Assessment of errors in Precipitable Water data derived from Global Navigation Satellite System observations

Combining METEOSAT-10 satellite image data with GPS tropospheric path delays to estimate regional Integrated Water Vapor (IWV) distribution

“…There are several approaches for estimating the amount of WV in the troposphere. The most common ones utilize radiosondes (Kley et al, 2000;Soden et al, 2004;Miloshevich et al, 2006), different techniques of GPS meteorology (Bevis et al, 1992(Bevis et al, , 1994Duan et al, 1996;Ware and Alber, 1997;Hagemann and Bengtsson, 2003;Vedel et al, 2004;Heise et al, 2009;Guerova et al, 2013;Hordyniec et al, 2015) or measurements from remote sensing satellites (Velden et al, 1997;Cresswell et al, 1999;Jiang et al, 2012). Radiosondes offer an essential component of the global observing system due to their extended lifetime and broad geographic coverage (Kley et al, 2000).…”

“…Currently, GNSS meteorology can provide continuous remote monitoring with high temporal and spatial resolution of the WV in the troposphere as long as the pressure and temperature are measured at the observation sites. However, systematic errors under 0.5 mm are still introduced into WV estimations and, when using pressure interpolation procedure, biases > 0.5 and 1 hPa standard deviation are introduced into the system (Hordyniec et al, 2015). Furthermore, the GPS integrated water vapor (IWV) estimations are generally validated by comparison either with radiosonde data (Moore et al, 2015), ECMWF IWV estimates (Heise et al, 2009) or water vapor radiometer (WVR) data (Shangguan et al, 2015), thus allowing us to obtain reasonable initial conditions data for creating new numerical models of zenith total delay (ZTD) and IWV for meteorological applications Figure 3.…”

“…In addition, retrieval of total WV from observations in the thermal IR split-window channels at 10.8 and 12.0 µm of the Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) over varying surface temperatures requires a nonlinear approach in order to make it applicable to the full range of global atmospheric conditions (Schroedter-Homscheidt et al, 2008). Recently, it has been shown that convective rainfall rate (CRR) can be derived using the 10.8 and 6.2 µm MSG-SEVIRI from the algorithm developed within the Satellite Application Facility with Support to Nowcasting and Very Short-Range Forecasting (NWC SAF), but it still needs to be calibrated using matrices generated from both SEVIRI and radar data (Ivančan-Picek et al, 2014).…”

Combining Meteosat-10 satellite image data with GPS tropospheric path delays to estimate regional integrated water vapor (IWV) distribution

“…The most common ones utilize radiosondes (Kley et al, 2000;Soden et al, 2004;Miloshevich et al, 2006), different techniques of GPS meteorology (Bevis et al, 1992(Bevis et al, , 1994Duan et al, 1996;Ware and Alber, 1997;Hagemann and Bengtsson, 2003;Vedel et al, 2004;Heise et al, 2009;Guerova et al, 2013;Hordyniec et al, 2015) or measurements from remote sensing satellites (Velden et al, 1997;Cresswell et al, 1999;Jiang et al, 2012). Radiosondes offer an essential component of the global observing system due to their extended lifetime and broad geographic coverage (Kley et al, 2000).…”

“…Currently, GNSS meteorology can provide continuous remote monitoring with high temporal and spatial resolution of the WV in the troposphere as long as the pressure and temperature are measured at the observation sites. However, systematic errors under 0.5 mm are still introduced into WV estimations and, when using pressure interpolation procedure, biases > 0.5 and 1 hPa standard deviation are introduced into the system (Hordyniec et al, 2015). Furthermore, the GPS integrated water vapor (IWV) estimations are generally validated by comparison either with radiosonde data , ECMWF IWV estimates (Heise et al, 2009) or water vapor radiometer (WVR) data (Shangguan et al, 2015), thus allowing us to obtain reasonable initial conditions data for creating new numerical models of zenith total delay (ZTD) and IWV for meteorological applications = 0.97) between the two data sets.…”