Influence of scintillation on quality of ozone monitoring by GOMOS

Sofieva, Viktoria; Kan, V.; Dalaudier, Francis; Kyrölä, E.; Tamminen, J.; Bertaux, Jean-Loup; Hauchecorne, Alain; Fussen, D.; Vanhellemont, F.

doi:10.5194/acp-9-9197-2009

Cited by 40 publications

(69 citation statements)

References 13 publications

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“…Other GOMOS self-collocations (less than 30 per year) are for different stars in successive orbits (Guirlet et al, ) in years 2002(Guirlet et al, -2004 The estimated retrieval uncertainty for GOMOS ozone is small in the stratosphere; for very bright stars it is ∼ 0.5-2 % (Sofieva et al, 2009;Tamminen et al, 2010). For an experimental estimation of precision in such occultations by the Fioletov method, a very large number of collocations with measurements from another instrument is needed, even in regions of low natural variability (Appendix B).…”

Section: Specifics Of Gomos Measurements and Challenges For Precisionmentioning

confidence: 99%

“…The residual scintillation error (i.e., the uncertainty due to incomplete scintillation correction) has been characterized in depth (Sofieva et al, 2009) and has been included in the inversion algorithm . The residual scintillation error depends on the obliquity of occultation; it vanishes for vertical (in-orbital-plane) occultations.…”

Section: Outlines Of Gomos Precision Derivation and Characterization mentioning

confidence: 99%

“…Precision of retrieved profiles depends slightly on obliquity of occultation due to the influence of scintillations (Sofieva et al, 2009). The GOMOS error estimates also slightly depend on ozone concentration, but this dependence is much weaker than the dependence on stellar magnitude and the spectral class.…”

Section: Specifics Of Gomos Measurements and Challenges For Precisionmentioning

confidence: 99%

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Validation of GOMOS ozone precision estimates in the stratosphere

et al. 2014

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Abstract. Accurate information about uncertainties is required in nearly all data analyses, e.g., inter-comparisons, data assimilation, combined use. Validation of precision estimates (viz., the random component of estimated uncertainty) is important for remote sensing measurements, which provide the information about atmospheric parameters by solving an inverse problem. For the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, this is a real challenge, due to the dependence of the signal-to-noise ratio (and thus precision estimates) on stellar properties, small number of self-collocated measurements, and growing noise as a function of time due to instrument aging. The estimated ozone uncertainties are small in the stratosphere for bright star occultations, which complicates validation of precision values, given the natural ozone variability.In this paper, we discuss different methods for geophysical validation of precision estimates and their applicability to GOMOS data. We propose a simple method for validation of GOMOS precision estimates for ozone in the stratosphere. This method is based on comparisons of differences in sample variance with differences in uncertainty estimates for measurements from different stars selected in a region of small natural variability.For GOMOS, the difference in sample variances for different stars at tangent altitudes 25-45 km is well explained by the difference in squared precisions, if the stars are not dim. Since this is observed for several stars, and since normalized χ 2 is close to 1 for these occultations in the stratosphere, we conclude that the GOMOS precision estimates are realistic in occultations of sufficiently bright stars. For dim stars, errors are overestimated due to improper accounting of the dark charge correction uncertainty in the error budget. The proposed method can also be applied to stratospheric ozone data from other instruments, including multi-instrument analyses.

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Section: Specifics Of Gomos Measurements and Challenges For Precisionmentioning

confidence: 99%

Section: Outlines Of Gomos Precision Derivation and Characterization mentioning

confidence: 99%

Section: Specifics Of Gomos Measurements and Challenges For Precisionmentioning

confidence: 99%

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Validation of GOMOS ozone precision estimates in the stratosphere

et al. 2014

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“…9). This causes about 0.5-1.5% additional error in the ozone retrievals at the altitude range of 20-45 km (Sofieva et al, 2009). The modelling error from the imperfect scintillation correction can be taken into account in the spectral inversion by introducing a wavelength correlated modelling error.…”

Section: Scintillation -Dilution Correctionmentioning

confidence: 99%

“…In the GOMOS retrievals, the perturbations in the stellar flux caused by scintillations are corrected by using additional scintillation measurements by the fast photometer operating in low-absorption wavelength region (Dalaudier et al, 2001;Sofieva et al, 2009). The quality of the scintillation correction and its limitations are discussed in Sofieva et al (2009) where it was shown that the applied scintillation correction removes significant part of the perturbations in the recorded stellar flux, but it is not able to remove scintillations generated by the isotropic turbulence.…”

Section: Scintillation -Dilution Correctionmentioning

confidence: 99%

GOMOS data characterisation and error estimation

et al. 2010

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Abstract. The Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument uses stellar occultation technique for monitoring ozone, other trace gases and aerosols in the stratosphere and mesosphere. The self-calibrating measurement principle of GOMOS together with a relatively simple data retrieval where only minimal use of a priori data is required provides excellent possibilities for long-term monitoring of atmospheric composition.GOMOS uses about 180 of the brightest stars as its light source. Depending on the individual spectral characteristics of the stars, the signal-to-noise ratio of GOMOS varies from star to star, resulting also in varying accuracy of retrieved profiles. We present here an overview of the GOMOS data characterisation and error estimation, including modeling errors, for O 3 , NO 2 , NO 3 and aerosol profiles. The retrieval error (precision) of night-time measurements in the stratosphere is typically 0.5-4% for ozone, about 10-20% for NO 2 , 20-40% for NO 3 and 2-50% for aerosols. Mesospheric O 3 , up to 100 km, can be measured with 2-10% precision. The main sources of the modeling error are incompletely corrected scintillation, inaccurate aerosol modeling, uncertainties in cross sections of trace gases and in atmospheric temperature. The sampling resolution of GO-MOS varies depending on the measurement geometry. In the data inversion a Tikhonov-type regularization with predefined target resolution requirement is applied leading to 2-3 km vertical resolution for ozone and 4 km resolution for other trace gases and aerosols.

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Greenhouse gas profiling by infrared‐laser and microwave occultation in cloudy air: Results from end‐to‐end simulations

et al. 2014

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The new mission concept of microwave and infrared‐laser occultation between Low Earth Orbit satellites (LMIO) is capable to provide accurate, consistent, and long‐term stable measurements of many essential climate variables. These include temperature, humidity, key greenhouse gases (GHGs) such as carbon dioxide and methane, and line of sight wind speed, all with focus on profiling the upper troposphere and lower stratosphere. The GHG retrieval performance from LMIO data was so far analyzed under clear‐air conditions only, without clouds and scintillations from turbulence. Here we present and evaluate an algorithm, built into an already published clear‐air algorithm, which copes with cloud and scintillation influences on the infrared‐laser transmission profiles used for GHG retrieval. We find that very thin ice clouds fractionally extinct the infrared‐laser signals, thicker but broken ice clouds block them over limited altitude ranges, and liquid water clouds generally block them so that their cloud top altitudes typically constitute the limit to tropospheric penetration of profiles. The advanced algorithm penetrates through broken cloudiness. It achieves this by producing a cloud flagging profile from cloud‐perturbed infrared‐laser signals, which then enables bridging of transmission profile gaps via interpolation. Evaluating the retrieval performance with quasi‐realistic end‐to‐end simulations, including high‐resolution cloud data and scintillations from turbulence, we find a small increase only of GHG retrieval RMS errors due to broken‐cloud scenes and the profiles remain essentially unbiased as in clear air. These results are encouraging for future LMIO implementation, indicating that GHG profiles can be retrieved through broken cloudiness, maximizing upper troposphere coverage.

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Influence of scintillation on quality of ozone monitoring by GOMOS

Cited by 40 publications

References 13 publications

Validation of GOMOS ozone precision estimates in the stratosphere

Validation of GOMOS ozone precision estimates in the stratosphere

GOMOS data characterisation and error estimation

Greenhouse gas profiling by infrared‐laser and microwave occultation in cloudy air: Results from end‐to‐end simulations

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