GEOPHYSICAL CONSISTENCY OF ENVISAT OZONE PROFILE DATA WITH GLOBALATMOSPHERE WATCH POLE-TO-POLE NETWORK MEASUREMENTS

C. De Clercq1, P. Gerard1, J. Granville1, J-C. Lambert1, and the ACVT-GBMCD Ozone Profile Team2

1Belgian Institute for Space Aeronomy, 3 Avenue Circulaire, B-1180 Brussels, Belgium, Email :coralie.declercq@aeronomie.be

ABSTRACT

The vertical distribution of atmospheric ozone is mon-itored by three Envisat ozone profilers, Global OzoneMonitoring by Occultation of Stars (GOMOS), Michel-son Interferometer for Passive Atmospheric Sounding(MIPAS), and SCanning Imaging Absorption spectroMe-ter for Atmospheric CHartographY (SCIAMACHY). Wepresent here the outcome of a large-scale ground-basedvalidation of the latest versions of the Envisat ozone pro-file data. The latter are confronted qualitatively and quan-titatively to ground-based observations collected fromozonesonde, lidar, and microwave radiometer stationsperforming network operation as part of WMO’s GlobalAtmosphere Watch programme. Envisat ozone profiledata are investigated from pole to pole and from theground up to the lower mesosphere. The study concludesto a reasonable quality and consistency of Envisat profileswhen adequate data selection and vertical ranges are en-visaged. Seasonal variations, altitude registration issuesand error budgets are further investigated.

1. INTRODUCTION

The atmospheric chemistry payload of ESA’s environ-mental satellite Envisat includes three instruments mon-itoring the vertical distribution of atmospheric ozoneon the global scale: Global Ozone Monitoring by Oc-cultation of Stars (GOMOS), Michelson Interferome-ter for Passive Atmospheric Sounding (MIPAS), andSCanning Imaging Absorption spectroMeter for Atmo-spheric CHartographY (SCIAMACHY). Although oper-ating from the same orbiting platform, the three instru-ments rely on substantially different remote soundingtechniques and strategies, leading to different perceptionsof the ozone profile. Following the recommendationsdrawn after a first validation exercise conducted duringthe commissioning phase in 2002 [1, 2] and a main val-idation carried out in 2004-2005 [3, 4], retrieval algo-rithms of the three Envisat ozone profilers have been up-graded. The entire GOMOS data record has been repro-cessed with the prototype processor version 6.0f and itsoperational implementation IPF 5.0. The entire MIPAS

data record acquired in full resolution mode is availablein version IPF 4.61- 4.62. The development of adaptedprocessors for the analysis of MIPAS spectra acquired inReduced Resolution mode is near completion. Data havenot been available for the present study. For the first time,the Off-line processor (version 3.0) of SCIAMACHY hasgenerated limb ozone profile data suitable for validation,although for a limited subset of orbits.

In 2004, we reported (at the Envisat Symposium inSalzburg) an integrated characterisation of Envisat ozoneprofile data using ground-based network data [3]. Thispaper updates this work with the latest versions of theEnvisat ozone profile data.

2. CORRELATIVE MEASUREMENTS

Three instruments measuring the vertical distributionof ozone and relying on different techniques constitutethe backbone of ground-based ozone profile monitoring:ozonesonde, lidar and microwave radiometer.

Electrochemical cell (ECC) ozonesondes are launchedmore or less regularly on board of small meteorologicalballoons at a variety of stations from pole to pole. Theyyield the vertical distribution of ozone volume mixing ra-tio (VMR) from the ground up to burst point, the latteroccurring typically around 30 km. Ozone VMR recordedat a typical vertical resolution of 100-150 m is convertedinto ozone number density using pressure and tempera-ture data recorded onboard the same balloon [5]. Erroron the ozone profile of ozonesonde depends of a largenumber of parameters. For ECC sonde important param-eters are: the manufacturer of the sonde (SPC or EnSci),the percentage of the sensing solution used in the elec-trochemical cell and the type of correction applied forpump efficiency. Unfortunately, this information is notalways given or well identified in the data files. However,as shown during the JOSIE chamber comparison [6], ifozonesondes are operated in a specific way, a similar levelof precision and accuracy is achievable from the differentsondes types. Typical error estimates are : systematic er-ror from 3% (0-20km) to 5% (20-35km); precision from5% (0-20km) to 7% (20-35km).

Proceedings of the Third Workshop on the Atmospheric Chemistry Validation of Envisat (ACVE-3) 4-7 December 2006, ESRIN, Frascati, Italy (ESA SP-642,February 2007)

Differential absorption ozone lidar (DIAL) systems pro-vide the vertical distribution of night-time ozone numberdensity at altitudes between 8-15 km and 45-50 km, de-pending on the cloud cover and other measurement con-ditions. The typical integration time of an ozone mea-surement in the whole stratosphere is 4 hours. Typicalvertical resolution ranges from 300 m up to 3 km depend-ing on the altitude. The accuracy of the lidar ozone profiledepends on the duration of the measurement and on thevertical resolution chosen to process the data. Individualerrors bars are given in each ozone file. Typical accu-racy estimates range from 3 to 7% from 15 to 40km. At40-45km and above, due to the rapid decrease in signalto noise ratio, the error bars increase and significant biasreaching 10% may exist [7].

Millimetre wave radiometers (MWR) operate night andday, providing ozone VMR integrated over typically 2hours (a few stations provide shorter integration time)from 20-25 to 70km, with a vertical resolution of 8 to12km. Ozone VMR is converted into number density us-ing ECMWF or NCEP meteorological analyses of pres-sure and temperature. The individual errors bars are givenin each ozone file. Typical accuracy ranges from 5% at20km to 20% at 70km where the information content issmaller leaving a larger weight to a priori constraints [8].Its low vertical resolution poses additional problems forcomparisons, for which dedicated methods have been de-veloped [9].

Most of ozone profilers perform network operation in theframework of international structures like the Networkfor the Detection of Atmospheric Composition Change(NDACC, formerly the NDSC) and the World Ozone andUltraviolet radiation Data Center (WOUDC), two majorcomponents of WMO’s Global Atmospheric Watch pro-gramme (GAW).

We have found collocation with Envisat measurementat 39 ozonesonde stations, 8 lidar and 7 microwave ra-diometer between July 2002 and now (See Tab. 1 for alist). They form a robust set of independent and of well-known quality correlative measurements. Their comple-mentary altitude ranges offer a ground to mesosphere ac-cess to the ozone vertical distribution and the variety ofstations with different geo-location ensures a quasi poleto pole coverage. We use them as a common reference tocharacterise the absolute and relative consistency of thethree different Envisat perceptions of atmospheric ozonevertical distribution.

3. VALIDATION METHODOLOGY

The correlative data and MIPAS data record (respectivelyGOMOS and SCIAMACHY) ozone profiles have beenprocessed to select collocated pairs of profiles. A colloca-tion criteria was chosen as the best compromise betweena sufficient amount of comparison points and a sufficientcollocation of the probed air masses. A maximum dis-tance of 500km from station to Envisat profile tangent

point and a maximum time difference of 6 hours seem towork properly although not optimally. This time coinci-dence criteria can be reduced to a maximum 2 hour to15 min for microwave radiometer instruments that haveshorter integration time. Stronger criteria result in insuf-ficient amounts of comparison points, especially in theSouthern Hemisphere and for SCIAMACHY. Errors inthe comparisons due to non-perfect coincidence are illus-trated here in details for MIPAS correlative studies.

The correlative analysis of Envisat ozone profile datastarts with visual and statistical studies of the differenceswith correlative ozone profile data. The time series ofozone partial columns and of ozone profile over their fullaltitude/pressure range are analysed. The objective is toidentify global features and trends. From the analysisof times series, time periods with homogeneous results,from which statistical values may be deduced and aremeaningful, are identified. In a second step, the verti-cal structure of the differences is analysed within thesetime periods where statistical analysis is relevant.

4. MIPAS

4.1. Error Budget of the Comparison

Instead of comparing the observed relative differenceswith errors bars of the measurements, they should becompared to the error budget of the comparison. As thedocumentation about operational retrievals and related er-rors is detailed both for MIPAS and ground-based data,we have been able to compute the total error budget forcomparison. To transpose this work to GOMOS andSCIAMACHY, more detailed information than actuallyavailable is required

Using the formalism adopted by Rodgers [10], the com-parison error budget can be expressed in terms of differ-ent covariance contributions:

SMN = SM + SN

+ (AM −AN )SV (AM −AN )T

+ (AM −AN )SH(AM −AN )T

+ S∆O3

+ ...

with:

• SMN = total error covariance of the MIPAS/NDACC comparison

• SM = MIPAS error (measurement and retrieval)

• SN = correlative instrument error (measurement andretrieval)

• AM = MIPAS averaging kernels

• AN = correlative instrument averaging kernels

Table 1. List of contributing ground-based instruments.s are ozonesonde and lidar stations where collocations withSCIAMACHY limb profile data have been found.

OZONESONDEStation Location Latitude Longitude InstituteAlert Canada 82.5 -62.33 MSCEureka Canada 80.05 -86.42 MSCNy-Alesund Svalbard 78.91 11.88 AWIThule Greenland 76.51 -68.76 DMIResolute Canada 74.72 -94.98 MSCScoresbysund Greenland 70.48 -21.97 DMIEsrange Sweden 67.88 21.06 NIESSodankyläs Finland 67.37 26.67 FMIKeflavik Iceland 63.97 -22.6 INTAOrlandet Norway 63.42 9.24 NILUJokioinen Finland 60.82 23.48 FMIChurchill Canada 58.75 -94.07 MSCEdmontons Canada 53.55 -114.1 MSCGoose Bay Canada 53.32 -60.38 MSCLegionowos Poland 52.4 20.97 INWMDebilts Netherlands 52.1 5.18 KNMIValentia Ireland 51.93 -10.25 MEUccles Belgium 50.8 4.35 KMIPrahas Czech Republic 50.02 14.45 CHMIHohenpeissenbergs Germany 47.8 11.02 DWDPayernes Swiss Alps 46.49 6.57 MCHTsukubas Japan 36.05 140.13 JMAParamaribos Surinam 5.81 -55.21 KNMISan Cristobal Galapagos -0.92 -89.6 NOAANairobi Kenya -1.27 36.8 MCHMalindi Kenya -2.99 40.19 RPSMNatal Brazil -5.42 -35.38 INPEWatukosek Java -7.5 112.6 JAXAAscension Island Congo -7.98 -14.42 NASATutuila Samoa -14.23 -170.56 NOAAFiji Fiji -18.13 178.42 NOAASaint-Denis Reunion -21.06 55.47 CNRSIrene South Africa -25.25 28.18 SAWSLauders New Zealand -45.03 169.68 NIWAMarambio Antarctica -64.28 -56.72 INTADumontd’Urville Antarctica -66.67 140.01 CNRSSyowa Antarctica -69 39.58 JMANeumayer Antarctica -70.65 -8.25 AWIBelgrano Antarctica -77.87 -34.63 INTA

LIDARStation Location Latitude Longitude InstituteNy-Alesund Svalbard 78.91 11.88 AWIAndoyas Norway 69.28 16.02 NILUHohenpeissenbergs Germany 47.8 11.02 DWDHaute Provences French Alps 43.94 5.71 CNRSTsukuba Japan 36.05 140.13 NIESTable Mountains California 34.23 -117.41 JPLMauna Loas Hawaii 19.54 -155.58 JPLLauders New Zealand -45.03 169.68 RIVM

MICROWAVE RADIOMETERStation Location Latitude Longitude InstituteNy-Alesund Svalbard 78.91 11.88 IFEKiruna Sweden 67.84 21.06 IMKBremen Germany 53.11 8.86 IFEZugspitze German Alps 47.42 11.98 IMKPayerne Swiss Alps 46.49 6.57 MCHMauna Loa Hawaii 19.54 -155.58 UMASLauder New Zealand -45.03 169.68 UMAS

• SV = atmospheric variability covariance (vertical)

• SH = atmospheric variability covariance (horizon-tal)

• S∆O3 = ozone difference between actual air masses

• ... = all other errors

MIPAS [11] and ground-based instrument [6, 7, 8] er-ror budgets are described in the literature. Ideally, theyare the error bars that comparison results (MIPAS/grounddifferences) should fit within if the compared air masseswere perfectly coincident. Smoothing and collocationdifferences increase the comparison error. In our study,we estimate separately errors due to those differencesin horizontal and vertical resolution and to difference ingeolocation. The full description of this experimentalmethod falls beyond the scope of this paper. More de-tails could be found in [12] and [13].

4.2. Time Series

MIPAS full resolution ozone profiles processed with soft-ware version 4.61 and 4.62 (off line) were compared withdata from three different ground-based techniques. Asthere is still an error in tangent altitude registration, MI-PAS profiles have been studied versus pressure.

Comparison results vary significantly between the lowerstratosphere (LS), where dynamics and chemistry inter-fere, and the higher stratosphere (HS) dominated at firstorder by photochemistry. They have been classified ac-cording to the behaviour that they reflect. In the LS theozone profile is highly dependent both on troposphericdynamics (ex : direct link with tropospause height) andon stratospheric dynamics. Consequently, the compari-son results can be grouped around the major synoptic andregional systems and the systems linked to stratospherictransport. As dynamical influences from the tropospheretend to vanish at higher altitudes, we move from largesynoptic groups to a more zonal behaviour and we cangroup more stations. In the middle and high stratosphere,zonal symmetry becomes dominant and comparisons re-sults follow this behaviour. Deviations from zonal sym-metry nevertheless exist and must be taken into account.

An example of the time series analysis is shown on Fig. 1.This plot depicts relative differences between MIPAS par-tial columns and correlative data influenced by the North-ern Atlantic system in 2003.

For each collocated pair of profiles, the relative differencebetween MIPAS and correlative ozone partial columns isshown (black bullets). Grey rectangles depict statisticalvalues (mean±1σ standard deviation) associated withthe comparisons. Different contributions to the total errorbudget are shown separately. The red lines depict the up-per limit for the uncertainties associated with differencesin smoothing of horizontal inhomogeneity. The blue lines

Figure 1. Time series of MIPAS 75-35 hPa ozone partialcolumns percentage relative differences with coincidentozonesondes measurements at Northern Atlantic stations.Individual comparisons and monthly statistics. Verticaland horizontal smoothing and spatial distance error bud-get contribution are depicted.

depict non-perfect collocation systematic (plain) and ran-dom (dashed) error contributions. The green lines depictdifference in vertical smoothing systematic (plain) andrandom (dashed) error contributions.

This example show that the standard deviation of thecomparisons fits well within error contribution due todifference in MIPAS/ozonesonde horizontal smoothing.The mean positive difference between MIPAS and correl-ative partial columns is of the order of magnitude of theMIPAS vertical smoothing effect. The spatial distanceeffect is smaller and dominated by the two other effects.

Based on the results from the time series of O3 partial col-umn differences, we have identified time periods wherethe agreement has a constant behaviour, and thus allowsus to derive meaningful statistics. At Arctic, Northernand Southern middle latitudes, we have separated theresults between "winter" (1 October to 31 March) and"summer" (1 April to 30 September). At tropical andequatorial stations, the weak seasonal variation allows usto draw annual plots. At Antarctic stations we can sepa-rate results between "ozone hole" (21 August to 15 Octo-ber) and "normal ozone" periods (16 October to 20 Au-gust).

4.3. Vertical and Meridian Structures

Fig. 2 shows corresponding vertical statistics results forthe "winter" time period at Northern Atlantic stations.The plot shows, for each collocated pair of profiles, therelative difference between MIPAS and correlative mea-surements (grey lines). The high-resolution correlativemeasurements have been previously convolved with MI-PAS averaging kernels and first-guess profile, followingRodgers equation in order to reduce effect of vertical

Figure 2. Percentage relative differences with coincidentozonesondes measurements at Northern Atlantic stationsversus pressure, for the "Winter" time period. Individ-ual comparisons, mean and standard deviation and cor-responding random and systematic total error budget ofthe comparison are depicted.

smoothing differences. Black lines depict statistical val-ues (mean and 1σ standard deviation) associated with thecomparisons. Red lines depict the limits for the total sys-tematic error of the comparison. The mean differencebetween MIPAS and ground station data should be com-pared to these lines. The total systematic error of thecomparison is calculated as the sum of MIPAS system-atic error and the systematic bias due to non-perfect col-location (spatial distance). The yellow block delimitedby dashed red lines depicts the total random error of thecomparison. This value should be compared with the 1σstandard deviation of the differences. This total randomerror of the comparison is calculated as the quadratic sumof MIPAS random error, ground-based random error, ran-dom contribution of spatial distance and LOS inhomo-geneity.

In general MIPAS ozone profiles show a good agree-ment with correlative data and the mean differences fallwithin the systematic error budget, except in the follow-ing cases. MIPAS reports larger ozone concentration thanthe ground based-instruments: (a) in the lower strato-sphere at stations from Northern and Southern mid lati-tudes, Equator and Tropics; (b) in the middle stratosphereover stations at the Equator, in the Tropic of Capricorn,

and in Antarctica during ozone hole events; (c) at alti-tudes higher than 40km, MIPAS ozone profiles underes-timate correlative observations in Hawaii.

These validation results are summarised in Fig.3, that re-ports mean relative differences for the two consideredtime periods of 2003 versus latitude. Results for all sta-tions have been averaged within bin of 5˚of latitude. MI-PAS overestimation of the ozone concentration in thelower stratosphere of the inter-tropical zone is clearlyviewed. Below the tropopause more scattered results areobtained. Weight of MIPAS overestimation of the ozoneamount during ozone hole period is also observed in the"January-March and October-December" Antarctica re-sults. For other geophysical states, the mean agreementbetween MIPAS and correlative instruments usually fallwithin the 10% level and often better.

5. GOMOS

5.1. Limb Illumination States

Previous versions of GOMOS retrieved profile werestrongly affected by the brightness of the limb in whichthe star occults. Bright limb occultations gave poor re-sults, and only dark limb occultations were suitable forvalidation and scientific usage. Data measured under twi-light condition were questionable. We will verify if it isstill the case for the GOMOS data set reprocessed withGOPR 6.0f.

Fig. 4 and Fig. 5 show GOMOS ozone profile datarecords plotted versus altitude and solar zenith angle(SZA) around the NDACC/Arctic station of Ny-Ålesundand the NDACC/New Zealand station of Lauder, respec-tively. These plots show clearly that GOMOS ozoneprofiles retrieved under bright limb condition still giveunrealistic results. When SZA is larger than 100˚, theozone profile seems suitable showing the ozone layerwith ozone maximum values of about6.1012 moleculecm−2, that correspond to standard ozone concentration atthese altitudes. During twilight, for SZA between 90˚and100˚, a specific pattern appears at high latitudes. In theupper layers that are already in the dark, GOMOS re-trieve standard ozone concentration levels; while in thelower altitude layers unrealistic ozone values still appear.The altitude of the transition between realistic and un-realistic ozone concentrations varies with SZA between90˚and about 100˚, while the Sun is setting. Due to theorbital configuration of ENVISAT and the limited az-imuthal range of allowed occultations (-10.8˚to 90.8˚re-duced to -5˚to +20˚, after 2005 instrument anomaly) thesetwilight configuration happen mostly at high latitudes, atlower latitudes the transition is sharper. We also remarkthat below about 15km, spots of large positive or evennegative ozone densities are retrieved by GOMOS, alsounder dark limb conditions.

Figure 3. Mean relative difference between MIPAS and correlative ozone concentration versus pressure and latitude.Data from January to March and October to December 2003 (left) and from April to September 2003 (right).

Figure 4. GOMOS ozone number densities over theNDACC/Arctic station of Ny-Ålesund, as a function of al-titude and solar zenith angle (SZA) for 2003.

Figure 5. Same as Fig. 4 but around the NDACC/NewZealand station of Lauder and for 2004.

For the rest of this analysis, only data measured underdark limb condition, for SZA larger than 100˚have beenselected. They represent about 45% of the profiles suit-able for validation.

5.2. Time Series

Three examples of individual stations time series areshown in Fig. 6, Fig. 7 and Fig. 8, illustrating compar-isons with ozonesonde and lidar data at Northern high,middle and tropical latitudes. The figures present the rel-ative difference between GOMOS and correlative instru-ments.

In the stratosphere, between 15-20 and 50 km, compar-isons show a good agreement between GOMOS and cor-relative data. Mean differences are lower than 10% andeven better. Below 15-20km, larger differences are ob-served. This conclusion confirms results from the direct

Figure 6. Time series of the percentage relative differ-ence between GOMOS and The NDACC/Arctic station ofNy-Ålesund ozonesonde ozone profiles versus altitude. Athree month running mean has been applied.

Figure 7. Same as Fig. 6 but with Belgian NDACC stationof Uccle ozonesonde data.

Figure 8. Same as Fig. 6 but with the NDACC/Hawaii ofMauna Loa lidar data.

ozone analysis, where large positive and negative ozonevalue where observed below this limit. GOMOS retrievegood ozone values to an altitude of about 15km at highlatitudes while in the inter-tropical zone large differencesappear already below 20km. Above 50km, comparisonswith lidar and microwave radiometer show that GOMOSozone concentrations are lower than the correlative val-ues by more than 20%. Part of this difference could beattributed to increasing lidar error bars and microwaveradiometer a-priori information content above 50km. Be-sides, GOMOS-related problems cannot be ruled out es-pecially if we consider the better agreement obtained withMIPAS at such altitudes.

At high latitudes GOMOS show lower ozone values thanthe correlative ozonesonde near the terminator and in thebottom part of the profile. This feature may be linked tolimb illumination condition issue as the Sun keeps closeto the horizon at these latitudes and time periods andbright limb problems begin to appear.

5.3. Vertical Structures

The time series do not show any evident time depen-dences of the relative differences. The absence of timestructure allows drawing annual statistics of the agree-ment between GOMOS and correlative data. A few ex-amples of these vertically resolved statistics for year 2004are shown on Fig 9 and Fig.10. This analysis confirmsthe results from time series study. GOMOS agree wellwith the correlative instrument between 15-20 and 50km.Larger mean difference are obtained above and belowthis altitude range. Similar results are obtained at otherground-based stations and other time periods.

6. SCIAMACHY

A reduced data set of SCIAMACHY ozone limb profilehas been retrieved at DLR with off-line processor ver-sion 3.0. This set is limited and it reduces the number ofground based stations where coincidences can be foundto: 11 ozonesonde and 6 lidar stations, see Tab. 1 for alist.

Previous versions of ESA operational SCIAMACHYozone limb profile profiles, as well as preliminary SCIA-MACHY ozone limb profiles retrieved at University ofBremen, have demonstrated an altitude shift problem[14, 3]. A problem exists with the accuracy of the limbpointing information in the Level 0 (and then in Level 1)data sets. The pointing errors were found to be caused byan incorrect knowledge of the satellite position.

A study of the resulting altitude shift for these prelimi-nary versions of SCIAMACHY ozone profiles concludedto a mean shift of 1.5km varying with longitude and lat-itude. On-board orbit propagator is updated twice a day,always roughly at the same geographical locations : (i)

Figure 9. Mean relative difference and standarddeviation of comparisons between GOMOS and theNDACC/Alpine station of Haute Provence lidar ozoneprofile as a function of altitude for 2004.

Figure 10. Same as Fig. 9 but with Kenyan NDACC sta-tion of Nairobi ozonesondes data.

around 60˚- 70˚W between approximately 20˚N and theequator, i.e., the Caribbean and/or the northern part ofSouth America; and (ii) around 100˚E and 45˚S, i.e.,south-west of Australia. Just after this sudden update ofthe on-board orbit model, the limb pointing appears to bemore accurate and then deviates slowly from the nominalpointing.

Corrections for this pointing error have been imple-mented to correct the problem. First, an upgrade of theon-board orbit propagator was implemented in December2003. Further corrections in the retrievals algorithm aresupposed to correct the remaining shift.

6.1. Individual Ozone Profile Comparisons

New SCIAMACHY 3.0 ozone limb profiles should beaccurate enough for validation, with a reduced altitudepointing shift. Fig.11 shows an example of retrievedSCIAMACHY ozone profile and coincident ozonesondeprofile at the NDACC/Alpine station of Payerne. SCIA-MACHY averaging kernel matrix and a priori data arepart of the product files. Ozonesonde and lidar ozoneprofiles have been convolved with SCIAMACHY aver-aging kernel matrix and a priori data, the correspondingsmoothed correlative profile is also shown on the plots.

No obvious altitude shift between the profiles appears.This example represents the majority of the analysedpairs of coincidence at middle and high norhtern lati-tudes. However, in a few cases SCIAMACHY seemsto be shifted downward compared to coincident ground-based profiles (while known altitude pointing error is ashifted upward of SCIAMACHY ozone profiles). Oneof this cases is illustrated in Fig.12 with collocatedSCIAMACHY and the NDACC/Alpine station of Hohen-peißenberg lidar ozone profiles.

At lower latitudes, in the inter-tropical zone, SCIA-MACHY ozone profiles show a remaining positive alti-tude shift compared to ground-based ozonesondes and li-dars. The maximum shifted is observed at the equatorialNDACC stations of Paramaribo as illustrated in Fig. 13

Individual comparisons were plotted for pairs of coin-cident profiles at all considered stations. This analysisreveals that SCIAMACHY gives lower ozone concen-tration at the altitude of the ozone maximum. This be-haviour seems to be more marked at Paramaribo stationin the equatorial latitude range. The relative agreementbetween SCIAMACHY and coincident correlative datawill be studied in more details hereafter, but we will firstinvestigate the altitude shift issue.

6.2. Altitude Shift

In order to analyse in detail the altitude shift issue, wehave applied the method of cross-correlation to this newSCIAMACHY data set. This method has been developed

Figure 11. SCIAMACHY ozone profile plotted versusaltitude and coincident ozonesonde profile measured atthe NDACC/Alpine station of Payerne. Correspondingcorrelative profile smoothed to the SCIAMACHY verticalperception using averaging kernel matrix and a priori in-formation.

Figure 12. Same as Fig. 11 but for SCIAMACHY and theNDACC/Alpine station of Hohenpeißenberg lidar coinci-dent ozone profiles.

Figure 13. Same as Fig. 11 but for SCIAMACHY andthe equatorial NDACC station of Paramaribo ozonesondecoincident ozone profiles.

and successfully used to study SCIAMACHY pointingerror effects previously [3]. The satellite profiles are com-pared with high resolution and of well known altitudepointing ozone profile given by ozonesonde and lidar in-struments. The cross correlation function is given by:

Corr(f, g)(x) =∫

f(y)g(x + y)dy (1)

where f and g represent the two ozone profiles to be com-pared and x the altitude. The altitude shift between thetwo functions is given by abscissa of the maximum of thecross correlation.

Mean altitude shifts, at all stations located between 50˚Sand 50˚N of latitude have been calculated. Results areplotted in Fig.14. Previous version of SCHIAMACHYprofile had shown dependences of the mean shift versuslongitude, due to updates of the on-board orbit propaga-tor at two permanent locations (Carribean and Australia),consequently, we have investigated the zonal variation ofthe shift.

The mean altitude shift is strongly reduced compared tothe previous version, with a mean value lower than 1kmand stations where it is quasi null. We should remark thatthis method has a resolution limited to the resolution ofthe correlative profile and is not precise enough to detectshifts smaller than a few hundred meters. The amount ofavailable SCIAMACHY profiles is limited and does notallow drawing global statistics. One point of 1km meanshift appear at -50˚of longitude, this could be a remain-ing effect of the Caribbean update of the on-board orbit

Figure 14. Mean altitude shifts between SCIAMACHYlimb ozone profiles and coincident correlative data plot-ted according to longitude. Stations located between 50˚Sand 50˚N of latitude have been selected.

Figure 15. Mean altitude shifts between SCIAMACHYlimb ozone profiles and coincident correlative data plot-ted according to latitude.

propagator. However, due to the low number of processedprofile it is difficult to conclude and no evident longitudi-nal dependency of the altitude shift could be confirmed.We have also studied meridian dependency of the SCIA-MACHY altitude shift. Mean shifts at all stations plottedaccording to latitude are shown on Fig.15.

This plot makes appear an increase of the altitude shiftfor stations located in the inter-tropical zone. The meanaltitude shift is negligible at northern middle latitude sta-tions in Europe and increase up to 1km when going downto equator. This behaviour is observed in the northernhemisphere. Due to the lack of coincidence in the south-ern hemisphere it was impossible to see if a similar pat-tern exists.

In principle, the off-line 3.0 processor includes a point-ing error correction that should reduce the altitude shiftsignificantly. This is what we observe. However ourstudy highlights also a persistent altitude shift which ex-hibits a clear meridian structure. Further validation stud-ies should discriminate possible contributions of the limbprofile retrieval algorithm adding to Envisat pointing er-rors. Although some remaining features are still present,profile are suitable for a preliminary validation especiallyover Europe where remaining shift is quasi null.

6.3. Time Series

As for MIPAS and GOMOS we have computed time se-ries of the relative difference between SCIAMACHY and

correlative ozonesonde and lidar profiles. Ozonesondeand lidar high resolution profiles are convolved withSCIAMACHY averaging kernel matrix and a priori data.Results are illustrated in Fig.16 and Fig.17 with com-parisons at the NDACC/Alpine station of Payerne andat the NDACC/Hawaii station of Mauna Loa. SCIA-MACHY ozone profiles show an agreement within 10%with ozonesonde and lidar data. They are generally lowerthan the correlative ozone profile by -10% in the altituderange of the ozone maximum. This confirms conclusiondrawn from individual comparisons. Comparisons withlidar data also show negative differences of -10% at 40kmof altitude. No evident time structure was found, but hereagain the few number of coincidence limit the analysis.Similar results are obtained at other middle latitude sta-tions and in the Southern Hemisphere.

Results at the equatorial NDACC station of Paramariboare shown at Fig.18. Here the negative differences ob-served are larger, up to -20%, and may be partly due toremaining altitude shift problem.

Figure 16. Time series of percentage relative differencesbetween SCIAMACHY and the NDACC/Alpine station ofPayerne ozonesonde ozone profiles versus altitude. Athree month running mean has been applied.

Figure 17. Same as Fig. 16 but with the NDACC/Hawaiistation of Mauna Loa lidar data.

Figure 18. Same as Fig. 16 but with the equatorialNDACC station of Paramaribo ozonesonde data.

6.4. Vertical Structures

Similar to what has been done for MIPAS and GO-MOS, we have computed vertical statistics for homoge-nous time period. Due to the few number of retrieved

SCIAMACHY profile, we have computed annual statis-tics to have a sufficient number of coincidences. Com-parisons have been made with and without convolvingcorrelative ozone profile with SCIAMACHY averagingkernel matrix and a priori data. The difference givesan estimate of the smoothing error associated with theSCIAMACHY data. Mean (plain line) and standard de-viation (dashed) are depicted for the two cases. Resultsat the NDACC/Alpine station of Payerne in 2005, at theequatorial NDACC station of Paramaribo in 2003 and atthe NDACC/New Zealand station of Lauder in 2004 areshown on Fig. 19, Fig. 20 and Fig. 21 and illustrate wellthe general behaviour observed. In general, the mean rel-ative difference between SCIAMACHY and ozonesondeand lidar correlative ozone profiles fits within the±10%level, except in the following cases: (a) SCIAMACHYreports lower ozone concentration at altitude of the ozonemaximum, from about 15 to 25km. (b) SCIAMACHYreports lower ozone concentration than lidars at 40km ofaltitude. (c) at the Paramaribo equatorial station, the un-derestimation of the ozone maximum is more marked andcould be due to remaining altitude shift problems. Addi-tional orbits overpassing low latitude stations located be-fore and after the on-board orbit propagator update mighthelps to confirm this finding.

Figure 19. Mean relative difference and standard de-viation of comparisons between SCIAMACHY and theNDACC/Alpine station of Payerne ozonesonde ozone pro-file as a function of altitude for year 2004. Correspondingstatistic for comparisons with correlative data smoothedto the SCIAMACHY vertical resolution using Averagingkernel matrix and a priori data.

Figure 20. Same as Fig. 19 but with the equatorialNDACC Paramaribo station ozonesondes data in 2003.

Figure 21. Same as Fig. 19 but with the NDACC/NewZealand station of Lauder lidar data in 2004.

7. CONCLUSION

Independent ground-based measurement techniques havebeen used to validate ozone profile data reported bythe three Envisat ozone profilers, GOMOS, MIPASand SCIAMACHY. Correlative profiles measured byozonesonde, lidar and microwave radiometry in theframework of GAW/NDACC ground-based networkshave been used as a common reference of well knownquality. The different geo-locations of the ground-basedstations and the different altitude range covered by thethree techniques allow pseudo-global investigations.

As detailed documentation about operational retrievalsand related errors is available both for MIPAS andground-based data, we have been able to calculate the to-tal error budget of the comparisons. Horizontal ozonegradient and geolocation difference contributions to thecomparison error budget have been estimated experimen-tally. Temporal analysis of the relative differences be-tween MIPAS and correlative data helps to identify timeperiods were statistical analysis is relevant. Vertically-resolved statistics computed for these time periods (meanand standard deviation) have been compared to the sys-tematic and random components of the comparison errorbudget. The standard deviation of the comparisons cor-relates well with the estimated random error. The anal-ysis shows that horizontal inhomogeneities captured byMIPAS air masses are the main contribution to this ran-dom error. In general MIPAS/ground mean differencesfit within the systematic error budget and are within 10%level, except a few cases. MIPAS reports larger ozonevalues in the lower stratosphere of the inter-tropical zoneand in Antarctica during the ozone hole. Below thetropopause more scattered results are obtained.

GOMOS ozone profiles are still strongly affected by thelimb illumination conditions of the star occultation. Pro-files retrieved in bright limb situation, for SZA smallerthan 100˚, contain unrealistic ozone values. When se-lecting only dark limb occultations, ozone profiles agreewell with the correlative data. Between 15-20km and50km, mean differences are within the 10%level. Below15km at high latitude and 20km in the inter-tropical zone,GOMOS ozone profiles show unrealistic large positive oreven negative ozone values. Above 50km, negative dif-ferences are observed between GOMOS and correlativelidars and microwave radiometers. The time series analy-sis does not reveal any evident time dependence, except athigh latitudes where a poorer agreement is obtained closeto the terminator.

Previous versions of SCIAMACHY limb ozone profiledata suffered from a know altitude pointing problem, re-sulting in an altitude shift of the profile. A limited datasubset of SCIAMACHY limb ozone profiles have beenreprocessed with a new version 3.0 of the off-line pro-cessor, including some correction for this shift. A spe-cific study of the altitude shift reveals that it is effectivelyreduced with the new version. The mean altitude shiftis now less than 1km and quasi null at some locations;

while it was between 1.5 and 3km for the previous ver-sions. However, meridian and zonal dependences of theSCIAMACHY pointing error persist. Despite remainingissues, ozone profile data are suitable for a preliminaryvalidation, especially over Europe where the remainingshift is quasi null. Time series and vertical statistics ofthe relative differences have been studied. The analy-sis reveals that SCIAMACHY underestimates the ozonemaximum between 15 and 25km. This underestimation ismore marked at the equatorial station of Paramaribo andcould be due to remaining altitude shift problems. Fur-ther validation studies should discriminate possible con-tributions of the limb profile retrieval algorithm addingto Envisat pointing errors. Comparisons with lidar dataalso show that SCIAMACHY gives lower ozone concen-tration than lidars around 40km of altitude. For otheraltitude ranges, mean relative difference fits within the±10% level.

The three Envisat ozone profilers rely on totally differ-ent techniques. Consequently our validation methodol-ogy, although being established on the same base, hasbeen adapted to each instrument and retrieval characteris-tics. Limb illumination condition is an issue for GOMOSwhile altitude shift of the retrieved profile is investigatedfor SCIAMACHY. When adequate data selection and ver-tical ranges are envisaged, validation results show an ac-ceptable agreement between ground-based data and MI-PAS and GOMOS ozone profiles in the stratosphere (thatis the standard accessible altitude range reported in the lit-erature for well-proven satellite ozone profilers). SCIA-MACHY ozone profile still show some problems linkedwith altitude pointing errors.

ACKNOWLEDDEMENTS

The ground-based measurements used in this work wereobtained as part of the WMO/GAW Network for the De-tection of Atmospheric Composition Change (NDACC)and are publicly available (see http://www.ndacc.org).Fast availability of preliminary data was facilitated by theframework of ESA’s Atmospheric Chemistry ValidationTeam (ACVT-GBMCD) and the ESA-funded TASTEproject. Scientific and technical staffs at the stations arewarmly thanked for their support and for fruitful discus-sions. This work has been funded partly by the CINA-MON project of PRODEX and the Belgian Science Pol-icy Office and partly by ESA.

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