Overview

This document describes the OMI SO₂ product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45 pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimal volcanic SO₂ mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO₂ data (1978-2005) [Krueger et al 1995]. OMI also enables the detection of anthropogenic SO₂ pollution in the lowest part of the atmosphere. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O₃ from SO₂.

The product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath. Each swath normally contains approximately 1600 viewing lines along the ground track of the satellite, with each viewing line containing 60 pixels or scenes across the satellite track. Scenes from all viewing lines with the same cross-track scene number are referred to as a row of the OMI swath. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the zoom mode for one day every 452 orbits.

Since 25 June 2007 signal suppression (anomaly) has been observed in Level 1B Earth radiance data for scenes in rows 53-54 (0-based). This anomaly is also known as the OMI row anomaly since it affects some particular rows of the CCD detector. It has since expanded to affect more rows. In SO₂ data, the row anomaly manifests itself as positive or negative stripes (discontinuity in SO₂ with cross-track viewing angle). Efforts have been made to flag the affected scenes. SO₂ data fields for scenes determined to have been influenced by the row anomaly have been assigned a large negative fill-value. More information about the OMI row anomaly can be found from KNMI.

For each OMI scene we provide 4 different estimates of the vertical column density of SO₂ in Dobson Units (1 DU = 2.69 ∙10¹⁶ molecules/cm²) obtained by making different assumptions about the vertical distribution of the SO₂. However, it is important to note that in most cases the precise vertical distribution of SO₂ is unimportant. The users can use either the SO₂ plume height, or the center of mass altitude (CMA) derived from SO₂ vertical distribution, to interpolate between the 4 values:

Planetary Boundary Layer (PBL) SO₂ column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km, recommend for use in studies on near-surface pollution. Please check the following section for important updates to the PBL SO₂ data in OMSO2 product version 1.2 and later.

Lower tropospheric SO₂ column (ColumnAmountSO2_TRL) corresponding to CMA of 3 km, recommended for use in studies on degassing from volcanic sources. Please check the section following the next section for important updates to the TRL, TRM, and STL SO₂ data in OMSO2 product version 1.3.

Middle tropospheric SO₂ column (ColumnAmountSO2_TRM) corresponding to CMA of 8 km, recommended for use in studies on moderate eruptions and long-range transport of sulfur pollution,

Lower stratospheric SO₂ column (ColumnAmountSO2_STL) corresponding to CMA of 18 km, recommended for use in studies on explosive volcanic eruptions.

The accuracy and precision of the derived SO₂ columns vary significantly with the SO₂ CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO₂ above clouds and snow/ice, and less sensitive to SO₂ below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment).

Important Updates to OMI PBL SO₂ Data (Version 1.2 and later)

The SO₂ data in ColumnAmountSO2_PBL are now produced with a retrieval algorithm based on principal component analysis (PCA) of the OMI radiance data [Li et al 2013]. Previously the OMI PBL SO₂ data were produced using the Band Residual Difference (BRD) algorithm [Krotkov et al 2006]. While the BRD algorithm is sensitive to SO₂ pollution in the PBL, it tends to have large noise and unphysical biases particularly at high latitudes. The PCA algorithm greatly improves the quality of OMI SO₂ retrievals and has been implemented for operational production of the next generation OMI standard SO₂ product. In OMSO2 product version 1.2 or later, the entire PBL SO₂ data record has been reprocessed with the PCA algorithm. PBL SO₂ Users who had previously acquired the old version OMSO2 data before the data release in October 2014 are strongly encouraged to download and use the new OMSO2 product (version 1.2 or later). There is no difference in the PBL SO₂ between version 1.2 and version 1.3 of the OMSO2 product. All SO₂ data fields ending with BRD are obsolete and only used for internal diagnostic purposes.

Important Updates to Version 1.3 OMI Volcanic (TRL, TRM, STL) SO₂ Data

The SO₂ data in ColumnAmountSO2_TRL, ColumnAmountSO2_TRM, and ColumnAmountSO2_STL data fields in the version 1.3 OMSO2 product are now produced with an extended version of the PCA algorithm [Li et al 2016]. Previously the OMI TRL, TRM, and STL SO₂ data were produced using the linear fit (LF) algorithm [Yang et al 2007]. While the LF algorithm is fast and sensitive to SO₂ from volcanic eruptions, it has a tendency to underestimate large volcanic SO₂ signals due to saturation of SO₂ absorptions at the strongly absorbing short UV wavelengths (< 315 nm). It also has relatively large noise and artifacts as compared with the PCA algorithm. The entire OMI TRL, TRM, and STL SO₂ datasets in the OMSO2 product have been reprocessed with the new PCA-based algorithm. Volcanic (TRL, TRM, and STL) SO₂ data users who had previously acquired OMSO2 data prior to the data release in June 2016 are strongly encouraged to download and use the new version 1.3 OMSO2 product.

Algorithm Description

All OMI SO₂ data are now generated with principal component analysis (PCA) -based algorithms. The PBL columns in OMSO2 version 1.2 and later are produced using the original PCA algorithm with a fixed spectral fitting window and a fixed SO₂ Jacobian spectrum that are appropriate for pollution SO₂ near the surface [Li et al 2013]. There is no difference in the PBL SO₂ between version 1.2 and version 1.3 of the OMSO2 product. TRL, TRM, and STL SO₂ columns in the new OMSO2 version 1.3 are produced with an extended version of the PCA algorithm, using a spectral fitting window and SO₂ Jacobians lookup table (LUT) suitable for volcanic SO₂ signals [Li et al 2016].

In the PBL SO₂ PCA algorithm, we apply a principal component analysis technique to radiance data over a presumably SO₂-free region (e.g., the equatorial Pacific). The resulting principal components (PCs) can capture most (> 99.9999%) of measurement-to-measurement variation of the radiances. The PCs are ordered so that the first PC explains the most of spectral variance, the second PC explains the second most of spectral variance, and so on. The first few leading PCs are generally associated with geophysical processes including ozone absorption, surface reflectance, and rotational-Raman scattering effects (RRS, also known as the Ring effect), while the following PCs often have high-frequency features likely originating from measurement noise and detector artifacts such as wavelength shift and stretch. These physical processes and measurement details can cause strong interferences in SO₂ retrievals, and the PCs enable us to appropriately account for them. By fitting a set of n_ν PCs (ν_i) along with the SO₂ Jacobians, which represents the sensitivity of the radiances to the SO₂ column (), to the measured Sun-normalized radiances, we can simultaneously obtain estimates of SO₂ column density (Ω_SO2) and coefficients of the PCs (ω):

, (1)

Here N is the measured N-value spectrum (N(λ) = -100log₁₀(I(λ)/I₀(λ), I and I₀ are radiance and irradiance at wavelength λ, respectively) for a given OMI scene. The PCA algorithm shares the same overall physics concept with the widely used Differential Optical Absorption Spectroscopy (DOAS) method, but the data-driven (vs. forward modeling) approach used to account for retrieval interferences reduces modeling uncertainties, enhances computation efficiency, and makes the PCA algorithm much less sensitive to instrument calibration issues. A more detailed discussion of the PCA algorithm can be found in Li et al [2013] and Joiner et al [2013].

For input data, the PCA algorithm uses OMI level 1B (L1B) radiance and irradiance data in the spectral window of 310.5-340 nm, as well as the O₃ column amount (Ω_O3) from the OMTO3 product [Bhartia and Wellemeyer 2002]. The spectral window includes the strong SO₂ absorption band at 310.8 nm and minimizes potential interferences due to stray light at shorter wavelengths. To better account for the orbit-to-orbit measurement artifacts and the different characteristics of the 60 rows of the OMI detector, we process data from each row of each orbit separately. Scenes having strong O₃ absorption due to large slant column O₃ (S_O3 > 1500 DU) are filtered out before PCA, given the much smaller expected SO₂ sensitivity for these scenes. After data filtering, we first conduct PCA on the approximately 900-1300 remaining scenes for an entire row, without screening out polluted areas. Since SO₂ absorption is generally very weak outside of polluted and volcanic-affected areas, it is unlikely for the PC(s) associated with or affected by SO₂ absorption (v_so2) to be among the first few leading PCs. A correlation analysis between the PCs and the SO₂ Jacobians is then conducted to determine the number of PCs (n_v) to be included in the fitting. This ensures that n_v is sufficiently small to prevent the inclusion of v_so2 and collinearity in Eq. 1, and allows reasonable initial estimates of SO₂ (Ω_{SO2_ini}) to be obtained. To maintain computational efficiency, we set an upper limit of 20 for n_v. A second step PCA is then applied to scenes with small Ω_{SO2_ini} (within +/-1.5 standard deviations for each orbit/row) to extract a new set of PCs to update Eq. 1, followed by updated retrievals of SO₂. This step is repeated twice, as the changes in the retrieved SO₂ generally become very small within two iterations. The second step PCA and retrievals are carried out separately for three segments of each row: a tropical region with S_O3 < 100 DU + min(S_O3), and two regions north and south of it. These regionally derived PCs more closely match the measurements and help reduce retrieval biases.

The SO₂ Jacobians used in the current version of the PBL SO₂ PCA algorithm are calculated with the VLIDORT radiative transfer code [Spurr 2008]. The calculation assumes the same measurement conditions as those in the BRD algorithm. More specifically, we assume fixed surface albedo (0.05), surface pressure (1013.25 hPa), as well as fixed solar zenith angle (30 deg) and viewing zenith angle (0 deg). For SO₂, a climatological profile over the summertime eastern U.S. are used. For O₃ and temperature, the OMTO3 standard mid-latitude profiles with Ω_O3 = 325 DU are used. This setup allows direct comparison between the new and old OMI PBL SO₂ data. In the future, we plan to expand the look-up table for SO₂ Jacobians to more realistically account for different measurement conditions.

New for version 1.3 OMSO2 data: the PCA-based volcanic (TRL, TRM, and STL) SO₂ retrieval algorithm [Li et al 2016] is an extended version of the PBL SO₂ PCA algorithm. The algorithm consists of two parts. The first part is essentially identical to the PBL SO₂ algorithm as described above and is used to provide input to the second part of the algorithm, including initial estimates of SO₂ column amounts (Ω_{SO2_ini}) and principal components (PCs) to be used in spectral fitting. The second part of the algorithm produces more accurate estimates of volcanic SO₂ column amounts by conducting iterative spectral fitting and by using a more comprehensive lookup table for SO₂ Jacobians. We followed an approach similar to that in TOMS and OMI total column O₃ retrievals [Bhartia and Wellemeyer 2002], and used simple Lambertian equivalent reflectivity (SLER or R) derived at the surface [Ahmad et al 2004] to implicitly account for the combined effects of aerosols, clouds, and the surface on the spectral dependence of TOA (top of the atmosphere) radiances and SO₂ Jacobians. We also neglected the effects of non-elastic rotational Raman scattering (RRS) on SO₂ Jacobians. As a result, the backscattered radiances at TOA (I) for multiple elastic Rayleigh scattering can be calculated with the following equation:

. (2)

The first three terms (I₀, I₁, and I₂) on the right-hand side (RHS) of the equation represent the atmospheric component the back-scattered radiances, while the last term represents the surface component. θ₀, θ, and ϕ stand for solar zenith angle (SZA), viewing zenith angle (VZA), and relative azimuth angle (RAZ), respectively. Taking the partial derivative with respect to Ω_SO2, we obtained the following equation used for the determination of SO₂ Jacobians:

(3)

Using VLIDORT, we built a set of pre-computed multi-dimensional SO₂ Jacobian lookup tables, with eight SZA nodes (0-81 deg), eight VZA nodes (0-80 deg), and 15 SO₂ nodes (0-1000 DU), for each of the 21 standard O₃ climatology profiles used in OMI total O₃ retrievals [Bhartia and Wellemeyer 2002]. This was done separately for four different prescribed SO₂ profiles (TRL, TRM, and STL) at 0.05 nm spectral resolution for the spectral range of 311-342 nm.

For a given pixel with SZA = θ₀, VZA = θ, RAZ = ϕ, initial estimate of SO₂ total column amount of Ω_{SO2_ini}, and O₃ column amount of Ω_O3, the algorithm first determines SLER (R) 342.5, 354.1, 367.04 nm, where contributions from gaseous absorption and non-elastic RRS processes are minimal, and then extrapolates R at these wavelengths to shorter wavelengths (310-340 nm). The algorithm then determines the SO₂ Jacobian spectrum for the pixel by interpolating Jacobians calculated using equation (3) for two O₃ (corresponding to two different O₃ climatology profiles), two SO₂, two SZA, and two VZA nodes that bracket Ω_O3, Ω_{SO2_ini}, θ₀, and θ, respectively. Details on how the SO₂ Jacobians are calculated are given in Li et al [2016].

The SO₂ Jacbobian spectrum and the principal components from the first part of the algorithm are fit to the measured radiances in the nominal fitting window of 313-340 nm to produce an updated estimate of SO₂ VCD (Ω_{SO2_step1}) for the pixel and compared with Ω_{SO2_ini}. If the difference is greater than 0.1 DU or 1% for pixels with SO₂ > 100 DU, Ω_{SO2_step1} is used as input to the lookup table to update the SO₂ Jacobian spectrum. The iterations continue until the results converge or the number of iterations exceeds 15. In each iteration step, the left edge of the actual fitting window is determined by locating the wavelength with the largest SO₂ Jacobian within 313-340 nm (up to 326.5 nm or roughly the mid-point of this nominal fitting window). All wavelengths shorter than this wavelength are excluded in the spectral fitting. This approach allows the interpolation error due to signal saturation at short UV wavelengths to be minimized.

Data Quality Assessment

Errors in OMI SO₂ data can arise from both the input radiance/residual data and the SO₂ Jacobians used in retrievals. The resulted errors are best described as pseudo-random (i.e. having different systematic and random components depending on spatial and temporal scales) Gaussian-like distribution with a nominal mean of zero. The errors usually reduce much slower than the square root of the number of measurements averaged.

We provide separate Quality Flags (QF) for each of the products that are based on SO₂ consistency criteria between the individual wavelength pairs. The OMSO2 scene quality flag is an automatic assessment of the SO₂ values for the corresponding scene by the OMSO2 retrieval algorithm. It is used primarily as an indicator of the validity of the retrieved SO₂ values. For detailed information about the OMSO2 quality flag, please consult the OMSO2 file specification). While the quality flag may provide some information on the usefulness of retrievals, we have found it to be too restrictive and not very useful in its current form. Preliminary analysis of the QF values has shown that they may miss many real PBL and low level degassing emissions. Therefore, independent verification of the real SO₂ signal is strongly recommended. OMSO2 data users are advised to ignore the quality flag in the current version and use other parameters such as solar zenith angle for data filtering, as specified below for the PBL data and volcanic SO₂ data. Below are data quality assessments for each SO₂ product. For all products the noise increases with increasing solar zenith angle at high latitudes and in the region of South Atlantic radiation Anomaly.

ColumnAmountSO2_PBL: As a measurement of retrieval noise, the standard deviation (sigma) for instantaneous field of view (IFOV) is ~0.5 DU over the presumably SO₂-free equatorial Pacific for PBL SO₂ in OMSO2 product version 1.2 or later, or about half that of the BRD algorithm. The root mean square (RMS) for IFOV in different latitude bands over the Pacific can be viewed as a measure of both noise and biases in retrievals, and is estimated at ~0.5 DU for regions between 30 deg S and 30 deg N, suggesting very small systematic biases in PCA retrievals over the tropics. The IFOV RMS of PCA retrievals increases to ~0.7-0.9 DU for high latitude regions with large slant column O₃, but is still more than a factor of two smaller than that of BRD retrievals. Data users are advised to use caution when analyzing data from the edges of the OMI swath (rows 0 and 59, 0-based), as they tend to have greater noise. For best data quality, use data from scenes near the center of the swath (rows 4-54, 0-based) with slant column O₃ < 1500 DU. Retrievals for OMI scenes from the descending node of the Aura satellite should not be used. The PCA retrievals also have a negative bias over some highly reflective surfaces such as certain areas in the Sahara (up to about -0.5 DU in monthly mean). This negative bias is small as compared to the biases in the BRD retrievals, and is expected to be further reduced after the implementation of a more extensive Jacobians lookup table (see below). For cloudy scenes, the BRD algorithm sometimes produces large negative retrievals, a bias that is now eliminated in the PCA retrievals.

The SO₂ retrieval accuracy also depends on the error in the SO₂ Jacobians. This error is systematic and increases with deviation of the observational conditions from those assumed in the Jacobian calculation. SO₂ will likely be overestimated for remote oceanic regions where SO₂ is transported from source regions and likely located at elevated levels above the PBL. Likewise, SO₂ will also be overestimated for scenes with snow/ice and/or clouds. We plan to expand the look-up table for SO₂ Jacobians to more realistically account for different measurement conditions. Before this improvement, only snow/ice-free scenes with radiative cloud fraction < 0.3 should be used in studies on SO₂ emission sources. The higher reflectivity scenes can still be used to track long-range transport of sulfur pollution. Finally, there is also a small but noticeable dependence of retrieved SO₂ on the number of PCs included in the fitting. We expect to provide a more complete error estimate in follow-up releases.

New for version 1.3 OMSO2 data:

ColumnAmountSO2_TRL: Due to increased sensitivity to elevated SO₂, the pixel-level 1 sigma noise in TRL data is estimated at ~0.2 DU under optimal observational conditions in the tropics. The noise is about 0.3 DU for high latitudes. The data can be used for cloudy, clear and mixed scenes as well as for elevated terrain, but will overestimate SO₂ plumes at altitudes higher than 3 km. Since the noise level of the new PCA TRL SO₂ is about a factor of two smaller than the previous LF TRL SO₂, we now recommend that the TRL retrievals be used for estimating emissions from degassing volcanoes.

ColumnAmountSO2_TRM: The standard deviation of TRM retrievals in background areas is about 0.1 DU in the tropics and about 0.15 DU at high latitudes, again about a factor of two smaller than the previous LF TRM retrievals. Like the TRL data, the TRM data can be used for various sky conditions. The TRM data can be used for investigating SO₂ plumes from moderate eruptions.

ColumnAmountSO2_STL data are intended for use for explosive volcanic eruptions where SO₂ is injected into the upper troposphere or lower stratosphere (UTLS). The standard deviation over background areas is around 0.1 DU for all latitudes for STL data.

Unlike the LF algorithm that has large negative bias for high SO₂ loading cases (> 100 DU), the new PCA volcanic SO₂ algorithm has greatly reduced this error and compares well with other retrieval methods that also utilize the full spectral content of UV hyperspectral measurements. For example, for the Kasatochi eruption in August 2008, the PCA-estimated total SO₂ loading is ~1700 kt, about a factor of two higher than the estimate based on LF retrievals and generally in good agreement with the offline OMI iterative spectral fitting (ISF) retrievals and GOME-2 optimal estimation retrievals. For the Sierra Negra eruption in October 2005, the PCA algorithm yields a max SO₂ of over 1100 DU. This agrees well with the offline OMI ISF retrievals and is several times greater than the max SO₂ from the LF algorithm. It should be noted that the PCA algorithm might still underestimate SO₂ loading for highly concentrated plumes immediately after large eruptions. Additionally, some eruptions also emit large amounts of dust into the atmosphere that may interfere with SO₂ retrievals. This interference may not necessarily be appropriately accounted for using the SLER approach and may cause errors in the retrieved SO₂.

Volcanic SO₂ data from all rows of the OMI, with the exception of rows affected by the row anomaly, can be used. As with the PBL SO₂ data, it is best to use retrievals from scenes with SZA < 70 deg, and retrievals for OMI scenes from the descending node of the Aura satellite should not be used. When estimating the SO₂ loading from a volcanic plume within a given domain, it is recommended that only OMI scenes or pixels exceeding a certain threshold (e.g., 1 DU) be included in the calculation. This helps to filter out occasional negative retrieval noise.

Product Description

The OMSO2 product is written as HDF-EOS5 swath file. Data files are available from Goddard Earth sciences Data and Information Services Center (GES DISC) web site. For a list of tools that read HDF-EOS5 data files, please visit this link:

http://disc.gsfc.nasa.gov/Aura/tools.shtml

A file, also called a granule, contains SO₂ and associated information retrieved from each OMI scene from the sun-lit portion of an Aura orbit. The data are ordered in time sequence. The information provided on these files includes: latitude, longitude, solar zenith angle, OMTO3 reflectivity (LER) and independent estimates of the SO₂ vertical columns, as a well as a number of ancillary parameters that provide information to assess data quality. Four values of SO₂ column amounts are provided corresponding to four assumed vertical profiles. Independent information is needed to decide which value is most applicable. For a complete list of the parameters, please read the OMSO2 file

Specification.

For general assistance with data archive, please contact GES DISC. For questions and comments related to the OMSO2 algorithm and data quality please contact Nickolay Krotkov (Nickolay.A.Krotkov@nasa.gov), who has the overall responsibility for this product, with copies to Can Li (Can.Li@nasa.gov).

The subsets of OMSO2 data over many ground stations and along Aura validation aircraft flights paths are also available through the Aura Validation Data Center (AVDC) web site.

References

Ahmad, Z., P. K. Bhartia, P. K., and N. Krotkov (2004), Spectral properties of backscattered UV radiation in cloudy atmospheres, J. Geophys. Res., 109, D01201, doi:10.1029/2003JD003395.

Bhartia, P. K. and C. W. Wellemeyer (2002), OMI TOMS-V8 Total O3 Algorithm, Algorithm Theoretical Baseline Document: OMI Ozone Products, edited by P. K. Bhartia, vol. II, ATBD-OMI-02, version 2.0. Available: http://eospso.gsfc.nasa.gov/eos_homepage/for_scientists/atbd/docs/OMI/ATBD-OMI-02.pdf

Joiner, J., L. Guanter, R. Lindstrot, M. Voigt, A. P. Vasilkov, E. M. Middleton, K. F. Huemmrich, Y. Yoshida, and C. Frankenberg (2013), Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2, Atmos. Meas. Tech., 6, 2803-2823, doi:10.5194/amt-6-2803-2013.

Krotkov,N.A., B. McClure, R. Dickerson, S. Carn, Can Li, P.K. Bhartia, K. Yang, A. Krueger, Z. Li, P. Levelt, H. Chen, P.Wang, and D. Lu (2008), Ozone Monitoring Instrument (OMI) SO₂ validation over NE China, J. Geophys. Res., Aura validation special issue, (in press)

Krotkov, N.A., S.A. Carn, A.J. Krueger, P.K. Bhartia, and K. Yang (2006). Band residual difference algorithm for retrieval of SO₂ from the Aura Ozone Monitoring Instrument (OMI). IEEE Trans.Geosci. Remote Sensing, AURA special issue, 44(5), 1259-1266, doi:10.1109/TGRS.2005.861932, 2006

Krueger, A.J., L.S. Walter, P.K. Bhartia, C.C. Schnetzler, N.A. Krotkov, I. Sprod, and G.J.S. Bluth (1995) Volcanic sulfur dioxide measurements from the total ozone mapping spectrometer instruments. J. Geophys. Res., 100(D7), 14057-14076, 10.1029/95JD01222.

Li, C., J. Joiner, N. A. Krotkov, and P. K. Bhartia (2013), A fast and sensitive new satellite SO₂ retrieval algorithm based on principal component analysis: Application to the ozone monitoring instrument, Geophys. Res. Lett., 40, doi:10.1002/2013GL058134.

Li, C., N. A. Krotkov, S. Carn, Y. Zhang, R. J. D. Spurr, and J. Joiner (2016), New generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO₂ dataset: Algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS), Atmos. Meas. Tech., to be submitted.

Bogumil, K., J. Orphal, T. Homann, S. Voigt, P. Spietz, O.C. Fleischmann, A. Vogel, M. Hartmann, H. Kromminga, H. Bovensmann, J. Frerick, J.P. Burrows (2003), Measurements of molecular absorption spectra with the SCIAMACHY pre-flight model: instrument characterization and reference data for atmospheric remote-sensing in the 230-2380nm region, Journal of Photochemistry and Photobiology, A:Chemistry, 157 , 167-184.

Yang, K., N. Krotkov, A. Krueger, S. Carn, P. K. Bhartia, and P. Levelt (2007), Retrieval of Large Volcanic SO₂ columns from the Aura Ozone Monitoring Instrument (OMI): Comparisons and Limitations, J. Geophys. Res., 112, D24S43, doi:10.1029/2007JD008825