-based stations of the GAW/WMO/NDSC network. These stations were explicitly specified for use in the present exercise by ESA. They included a combination of UV-visible zenith-sky, Brewer and Dobson spectrophotometers. Validation of the GDOAS algorithm, based on comparisons with ground-based correlative data, show a marked improvement in retrieved total ozone values in two important aspects. First the cyclic signatures in the GOME versus ground-based total ozone comparisons, identified as one remaining problem with GDP 3.0, have been reduced by more than a factor of two in average at all latitudes except Southern polar regions. Likewise solar zenith angle dependencies and total ozone column dependencies reported in GDP 3.0 have been reduced. This improvement is mostly attributed to the use of an improved Ring correction that accounts properly for the impact of ozone absorption line filling. Second, the ozone columns retrieved by GDOAS are systematically larger (by 1.5% in average) than equivalent GDP 3.0 values, leading to better agreement with ground-based data. This improvement is especially visible in tropical regions where the scatter of the comparisons is minimised due to the general stability of the ozone field. The comparison of GDOAS retrievals with the TOMS v.8 total ozone product, performed in a systematic way for one full year of GOME and TOMS measurements (1997), show a good agreement for the mid- and low latitudes. However larger discrepancies are obtained in polar regions where GDOAS total ozone tends to be systematically higher than TOMS. Results from the GDOAS-TOMSv8 comparison also suggest that the differences between the two algorithms are strongly connected to the solar zenith angle. The agreement between GDOAS and the two other GOME DOAS algorithms (TOGOMI and WDOAS) is generally very good. In particular the agreement with TOGOMI is virtually perfect up to 60° SZA and remains very good up to 85° SZA (average differences smaller than 2%). Again, largest differences between the three DOAS algorithms are found in polar regions close to the terminator, especially under ozone hole conditions. In those conditions, GDOAS tends to retrieve slightly smaller ozone columns than WDOAS and TOGOMI. Acknowledgements

Contributing NDSC PIs and ground-based instrument operators (AWI, BAS, CAO, CNRS, DMI, DWD, ETH, FMI, IASB, IFE, KMI, KNMI, KTSU, NILU, SMI, U. Bordeaux, U. Reims, U. Réunion, U. Sao Paulo, U. Wales), DLR-IMF, science and Operation Teams are acknowledged for data provision. GOME level-1 and level-2 data products used in this work


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were processed at DLR-IMF on behalf of ESA. For general help, support and useful discussions, we would like to thank N. Theys, C. Fayt, V. Soebijanta, J. Granville and P. Gerard at BIRA-IASB, as well as W. Thomas at DLR-IFM. References

Bhartia, P.K., Algorithm Theoretical Baseline Document, TOMS v8 Total ozone algorithm, 2003 (http://toms.gsfc.nasa.gov/version8/version8_update.html )

Koelemeijer, R.B.A., and P. Stammes, A fast method for retrieval of cloud parameters using oxygen A band measurements from the Global Ozone Monitoring Experiment, J. Geophys. Res., 106, 3475-3490, 2001.

Lambert, J.-C., et al., ERS-2 GOME GDP 3.0 Implementation and Delta Validation, Validation Report for GOME Level-1-to-2 Data Processor Upgrade to Version 3.0, ERSE-DTEX-EOAD-TN-02-0006, ed. J.-C. Lambert, 2002.

Van Roozendael, M., V. Soebijanta, C. Fayt, and J.-C. Lambert, Investigation of DOAS Issues Affecting the Accuracy of the GDP Version 3.0 Total Ozone Product, in ERS-2 GOME GDP 3.0 Implementation and Delta Validation, Ed. J.-C. Lambert, ERSE-DTEX-EOAD-TN-02-0006, ESA/ESRIN, Frascati, Italy, Chap.6, pp.97-129, 2002.

Wellemeyer, C.G., S.L. Taylor, C.J. Seftor, R.D. McPeters, and P.K. Barthia, A correction for total ozone mapping spectrometer profile shape errors at high latitude, J. Geophys. Res.,102, 9029-9038, 1997.

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