The purpose of the data assimilation is to optimally use all the available information, to determine as accurately as possible the state of the atmosphere. In this context is shown the assimilation of the GPS-ZTD (Global Positioning System - Zenith Total Delay) by a 3D-Var data assimilation system that can be used in cycling mode with the Regional Atmospheric Modeling System (RAMS; Federico, 2013). The water vapour mixing ratio (q) and temperature (T) given by the RAMS model (background) are modified according to the assimilation of the GPS-ZTD with the purpose of improving the representation of the humidity and temperature fields. To verify the impact of the GPS-ZTD data assimilation on the representation of the atmospheric humidity field, especially at the local scale, a numerical experiment was performed over an Italian central region, over a one-month time frame. The studied network consists of 28 geodetic receivers and 3 single frequency receivers, this latter category of receivers has turned up more and more in marketing for the very low cost with respect to high level of performances. For what concerns all devices, processing was carried out in PPP (Precise Point Positioning) using RTKLIB, an Open Source Program Package for GNSS Positioning (http://www.rtklib.com/). For the application of PPP processing to single-frequency receivers, a new ground-based augmentation strategy for centimetric PPP solution with GNSS single frequency receiver was used (Mascitelli et al., 2019); the developed algorithm, starting from the observations of a unique dual frequency “reference” receiver, is able to reconstruct L2 synthetic observations for any single frequency receiver located in its surroundings (15-20 km). For the verification of 3D-Var system, the whole dataset was divided in two parts: a set of receivers was used for the data assimilation, while the remaining stations were used for verification. Results show a substantial decrease of the RMSE of the GPS-ZTD and IWV for the stations used for the verification of the methodology. The impact on precipitation forecast was also assessed and results are promising.

Main objective of the “BalkanMed real time severe weather service” (BeRTISS, 2017-2019) project is to establish a pilot transnational severe weather service by exploiting Global Navigation Satellite Systems (GNSS) tropospheric products to enhance the safety, the quality of life and environmental protection in the Balkan-Mediterranean region. In Bulgaria severe weather events (intense precipitation, hail and thunderstorms) are common in the summer months and are associated with large economic losses for example in agriculture. The Bulgarian Hail Suppression Agency and the Sofia University are partners in the BeRTISS project with final aim to develop the Bulgarian Integrated NowCAsting tool (BINCA). BINCA will use data from the recently deployed ground-based GNSS network of 12 stations in Bulgaria. The GNSS, weather radar as well as surface atmospheric observation will be combined with the Weather Research and Forecast (WRF) model simulations covering Bulgaria. BINCA will provide products in near-real time on a publicly accessible web platform to facilitate the operational tasks in hail suppression in Bulgaria but also other operational and public services. In this work the BeRTISS progress and the future work in Bulgaria are discussed.

Within the project Greenlight the Austrian Federal Railways have equipped a large number of trains with high-quality dual-system single frequency receivers with RTK-capability. The goals of this project are manifold comprising the real-time train positing to support passenger information systems or the online monitoring of cargo and many more. In near future several hundreds of additional vehicles will be equipped with such receivers. Although the current configuration is based on GPS/GLONASS receivers the upcoming generation will consist of combined GPS/Galileo single frequency receivers.
On basis of real observation data gathered along various railway tracks in Austria this presentation studies the potential of deriving tropospheric delay information useful for numeric weather models (NWM). The challenges are to retrieve tropospheric parameters (in first place vertical delays) with sufficient accuracy from data tracked by a fast moving object as well as the real-time or close-to-real-time data transmission between train and processing center. Further problems arise due to signal masking, multi-path and the required modelling of the ionospheric delay. On the other hand, compared to the current static networks of a few high-quality meteorological sites and several GNSS reference stations, the beauty of the solution is to make use of a fleet of almost 1000 GNSS sensors permanently moving over the Austrian territory. The presented study is based on post-processed raw observation data. Nevertheless, taking into consideration the upcoming 5G-network roll-up also a fast data transmission, at least when the trains pass railroad stations, can be assured in future.
Aside of the discussion of quality aspects of the delay parameters derived from GPS/Galileo L1/E1 data also a NWM data assimilation step based on a few hundreds of simulated delays will be presented.

Since the early 80’ ies, the ‘centimetric’ tropospheric corrections for GNSS attracted researchers’ interest. This was strongly triggered by the advent of carrier phase positioning solutions, requiring an adequate treatment of atmospheric or, as discussed in this paper, of tropospheric corrections. Of course the corrections for satellite communication and measurements were not completely unknown at that point, handy formulae, still in use today were developed in the 60’ies and 70’ies. Now, even for differential carrier phase calculations the correction turned out not to be precise enough. Especially in areas with large height differences and short correlated weather effects, the differential path delays would not cancel out to the desired level. Two approaches, one might be called ‘meteo modeling’ and the other one ‘estimation’, found their adherers, a third being a combination of both. The estimation of path delays, however, allowed the derivation of meteorological parameters by inversion. In montane regions as Switzerland the problem of tropospheric refraction, affecting the high precision baseline determination over noticeable height differences led, starting in the early 80’ies, to the installation of an alpine GPS test field and to the development of different algorithmic approaches to solve the refraction problem. Some emphasis was put onto the tomographic inversion of path delay estimations (AWATOS) and onto 4-dimensional functional and statistical interpolation of path delays and meteorological fields (COMEDIE). 4-dimensional signifies the inclusion of time correlation into the calculation. In the past years, we studied different scales of networks, from the size of an alpine catchment to regional areas including also flat lowlands. Newest developments are directed towards GNSS application in SAR observations. There, the development of short wavelength (x-band) and the quasi real-time usage of the measurements for hazardous movement detections especially in mountainous terrain makes it necessary to work on the determination of precise refraction corrections. This paper shortly describes the methods, unveils a few pitfalls, and shows examples of the different activities with their results and outlooks.

Tropospheric Zenith Delay (ZTD) is one of the main error source in Global Navigation Satellite Systems (GNSS). It is also a meteorological variable that contains temporal and spatial variations owing to the fact that it can be converted to the precipitable water vapor.

Empirical Orthogonal Function (EOF) analysis is a statistical method to decompose a multi-variate data set into its principal components (EOF modes). The covariance matrix decomposition is applied to the data set to derive EOF modes with which the temporal and spatial data matrix is reconstructed. Additionally, the differences between original matrix and reconstructed matrix are computed and taken as the residual errors. Also, Root Mean Square Residual Error (RMSE) of each matrix which is a good indicator of the significant EOF modes and noise are calculated.

In this study, EOF analysis was employed to analyze the separation capabilities of the noise and the principal components of ZTD derived at 14 TUSAGA-Active (Turkish CORS) stations for 32 days in a test area limited to 30°–34° northern latitudes and 39°–42° eastern longitudes. Firstly, the EOF modes and RMSE of each reconstructed matrix were calculated. The RMSE value corresponding to the lowest significant EOF value was taken as the precision of ZTD for the evaluation time. In addition, the total variances of significant modes was used in the determination of the optimal level of the EOF modes.