Comparisons of Tropospheric Emission Spectrometer (TES)

ozone profiles to ozonesondes: Methods and initial results

H. M. Worden,1 J. A. Logan,2 J. R. Worden,1 R. Beer,1 K. Bowman,1 S. A. Clough,3

A. Eldering,1 B. M. Fisher,1 M. R. Gunson,1 R. L. Herman,1 S. S. Kulawik,1

M. C. Lampel,4 M. Luo,1 I. A. Megretskaia,2 G. B. Osterman,1 and M. W. Shephard3

Received 6 March 2006; revised 31 May 2006; accepted 30 August 2006; published 15 February 2007.

[1] The Tropospheric Emission Spectrometer (TES) on the Earth Observing System(EOS)-Aura spacecraft measures global profiles of atmospheric ozone with verticalresolution of 6–7 km in the troposphere for the nadir view. For a first validation of TESozone measurements we have compared TES-retrieved ozone profiles to ozonesondesfrom fall, 2004. In some cases the ozonesonde data are from dedicated launches timed tomatch the Aura overpass, while other comparisons are performed with routine dataavailable from the Southern Hemisphere Additional Ozonesonde (SHADOZ) archive andWorld Ozone and Ultraviolet Data Center (WOUDC) data archives. We account forTES measurement sensitivity and vertical resolution by applying the TES-averagingkernel and constraint to the ozonesonde data before differencing the profiles. Overall, forV001 data, TES ozone profiles are systematically higher than sondes in the uppertroposphere but compare well in the lower troposphere, with respect to estimated errors.These comparisons show that TES is able to detect relative variations in the coarse verticalstructure of tropospheric ozone.

Citation: Worden, H. M., et al. (2007), Comparisons of Tropospheric Emission Spectrometer (TES) ozone profiles to ozonesondes:

Methods and initial results, J. Geophys. Res., 112, D03309, doi:10.1029/2006JD007258.

1. Introduction

[2] Distributions of the tropospheric column of ozone, asviewed from space, were first derived from measurements oftotal column ozone from the Total Ozone MeasurementSpectrometer (TOMS) [Fishman and Larsen, 1987; Fishmanet al., 1990]. These observations provided valuable informa-tion about the global distribution of tropospheric ozone andthe influence of biomass burning in the tropics. Estimates oftropospheric ozone from TOMS rely on various residualmethods, in which the column of stratospheric ozone issubtracted from the total column of ozone, [e.g., Fishmanand Larsen, 1987; Hudson and Thompson, 1998; Ziemke etal., 1998, 2005]. The global distribution of troposphericozone has also been retrieved directly from the Global Ozoneand Monitoring Experiment GOME [Liu et al., 2005, 2006].Neither of these observing methods provide vertical profilesof ozone. In order to investigate the mechanisms that controltropospheric ozone, vertical information is required.

[3] The Tropospheric Emission Spectrometer (TES) onEOS-Aura was designed to measure the global, verticaldistribution of tropospheric ozone and ozone precursorssuch as carbon monoxide [Beer et al., 2001; Beer, 2006].In cloud-free conditions, the vertical resolution of TES nadirozone estimates is about 6 km with sensitivity to both thelower and upper troposphere, as well as the stratosphere[Bowman et al., 2002; Worden et al., 2004].[4] Ozonesondes fill a critical need for the validation of

TES ozone profiles by providing in situ data from thesurface to the stratosphere, with fine vertical resolution(�150 m). The primary challenge with using ozonesondesfor TES validation is the relatively small number of accept-able coincident measurements for use in a statistical anal-ysis. We apply an initial set of relatively loose coincidencecriteria, described in section 2.3, followed by more selectivecriteria, based on temperature differences as discussed insection 4.2. These combined criteria result in a set ofcomparisons that are examined for statistical biases betweenTES and ozonesondes.[5] To make comparisons between TES and ozonesondes,

we must account for the vertical resolution and sensitivity ofTES by applying the TES-averaging kernel and constraint tothe sonde data. This results in a vertical profile thatrepresents what TES would estimate for the atmosphericstate as measured by the sonde and will be referred to as thesonde profile with the TES operator. Differences betweenTES ozone profiles and ozonesonde profiles with the TESoperator are compared below to known systematic andrandom errors.

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1Jet Propulsion Laboratory, California Institute of Technology,Pasadena, California, USA.

2Division of Engineering and Applied Sciences, Harvard University,Cambridge, Massachusetts, USA.

3Atmospheric and Environmental Research (AER), Lexington, Massa-chusetts, USA.

4Raytheon Information Solutions, Pasadena, California, USA.

Copyright 2007 by the American Geophysical Union.0148-0227/07/2006JD007258$09.00

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[6] The analysis presented in this paper is limited toobservations between 20 September 2004 and 17 November2004. The TES data used here, the V001 Beta Release, areavailable from the Langley Atmospheric Science DataCenter (ASDC): http://eosweb.larc.nasa.gov/PRODOCS/tes/table_tes.html. Our purpose is to document the accuracyof the first release of TES data. The TES calibration hasbeen improved over that used in the V001 Beta Release[Worden et al., 2006]. Future validation efforts will use TESdata with the updated calibration (V002), and cover a moreextensive period of ozonesonde data. In order for the user tobe able to explicitly account for vertical sensitivity whenusing or interpreting TES data, all the data versions includethe averaging kernel and a priori constraint vector for eachestimated profile.

2. Data

2.1. TES Data

[7] TES is a nadir and limb viewing infrared Fouriertransform spectrometer (FTS) with an apodized resolutionof around 0.10 cm�1 and a spectral range from 650 to3250 cm�1. The footprint of each nadir observation is 5 kmby 8 km. TES is on the EOS-Aura platform [Schoeberl et

al., 2006] (http://aura.gsfc.nasa.gov/) in a near-polar, sun-synchronous, 705 km altitude orbit. The ascending nodeequator crossings are near 1:45 pm local solar time. TheTES instrument and data acquisition modes are described byBeer et al. [2001] and Beer [2006]. The data used for thesecomparisons are nadir profiles only and include the first full16-orbit Global Survey run, taken on 20 September 2004and all subsequent Global Survey and Step/Stare runs up to17 November 2004. Nadir observations for the GlobalSurvey runs used here are about 5� apart along the orbittrack, with successive orbits 22� apart in longitude. Step/Stare runs have denser nadir coverage, about 0.4� apart, andtypically cover a 60� latitude range. Several of the TESStep/Stare runs were taken during the AVE (Aura ValidationExperiment) campaign in Houston October–November2004 where both aircraft (WB-57) and sonde data werecollected. In all, eight Global Surveys and nine Step/Stareswere used for this study. Representative data coverage over16 orbits of Global Survey measurements is shown inFigure 1.[8] Atmospheric ozone concentrations are estimated from

the 9.6 mm ozone absorption band using a spectral rangefrom 995 to 1070 cm�1. The algorithms and spectralwindows used for TES retrievals of atmospheric state

Figure 1. Example of a Tropospheric Emission Spectrometer (TES) Global Survey, showing estimatedozone values at 681.3 hPa. This map illustrates the coverage obtained in 16 orbits (�26 hours), in thiscase, starting on 10 November 2004. Boxes indicate measurement locations, but are larger than the actualTES footprint.

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parameters with corresponding error estimation are de-scribed by Worden et al. [2004] and Bowman et al. [2002,2006]. TES retrievals use an optimal estimation approachfollowing Rodgers [2000]. Kulawik et al. [2006b] showhow individual TES profiles are characterized for errors andvertical information. Temperature and water vapor areretrieved concurrently with ozone. For operational simplic-ity, we set the initial guess to be equal to the a prioriconstraint in the retrieval. TES ozone retrievals use a prioriprofiles and covariance matrices from a climatology devel-oped using the MOZART model [Brasseur et al., 1998;Park et al., 2004]. A priori information for temperature andwater vapor is obtained from Goddard Space Flight Center(GSFC) Global Modeling and Assimilation Office (GMAO)[Bloom et al., 2005].[9] The TES data used in this analysis include both clear

and cloudy nadir target scenes. The TES algorithms formodeling the atmospheric radiance and retrieving atmo-spheric parameters in the presences of clouds are describedby Kulawik et al. [2006a]. TES nadir ozone profilestypically have a vertical resolution of around 6–7 km inthe troposphere. The vertical resolution of tropospherictemperature is about 2–3 km. The primary diagnosticinformation used for screening failed retrievals in thisanalysis is the square of the radiance residual (data: forwardmodel radiance), normalized by the NESR (Noise Equiva-lent Spectral Radiance), and is denoted as c2. Retrievalswith c2 < 1.6 were eliminated from the analysis.

2.2. Sonde Data

[10] Almost all the data used here were obtained usingelectrochemical concentration cell (ECC) ozonesondes,which rely on the oxidation reaction of ozone with potas-sium iodide in solution [Komhyr et al., 1995]; the excep-tions are sondes used at Hohenpeissenberg, Germany(Brewer Mast sondes) and those used in Japan (Kagoshimaand Naha, type KC) (World Meteorological Organization(WMO), Assessment of Trends in the Vertical Distributionof Ozone, SPARC Report 1, WMO-Ozone Research andMonitoring Project Report 43, 1998, available at http://

www.atmosp.physics.utoronto.ca/SPARC/; hereinafter re-ferred to as WMO, 1998). The ozonesondes are flown withradiosondes, so that temperature data are also available. Thesondes provide profiles to a maximum altitude of about35 km (not all balloons reach this altitude) with verticalresolution of �150 m for ozone. The sonde data areprovided in units of ozone partial pressure on a verticalscale of atmospheric pressure. The accuracy of the ozonemeasurement is about ±5% in the troposphere (WMO,1998). Ozonesonde data used in this analysis were obtainedfrom the World Ozone and Ultraviolet Data Center(WOUDC) (http://www.woudc.org), and the SouthernHemisphere Additional Ozonesonde (SHADOZ) archive(http://croc.gsfc.nasa.gov/shadoz). Most sonde stationsmake measurements weekly, but several in Europe makemeasurements 2–3 times a week. A few of the sondes usedfor validation were launched at the time of TES overpasses,including those from the AVE campaign [Morris et al.,2006], and sondes from selected SHADOZ sites. Table 1lists the sonde sites for the data used in these comparisons.Although more sondes were launched to coincide with Auraoverpasses, and many other sites were checked for coinci-dences, only those passing both the initial criteria and themore restrictive temperature comparison criterion (dis-cussed in section 4.2) are listed in Table 1.[11] Most of the sonde data at the WOUDC have been

normalized to the overhead column of ozone measured by aDobson or Brewer instrument. The normalizing, or correc-tion, factor (CF) was used here to screen the data. Weincluded profiles for which the CF was in the range 0.85 to1.15. The data provided at the SHADOZ archive have notbeen normalized [Thompson et al., 2003]. We screened theSHADOZ data by integrating each sonde profile andcomparing it to the ozone column from TOMS (Total OzoneMapping Spectrometer) overpass data (http://toms.gsfc.nasa.gov); the amount of ozone above balloon burst istaken from a climatology which is an update of McPeterset al. [1997]. We eliminated from further analysis profilesfor which the ratio of the integrated sonde/TOMS columndiffered from the mean value for that location by more than

Table 1. Sonde Sites With Fall 2004 Tropospheric Emission Spectrometer (TES) Coincident Measurements Used for Analysis

Sonde StationLatitude,

�NLongitude,

�ENumber ofSondes Used

Number Timedfor Aura Overpass

DistanceRange, km

Time Difference,hours

Churchill 59 �94 1 0 159 43Legionowo 52 21 1 0 238 12Lindenberg 52 14 2 0 226, 375 13, 24Hohenpeissenberg 48 11 3 0 232 � 483 18–20Payerne 47 7 4 0 193 � 574 2–33Trinidad Head 41 �124 2 0 208, 365 21, 34Boulder 40 �105 2 0 93, 518 4, 44Wallops 38 �76 2 0 247, 316 7, 18Tateno 36 140 1 0 350 14Huntsville (AVE) 35 �87 1 1 216 0.3Kagoshima 32 131 2 0 276, 291 15, 27Houston (AVE) 30 �95 4 4 145 � 720 0.1–1.2Naha 26 128 2 0 154, 344 1,12Hilo 20 �155 2 0 179, 480 36, 12Sepang 3 102 1 0 285 3Nairobi �1 36 1 0 167 9San Cristobal �1 �90 2 0 295, 304 17, 31Natal �6 �35 4 3 158 � 520 1–4Ascension �8 �15 5 3 101 � 438 0.2–12Samoa �14 �170 1 0 265 19

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15%. Although all the SHADOZ stations use ECC sondes,there are differences due to procedures, the solution strengthof KI, and the instrument type (there are two manufacturers),as described by Thompson et al. [2003, 2007]. Consequently,when the integrated profiles are compared to TOMS columndata there are station-to-station differences of up to 10%.These differences are small compared with the TES-sondedifferences found below, but must be considered in futurework as the TES calibration and retrieval is refined.

2.3. Coincidence Criteria

[12] Sonde–TES measurement pairs are selected usingcriteria required to obtain a sufficient number of matchesfor reasonable statistics. These criteria should be consideredcarefully with respect to expected scale dependencies foratmospheric variability. For example, Sparling and Bacmeister[2001] suggest a distance criterion of 100 km. However,applying this criterion we would obtain only three sonde-TES matches in this data set. Following the experience ofprevious satellite/sonde intercomparisons such as those withthe Michelson Interferometer for Passive AtmosphericSounding (MIPAS) on ENVISAT [Steck et al., 2003;Migliorini et al., 2004], we selected standard criteria of600 km (TES measurement to sonde launch site) and48 hours. To take advantage of all the sonde launchesduring the AVE campaign timed for the Aura overpass, weused a looser distance criterion of 800 km. Although thesecoincidence criteria might be more appropriate for strato-spheric variability than for the troposphere, we found thattighter criteria yielded an insufficient number of matches fora meaningful analysis of the comparisons. Using thesecriteria for the September to November 2004 TES data gives

55 sonde-TES measurement pairs, shown in Figure 2. Therefinement of these initial criteria is discussed in section 4.2.

3. Comparing In Situ and Remotely Sensed Data

3.1. Procedure for Sonde and TES Data Comparisons

[13] In order to compare TES profiles with in situmeasurements, we must first account for the verticalsmoothing and variable sensitivity inherent to trace gasand temperature profiles obtained by remote sensing, asshown in equation (1). Along with the sensitivity to theestimated parameters, the relative effect of the retrievalconstraint vector, or a priori, varies with pressure. For eachTES-sonde measurement pair, we apply the TES averagingkernel and constraint vector, or TES operator, to the sondedata, producing a profile that represents what TES wouldmeasure for the same air sampled by the sonde, in theabsence of other error.[14] The method of measurement intercomparison for

remote sensing, accounting for averaging kernels and thea priori information used in the estimates, is described byRodgers [2000] and Rodgers and Connor [2003], and was acritical step in the validation of MOPITT (Measurement ofPollution in the Troposphere) CO profiles [Emmons et al.,2004]. We can neglect the averaging kernels associated withthe sonde profiles since they are close enough to identitymatrices due to vertical resolution (�150 m) that is muchfiner than that of TES. As described in the equations below,the adjusted sonde profile, hereafter referred to as the sondeprofile with TES operator (sonde w/TESop), can then bedifferenced with the corresponding TES profile and com-pared to estimated errors.

Figure 2. Map of TES � ozonesonde coincidences for 20 September 2004 to 17 November 2004. Redcircles indicate sonde sites and + symbols indicate TES measurements. These matches satisfy the initialcoincidence criteria, but not all pass the more selective criteria to be considered in statistical analysis.

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[15] Given a state vector x, in this case either temperaturein K or ozone in logarithm of volume mixing ratio (VMR)as a function of pressure, the TES estimate can be written asthe linear expression:

bx ¼ xa priori þ Axx x� xa priori

� �þGnþ dcs ð1Þ

where xa priori is the a priori constraint vector, n is thespectral noise, G is the gain matrix, which describes thesensitivity of the estimate to the measured radiance with

G ¼ @x

@F¼ KTS�1

n K þ L� ��1

KTS�1n ð2Þ

F is the forward model radiance, K is the Jacobian matrix,Sn is the measurement covariance, and L is the constraintmatrix. These give the averaging kernel Axx = GK, which isthe sensitivity of the retrieval to the true state. We alsodefine the ‘‘cross-state’’ errors incurred from retrievingmultiple parameters (e.g., temperature, water vapor andsurface emissivity) as:

dcs ¼ Acs xcs � xcsa priori

� �ð3Þ

where Acs is the submatrix of the averaging kernel for thefull state vector of all jointly retrieved parameters thatrelates the sensitivity of x to xcs, the vector of cross-stateparameters and corresponding cross-state a priori constraintvector. (see Worden et al. [2004] and Bowman et al. [2006]for more details on notation and definitions.)[16] The in situ measurement for the same atmospheric

state x is:

xsonde ¼ xþ dsonde ð4Þ

with error vector dsonde for the sonde profile. (We assumethe sonde errors have an uncorrelated, i.e., diagonal onlycovariance). For sonde profiles that did not reach 10 hPa,the unmeasured part of the stratosphere is approximated byappending the TES a priori. For ozone, the TES a priori isscaled to the last available sonde point and for temperatureit is shifted. The sonde data are interpolated andextrapolated to a fine level pressure grid (180 levels perdecade pressure; 800 levels from 1260 hPa to 0.046 hPa).This ensures a robust mapping procedure since the pressuregrids of the original sonde data are irregular and variable.The sonde profiles were then mapped to the 87 levelpressure level grid, covering 1212 hPa to 0.1 hPa, used forTES profiles and averaging kernels with a mapping matrix(M*) that is the pseudo-inverse of the matrix (M) thatinterpolates from the 87 TES pressure levels to the fine levelpressure grid with M* = (MTM)�1 MT. The equivalent TESestimate for the sonde measurement, sonde w/TESop, isthen:

bxsonde ¼ xa priori þ Axx M*xsonde � xa priori

� �ð5Þ

We can now difference the TES (equation (1)) and sonde w/TESop (equation (5)) estimates:

DTES�sonde ¼ bx� bxsonde ¼ Axx x�M*xsonde½ � þGnþ dcs ð6Þ

For the following analysis, we only consider the differencesin the troposphere. It is important to note that the differenceabove does not include the constraint vector xa priori, whichsimplifies the errors in the comparison. Because thedifference is not biased by the a priori, we can use thecomparisons to identify other biases in TES ozone profiles,such as those due to radiometric calibration error. Theexpected error for this difference is:

E bx� bxsondeð Þ bx� bxsondeð ÞTh i

¼

E Axx x�M*xsonde½ � þGnþ dcsð Þ Axx x�M*xsonde½ � þGnþ dcsð ÞTh i

¼

E AxxM*dsondeð Þ AxxM*dsondeð ÞTh i

þ E Gnð Þ Gnð ÞTh i

þ E dcsdTcs� �

¼

AxxM*SsondeMTAT

xx|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}Sonde error

þGSnGT|fflfflffl{zfflfflffl}

TESmeas:error

þAcsScsATcs|fflfflfflfflfflffl{zfflfflfflfflfflffl}

TES cross�state error

ð7Þ

where Ssonde is the sonde error covariance, Sn is the spectralradiance measurement error covariance and Scs is a blockdiagonal matrix containing the (uncorrelated) a prioricovariances for the other jointly retrieved parameters, e.g.,TATM (atmospheric temperature) and H2O (water vapor):

Scs ¼STATM 0

0 SH2O

�: ð8Þ

[17] The main contributions to the difference error are theTES measurement and cross-state terms which vary withaltitude, and can be as high as 15–20% (each) for ozone.The sum of measurement and cross-state errors is labeledthe observational error, which is provided in TES V002 dataproducts. For this analysis, we neglect the errors associatedwith the sonde measurements (±5%) since they are signif-icantly smaller than the TES error terms. We also neglectthe approximation of the stratosphere that is applied in somesonde cases since the effects to the troposphere are minor.The error for the TES-sonde difference assumes that bothmeasure the same atmospheric state. This, of course, is notthe case, and differences in the sampled atmosphere areexamined further in the following sections.

3.2. TES Nadir Ozone-Averaging Kernel Examples

[18] Averaging kernels are essential for understanding TESestimated profiles because they show where the retrieval ismost sensitive vertically and how information is smoothed,thus giving a measure of the vertical resolution. They arecomputed and reported for each TES profile and provide themeans for intercomparisons as well as for data assimilation[Jones et al., 2003]. Figure 3 shows examples of TES nadirozone-averaging kernels for clear and cloudy conditions.These plots illustrate how vertical smoothing in TES retriev-als combines the information from different altitudes. Inparticular, the ozone abundance in the stratosphere has asignificant influence on the TES retrieval of ozone in theupper troposphere (magenta and dark blue lines). By contrast,the TES retrievals in the lower troposphere are relatively freeof stratospheric influence (green lines).

4. Results

4.1. Comparison Examples

[19] To demonstrate the TES-sonde comparison method,we examine two particular cases to see the effects of the

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TES operator (using equation (5)) on the input sonde data.Figure 4 shows the comparisons of TES data acquired on11 October 2004 to a sonde launched from Ascension Islandwithin an hour of the Aura overpass and TES data acquired

on 10 November 2004 to a sonde launched a day earlierfrom Kagoshima. The corresponding TES ozone-averagingkernel is shown in Figure 3, right. Figure 4 shows how thefine vertical structure of the original sonde data is smoothed

Figure 3. TES nadir ozone-averaging kernels under clear and cloudy conditions. The colors indicateaveraging kernel rows corresponding to the pressure levels as noted in the legend.

Figure 4. TES and ozonesonde comparisons for (a) TES measurement on 11 October 2004 separated by229 km from Ascension Island sonde launched for the Aura overpass (less than 1 hour time difference)and (b) TES measurement on 10 November 2004 with sonde from Kagoshima launched a day earlier(separated by 291 km). The corresponding TES-averaging kernel is shown in Figure 3, right, with cloudobserved at 483 hPa. Extension of the original sonde profile above 23.7 hPa, by scaling the ozone initialguess to match the last available sonde value, was performed in this case.

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by the averaging kernel. In the altitude range below theobserved cloud in the Kagoshima case, where TES has nosensitivity, both TES and sonde with TES operator profileshave the values of the a priori. Since the Kagoshima sondedata had a minimum pressure of 23.7 hPa, the top of theprofile was extended by scaling the a priori to match the lastavailable sonde value. Applying the same method, TES andsonde temperature profiles are also compared.

4.2. Trajectory Analysis and TemperatureDifference Criterion

[20] In order to understand which comparisons are ap-propriate within the coincidence criteria used in our initialselection of TES measurement with sonde matches, we haveperformed backward trajectories for the locations and timesof several TES and sonde measurement pairs. The trajecto-

ries are computed with the HYSPLIT transport and disper-sion model (R. R. Draxler and G. D. Rolph, Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT),2006, available at NOAA Air Resources Laboratory Real-time Environmental Applications and Display System(READY) Web site (G. D. Rolph, 2003), http://www.arl.noaa.gov/ready/hysplit4.html). We found that there was adistinct relationship between cases with poor temperaturecomparisons (several pressure levels with >5 K differencesbetween TES and sonde in the troposphere) and trajectoriesthat represented obviously different source regions, asshown in Figure 5. In contrast, Figure 6 shows a case wherethe temperature difference falls within the criteria for anacceptable match (<5 K differences in the troposphere) andthe corresponding similar back trajectories. We thereforeuse sonde-TES temperature differences as an additional

Figure 5. Backward trajectories at 2, 10 and 14 km above ground level (AGL) to sonde site (left) andTES measurement location (middle) with corresponding sonde (w/TESop) � TES temperature profilecomparison (right) for a TES measurement 193 km from Boulder on 17 November 2004. Sonde datawere acquired 15 hours later.

Figure 6. Backward trajectories at 1, 5, and 10 km above ground level (AGL) to sonde site (left) andTES measurement location (middle) with corresponding sonde (w/TESop) � TES temperature profilecomparison (right) for a TES measurement 269 km from Natal on 20 September 2004. Sonde data wereacquired 1.3 hours earlier.

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filter to select comparison cases for the statistical analysisthat follows.

4.3. Ensemble Results

[21] When we apply the temperature difference criteriadescribed above, and exclude latitudes >60� where TESmeasurements are less reliable because of poor surfacecharacterization, we are left with 43 coincident pairs, listedin Table 1. These represent a range of latitudes and cloudcover conditions. There were three cases, all in the northernmidlatitudes, with high thick clouds obscuring the lowertroposphere (effective cloud optical depth >3 at pressuresless than 750 hPa). These cases were excluded from thestatistics for the lower troposphere. Three other cases hadlow thick clouds (cloud top pressure >750 hPa). All othercases had effective cloud optical depths ranging from 0.01 to1.3, with corresponding effects to TES sensitivity in thelower troposphere. Under these varying cloud conditions,the application of the TES operator to the sonde data ensuresthat the comparisons are unbiased by the TES a priori.[22] Figure 7 shows an ensemble plot of all 43 sonde

(w/TESop)-TES ozone profile differences in ppb and thecorresponding temperature profile differences in Kelvin(K). The figure reveals a clear bias, with TES measuringhigher ozone in the upper troposphere, peaking around200 hPa. There are also systematic temperature biases �the TES temperatures too high in the upper troposphereand too low in the lower troposphere. The temperaturebias is corroborated by other comparisons to radiosondesand AIRS retrievals (G. B. Osterman (Ed.), TES Valida-tion Report, version 1.00 (available at http://eosweb.larc.nasa.gov/PRODOCS/tes/table_tes.html), and is discussedbelow in terms of the effects of a calibration error insection 5.4.3.1. Tropics and Midlatitude Characteristics[23] Figure 8 compares vertical averages of TES and

sonde with the TES operator data so that we can summarizeTES ozone estimates for different latitudes and ozoneabundance conditions. The plots show unweighted averagesover TES vertical levels in the lower troposphere (surface to

500 hPa) and upper troposphere (500 hPa to tropopause or200 hPa, whichever is at higher pressure). The tropopausepressures are computed by fitting the TES temperatureprofile minima, and they range from 300 hPa (higherlatitudes) to 100 hPa for the tropics. In order to minimizethe influence of the stratosphere in the upper troposphere,due to the averaging kernel, we set the minimum pressurefor the averages at 200 hPa. Results are also shownseparately for the tropics (latitude <25�) and midlatitudes(25� > latitude > 60�). The figure includes the mean biasand root mean square (rms) of the differences. There isclearly a linear relationship between TES and sondemeasurements in the tropics, for both the upper and lowertroposphere, and in the upper troposphere at midlatitudes.This is the case even though the upper tropospheric bias ineach region is larger than the TES measurement and cross-state errors. This linearity gives confidence to users of TESdata that relative variations as observed on a global map aresignificant, even though biased.4.3.2. Correlations of Error-Weighted Differences[24] In order to test our assumptions for coincidence

criteria, we calculate the error-weighted differencesbetween the sonde with TES operator and TES profiles,averaged for the upper and lower troposphere, denoted asjhDTES-sondeijs. The difference DTES-sonde is defined inequation (6), with averaging over pressures indicated byh i. The vertical bars indicate magnitude values andsubscript s indicates that the difference is divided by thediagonal error from the terms in equation (7) for TESmeasurement and cross-state errors. The error weightingallows us to test for correlations in the ozone differenceswhile accounting for the possible dependence on TESmeasurement errors. We have found only weak correlations,listed in Table 1, for ozone differences with distance andtime coincidence criteria, although this could be due to thesmall number of comparisons. These low correlations sug-gest that our bias is dominant over differences that might beexpected from horizontal-scale variations. We have alsoconsidered correlations with the TES retrieved cloud toppressure. Although these correlations are negative as

Figure 7. Sonde (w/TESop)–TES profile differences for 44 comparisons (cases passing the moreselective criteria) with (a) ozone and (b) temperature.

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expected; that is, higher clouds (lower pressure) wouldinterfere more with the comparison, they are also relativelyweak. For comparison, the correlations for the pressureaveraged TES and sonde with TES operator abundances,shown in Figure 8, are given in the last row of Table 2.

4.4. Sensitivity to Profile Shape

[25] Finally, we use ozonesonde-TES comparisons todetermine if TES measurements can differentiate large-scaleprofile features, such as enhancements due to biomassburning downwind of source regions such as those observedroutinely at Ascension Island. [Thompson et al., 1996].Figure 9 shows the comparison of three ozonesonde profiles(two from Natal and one from Ascension), the correspondingcomparison of the closest TES measurements and the a prioriused for the TES retrievals, where differences in the latter aredue only to changes in the monthly climatology data. It isclear that even with a bias in the upper troposphere, TES issensitive to the enhanced tropospheric ozone observed atAscension. Figure 10 shows a similar comparison for sondeand TES profiles at subtropical and midlatitudes and dem-onstrates TES sensitivity to the different ozone gradientscorresponding to latitudinal variations in tropopause height.

5. Expected Calibration Improvements

[26] The TES radiance spectra used for these comparisons(V001) have known calibration errors that will be corrected

in the next data version, [Worden et al., 2006]. For one TESGlobal Survey, run 2147, taken 20 September 2005, wehave processed the data with an improved L1B calibrationalgorithm and we examine the expected changes for ozo-nesonde comparisons with V002 data. The primary valida-tion for TES radiances is a comparison with measurementsfrom AIRS on EOS-Aqua [Pagano et al., 2003], takenabout 12 minutes earlier along the same orbit. TES spectraldata are convolved with the AIRS spectral response func-tion (SRF) before the comparison is made. For the data setused in this study, there are differences >1 K in brightnesstemperature for the TES-AIRS comparisons. Improvedcalibration algorithms reduce this to better than 0.5 K. Anexample of the resulting changes to the ozone retrievals isshown in Figure 11. Only three cases were available forcomparing with ozonesonde data, but all showed significant

Figure 8. Comparisons of TES (with error bars) and sonde with TES operator average abundances. Thefour panels show upper troposphere (UT) averages (500 hPa to tropopause or 200 hPa, whichever is athigher pressure) separated for midlatitudes (25� < latitude < 60�) and tropics (latitude < 25�) and lower-troposphere (LT) averages (surface to 500 hPa) separated for midlatitudes and tropics. Dashed lines showthe 1:1 reference. TES-sonde (w/TESop) bias and RMS values, in ppbV are shown for the N number ofcases in each panel. Three cases were excluded for midlatitude LT due to optically thick clouds above750 hPa.

Table 2. Correlations of TES: Sonde with TES Operator

Differences With Distance, Time Difference, and Retrieved Cloud

Top Pressure

Correlation Pair ha,bi

UpperTropospherecorrelation

LowerTroposphereCorrelation

hjhDTES-sondeijs, jdistanceji �0.15 0.03hjhDTES-sondeijs, jtime_differenceji 0.08 �0.25hjhDTES-sondeijs, cloud top pressurei �0.09 �0.02hhTESi,hsonde w/TESopii 0.80 0.76

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decreases in both temperature and ozone biases, especiallyin the upper troposphere. This was expected since thecalibration corrections were largest for lower radiancevalues, such as those emitted near the tropopause.

6. Conclusions and Outlook

[27] We have established that TES V001 ozone retrievalsin the upper troposphere are biased high compared to sondemeasurements. Despite this bias, TES is able to distinguishbetween high and low ozone abundances in both the lowerand upper troposphere and can detect large-scale features inozone profiles.[28] We plan further validation of the TES ozone prod-

uct using TES retrievals with the improved calibration

discussed in section 5. Future TES-sonde comparisons willhave better statistics because of increased nadir samplingfor the TES Global Survey mode since 21 May 2005.Nadir scans are now spaced �1.6� along the orbit track,instead of every 5�, which is the spatial sampling of theearlier TES nadir data, such as shown in Figure 1. Thisincrease was at the expense of routine limb observations,but has significantly improved the number of usefultropospheric ozone measurements. In our future work,we expect to be able to use stricter spatial and temporalcriteria for matches, in part because of the higher frequencyof TES nadir sampling, which should lead to morematches with routine ozonesonde launches and also be-cause sondes are being launched coincident with Auraoverpasses as part of planned validation campaigns. We

Figure 9. Tropical ozone profiles from ozonesondes with TES operator (left), TES retrievals (middle)and for reference, TES a priori (right) from the MOZART climatology (same as initial guess, I.G.).

Figure 10. Subtropical and midlatitude ozone profiles from ozonesondes with TES operator (left), TESretrievals (middle) and for reference, TES a priori (right) from the MOZART climatology (same as initialguess, I.G.).

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will also examine the role of natural variability in influ-encing sonde-TES differences.

[29] Acknowledgments. This work was performed, in part, at the JetPropulsion Laboratory, California Institute of Technology, under a contractwith the National Aeronautics and Space Administration. J.A.L. and I.A.M.were funded by a grant from NASA to Harvard University. The authorsgratefully acknowledge Frank Schmidlin, Michael Newchurch, and GaryMorris for dedicated sonde launches corresponding to Aura overpasses.Gary Morris of Valparaiso University had sponsorship for A.V.E. (AuraValidation Experiment) from the Shell Center for Sustainability andNASA’s IONS program. We also thank the World Ozone Data Centreand the SHADOZ program for making the routine sonde data accessibleand the NOAA Air Resources Laboratory (ARL) for the provision of theHYSPLIT transport and dispersion model used in this analysis (http://www.arl.noaa.gov/ready/hysplit4.html).

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�����������������������R. Beer, K. Bowman, A. Eldering, B. M. Fisher, M. R. Gunson, R. L.

Herman, S. S. Kulawik, M. Luo, G. B. Osterman, H. M. Worden, and J. R.Worden, Jet Propulsion Laboratory, California Institute of Technology,4800 Oak Grove Drive, Pasadena, CA 91109, USA. (helen.worden@jpl.nasa.gov)S. A. Clough and M. W. Shephard, Atmospheric and Environmental

Research, (AER), 131 Hartwell Avenue, Lexington, MA 02420, USA.M. C. Lampel, Raytheon Information Solutions, 299 N. Euclid Avenue,

Suite 500, Pasadena CA, 91101, USA.J. A. Logan and I. A. Megretskaia, Division of Engineering and Applied

Sciences, Harvard University, Pierce Hall, 29 Oxford Street, HarvardUniversity, Cambridge, MA 02138, USA.

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