Troposphere: Signal or Noise? (2003)

The motivation for studying the non-dispersive part of the atmosphere, also known as troposphere, is manifold. Meteorologists study the atmosphere to learn about its physical properties and to do weather predictions. For GPS positioning the modeling of the troposphere is essential for unbiased coordinate results. One of the common interests for both parties is the zenith total delay (ZTD) of troposphere. GPS networks can serve as an array of sensors to provide continuous tropospheric zenith total delay for numerical weather prediction applications. Several research institutes work actively in this area to derive more accurate estimates for ZTD in near real time (normally with one or two hours delay). In this paper, we will show that ZTD estimation in real-time can give comparable results as in postprocessing. Since 1999 the Swiss Federal Office of Topography has been active in the European project COST-716 (exploitation of ground-based GPS for climate and numerical weather prediction application). After a successful benchmarking (van der Marel et al., 2001), swisstopo has been contributing zenith total delay (ZTD) estimates in near real-time (NRT-ZTD) since December 2001. In addition to the 29 Swiss AGNES sites, 20 EUREF sites are processed on an hourly basis. Furthermore, about 12 sites from other networks, mainly in France, are being used in order to improve the station distribution in the western part of Europe. This area is important because the dominating weather conditions from the Atlantic Ocean usually pass over France before they reach Switzerland. Real-time availability of the zenith total delays is an interesting alternative to the postprocessing values if the same quality of the estimates is guaranteed. As reference also post-processing ZTD values using the final IGS orbits are generated with a time delay of 21 days. Networked reference station software for GPS processing must estimate tropospheric delays to provide a reliable network solution of the error models. The aim is to provide the field users with information about their local errors to improve productivity and accuracy of the positioning. In addition to the tropospheric delay, ionospheric delays and satellite orbit improvements are applied. Since January 2003 ZTD values can be extracted for the Swiss AGNES network using the real-time network RTK software GPSNet 2.0. This is possible with accumulation intervals of 1 minute and a negligible time delay. A comparison of the postprocessing and real-time results proves to be interesting for meteorology as well as for networked RTK. Therefore the GPS networking software benefits from the additional know-how provided by the meteorology data, and the meteorologists gain access to real-time data. This paper assesses real time ZTD estimation with postprocessing results. For the AGNES network, the absolute ZTD as well as the differential values between the reference stations were compared. The analyses show that 1 cm accuracy for absolute ZTD and 7 mm for relative ZTD can be achieved in real time. Finally it can be said that coordinating both the needs and the efforts of meteorology and GPS networking leads to better stochastic models for the networked GPS and realtime availability of absolute tropospheric zenith delays for meteorology. TROPOSHERE IS SIGNAL Water vapor is one of the most highly variable atmospheric quantity, both spatially and temporally, present within the lower troposphere (0-5km). The distribution of water vapor is traditionally monitored on an operational basis by balloon soundings and surface stations (Bock et al, 2001). From last decade, GPS technique has been successfully applied to the remote sensing of integrated water vapor (IWV). Parallel to the validation activities within the COST-716 project the necessary algorithms were developed in order to directly assimilate the GPS ZTD estimates as additional observations in the numerical weather prediction (Guerova et al., 2002). Several test periods with different weather conditions were selected. For all these periods of about 1 month of data the numerical weather prediction was recomputed once with using GPS, once without GPS. Figure 1: Differences between IWV values derived from numerical weather prediction models with and without using GPS (September 9, 2001, 12:00 UTC) The differences in terms of the integrated water vapor, which can be computed from the ZTD with known pressure and temperature, are given in Fig. 1. It is a special situation in September 2001, where bigger differences between the 2 predictions were detected. Discrepancies are obvious mainly in the Genoa region. Fig. 2 shows the IVW estimates from the numerical weather prediction model without using GPS and with using GPS for the GPS site GENO. This example shows quite nicely that the numerical weather prediction model without using GPS predicts a considerable drop in IWV, which was not realistic when comparing with the actual weather situation. The use of the GPS ZTD values was able to stabilize the numerical weather prediction model. Figure 2: IWV estimates from the numerical weather prediction model without using GPS (aLMo) and with using GPS (aLMo + GPS) for the GPS site GENO. The results of the assimilation tests can be roughly summarized: • GPS has a significant influence on the prediction up to 6 hours • Positive impact of GPS ZTD estimates on numerical weather prediction was found mainly in summer time. TROPOSPHERE IS NOISE In GPS positioning, tropospheric delay is treated as systematic error. It mainly affects the height component. Uncorrected tropospheric residuals will cause systematic biases in the height component of the position. This kind of effect can be easily seen from the long-term height variation when processing a static baseline using kinematic post-processing. Fig. 3 shows the rover height error versus time for a 32 km static baseline (Germany, Hoehenkirchen to Neufahrn, Nov. 22, 2001 0:00-6:00). The dataset was processed by the Trimble Total Control (TTC) kinematic processor using the ionosphere free observable with default tropospheric model (modified Hopfield, standard met conditions). It can be seen clearly that the height is biased up to 10 cm from 3 to 6 o’clock. However, if tropospheric scaling (it is a measure equivalent to ZTD estimation, it equals to estimated ZTD divided by ZTD from tropospheric model) is applied to the rover (Table. 1) in the processing, the height is almost flat as shown in Fig. 4.