2014
DOI: 10.5194/amt-7-2487-2014
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A multi-site intercomparison of integrated water vapour observations for climate change analysis

Abstract: Abstract. Water vapour plays a dominant role in the climate change debate. However, observing water vapour over a climatological time period in a consistent and homogeneous manner is challenging. On one hand, networks of groundbased instruments able to retrieve homogeneous integrated water vapour (IWV) data sets are being set up. Typical examples are Global Navigation Satellite System (GNSS) observation networks such as the International GNSS Service (IGS), with continuous GPS (Global Positioning System) obser… Show more

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Cited by 71 publications
(63 citation statements)
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References 71 publications
(107 reference statements)
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“…Finally, the result for Edwards shows a consistent, strong wet bias of TANSO-FTS for almost all coincidences (a tentative explanation is given in Section 5.3). Similar general features-overall dry bias with respect to ground-based instruments, reasonably small biases associated with large standard deviations, significant biases in high-humidity cases or cloudy conditions even if the measurements pass the cloud filters-have already been noted for other satellite instruments when compared to ground-based measurements (e.g., [15,[63][64][65]). …”
Section: Statistical Comparisonmentioning
confidence: 49%
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“…Finally, the result for Edwards shows a consistent, strong wet bias of TANSO-FTS for almost all coincidences (a tentative explanation is given in Section 5.3). Similar general features-overall dry bias with respect to ground-based instruments, reasonably small biases associated with large standard deviations, significant biases in high-humidity cases or cloudy conditions even if the measurements pass the cloud filters-have already been noted for other satellite instruments when compared to ground-based measurements (e.g., [15,[63][64][65]). …”
Section: Statistical Comparisonmentioning
confidence: 49%
“…Furthermore, existing validation studies have shown that the observed differences are study-dependent and that no single instrument or technique can yet provide continuous data of sufficiently good quality for accurate climate model predictions [14,15]. When new data become available, it is thus of foremost importance to evaluate their quality and limitations in order to use them efficiently in long-term trend evaluation (jointly with pre-existing datasets) and climate modeling.…”
Section: Introductionmentioning
confidence: 99%
“…We must note that, for Equation (8) to be true, it is crucial that the TANSO-FTS TIR XH 2 O data are independent of the TANSO-FTS SWIR data. This is discussed in Appendix A.…”
Section: Intercomparison Between Three Datasetsmentioning
confidence: 99%
“…σ TCCON and σ R values for all TCCON sites were calculated as an ensemble of all data from each site. Finally, the unknown variables σ TIR , σ SWIR and σ S were determined using Equations (6) to (8). Figure 4b, and the amounts of data employed for comparisons are shown in Figure 4c.…”
Section: Intercomparison Between Three Datasetsmentioning
confidence: 99%
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