Total ozone retrieval from GOME UV spectral data using the weighting function DOAS approach

Abstract. The quality of trace gas products derived from measurements of a space-borne imaging spectrometer is affected by the inhomogeneity of the illumination of the instrument slit and thus by the heterogeneity of the observed scene. This paper aims to quantify this effect and summarise findings on how to mitigate the impact of inhomogeneous slit illumination on tropospheric O 3 , NO 2 , SO 2 and HCHO columns derived from measurements of the Sentinel-4 UVN imaging spectrometer. For this purpose, spectra for inhomogeneous ground scenes have been simulated based on a combination of a radiative transfer model and spatially high resolved MODIS (Moderate Resolution Imaging Spectroradiometer) data. The resulting errors on tropospheric O 3 , NO 2 , SO 2 and HCHO columns derived from these spectra have been determined via an optimal estimation approach. We conclude that inhomogeneous illumination results in significant errors in the data products if the natural inhomogeneity of the observed scenes are not accounted for. O 3 columns are less affected than the other data products; largest errors occur for NO 2 (mean absolute errors about 5 %, maximum error exceeding 50 %, standard deviation of the errors about 8 %). These errors may be significantly reduced (by factors up to about 10) by an appropriate wavelength calibration applied individually to each Earthshine radiance spectrum. With wavelength calibration the estimated mean absolute errors due to inhomogeneity are for all gases well below 1 %; standard deviations of the errors are 1.5 % or lower; maximum errors are about 10 % for NO 2 and around 5 % for the other gases.

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“…(B6) was inspired by the weighting function DOAS method (see, e.g. Coldewey-Egbers et al, 2005). As y ref , α is determined with SCIATRAN.…”

Section: Spectral Calibration Algorithmmentioning

confidence: 99%

Quantification and mitigation of the impact of scene inhomogeneity on Sentinel-4 UVN UV-VIS retrievals

et al. 2012

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“…Several different algorithms make use of cloud OCPs or similar quantities to supplement and improve retrieved information about O 3 , including estimates of the total column (e.g., Coldewey-Egbers et al, 2005;van Roozendael et al, 2006;Veefkind et al, 2006) and tropospheric concentrations (e.g., Ziemke et al, 2009;Joiner et al, 2009). Other studies have focused on various aspects of cloud-related errors on O 3 retrievals (e.g., Koelemeijer et al, 1999;Vasilkov et al, 2004;Kokhanovsky et al, 2007b;.…”

Section: Introductionmentioning

confidence: 99%

Fast simulators for satellite cloud optical centroid pressure retrievals; evaluation of OMI cloud retrievals

et al. 2012

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Abstract. The cloud Optical Centroid Pressure (OCP) is a satellite-derived parameter that is commonly used in tracegas retrievals to account for the effects of clouds on nearinfrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosols. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals, from the Ozone Monitoring Instrument (OMI), with estimates based on collocated cloud extinction profiles from a combination of CloudSat radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, cases of low clouds obscurred by ground-clutter in CloudSat observations and by opaque high clouds in CALIPSO lidar observations, and the fact that CloudSat/CALIPSO only observes a relatively small fraction of an OMI field-of-view.

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“…As with the OMTO3 v8.5 algorithm, actual cloud pressures are used in the DOAS algorithm to measure total ozone and other trace gases. All satellite DOAS techniques as applied to GOME, SCIAMACHY, OMI, and GOME2 satellite data derive total column amounts using cloud parameters retrieved with the same instrument (e.g., Coldewey-Egbers et al, 2005;Eskes et al, 2005;Roozendael et al, 2006;Kroon et al, 2008b).…”

Section: Introductionmentioning

confidence: 99%

Ozone mixing ratios inside tropical deep convective clouds from OMI satellite measurements

et al. 2009

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Abstract.We have developed a new technique for estimating ozone mixing ratio inside deep convective clouds. The technique uses the concept of an optical centroid cloud pressure that is indicative of the photon path inside clouds. Radiative transfer calculations based on realistic cloud vertical structure as provided by CloudSat radar data show that because deep convective clouds are optically thin near the top, photons can penetrate significantly inside the cloud. This photon penetration coupled with in-cloud scattering produces optical centroid pressures that are hundreds of hPa inside the cloud. We combine measured column ozone and the optical centroid cloud pressure derived using the effects of rotational-Raman scattering to estimate O 3 mixing ratio in the upper regions of deep convective clouds. The data are obtained from the Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite. Our results show that low O 3 concentrations in these clouds are a common occurrence throughout much of the tropical Pacific. Ozonesonde measurements in the tropics following convective activity also show very low concentrations of O 3 in the upper troposphere. These low amounts are attributed to vertical injection of ozone poor oceanic boundary layer air during convection into the upper troposphere followed by convective outflow. Over South America and Africa, O 3 mixing ratios inside deep convective clouds often exceed 50 ppbv which are comparable to mean backCorrespondence to: J. R. Ziemke (jerald.r.ziemke@nasa.gov) ground (cloud-free) amounts and are consistent with higher concentrations of injected boundary layer/lower tropospheric O 3 relative to the remote Pacific. The Atlantic region in general also consists of higher amounts of O 3 precursors due to both biomass burning and lightning. Assuming that O 3 is well mixed (i.e., constant mixing ratio with height) up to the tropopause, we can estimate the stratospheric column O 3 over clouds. Stratospheric column ozone derived in this manner agrees well with that retrieved independently with the Aura Microwave Limb Sounder (MLS) instrument and thus provides a consistency check of our method.

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Total ozone retrieval from GOME UV spectral data using the weighting function DOAS approach

Cited by 126 publications

References 32 publications

Quantification and mitigation of the impact of scene inhomogeneity on Sentinel-4 UVN UV-VIS retrievals

Quantification and mitigation of the impact of scene inhomogeneity on Sentinel-4 UVN UV-VIS retrievals

Fast simulators for satellite cloud optical centroid pressure retrievals; evaluation of OMI cloud retrievals

Ozone mixing ratios inside tropical deep convective clouds from OMI satellite measurements

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