Estimating ozone variability and instrument uncertainties from

SBUV(/2), ozonesonde, Umkehr, and SAGE II measurements:

Short-term variations

V. E. Fioletov and D. W. TarasickEnvironment Canada, Toronto, Ontario, Canada

I. PetropavlovskikhCooperative Institute for Research in Environmental Studies, University of Colorado, Boulder, Colorado, USA

Received 7 June 2005; revised 19 September 2005; accepted 24 October 2005; published 24 January 2006.

[1] Coincident ozone measurements by the Solar Backscattered Ultra Violet (SBUV) andStratospheric Aerosol and Gas Experiment (SAGE) II instruments and by ozonesondesand the Umkehr method are used to estimate stratospheric ozone variability and standarduncertainties of these different measurements. Below 20 km over northernmidlatitudes, estimated measurement uncertainties for SBUV(/2), sondes, and Umkehr aresimilar (�8% for 0 to 20 km integrated ozone), although only sondes have a highvertical resolution there. From 20 to 28 km., the estimated uncertainties (4–6%) for allfour measurement sources are substantially smaller than the ozone variability in winter(10–15%), but they are comparable in summer (�5%). Above 28 km, sonde uncertaintiesare larger than or comparable to the ozone variability, and much larger thanuncertainties of SBUV(/2), Umkehr, or SAGE II data. Umkehr measurement uncertaintiesat 24–32 km are about 5% and are lower than sonde uncertainties (7–13%) at these levels.SBUV(/2) data are used to evaluate differences between different types ofozonesondes, and to show that correction by total ozone measurements noticeably reducesozonesonde uncertainties. The latitudinal dependence of ozone variability and instrumentuncertainties is studied using pairs of collocated SBUV(/2) and SAGE IImeasurements. There is good correlation between these measurements over middle andhigh latitudes. Over the tropical region, the correlation coefficients are modest (about 0.5)but significant in all layers except at 28–31 km. It is noted that where ozonevariability is comparable to instrument uncertainties, comparison with a climatology basedon a large number of observations may provide better insight into instrument performancethan intercomparison of a small number of quasi-coincident measurements.

Citation: Fioletov, V. E., D. W. Tarasick, and I. Petropavlovskikh (2006), Estimating ozone variability and instrument uncertainties

from SBUV(/2), ozonesonde, Umkehr, and SAGE II measurements: Short-term variations, J. Geophys. Res., 111, D02305,

doi:10.1029/2005JD006340.

1. Introduction

[2] Ozone changes on regional and global scales havebeen an important subject of study because of the role thatozone plays in atmospheric chemistry and dynamics as wellas concern about global ozone decline [e.g., Solomon, 1999;Staehelin et al., 2001] and possible recovery [Reinsel, 2002;Newchurch et al., 2003; Steinbrecht et al., 2004]. Thecharacteristics of the global total ozone distribution andlong-term changes in it are well established from ground-based and satellite measurements [e.g., Fioletov et al., 2002;World Meteorological Organization (WMO), 2003]. Lessinformation, however, is available about the vertical struc-

ture of the ozone changes on a global scale [Randel et al.,1999; Logan et al., 1999; Tarasick et al., 2005]. This isprimarily due to lack of global information on the verticaldistribution of ozone.[3] There are a number of sources of vertically resolved

ozone data. Ozonesondes make in situ ozone measurementsup to 30–35 km, while lidar, and microwave instrumentsmeasure ozone vertical profiles from the ground to about50 km. The Umkehr method also provides vertical profiledata from ground-based observations by Dobson and Brewerinstruments. However, the number of ground-basedmeasurements, although substantially greater in recentyears, is still limited, and there is a need for a globalset of vertical distribution data for long-term ozone trendanalysis.[4] There are several methods of ozone profile retrieval

from satellite observations. Although there are a number of

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, D02305, doi:10.1029/2005JD006340, 2006

Copyright 2006 by the American Geophysical Union.0148-0227/06/2005JD006340$09.00

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other instruments with shorter records, long records areavailable only from the Stratospheric Aerosol and GasExperiment (SAGE) and Solar Backscattered Ultra Violet(SBUV) and SBUV/2 series of instruments. The SAGE I-IIIinstruments [e.g., Wang et al., 2002] employ solar occulta-tion, a reliable method but with coverage limited to about30 profiles a day (at sunrise and sunset). These have beenin operation since 1979. The SBUV and SBUV/2 instru-ments have been in operation since 1978, with sixSBUV(/2) instruments having flown since then. However,the use of SBUV(/2) data for the assessment of changesin ozone vertical distribution has been relatively limiteduntil recently owing to concerns about biases and othererrors in individual instrument records. For example, acomprehensive World Climate Research Programme(WCRP) [1998] report refers to only two SBUV(/2)instruments.[5] A new version 8 of SBUV(/2) data has been released

recently [Bhartia et al., 2004; Frith et al., 2004]. Firstresults have demonstrated a significant improvement of theSBUV(/2) data quality [McPeters et al., 2004; Ahn et al.,2004]. The version 8 algorithm is optimized to estimatelong-term ozone variations and is not necessarily optimalfor retrieving individual profiles. However, the nearlyglobal coverage provided by the SBUV(/2) instrumentsmakes their data very attractive for validation of other typesof ozone measurement, even on the level of individualprofiles.[6] In this study, comparisons with ozonesonde, Umkehr

and SAGE II measurements are used to evaluate SBUV(/2)version 8 performance as well as uncertainties in the othertypes of ozone measurement. Measurement uncertainty byitself does not necessarily determine the value of a particularmeasurement. It is important also to know the magnitude ofthe variability of the measured parameter, compared to theinstrument uncertainties. In the following we show that setsof parallel measurements of the same parameter by twodifferent instruments make it possible, under some basicassumptions, to estimate both the instrument uncertaintiesand parameter variability. It is demonstrated that SBUV(/2)data can be successfully used to estimate ozone variability,although they are not completely free from some instrumentor algorithm-related problems. It is shown how SBUV(/2)data can be used to evaluate the performance of differentinstruments, for example, between different types of ozo-

nesondes. This study is focused on relatively short time-scales: from individual profiles to monthly averages.

2. Data Sets

2.1. SBUV(/2) Data

[7] The SBUV/2 instrument is a scanning double mono-chromator measuring backscattered solar radiation in 12discrete wavelength bands ranging from 252.0 to 339.8nanometers. It is nadir viewing, with a field of view (FOV)of 11.3 by 11.3 degrees (160 � 160 km). Satellite tracks areseparated by approximately 26 degrees of longitude andthe satellite footprint moves at a speed of about 6 km/sec.In discrete mode, a set of 12 measurements, one foreach discrete wavelength band, is taken every 32 s. TheSBUV(/2) satellite data used here are reprocessed with theversion 8 algorithm and are available at http://daac.gsfc.nasa.gov/data/dataset/TOMS/DVD-ROMs. Data are fromthe Nimbus 7 SBUV instrument for the period November1978 to June 1990, from SBUV/2 on NOAA 9 (March 1985to February 1998), NOAA 11 (January 1989 to March2001), and NOAA 16 (from October 2000 to December2003). The data are available as column ozone in DobsonUnits (DU) for 13 layers, each 3.2 km thick except forLayer 1, which is about 18 km in depth. The layerboundaries are given in Table 1. This study is focused onLayers 1–8 which contain more than 97% of column ozone.Small absolute ozone amounts, low (compared to instrumen-tal errors) variability, and the existence of diurnal variationsmake it difficult to validate the data above Layer 8. To avoiderrors related to volcanic aerosols, data between 40�S and40�Nwere removed from the analysis for the one-year periodfollowing the major volcanic eruptions in 1982 and 1991, assuggested byWCRP [1998]. In addition, measurements withSBUV(/2) aerosol index greater than 5 were excluded fromthe analysis.

2.2. Ozonesonde Data

[8] Data from two types of ozonesondes, Brewer-Mast(BM) and ECC, were used in this study. Several stationshave long records of recently quality-controlled or reeval-uated BM data (Hohenpeissenberg, Payerne and Uccle inEurope) or of ECC data (Boulder, Edmonton, Churchill, andGoose Bay in North America, and Uccle, Lindenberg,Legionovo, Praha, and Debilt in Europe). These were used

Table 1. Pressure Boundaries and Mean Altitudes of the SBUV(/2) Layers and Standard Deviations of Ozone Variation and Instrument

Standard Uncertainties in These Layers for Individual Profiles Over Europea

LayerPressure

Range, hPa

ApproximateMean

Altitude, km

Ozone Variability, % Standard Uncertainties, %

Winter Summer SBUV(/2) Sondes SAGE II Umkehr

1 1013–63.93 9 21 12 7 (8) 12 (8) . . . 12 (8)2 63.93–40.33 20 15 8 9 7 4 (8) 83 40.33–25.45 23 10 5 7 5 3 (5) 74 25.45–16.06 26 10 5 6 6 4 55 16.06–10.13 29 12 4 5 8 5 56 10.13–6.39 32 12 4 5 11 5 57 6.39–4.03 36 9 4 5 . . . 3 68 4.03–2.55 39 9 4 4 . . . 6 8aMeasurements taken less than 500 km apart for Layers 2–8 and less than 200 km apart for Layer 1 were used. The numbers in brackets are standard

uncertainties for ‘‘corrected’’ data (see text).

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to estimate inter-instrument bias in the SBUV(/2) data andto estimate instrument uncertainties and ozone variability.All of these data are available from the WMO World Ozoneand UV Data Centre (WOUDC) (http://www.woudc.org).Comparisons in this study were performed on SBUV(/2)layers (see Table 1). Ozone integrals for the layers werecalculated from the ozonesonde profiles. For consistency,only sondes that reached Layer 5 were used for thecomparison.[9] There are known systematic differences between ECC

and BM ozonesonde response [WMO, 1994; Smit et al.,1996]. Figure 1 shows such differences, estimated usingSBUV(/2) data. To derive these estimates, sonde data werefit to SBUV(/2) overpasses using the following statisticalmodel: for each layer, Xsonde = (a + c � I)Xsbuv where Xsonde

and Xsbuv are ozonesonde and SBUV(/2) overpass data, a isthe sonde-SBUV difference for that layer (derived from thefit), and c is an additional bias between ECC and BMsondes (the parameter I = 0 for ECC data and 1 for BM). c istherefore a measure of the difference in response betweenthe two types of sondes, given in per cent by (c/a)�100%.From Figure 1, BM data are in agreement with ECC data inLayers 1–4, and 3% and 5% lower in Layers 5 and 6,respectively. These values are consistent with results ofinternational intercomparisons of ozonesondes [WCRP,1998] and with recent comparisons of ECC and BM sondeswith SAGE II [Lemoine and De Backer, 2001]. In theanalysis that follows the BM data have been adjusted viaFigure 1 to match that of ECC sondes.[10] Some information about the sonde instrumental un-

certainty can be derived from the total ozone correctionfactors [e.g., Logan et al., 1999; Tarasick et al., 2005].Individual soundings have slight differences in overallresponse to ozone. To compensate for these differences

the profile data are often scaled to match total ozonemeasured independently by an accurate ground-based in-strument (Dobson or Brewer spectrophotometer). The cor-rection factor is the ratio of the integrated ozone from theozonesonde profile (including an estimated amount abovethe sonde burst altitude) and that from direct measurementby the ground-based instrument. The standard deviation ofthe correction factor estimated from 5500 ECC ozonesondeflights that reached at least 26 km is 0.076, i.e., 7.6%. Thesame standard deviation for BM sondes at Hohenpeissen-berg is about 7% and it is about 10% at the other BM sites.In contrast, for well-calibrated instruments the standarduncertainty of direct sun total ozone measurements is lessthan 3% [Basher, 1982]. Thus the uncertainty of noncor-rected ozonesondes is about 7%, although the uncertaintyalso depends on altitude. The standard uncertainty ofcorrected data will be lower: greater than 3%, given timeand space differences between the total ozone and ozone-sonde measurements, altitude-dependent errors in the ozo-nesonde profile that are not removed by a linear correction,errors in estimating the residual ozone above the balloonburst, use of less accurate zenith sky total ozone measure-ments, etc., but probably about 4–5%.

2.3. Umkehr Data

[11] The Umkehr effect can be used to determine thevertical ozone distribution from ground-based spectropho-tometer measurements. There have been a number ofversions of the Umkehr algorithm since it was first intro-duced [e.g., Bojkov et al., 2002; Petropavlovskikh et al.,2005a]. In this study, we use data from four northernmidlatitude stations (Boulder, Arosa, Tateno and HauteProvance), processed with the new UMK04 algorithm[Petropavlovskikh et al., 2005b]. The algorithm descriptionand the data are available from (http://www.srrb.noaa.gov/research/umkehr). The UMK04 algorithm and the Version8 SBUV(/2) algorithm were simultaneously developed, andthey include similar features, such as a time-independent apriori profiles from an updated ozone climatology andsimilar concepts of forward and inverse models. Bothsystems also have pressure-based retrieval coordinates,enabling us to do a direct layer ozone comparison. For thisstudy, Umkehr profiles were interpolated to the samestandard SBUV(/2) version 8 levels shown in Table 1. Itshould be noted that these levels are different from thetraditional Umkehr levels. The 1982 and 1991 Umkehrdata were excluded from the analysis because they wereaffected by large amounts of stratospheric aerosol frommajor volcanic eruptions.

2.4. SAGE Data

[12] Data from the SAGE II sensor [McCormick, 1987]aboard the Earth Radiation Budget Satellite (ERBS) for theperiod October 1984 to December 2003 (http://www-sage2.larc.nasa.gov) were used for validation. SAGE II isa seven-channel spectrometer with channels at 385, 448,453, 525, 600, 940, and 1020 nm. Data were processed withthe version 6.20 algorithm. SAGE II data, presented asozone number density for 1-km-thick layers, were recalcu-lated to SBUV(/2) layers using pressure data from theSAGE II data files. The path length of the measurementsis 200 km along the line of sight by 2.5 km for a 1-km

Figure 1. Systematic differences between Brewer-Mastand ECC types of ozonesonde derived from the analysis ofSBUV(/2) overpass data over northern midlatitudes as afunction of SBUV level. The layer boundaries (in pressure)are shown on the right. The 95% confidence intervals arealso shown.

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retrieved layer [WCRP, 1998]. Only ozone number densitymeasurements with estimated error less than 50% wereused. Ozone content was not calculated for SBUV(/2) layerswith incomplete (missing) data. These conditions excludeall SAGE II Layer 1 data and some Layer 2 data from theanalysis.[13] The SAGE II instrument makes measurements dur-

ing sunrise and sunset. There is some systematic differencebetween these measurements due to diurnal variations inozone. However, the difference becomes noticeable onlyabove Layer 8, while this analysis was limited to Layers 1–8 only.[14] It is known that a small percentage of SAGE II data

is affected by errors caused by interference from aerosol(including stratospheric aerosol from major volcanic erup-tions), clouds and other, not always known, factors [Rind etal., 2005]. Sonde and Umkehr data records also contain asmall fraction of erroneous values. To eliminate these out-liers, we excluded data where integrated ozone values differfrom SBUV(/2) values by 50% or more and where the layerozone differs from the SBUV(/2) ozone in that layer by 70%or more. This screening procedure rejected less than 0.4%of SAGE II data and less than 0.1% of Umkehr and sondedata. This was in addition to SAGE II data excluded fromthe comparison by virtue of the fact that the correspondingSBUV(/2) data had already been excluded, as describedunder SBUV(/2), above.

3. SBUV(/2) Data Versus Other Data Sources

[15] Comparisons of SBUV(/2) data with measurementsof other types were made for pairs of collocated observa-tions. For each ground-based measurement or SAGE IIobservation, the nearest SBUV(/2) pixel available for thesame day was taken for comparison. Figure 2 shows the

standard deviation of the difference between SBUV(/2),ozonesonde, Umkehr and SAGE II measurements as afunction of the distance between measurement points. Thelargest differences can be seen in the troposphere–lower-most stratosphere (Layer 1), where the scale of ozonefluctuations is relatively small. As Figure 2 shows, thestandard deviation of differences between SBUV(/2) mea-surements and other types of data rises rapidly in Layer 1with the distance between the measuring points. For Layer 3and above (i.e., above 21 km), the standard deviation ofdifferences increases just slightly with distance, indicatingthat ozone variations have larger scales there. Furthercomparisons in this study were limited to measurementsseparated by no more than 1000 km for estimates ofmonthly or latitudinal characteristics and no more than500 km for individual profile comparisons. Setting the limitto 500 km gives better results in Layers 1 and 2, butotherwise the difference in the results between the 500 kmand 1000 km limits are negligible. Differences in timebetween SBUV(/2) and other observations had little effecton the results. Comparisons in this study were limited tomeasurements taken on the same day.[16] Figure 3 demonstrates the mean difference between

SBUV(/2) measurements and ozonesonde, Umkehr, andSAGE II data for Layers 1–8. All individual SBUV andSBUV/2 instruments demonstrate very similar biases withrespect to the other data sources, suggesting that the biasesare related to the algorithm and not to systematic errors ofindividual instruments. NOAA-16 SBUV/2 data showslightly larger differences than the other SBUV(/2) instru-ments, compared to sondes and Umkehr data, but thenumber of coincident measurements for NOAA-16 is alsomuch smaller than for the other SBUV(/2) instruments.[17] There is a significant difference between SBUV(/2)

data in Layers 2–5 and the data from the other sources as a

Figure 2. Standard deviation of the difference between ozonesondes and SBUV(/2) overpasses,between SAGE II and SBUV(/2) overpasses, and between Umkehr observations and SBUV(/2)overpasses as a function of the distance between SBUV(/2) overpass point and location of the ground-based station or SAGE II measurement for northern midlatitudes (30�N–60�N).

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function of total ozone. Apparently, the SBUV(/2) data areunbiased if the total ozone value is close to the climatolog-ical value (Figure 4), but significant biases appear for higherand lower values of total ozone. Differences can be as highas 15–20% in extreme cases. Figure 4 is based on measure-

ments taken at 30�N–60�N; however, this effect can be seenat all seasons and at all latitudes. The dependence on totalozone is obvious and could be fixed. However, that wouldmean that the retrieval algorithm would depend on columnozone and the total ozone trend would then automatically

Figure 3. Differences in percent between SBUV(/2) and (left) sonde, (middle) SAGE II, and (right)Umkehr data for different layers. Measurements between 30�N and 60�N were used.

Figure 4. Standard deviation of the difference between ozonesondes and SBUV(/2) overpasses,between SAGE II and SBUV(/2) overpasses and between Umkehr observations and SBUV(/2)overpasses as a function of SBUV(/2) total ozone, estimated using data at 30�N–60�N (left) for theseason of maximum ozone, February–April, and (right) for the season of minimum ozone, August–October.

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yield a trend in the ozone profile. This is something that theauthors of the version 8 algorithm tried to avoid [Bhartia etal., 2004]. Relative to the sonde and SAGE II measure-ments, differences with Umkehr data show a much smallerdependence on total ozone for Layers 2–3 and a largerdifference for Layer 1. This may indicate that the Umkehrdata also have systematic errors that depend on total ozone.The SBUV(/2) errors at very large and very small totalozone values have a relatively minor effect on SBUV(/2)data statistics, such as annual means, because these casescomprise a small part of the total data set.

4. Ozone Variability Versus InstrumentUncertainties

[18] Figure 5 shows ozone values measured by sondes,Umkehr and SAGE II plotted against SBUV(/2) ozone forLayer 5 for January (left) and for June–July (right). Datafrom the European region were used for this plot. While theJanuary data show a clear correlation between SBUV(/2)and the other three sources, the summer (June–July) data donot, except perhaps for the SBUV(/2)-SAGE II pair. High

correlations indicate that the instrument uncertainties arelow relative to the ozone variability. Conversely, if ozonevariability is low compared to the instrument uncertainties(provided that the measurement errors of the two instru-ments are independent), the correlation coefficient is small.This is what we see in summer.[19] These correlations are illustrated in more detail in

Figure 6 (left). In all cases correlation calculations wereperformed on the residuals after removal of the annual cycle(estimated by the two first harmonics of its Fourier expan-sion). This was necessary in order to avoid artificialcorrelations introduced by this common source of variation.The annual cycle was assumed to be the same for all datasources. The data were then grouped by season, to improvethe statistics for the correlation coefficient estimates. Win-tertime correlation coefficients are as high as 0.75–0.9,while some summer values are less than 0.3.

4.1. Estimating Ozone Variability and MeasurementUncertainties

[20] Information about the natural variability of ozoneand measurement uncertainties can be derived from themeasurements themselves. The result of a measurement(M) is the sum of the true ozone value (X) and an error(e). In the case of overpass data, we can assume that thereare two types of instruments (e.g., sondes and SBUV(/2))that measure the same parameter X, but with different errorse1 and e2. The results of their measurements (M1 and M2)can be used to estimate the variance of X, e1 and e2, asfollows: If we assume that the measured value and the errorsare independent, then the variance of M is the sum ofvariances of X and ei,

s2 Mið Þ ¼ s2 Xð Þ þ s2 eið Þ; i ¼ 1; 2: ð1Þ

[21] The difference of M1 and M2 does not depend on X.If, in addition, we assume that the errors of differentinstruments are not correlated, then the variance of thedifference is equal to the sum of s2(ei) and s2(ei),

s2 M1 �M2ð Þ ¼ s2 e1ð Þ þ s2 e2ð Þ: ð2Þ

The values of s2(M1), s2(M2), and s2(M1 � M2) can be

estimated from a set of parallel measurements. The threeresulting equations can be solved for s2(X), s2(e1), ands2(e2),

s2 Xð Þ ¼ 1=2 s2 M1ð Þ þ s2 M2ð Þ � s2 M1 �M2ð Þð Þ;

s2 e1ð Þ ¼ 1=2 s2 M1ð Þ � s2 M2ð Þ þ s2 M1 �M2ð Þð Þ;

s2 e2ð Þ ¼ 1=2 s2 M2ð Þ � s2 M1ð Þ þ s2 M1 �M2ð Þð Þ:

ð3Þ

Equations (3) were used to estimate the standard deviation(SD) of instrument errors (we will refer to it as to standardinstrument uncertainty) and the SD of ozone variability for agiven location, time of year, and layer.[22] There are several caveats related to the described

method. First, sondes and satellites do not measure thesame quantities. Satellite observations reflect ozone valuesaveraged over a large area, while sonde data represent

Figure 5. Ozone values in Layer 5 (10–16 hPa) forEurope (42�N–53�N, 5�W–25�E) measured by (top)sondes, (middle) SAGE II, and (bottom) Umkehr plottedagainst SBUV(/2) ozone for (left) January and (right) June–July. Values are given in DU.

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instantaneous values at a single point in space. Thus thevariance of ‘‘satellite’’ ozone is lower than the variance of‘‘sonde’’ ozone. The difference in the variances will beattributed to the sonde instrument errors if equation (3) isused. As Figure 2 shows, ozone variations have a largescale for all layers except, perhaps, Layer 1, thereforeoverestimation of sonde errors should not be large aboveLayer 1.[23] In addition, Umkehr and SBUV(/2) data do not

actually represent the ozone content of the 3.2-km-thicklayers presented in Table 1, but rather a weighted average ofozone content for a wider altitudinal range normalized tomatch the contribution from that 3.2-km-thick layer. Thismay cause (under) overestimation of (high) low ozonevalues where the profile has sharp changes in the vertical.A strongly layered structure to the ozone profile is oftenseen below 25 km at midlatitudes in wintertime, caused bythe quasi-isentropic mixing of air from higher and lowerlatitudes [Reid and Vaughan, 1991; Orsolini, 1995; Tarasicket al., 2005].[24] Second, we do not actually know the variances

s2(Mi) and s2(M1 � M2); we can only estimate them,with a certain error, from the available measurements. Thea-level confidence interval for the variance s2 depends on

the estimated variance value itself and the number of datapoints, n,

n� 1ð Þs2

c21�a=2 n� 1ð Þ

< s2 <n� 1ð Þs2

c2a=2 n� 1ð Þ

; ð4Þ

where s2 is the sample variance and c2(n � 1) is the chi-square distribution with n�1 degrees of freedom. The errorof the variance estimate depends on the variance itself. Allthree variances, s2(M1), s2(M2), and s2(M1 � M2),determine s2(X), s2(e1), and s2(e2) in equation (3). There-fore the errors in the s2(X), s2(e1), and s2(e2) estimatesdepend on the sum of all three variances s2(M1), s

2(M2),and s2(M1 � M2), and can be high even if the estimatedvariance itself is low (but one or more of the variancess2(M1), s

2(M2), or s2(M1 �M2) are high). The estimates are

thus only as accurate as the least accurate of theseparameters. The variance estimates can be improved byincreasing the number of data points. For this reason weestimated seasonal variances instead of, for example,monthly ones.[25] Finally, the assumption that measurement errors and

ozone values are uncorrelated may not be entirely valid,either because of instrument or retrieval effects, or for the

Figure 6. Correlation coefficient between SBUV(/2) data and (top) ozonesonde, (middle) Umkehr, and(bottom) SAGE II measurements over Europe (42�N–53�N, 5�W–25�E) as a function of season.Measurements separated by no more than 500 km were used. The correlation coefficients for daily dataare shown on the left panel and for monthly averages on the right panel. On average, there are 14 days permonth with collocated sonde and SBUV(/2) measurements and 13 days with collocated Umkehr andSBUV(/2) measurements. The number of SAGE II measurements was insufficient to calculate monthlyaverages correctly.

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reasons noted above. However, as we will show, if two ormore sources of ozone measurements are available, the linkbetween measurement error and measured ozone value canbe quantified.

4.2. Individual Profile Comparisons

[26] Figure 7 shows the standard deviations of ozonevariations and of instrument standard uncertainties calculatedusing equation (3), for four seasons at SBUV(/2) Layers 1–8 over Europe (42�N–53�N, 5�W–25�E) based on individ-ual measurements. These results are also summarized inTable 1. The SBUV(/2) data were used as one source ofdata, while for sonde, Umkehr, or SAGE II measurementswere used as the second source. Applying equation (3) tothese data sources creates three sets of SD of ozonevariability and SD of SBUV (/2) uncertainties estimates.There is not much difference between these estimates of thesame parameter from different data sources. This result

permits greater confidence in the validity of our method.Only one value of SD of ozone variability and SD ofSBUV(/2) uncertainties are therefore plotted in Figure 8.That value was calculated as square root of the meanvariance estimates from different data sources.[27] For Layer 1, the ozone variability is larger than

instrument errors. In Layer 2, the standard deviation ofSBUV(/2) errors exceeds the SD of ozone variability inwinter and spring, while the SD of sonde and SAGE IIdata are lower than the ozone variability SD at all seasons.The SBUV(/2) data are less accurate than sonde andSAGE II measurements at Layer 3, but they becomecomparable at Layer 4. The SBUV(/2) measurements arein general as accurate as SAGE II data at Layers 5–8,while sonde data are less accurate than satellite data atLayer 5 and above.[28] Pairs of collocated SAGE II and sonde measure-

ments can be also used to estimate the ozone variability and

Figure 7. Standard deviation (SD) of ozone variations and of instrument errors of individualmeasurements for sonde, SBUV(/2), Umkehr, and SAGE II data as a function of season for Europe. Thedashed lines show standard uncertainties for ‘‘corrected’’ data (see text).

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instrument uncertainties using equation (3). These estimatesare less reliable because the number of data points is anorder of magnitude less than the number of SBUV(/2)–sonde pairs. However, they represent an independent set ofestimates. The correlation coefficient between SAGE II andsonde data are similar to those between SBUV(/2) andsonde data. The values for winter, when the correlationcoefficients are the highest, are between 6.5 and 7.8 inLayers 2–5 for SAGE II–sonde pairs compared to 6.7–7.5for the SBUV(/2)–sonde pairs. In summer, the correlationcoefficients are somewhat higher for SAGE II–sonde pairsthan for SBUV(/2) sonde pairs in Layers 2–4 reflecting thefact that standard deviations of SAGE data are lower therethan standard deviations of SBUV(/2) data. However, thenumber of SAGE II–sonde pairs in summer is less than 100and these estimates are not very reliable.[29] The standard uncertainties estimated from sonde and

SAGE II data (not shown) also demonstrate similar patternsto those estimated from comparison to SBUV(/2) measure-ments: The uncertainties are substantially smaller than theozone variability in winter, but they are comparable insummer in Layers 2–4. Sonde uncertainties exceed ozonevariability in spring and summer in Layer 5 and during allseasons in Layer 6. This pattern can be also seen fromcomparison with SBUV(/2) (Figure 7).[30] Figure 7 also gives the impression that SBUV(/2)

performs better than sondes in Layer 1, particularly duringthe winter and autumn. However, ozone variability there hasa relatively small spatial scale and is likely underestimatedby SBUV(/2) with its large FOV. The low estimated SD ofSBUV(/2) uncertainties in Layer 1 and SAGE II uncertain-ties in Layers 2 and 3 could be also an artifact of theunderestimation of ozone variability by the satellite instru-ment, as discussed in section 4.1. Figure 8 shows thedifference between SBUV(/2) data and sonde, Umkehr,

and SAGE II data plotted as a function of deseasonalizedozone in Layers 1–4 for winter. The ozone value used herewas calculated as the average of the two compared measure-ments. Figure 8 suggests that SBUV(/2) measurements tendto underestimate large deviations from the climatologicalvalue in Layer 1. Above Layer 1, there is no dependence ofthe difference between SBUV(/2) data and sonde orUmkehr measurements on ozone. Figure 8 also indicatesthat SAGE II measurements tend to underestimate highozone values in Layers 2 and 3 relative to SBUV(/2)measurements. No clear dependence on ozone was seen inLayer 4 and above.[31] The difference between SAGE II and SBUV(/2) data

at high ozone values in Layer 3 is evidenced by the fact thatthe SAGE II instrument rarely reports ozone values above 70DU, while SBUV(/2) overpasses for SAGE II measurementsoften exceed the 70 DU limit. This difference in frequency ofhigh values is not caused by a difference in mean response, asFigure 3 shows no bias between SAGE II and SBUV(/2) datain Layer 3. In contrast, SBUV(/2) and ozonesonde data showsimilar ranges. There are too few collocated sonde and SAGEII measurements to draw conclusions about SAGE II perfor-mance at large ozone values.[32] Thus, SBUV (/2) data in Layer 1 and SAGE II data

in Layers 2 and 3 may be affected by an error that is afunction of the ozone amount. In order to correct theestimated uncertainties for this, the ozone dependence wasapproximated as a linear function of the ozone amount andthe measurements scaled accordingly. A linear scaling doesnot affect the correlation coefficients between differenttypes of measurements, but changes the estimates of thestandard uncertainties of the scaled data. The goal of thisadjustment is simply to properly compare data from differ-ent sources; we have not used the ‘‘corrected’’ data exceptto calculate measurement uncertainties. The estimated

Figure 8. Difference between SBUV(/2) data and the other sources in different layers as a function ofthe deseasonalized layer ozone amount for winter (December–February) over northern midlatitudes.

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ozone variability and standard measurement uncertaintiesfor the ‘‘corrected’’ data are shown in Figure 7 (the dashedlines) and in Table 1. The estimated uncertainties for sonde,Umkehr and ‘‘corrected’’ SBUV data are about 10% andnearly identical for all three data sources in Layer 1.This suggests that the uncertainty is largely caused by thenatural ozone variability and the difference in time andspace between observations of different types. The standarddeviation of the uncertainty drops from 10% to 8% ifSBUV(/2) overpasses within 200 km (instead of 500 km)from the ground-based sites are considered. Applying theozone value-based correction to SAGE II data increases theestimated measurement uncertainties for SAGE II andmakes them similar to those from sonde data in Layers 3and 4. The SAGE II uncertainties estimated here are similarto those obtained by direct comparisons to ozone sondes:4% above 25 km [Wang et al., 2002].[33] The sonde data have the lowest standard deviations

in Layers 3 and 4, i.e., at the ozone maximum (Figure 7).They are about 5–6% in Layer 3 and 5–7% in Layer 4, anddo not depend on season. The SDs are between 7 and 10%in Layer 5 and between 10 and 13% in Layer 6. Thesenumbers are similar to the SD values obtained from directozonesonde comparisons, where minimum SD values of 4%have been reported near the altitude of the ozone maximum,with values of 7% at 10 mb (the bottom of Layer 6), and20% at 5 mb (Layer 7) [WMO, 1994]. The relatively largeozonesonde uncertainties in Layer 5 explain the lack ofsignificant correlation between SBUV(/2) and sonde data insummer in Figure 5, while there is a correlation betweenSBUV(/2) and SAGE II. The sonde uncertainty (7%) in

summer in Layer 5 is noticeably larger than the ozonevariability (4%) and uncertainties of SBUV(/2) (4%).Umkehr (5%), and SAGE II (2.5%) data and the correlationcoefficients reflect these large sonde uncertainties.[34] Comparisons with SBUV(/2) data also make it pos-

sible to evaluate the performance of different types ofsondes (Figure 9, top). The Brewer-Mast and ECC typesondes demonstrate similar uncertainties in Layers 1–5.However, the SD of ECC sonde uncertainties in Layer 6 isnearly a factor of 2 lower than those for Brewer-Mastsondes for all seasons except winter. Similar findings werealso reported by Lemoine and De Backer [2001, Figure 3].This is also consistent with lidar and microwave compar-isons of BM and ECC ozonesondes [WCRP, 1998]. Therelatively high errors of ECC sondes in Layer 6 in wintersuggests that the performance of ECC cells may be poorer atthe low stratospheric temperatures typical of that time ofyear above 30 km.[35] The same approach can be used to estimate the effect

of total ozone correction on ozonesonde uncertainties.Several ozonesonde intercomparisons, and comparisonswith simultaneous measurements by UV photometers andlidar, have shown that some errors in sonde results, partic-ularly systematic errors that apparently result from differ-ences in preflight preparation, are reduced by normalizationto an accurate total ozone measurement [e.g., Kerr et al.,1994; Smit et al., 1996; Beekman et al., 1994, 1995]. Figure9 (bottom) shows that the ozonesonde uncertainties in Layer4 are 5–7% for corrected data and 7–10% for noncorrecteddata, i.e., applying the correction factor noticeably improvesthe quality of sonde data.

Figure 9. (top) Standard deviation of ozone variations and of instrument errors of individualmeasurements SBUV(/2) and SAGE II, and for the two types of ozonesondes (Brewer-Mast and ECC) inLayer 6. (bottom) Same as above, but for ECC sondes with and without the correction factor in Layer 4.

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[36] The performance of Umkehr measurements is similarto that of SBUV(/2) for Layers 2–6. The Umkehr measure-ment uncertainties in Layers 4–6 are about 5% and arelower than sonde uncertainties at these levels. This suggeststhat well-calibrated Umkehr measurements can be a valu-able source of information about the ozone distribution in

the 24–35 km altitude range. It should be noted, however,that SBUV(/2) and Umkehr algorithms use similar averag-ing kernels, i.e., they measure ‘‘the same’’ ozone that isdifferent from sonde and SAGE II ozone for the samelayers. Thus it is likely that Umkehr uncertainties calculatedhere are underestimates.

4.3. Monthly Averages

[37] Both ozone variations and instrument uncertaintieshave components with different timescales. Ozone varia-tions include short-term fluctuations caused by meteorolog-ical processes as well as long-term components such as thesolar cycle and chemical or climate-induced trends. Instru-ment uncertainties can be random uncorrelated errors orsystematic errors that vary on short or long timescales. It isnot therefore obvious that the correlation coefficients be-tween monthly averaged ozone measurements should bemuch higher than those for individual observations. Never-

Table 2. Same as Table 1, But for Monthly Averaged Data

Layer

Ozone Variability, % Standard Uncertainties, %

Winter Summer SBUV(/2) Sondes Umkehr

1 11 6 4 5 52 10 5 4 4 43 7 4 4 3 44 6 4 3 3 35 7 3 3 4 36 7 4 3 7 37 6 4 3 - 48 7 4 3 - 5

Figure 10. Correlation coefficients between deseasonalized ozone values measured by SBUV(/2) andSAGE II as a function of latitude for four seasons, and (top left) mean difference between SBUV(/2) andSAGE II data as a function of latitude in Layers 2–4.

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theless, comparing the right to the left panel of Figure 6shows that monthly averaging substantially increases thecorrelation coefficients. The correlation coefficient betweensondes and SBUV(/2) monthly averages over Europe are0.8–0.9 for winter, spring and autumn and 0.6–0.7 forsummer for most layers. A large improvement compared toindividual profiles can be seen for the Umkehr-SBUV(/2)comparison, although the correlation coefficients there aretypically not as high as for the sonde-SBUV(/2) pairs.[38] Table 2 shows the standard deviation of ozone values

and sonde and SBUV(/2) standard uncertainties for monthlyaveraged data. For these calculations, coincident ozone-sonde and SBUV(/2) measurements over central Europewere averaged for each day, and daily values then averagedto obtain the monthly mean. Sonde, Umkehr and SBUV(/2)measurements demonstrate similar uncertainties inLayers 2–4 and the SBUV(/2) instrument performs betterthan the sondes in Layers 5 and 6. The standard deviationsof ozone variations are typically 1.5–2 times higher than theSBUV(/2) uncertainties, suggesting that SBUV(/2) canmeasure monthly ozone values reasonably well. There areon average about 14 days with ozonesonde flights and13 days with Umkehr measurements per month in centralEurope. The standard deviations would be smaller and theratio of ozone variations to SBUV(/2) uncertainties wouldbe even higher if data for all days were available. As thereare typically only 2–3 days per month when SAGE IIdata are available for central Europe in summer, it is notpossible to estimate monthly ozone variability and instru-ment uncertainties from the SBUV(/2)–SAGE II collocatedmeasurements.

4.4. Latitudinal Effects

[39] The results discussed above are based on data fromnorthern midlatitudes, where the majority of ground stationsare located. As shown in Figure 3, there is a bias betweenSBUV(/2) data and SAGE II in some layers. This bias,estimated using observations at 30�N–60�N, was subtractedfrom the data for all latitudes. The results are shown inFigure 10 (top left corner). The mean residuals, calculatedfor 10-degree-wide belts, demonstrate that removing thebias at northern midlatitudes eliminates it as well at otherlatitudes. The apparent exception is Layer 2 at low latitudeswhere SAGE II data are up to 15% higher than SBUV(/2).Sonde data do not show the same difference there. This maybe an artifact related to sampling: some of the SAGE II datain Layer 2 are missing at low latitudes. If there are anymissing data within the layer, the ozone integral for thatlayer was not calculated and therefore was not used for thecomparison with SBUV(/2). As signal-to-noise ratio islower for low ozone amounts, it is more likely that ameasurement is rejected when ozone is low than whenozone is high, and therefore our estimate for the Layer 2integrals may be biased toward high values.[40] To compare SBUV(/2) and SAGE II data at different

latitudes, matched pairs of SBUV(/2) and SAGE II obser-vations were binned into 10� latitude by 30� longitude cells.The annual cycle was estimated for each cell and thenremoved from the data. Correlation coefficients, ozonevariations and instrument uncertainties were then calculatedfrom the residuals. Correlation coefficients were found to beuniform as a function of longitude and so are presented as a

function of latitude only. The results, for four seasons, areshown in Figure 10.[41] The correlation coefficients are relatively high (0.6–

0.8) over middle and high latitudes in Layers 4–8 except forthe summer. The correlation coefficients in summer aretypically lower, especially in the upper layers. This is dueto the fact that ozone variability is lower in summer, butinstrumental uncertainties remain the same as in the otherseasons. Over the tropical region, the correlation coeffi-cients are low in Layer 2. As noted, SAGE II errors arerelatively high there and the data could be biased by theintegration algorithm applied in this study. The correlationcoefficients are modest, about 0.5, but significant over thetropical region in all other layers except Layer 5. There ispractically no correlation between SBUV(/2) and SAGE IIdata over the equator in Layer 5. If SBUV(/2) and SAGE IIdata over the equator are plotted as a function of time inLayers 4–6, one can see that ozone variations in Layers 4and 6 are in the opposite phase, while Layer 5 is atransitional area. Apparently, the low vertical resolution ofSBUV(/2) data yields a large uncertainty of SBUV(/2) datarelative to the ozone variability there.[42] The instrument uncertainties and SD of ozone vari-

ability can be estimated from pairs of SBUV(/2) and SAGEII observations as described in section 4.1. The results arepresented in Figure 11. Data for all seasons were used, soFigure 11 gives the annual average picture of ozonevariability and instrument uncertainties as a function oflatitude. The uncertainties of both SBUV(/2) and SAGE IIare relatively high in Layer 2: about 8% in the tropics and abit less over midlatitudes. SAGE II appears to performbetterat northern midlatitudes, but as mentioned in section 4.2, thismay be an artifact. SBUV(/2) uncertainties are notably largerthan those for SAGE II in Layer 3, but above it they are nearlyidentical. The estimated SBUV(/2) and SAGE II instrumentuncertainties are 4–6% in Layers 5–8 and do not depend onlatitude. Ozone variability at these levels is declining withaltitude. It is much higher in the extratropics than theinstrument uncertainties in Layer 5, but only slightly higherin Layer 8. Ozone variability is low, and close to theinstrument uncertainties, at all levels at low latitudes.[43] The results discussed above are based on individual

measurements. As was shown in section 4.3, SBUV(/2) datado a better job at estimating monthly averages at midlati-tudes. This is probably true for other regions: A largefraction of ozone variability is related to long-term fluctua-tions, while part of the SBUV(/2) uncertainty is caused byindependent random errors. However, the limited number ofSAGE II measurements makes it impossible to estimatemonthly averages reliably.

5. Summary and Discussion

[44] Version 8 of SBUV(/2) data was primarily designedto estimate long-term ozone change. Individual ozoneprofiles from SBUV(/2) measurements may have systematicerrors of up to 20%, particularly in Layers 2 and 3 if thetotal ozone value is substantially different from the clima-tological one. However, a relatively small fraction ofSBUV(/2) profiles are affected by this type of error. Whensuitably averaged, SBUV(/2) data can be used as a bench-mark for validation of other sources of ozone data. As an

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example, SBUV(/2) data were used here to evaluate differ-ences between different types of ozonesonde.[45] Coincident ozone observations by SBUV(/2) and

SAGE II and by ozonesonde measurements and Umkehrretrievals were used to estimate the standard deviation ofozone variability and standard uncertainties of differentinstruments. At midlatitudes, the SD of ozone variabilityhas a strong annual cycle with its maximum in winter andminimum in summer, while standard uncertainties of theinstruments are much less affected by seasonal variations.The SD of ozone variability of individual ozone profiles ishigher than the standard uncertainties of available instru-ments at midlatitudes, except for the summer, when they arecomparable. For monthly averages, ozone variability isnoticeably higher than instrument uncertainty. This suggeststhat the validation of ozone profile measurements should bebased on a substantial number of coincident measurements.If the number of coincident (or, in practice, quasi-coinci-

dent) measurements is limited and ozone variability is low,comparison with a well-validated ozone climatology (i.e.,based on a large number of observations) may in factprovide better insight into instrument performance.[46] In general, there is good agreement between ozone

profiles measured by SBUV(/2) and SAGE II, with corre-lation coefficients between the two data sources of 0.7–0.9over middle and high latitudes. The standard uncertaintiesof the two instruments at 22–34 km (SBUV layers 3–6) arelower than average the SD of ozone variability there. Above34 km (Layers 7 and 8), while standard uncertainties remainthe same (4–5%) as at lower levels, the SD of ozonevariability declines and becomes comparable to the instru-ment uncertainties. Over low latitudes at 22–40 km (Layers3–8), ozone variability is lower than over middle and highlatitudes and is closer to the instrument standard uncertain-ties. The correlation coefficients between SBUV(/2) andSAGE II data are 0.4–0.6 but still statistically significant

Figure 11. Standard deviation of ozone variations and instrument uncertainties for SBUV(/2) andSAGE II as a function of latitude, and (top left) total number of pairs of measurements that were used forthe calculation.

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there. The only exception is Layer 5 (about 29 km) whereno correlation between the two data sources was found. Thiscould be related to fine vertical structure of ozone variationsthere that is not measured properly by the SBUV(/2)instrument with its low vertical resolution.[47] Correlation coefficients between SBUV(/2) and

sonde data are slightly smaller than between SBUV(/2)and SAGE II, at about 0.6–0.8, except in summer, whencorrelations are poor. Sonde data have the lowest instrumentuncertainties at 22–28 km (Layers 3 and 4), i.e., at theozone maximum. Their uncertainties above 28 km are largerthan or comparable to the ozone variability, and much largerthan the uncertainties of SBUV(/2), Umkehr (5%), or SAGEII data. This analysis also shows that correction by totalozone measurements can noticeably reduce ozonesondeuncertainties. Ozonesondes remain, of course, the mostimportant source of ozone data with high vertical resolutionin the troposphere and lower stratosphere.[48] The performance of Umkehr measurements is similar

to that of SBUV(/2) at 19–34 km (Layers 2–6). TheUmkehr measurement uncertainties at 25–34 km (Layers4–6) are about 5% and are lower than sonde uncertainties atthese levels. Well-calibrated Umkehr measurements maytherefore be a valuable source of information about theozone distribution in the 25–34 km altitude range. However,Umkehr uncertainties may be underestimated by thismethod, because SBUV(/2) uses similar averaging kernels.

[49] Acknowledgments. The authors would like to thank the NASALangley Research Center (NASA-LaRC) and the NASA Langley Radiationand Aerosols Branch for making SAGE II data available. The reprocessedSBUV/2 data were made available with support from the NOAA Climateand Global Change Program, Atmospheric Chemistry Element, and theNOAA National Environmental Satellite Data and Information Services,Product Systems Development and Implementation Program. In additionwe acknowledge the personnel who provide ozonesonde and Umkehrobservation and make them available through the World Ozone and UVradiation Data Centre. We also would like to thank D. I. Wardle and A. J.Miller for their comments and suggestions.

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�����������������������V. E. Fioletov and D. W. Tarasick, Environment Canada, 4905 Dufferin

Street, Toronto, ON, Canada M3H 5T4. (vitali.fioletov@ec.gc.ca; david.tarasick@ec.gc.ca)I. Petropavlovskikh, Cooperative Institute for Research in Environmental

Studies, University of Colorado, SRRB, 325 Broadway, Boulder, CO80305, USA. (irina.petro@noaa.gov)

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