Analysis of global water vapour trends from  satellite  measurements in the visible spectral range

Mieruch, S.; Noël, Stefan; Bovensmann, H.; Burrows, John P.

doi:10.5194/acp-8-491-2008

Cited by 89 publications

(68 citation statements)

References 29 publications

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“…As most the water vapour is found in the troposphere, increasing its volume mixing ratio towards the surface, a large impact of the shielding effect on the satellite TWVC retrievals is expected (Mieruch et al, 2008(Mieruch et al, , 2010. Thus, some retrieval algorithms make use of a cloud correction method to take into account the water vapour present below the clouds -e.g.…”

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%

mentioning

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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“…So it is not surprising that a number of publications have noted that statistically significant, long-term trends in climate variables are difficult to derive from satellite data because of problems with satellite intercalibration and sensor drift (Hurrel andTrenberth, 1997, 1998;Christy et al, 1998;Wenz andSchabel, 1998, Trenberth et al, 2007). First satellite based IWV trend studies via ERS-2/GOME and ENVISAT/SCIAMACHY data were reported only recently (Wagner et al, 2006;Mieruch et al, 2008).…”

Section: R Sussmann Et Al: Trends In Column-integrated Water Vapor mentioning

confidence: 99%

Technical Note: Harmonized retrieval of column-integrated atmospheric water vapor from the FTIR network – first examples for long-term records and station trends

et al. 2009

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Abstract. We present a method for harmonized retrieval of integrated water vapor (IWV) from existing, long-term, measurement records at the ground-based mid-infrared solar FTIR spectrometry stations of the Network for the Detection of Atmospheric Composition Change (NDACC). Correlation of IWV from FTIR with radiosondes shows an ideal slope of 1.00(3). This optimum matching is achieved via tuning one FTIR retrieval parameter, i.e., the strength of a Tikhonov regularization constraining the derivative (with respect to height) of retrieved water profiles given in per cent difference relative to an a priori profile. All other FTIR-sonde correlation parameters (intercept=0.02(12) mm, bias=0.02(5) mm, standard deviation of coincident IWV differences (stdv)=0.27 mm, R=0.99) are comparable to or better than results for all other ground-based IWV sounding techniques given in the literature. An FTIR-FTIR side-byside intercomparison reveals a strong exponential increase in stdv as a function of increasing temporal mismatch starting at t≈1 min. This is due to atmospheric water vapor variability. Based on this result we derive an upper limit for the precision of the FTIR IWV retrieval for the smallest t (=3.75 min) still giving a statistically sufficient sample (32 coincidences), i.e., precision(IWV FTIR )<0.05 mm (or 2.2% of the mean IWV). The bias of the IWV retrievals from the two different FTIR instruments is nearly negligible (0.02(1) mm). The optimized FTIR IWV retrieval is set up in the standard NDACC algorithm SFIT 2 without changes Correspondence to: R. Sussmann (ralf.sussmann@kit.edu) to the code. A concept for harmonized transfer of the retrieval between different stations deals with all relevant control parameters; it includes correction for differing spectral point spacings (via regularization strength), and final quality selection of the retrievals (excluding the highest residuals (measurement minus model), 5% of the total).As [1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008], whereas for the Jungfraujoch no significant trend is found. This confirms recent findings that strong variations of IWV trends do occur above land on the local to regional scale (≈250 km) in spite of homogeneous surface temperature trends. This paper provides a basis for future exploitation of more than a dozen existing, multi-decadal FTIR measurement records around the globe for climate studies.

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“…Various wavelength windows have been explored: around 650 nm (Wagner et al, 2003, around 700 nm (Noël et al, , 2005Mieruch et al, 2008), and around 590 nm (Maurellis et al, 2000;Lang et al, 2003). The advantage of using these windows is that the measurements are also sensitive to the boundary layer, where most of the water vapour resides.…”

Section: H Schrijver Et Al: Water Vapour Columns From Sciamachy 23mentioning

confidence: 99%

Water vapour total columns from SCIAMACHY spectra in the 2.36 μm window

et al. 2009

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Abstract. The potential of the shortwave infrared channel of the atmospheric spectrometer SCIAMACHY on Envisat to provide accurate measurements of total atmospheric water vapour columns is explored. It is shown that good quality results can be obtained for cloud free scenes above the continents using the Iterative Maximum Likelihood Method. In addition to the standard cloud filter employed in this method, further cloud screening is obtained by comparing simultaneously retrieved methane columns with values expected from models. A novel method is used to correct for the scattering effects introduced in the spectra by the ice layer on the detector window.The retrieved water vapour total vertical columns for the period 2003-2007 are compared with spatially and temporally collocated values from the European Centre for MidRange Weather Forecast (ECMWF) data. The observed differences for individual measurements have standard deviations not higher than 0.3 g/cm 2 and an absolute mean value smaller than 0.01 g/cm 2 with some regional excursions.The use of recently published spectroscopic data for water vapour led to a significant improvement in the agreement of the retrieved water vapour total columns and the values derived from ECMWF data. This analysis thus supports the superior quality of the new spectroscopic information using atmospheric data.

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Analysis of global water vapour trends from satellite measurements in the visible spectral range

Cited by 89 publications

References 29 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

Technical Note: Harmonized retrieval of column-integrated atmospheric water vapor from the FTIR network – first examples for long-term records and station trends

Water vapour total columns from SCIAMACHY spectra in the 2.36 μm window

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