Errors and accuracy of TOMS ozone data

Even though it is relatively easy to detect ozone in the atmosphere, it has proved difficult to make sufficiently precise and numerous measurements to determine changes of a few percent in a decade. Difficulties include knowing how the absolute calibrations of the instruments change with time; assessing how much variability in any set of measurements is caused by the instrument, and how much by the natural variability in the atmosphere; and interpreting comparisons of measurements made by different instruments, especially when different techniques are used (Harris et al. 1997).

Changes in operational satellites, recalibration of ground-based instruments, or interruptions in observation records result in data sets which may have systematic errors that change with time. These errors are usually significantly less than the ozone decline seen in recent years over middle and polar latitudes, and therefore have had limited adverse effects on the trend estimates for these regions. However, variations in global ozone are about 2-3% (WMO 1999), so a systematic error of 1%, which is typical even for the most reliable instruments, makes detection of real ozone changes on a global scale difficult. This problem has been addressed in recent years by comparing data from several sources and, if necessary, applying systematic corrections (Fioletov et al. 2002).

It is fortuitous that there is an ozone data source from the one instrument available for the duration of this project, which minimises the above-mentioned difficulty of multiple sources, although the problem of gaps in the data coverage persists. Because TOMS measures ozone using scattered sunlight, it is not possible to measure ozone when there is no sun (i.e. in polar regions in winter). Consequently, maps of ozone for the Antarctic will always have areas of missing data due to polar night. Data are also lost because of missing orbits and other technical problems (McPeters and Beach 1996). As an example, after failure of the ADEOS satellite on 29th June, 1997, it was decided to raise the EP/TOMS orbit to 750 km to provide more complete global coverage. This was accomplished over the period 4th December to 12th December 1997, during which time no EP/TOMS data are available. This resulted in an EP/TOMS data set of 1 ½ years of high resolution data taken at the expense of full global coverage (McPeters and Beach 1996).

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At the transition between good and flagged or missing data, an “edge effect” will appear, consisting of a few pixels of apparently low ozone values. These are artefacts generated when the original data were interpolated to produce the stored image data (McPeters and Beach 1996). Another fundamental problem with the use of data from polar orbiting satellites is that the observations are not all made simultaneously. Global maps can be constructed by overlaying subsequent tracks observed at different times. However, spurious gradients due to changes in the ozone distribution in time can occur between adjacent tracks measured at different times (Levelt et al. 1996).

Bhartia and Wellemeyer (2009) give details of the way that the TOMS V8 total ozone algorithm was designed to remove any latitude- and seasonally-dependent biases from the data, and how the residuals are used to detect and correct errors. However, like any remote sensing technique, the aforementioned algorithm is susceptible to the following distinctly different types of errors (Bhartia and Wellemeyer 2009): forward model errors, inverse model errors and instrumental errors.

In their ‘error summary’, Bhartia and Wellemeyer (2009) state that all the important error sources mentioned are systematic, i.e. the errors are repeatable given the same geophysical conditions and viewing geometry. However, most errors vary in a pseudo-random manner with space and time, so they tend to average out when data are averaged or smoothed.

Table 5.4: Estimated errors in retrieved TOMS ozone from McPeters et al. (1998).

Source Error (%)

Random – not applicable to long term change (typical values – may be larger in winter months or under disturbed atmospheric conditions)

Instrument noise 0.3

Instrument characterisation 0.3

Atmospheric temperature 1.0

Retrieval error 1.0*

Tropospheric ozone 1.5

Net (Root sum of squares) 2.0

Time Invariant

Rayleigh scattering <0.5

Ozone absorption cross-section <2.0**

Wavelength calibration 1.0

Radiometric calibration <1.0

Retrieval error <1.0

Net (Root sum of squares) 3.0

Time Dependent (over first year)

Radiometric calibration <0.5

Wavelength calibration <0.25

Atmospheric temperature 0.16/K

Tropospheric ozone 0.05/percent

change * May be 5 percent or higher at very high solar zenith angles

** Value for comparisons with non-UV instruments or UV measurements evaluated using different ozone absorption cross-sections

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There are three areas in which uncertainties can be introduced into the ozone data derived from TOMS: the accuracy and precision of the measurements; the value of the radiances calculated from the radiative transfer model; and the process of comparing the measured and calculated radiances to derive ozone values (McPeters et al. 1998). In each of these areas, errors of three kinds are possible: random errors, time-invariant systematic errors and time-dependent systematic errors. Table 5.4 summarises the estimated uncertainties in the retrieved EP ozone. They are organised by type of error rather than by where they originate in the ozone retrieval process. This organisation makes it clearer how the errors are to be combined to derive a total error for the retrieval.

Bhartia et al. (1993) examined the effect of Mount Pinatubo aerosols on total ozone measurements from backscatter ultraviolet (BUV) experiments. Radiative transfer calculations show that, except at very high SZAs, errors in total ozone derived from the aerosol-contaminated radiances are less than 2%, and vary both in magnitude and in sign with angles of observations. At SZAs greater than 75°, total ozone values may be underestimated by as much as 10% if a large concentration of aerosols is present near the ozone density peak. In subpolar latitudes, error in total ozone derived from TOMS as a function of scan angle is very sensitive to the aerosol size distribution parameters.

Email correspondence from A.R. Klekociuk (pers. comm., 2008) pointed out that there could be an instrumental effect at about the 10 DU level in the TOMS V8 data which gives a lower ozone column over ice. Cuevas et al. (2001) reported on the GHOST (Global Hidden Ozone Structures from TOMS) effect in a case study in the Iberian Peninsula. Over this land mass in summer, the magnitude of total ozone is 2.5% (8 DU) lower than over the surrounding seas. This phenomenon was tested by calculating what effect it would have on the output for this project as follows:

Software was written to extract a ‘slice’ of erythemal UV values from the Look-Up Table (LUT) for an ozone value, say, X DU (where X = 50, 100, 150, 200, 250, 300, 350, 400, 450, 500), then extract a slice for an ozone value (X + 10) DU. The difference and the percentage difference between each erythemal UV value in the slices were determined (Table 5.5) and plotted (Figure 5.3).

There is a stronger effect on erythemal UV values with lower ozone values. It can be assumed that TOC would not be less than 100 DU over sea ice. The percentage differences for erythemal UV values for SZA = 35° and TOC = 100 DU were extracted, and they showed that the maximum over-estimation in the erythemal UV values to be expected is 10%. Choosing a SZA of 65° and TOC of 300 DU, the maximum over-estimation in erythemal UV values is expected to be 3.5%. On this basis, the potential for the effect of GHOST-type errors in TOC values was deemed to be not highly significant.

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Table 5.5: Difference and percentage difference of erythemal UV values (mW m-2) for ozone values, X and (X + 10) DU.

Ozone

(DU) difference Max. difference Max %

50 401.567 20.0875 100 140.645 11.2122 150 66.7056 7.71405 200 37.2219 5.93678 250 23.0592 4.78884 300 15.3838 4.00956 350 10.8311 3.41937 400 7.95886 3.05657 450 6.05658 2.59792 500 4.7258 2.29715

Figure 5.3: Plot of percent difference in erythemal UV values for ozone values, X and (X + 10) DU.

To determine the extent to which the real TOC differs from the satellite data over a region Lambert et al. (2000) undertook the following research. They compared several years of total ozone measured from space by the ERS-2 GOME, the EP-TOMS and the ADEOS TOMS with high-quality ground-based observations associated with the Network for the Detection of Stratospheric Change (NDSC), over an extended latitude range and a variety of geophysical conditions. The space- and ground-based data are found to agree within a few percent on average. However, their analysis highlights for both GOME and TOMS several sources of discrepancies. In particular for this study they were: a SZA dependence with TOMS beyond 80° SZA, and a north/south difference of TOMS with ground-based observations.

It should be noted that the quality of the EP-TOMS data for 2000 is affected by changes in the optical properties of the scanning mirror, and therefore these data should be interpreted with caution (Fioletov et al. 2002). Despite the above discussion of errors and inaccuracies, the fact

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remains that the EP-TOMS is the best estimate of ozone that is available for inclusion in this project, which has the temporal coverage, 1996 to 2005.