First retrieval of global water vapour column amounts from SCIAMACHY measurements

Noël, Stefan; Buchwitz, Michael; Burrows, John P.

doi:10.5194/acp-4-111-2004

Cited by 87 publications

(56 citation statements)

References 18 publications

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“…Thus, some retrieval algorithms make use of a cloud correction method to take into account the water vapour present below the clouds -e.g. the AMC-DOAS (Air Mass Corrected Differential Absorption Spectroscopy) method used to retrieve TWVC from SCIAMACHY (Scanning and Imaging Absorption Spectrometer for Atmospheric Chartography) measurements in the visible spectral range (Noël et al, 2004). Du Piesanie et al (2013) checked this correction method by means of a detailed analysis of the sounding-satellite differences as a function of cloud fraction, cloud optical thickness and cloud top height.…”

Section: Dependence Of the Differences On Cloud Parametersmentioning

confidence: 99%

“…Kaufman and Gao, 1992;Bauer and Schlüssel, 1993;Noël et al, 1999Noël et al, , 2004Maurellis et al, 2000;Wagner et al, 2006;Li et al, 2006;Deeter, 2007;Lang et al, 2007;Mieruch et al, 2008;Pougatchev et al, 2009). Within this framework, the European satelliteborne atmospheric sensor Global Ozone Monitoring Experiment 2 (GOME-2) aboard the Meteorological Operational satellite program (MetOp-A and MetOp-B) provides the potential for a detailed analysis of the global distribution of the atmospheric water vapour .…”

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confidence: 99%

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Validation of GOME-2/MetOp-A total water vapour column using reference radiosonde data from the GRUAN network

et al. 2015

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Abstract. The main goal of this paper is to validate the total water vapour column (TWVC) measured by the Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensor and generated using the GOME Data Processor (GDP) retrieval algorithm developed by the German Aerospace Centre (DLR). For this purpose, spatially and temporally collocated TWVC data from highly accurate sounding measurements for the period January 2009-May 2014 at six sites are used. These balloon-borne data are provided by the GCOS Reference Upper-Air Network (GRUAN). The correlation between GOME-2 and sounding TWVC data is reasonably good (determination coefficient, R 2 , of 0.89) when all available radiosondes (1400) are employed in the intercomparison. When cloud-free cases (544) are selected by means of the satellite cloud fraction (CF < 5 %), the correlation exhibits a remarkable improvement (R 2 ∼ 0.95). Nevertheless, the analysis of the relative differences between GOME-2 and GRUAN data shows a mean absolute bias error (weighted with the combined uncertainty derived from the estimated errors of both data sets) of 15 % for all-sky conditions (9 % for cloud-free cases). These results evidence a notable bias in the satellite TWVC data against the reference balloon-borne measurements, partially related to the cloudy conditions during the satellite overpass. The detailed analysis of the influence of cloud properties -CF, cloud top albedo (CTA) and cloud top pressure (CTP) -on the satellitesounding differences reveals, as expected, a large effect of clouds in the GOME-2 TWVC data. For instance, the relative differences exhibit a large negative dependence on CTA, varying from −6 to −23 % when CTA rises from 0.3 to 0.8. Furthermore, the satellite-sounding TWVC differences show a strong dependence on the satellite solar zenith angle (SZA) for values above 50 • . Hence the smallest relative differences found in this satellite-sounding comparison are achieved for those cloud-free cases with satellite SZA below 50 • . Finally, the relative differences also show a negative dependence on the reference TWVC values, e.g. changing from +10 % (TWVC below 10 mm) to −10 % (TWVC above 40 mm) when cloud-free conditions with SZA below 50 • are selected. Overall, relative differences within ±10 % with respect to reference sounding data for a large range of TWVC values can be considered as a good result for satellite retrievals.

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Section: Dependence Of the Differences On Cloud Parametersmentioning

confidence: 99%

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confidence: 99%

Validation of GOME-2/MetOp-A total water vapour column using reference radiosonde data from the GRUAN network

et al. 2015

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“…The data have been retrieved by the AMC-DOAS method (Noël et al, 2004). The main advantages of the GOME/SCIAMACHY data are the independence from external information and the ability to retrieve water vapour also over land, which is e.g.…”

Section: Data Basementioning

confidence: 99%

Markov chain analysis of regional climates

Mieruch

Noël

Bovensmann

et al. 2010

Nonlin. Processes Geophys.

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Abstract. We present a novel method for regional climate classification that is based on coarse-grained categorical representations of multivariate climate anomalies and a subsequent Markov chain analysis. From the estimated transition matrix several descriptors, such as persistence, recurrence time and entropy, are derived. These descriptors characterise dynamic properties of regional climate anomalies and are connected with fundamental concepts from nonlinear physics like residence times, relaxation process and predictability. Such characteristics are useful for a comparative analysis of different climate regions and, in the context of global climate change, for a regime shift analysis.We apply the method to the bivariate set of water vapour and temperature anomalies of two regional climates, the Iberian Peninsula and the islands of Hawaii in the central Pacific Ocean. Through the Markov chain analysis and via the derived descriptors we find significant differences between the two climate regions. Since anomalies are departures from seasonal and long term components, these differences relate to differences in the short term stability of both regional climates.

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“…Satellite observations of water vapor have used microwave and radio wave (e.g., Advanced Microwave Sounding Unit (AMSU), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), Special Sensor Microwave/Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMI/S) (Ferraro et al, 2005;Boukabara et al, 2010), Global Navigation Satellite System (GNSS) (Lee et al, 2013)), thermal infrared (e.g., Moderate Resolution Imaging Spectrometer (MODIS) (King et al, 2003), Tropospheric Emission Spectrometer (TES) (Worden et al, 2012), Atmospheric Infrared Sounder (AIRS) (Aumann et al, 2003), Infrared Atmospheric Sounding Interferometer (IASI) (Schneider and Hase, 2011)), and nearinfrared and visible sensors (e.g., MODIS (King et al, 2003), Global Ozone Monitoring Experiment (GOME) (Noël et al, 2002;Wagner et al, 2003), GOME-2 (Grossi et al, 2014), Scanning Imaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) (Noël et al, 2004), Medium Resolution Imaging Spectrometer (MERIS) (Lindstrot et al, 2012) Figure 1. Reference spectra used in the standard operational water vapor retrieval.…”

Section: Introductionmentioning

confidence: 99%

Water vapor retrieval from OMI visible spectra

et al. 2014

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Abstract. There are distinct spectral features of water vapor in the wavelength range covered by the Ozone Monitoring Instrument (OMI) visible channel. Although these features are much weaker than those at longer wavelengths, they can be exploited to retrieve useful information about water vapor. They have an advantage in that their small optical depth leads to fairly simple interpretation as measurements of the total water vapor column density. We have used the Smithsonian Astrophysical Observatory (SAO) OMI operational retrieval algorithm to derive the slant column density (SCD) of water vapor using the 430-480 nm spectral region after extensive optimization. We convert from SCD to vertical column density (VCD) using the air mass factor (AMF), which is calculated using look-up tables of scattering weights and assimilated water vapor profiles. Our Level 2 product includes not only water vapor VCD but also the associated scattering weights and AMF. In the tropics, our standard water vapor product has a median SCD of 1.3 × 10 23 molecules cm −2 and a median relative uncertainty of about 11 %, about a factor of 2 better than that from a similar OMI algorithm that uses a narrower retrieval window. The corresponding median VCD is about 1.2 × 10 23 molecules cm −2 . We have examined the sensitivities of SCD and AMF to various parameters and compared our results with those from the GlobVapour product, the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Aerosol Robotic NETwork (AERONET).

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First retrieval of global water vapour column amounts from SCIAMACHY measurements

Cited by 87 publications

References 18 publications

Validation of GOME-2/MetOp-A total water vapour column using reference radiosonde data from the GRUAN network

Validation of GOME-2/MetOp-A total water vapour column using reference radiosonde data from the GRUAN network

Markov chain analysis of regional climates

Water vapor retrieval from OMI visible spectra

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