Evaluation of GPS Precipitable Water over Canada and the IGS Network

Abstract

Precipitable water (PW) derived from the GPS zenith tropospheric delay (ZTD) is evaluated (as a first step toward variational data assimilation) through comparison with that of collocated radiosondes (RS_PW), operational analyses, and 6-h forecasts (from the Canadian Global Environmental Multiscale model) of the Canadian Meteorological Centre. Two sources of ZTD data are considered: 1) final ZTD (over Canada), computed by the Geodetic Survey Division (GSD) of Natural Resources Canada, and 2) final ZTD (distributed globally), obtained from the International GPS Service (IGS). The mean GSD GPS–derived PW (GPS_PW) is 14.9 mm (reflecting the relatively cold Canadian climate), whereas that of the IGS dataset is 20.8 mm. Intercomparison statistics [correlation, standard deviation (SD), and bias] between GPS_PW and RS_PW are, respectively, 0.97, 2.04 mm, and 1.35 mm for the GSD data and 0.98, 2.6 mm, and 0.67 mm for the IGS data. Comparisons of GPS_PW with 6-h forecast PW (TRIAL_PW) show slightly lower correlations and a higher SD. The increase in SD is greater for the IGS data, which is not surprising, because in regions such as the Tropics and subtropics, moisture forecasts are of a lower quality and the RS observation network is sparse. From a three-way intercomparison (IGS GPS_PW, RS_PW, and TRIAL_PW) of the SD statistics, it is found that GPS_PW has the lowest estimated PW error (≈1 mm) for PW in the 5–30-mm range. For PW greater than 30 mm, the RS_PW estimated error is ≈2 mm, and that of GPS_PW is ≈2.5 mm. The TRIAL_PW estimated error increases with PW, reaching 5.5 mm in the 40–55-mm PW range. These intercomparison results indicate that GPS_PW should be a useful source of humidity information for NWP applications.

An observation campaign of precipitable water vapor with multiple GPS receivers in western Java, Indonesia

“…The magnitude of the deviation is consistent with that reported in previous studies Sato et al 2013). As regards the bias, previous comparisons of GPS-derived PWV with respect to Vaisala radiosondes, both in mid-latitude (by using the RS80H model (Deblonde et al (2005) and the RS90 model (Van Baelen et al 2005)) and tropical zones (RS80-15G model (Sapucci et al 2007), RS80A, RS80H, and RS90 models (Wang et al 2007), and RS92 model )), evidenced a negative (dry) bias of the radiosonde measurements. This systematic error, especially significant during daytime, is generally attributed to the solar heating of the radiosonde humidity sensor (Vömel et al 2007).…”

Integrated water vapor from IGS ground-based GPS observations: initial results from a global 5-min data set

“…Also here deficiencies in vertical pressure or humidity extrapolation are unlikely since five of these stations show an altitude difference of less than 200 m between lowest ECMWF level and GPS antenna. In general the observed GPS-ECMWF IWV standard deviation is consistent with previous GPS IWV validation studies (less than 3 mm) (e.g., Li et al, 2003;Hagemann et al, 2003;Deblonde et al, 2005;Wang et al, 2007). The P s comparisons between local measurements at 83 stations and interpolated ECMWF (at 03:00, 09:00, 15:00, 21:00 UT) data are shown in Fig.…”

“…The GPSbased IWV observation technique is characterized by several advantages in comparison to the traditional observing systems: independence on sensor calibrations and therefore long-term stability, all-weather capability, high accuracy and low cost. Several validation studies show well agreement between IWV from GPS and other observing systems like radiometers or radiosondes (e.g., Rocken et al, 1993;Tregoning et al, 1998;Deblonde et al, 2005;Wang et al, 2007). Thus, GPS IWV is used in several applications like numerical weather prediction (e.g., Gendt et al, 2004;Gutman et al, 2004;Vedel et al, 2004;Guerova et al, 2006), validation of IWV results from other sensors (e.g., Li et al, 2003;Van Baelen et al, 2005) and first climatological investigations (e.g., Gradinarsky et al, 2002;Nilsson and Elgered, 2008).…”

GNSS Atmospheric and Ionospheric Sounding – Methods and Results

“…Sites on the eastern Atlantic and northeast Pacific coasts have lower annual variations, probably because of the moderating effect of the ocean on climate. Sites on the lee side of the Alps have higher annual variation, possibly due to the combined effects of a rain shadow in the winter and high moisture from the Mediterranean in the summer (Haase et al, 2003;Deblonde et al, 2005). Figure 7 shows the annual phase distribution with the latitude, where phase values are counted as clockwise from the north.…”