## 1 Introduction
In addition, integer ambiguity resolution is possible only if precise observation phase biases are available, thus often non-integer-fixed ambiguities are usually estimated. First ZTD products using the PPP method and new IGS RTS products were demonstrated in 2014 [16; 17; 18]. Though the ZTD estimates already reached an internal precision below 10 mm when compared to GNSS final products, the studies also revealed significant station-/product-specific systematic errors attributed to inconsistencies in precise models applied in PPP and to the precise product generation. Fortunately, a large portion of the systematic errors are changing slowly over time and are thus not critical for an assimilation into NWMs which were designed to identify and remove biases on monthly basis by comparing station/product-specific GNSS ZTDs with their counterparts from the model background information [10]. However, the precision of the real-time ZTDs obtained using the Kalman filter still remained worse by a factor of 1.5 compared to the E-Gvap standard NRT ZTD products. The reasons were twofold: (1) dependence on the quality of the RT products, and (2) use of the strategy for real-time data analysis. The E-Gvap solutions utilizing the batch processing and data files can be characterized with a standard deviation of 3-6 mm and 3-8 mm, for regional and global products, respectively, and a systematic error within 1-3 mm [19]. The accuracy evaluated with respect to external data, such as radiosonde, corresponds to 1-2 mm in the precipitable water vapor [15]. Anyway, real-time tropospheric products are ready to support assimilation in the rapid update cycle of NWM prediction or nowcasting, and target a short-term weather prediction or severe weather events monitoring [20]. Nowadays, multi-GNSS offers many satellites and signals that are expected to strengthen all estimated parameters, in particular the ZTD and horizontal tropospheric gradients. For the E-Gvap production, data from the US NASTAR Global Positioning System (GPS) have been initially used. In 2008, the IGS started a provision of ultra-rapid precise orbit products for the Russian GLONASSsatellites too. However, GLONASS data could have been included in NRT analyses only after resolving a 1.5 mm systematic difference in the ZTD when estimated independently from GPS and GLONASS data [21]. The bias was caused by using the IGS05 model of phase center offsets (PCO) for GLONASS satellite antennas [22], and the problem has been eliminated in 2012 [19] by adopting consistent PCO models for both constellations in new IGS08 realization [23]. Besides others, general limitations for the use of GNSS data from other global systems, European Galileo and Chinese BeiDou, persist mainly in (1) incompleteness of the constellations, (2) lack of precise models and calibrations for new signals, receiver and satellite instrumentations, and (3) lack of precise orbit and clock products supporting the ultra-fast processing mode. The situation will change soon as both global systems will become operational in next years. Since 2012, the IGS Multi-GNSS Experiment (MGEX, [[http://mgex.igs.org](http://mgex.igs.org)]([http://mgex.igs.org](http://mgex.igs.org))) has been successfully filling the gaps in data, metadata, models, formats, standards and products for an optimal exploitation of all global satellite constellations and other regional augmentations. Although Galileo and BeiDou systems have not been completed yet, several groups reported an initial positive impact when using multi-constellation for estimating ZTDs and horizontal tropospheric gradients [24; 25; 26; 27]. The GNSS ZTD estimated from a single hour of data is not stable and accurate enough for the E-GVAP usage due to high correlations with coordinates and initial phase ambiguities. At least 12-h data batch is usually used for the NRT processing in E-GVAP, which means a high degree of redundancy in data processing updated on an hourly basis.