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) observations spanning over the last 15+ years, and the AErosol RObotic NETwork (AERONET), providing long-term observations performed with standardized and well-calibrated sun photometers. On the other hand, satellite-based measurements of IWV already have a time span of over 10 years (e.g. AIRS) or are being merged to create long-term time series (e.g. GOME, SCIAMACHY, and GOME-2).This study performs an intercomparison of IWV measurements from satellite devices (in the visible, GOME/SCIAMACHY/GOME-2, and in the thermal infrared, AIRS), in situ measurements (radiosondes) and ground-based instruments (GPS, sun photometer), to assess their use in water vapour trends analysis. To this end, we selected 28 sites world-wide for which GPS observations can directly be compared with coincident satellite IWV observations, together with sun photometer and/or radiosonde measurements. The mean biases of the different techniques compared to the GPS estimates vary only between −0.3 to 0.5 mm of IWV. Nevertheless these small biases are accompanied by large standard deviations (SD), especially for the satellite instruments. In particular, we analysed the impact of clouds on the IWV agreement. The influence of specific issues for each instrument on the intercomparison is also investigated (e.g. the distance between the satellite ground pixel centre and the co-located ground-based station, the satellite scan angle, daytime/nighttime differences). Furthermore, we checked if the properties of the IWV scatter plots between these different instruments are dependent on the geography and/or altitude of the station. For all considered instruments, the only dependency clearly detected is with latitude: the SD of the IWV observations with respect to the GPS IWV retrievals decreases with increasing latitude and decreasing mean IWV.

Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network

“…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]). …”

“…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.…”

Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison

“…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.…”

“…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.…”

“…The standard deviation of the mean difference between the IWV, which is obtained from the intercomparison, includes the spatial and temporal representativeness (variability) and the effect of difference in the vertical resolution between the two measurements, in addition to the respective measurement precisions. Because of high spatial and temporal variability of atmospheric H 2 O, it is difficult to rigorously estimate the measurement precision by the intercomparison of IWV, even if side-by-side observations are performed (e.g., [3][4][5][6][7][8]). The spatial and temporal variability can be estimated by selecting subsets of data from both instruments with optimal volume matching and acceptable temporal matching [9,10].…”