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The conventional Global Navigation Satellite System (GNSS) processing is typically contaminated with errors due to atmospheric variabilities, such as those associated with the mesoscale phenomena. These errors are manifested in the parameter estimates, including station coordinates and atmospheric products. To enhance the accuracy of these GNSS products further, a better understanding of the local-scale atmospheric variability is necessary. As part of multi-GNSS processing, station coordinates, carrier phase ambiguities, orbits, zenith total delay (ZTD) and horizontal gradients are the main parameters of interest. Here, ZTD is estimated as the average zenith delay along the line-of-sight to every observed GNSS satellite mapped to the vertical while the horizontal gradients are estimated in NS and EW directions and provide a means to partly account for the azimuthally inhomogeneous atmosphere. However, a better atmospheric description is possible by evaluating the slant path delay (SPD) or slant wet delay (SWD) along GNSS ray paths, which are not resolved by ordinary ZTD and gradient analysis. SWD is expected to provide better information about the inhomogeneous distribution of water vapour that is disregarded when retrieving ZTD and horizontal gradients. Usually, SWD cannot be estimated directly from GNSS processing as the number of unknown parameters exceeds the number of observations. Thus, SWD is generally calculated from ZTD for each satellite and may be dominated by un-modelled atmospheric delays, clock errors, unresolved carrier-phase ambiguities and near-surface multipath scattering. In this work, we have computed multipath maps by stacking individual post-fit carrier residuals incorporating the signals from four GNSS constellations, i.e. BeiDou, Galileo, Glonass and GPS. We have selected a subset of global International GNSS Service (IGS) stations capable of multi-GNSS observables located in different climatic zones. The multipath effects are reduced by subtracting the stacked multipath maps from the raw post-fit carrier phase residuals. We demonstrate that the multipath stacking technique results in significantly reduced variations in the one-way post-fit carrier phase residuals. This is particularly evident for lower elevation angles, thus, producing a retrieval method for SWD that is less affected by site-specific multipath effects. We show a positive impact on SWD estimation using our multipath maps during increased atmospheric inhomogeneity as induced by severe weather events.

As one of the most important components of the global hydrologic cycle, atmospheric water vapor shows significant variability in both space and time over a large range of scales. This variability results from the interactions of many different factors, including topography and the presence of specific atmospheric processes. One of the key regions for affecting global climatic variations lies in the sub-Antarctic zone over the Southern Ocean with its Antarctic Circumpolar Current and the associated Antarctic Convergence. There, in this cold and maritime region, lies South Georgia Island with its weather and climate being largely affected by both the dominating ocean currents and the westerly winds in this zone. While the island forms an important outpost for various surface observations in this largely under sampled and extremely remote region, it also forms a barrier for these winds due to its high topography. This, in turn, leads to various local meteorological phenomena, such as warm Foehn winds, which have a significant impact on the near-surface meteorology and contribute to the accelerated glacier retreat observed for the northeast of the island.Surface meteorological data have been available for several stations near King Edward Point (KEP) in South Georgia for much of the 20th century. Since 2013 and 2014, Global Navigation Satellite System (GNSS) data have been available at five locations around the periphery of the island. In this study, we investigate the consistency between the different surface meteorological data sets and along with GNSS Precipitable Water Vapour we use these to analyse historic Foehn events.

Multipath is a largely unmodelled source of error and causes large range errors in Global Navigation Satellite System (GNSS) observations. The effects have strong site-specific characteristics and impact each receiver differently. Multipath errors can propagate and can cause in-situ position and velocity biases and are also contributing to the pervasive draconitic harmonic signals. We employ an empirical approach to reducing the effects of multipath by stacking one-way post-fit carrier phase residual observations by applying an appropriate averaging scheme. Our processing is based on static multi-GNSS observations using various scientific GNSS software packages (Bernese GNSS Software, NAPEOS, GAMIT-GLOBK, PRIDE and GINS). Our multipath stacking (MPS ) uses the stacking of individual residuals generated by variable azimuth cell size (congruent cells) by allocating carrier phase residuals in each cell, unlike fixed azimuth cell resolution in the standard MPS approaches. This reduces the binning of fewer residuals at higher elevation angles. Before stacking, we also apply rigorous statistical outlier screening tests for each one-way post-fit carrier phase residual assigned to each of the congruent cells. We thus correct the multipath effects by subtracting the stacked multipath map from the post-fit carrier phase residual. Using this technique we produce a model available in the form of the Antenna Exchange (ANTEX) file format, that can potentially be implemented in routine GNSS analysis with no or little additional overhead for individual analysis centers (ACs).In this study, we assess the feasibility and applicability of the MPS maps as an International GNSS Service (IGS) product for routine GNSS analysis. We have selected a subset of IGS stations with and without known multipath issues in different climatic zones. We demonstrate the multipath stacking technique to result in a significant reduction of the variation in the one-way post-fit carrier phase residuals. For GPS-only solutions, the MPS technique shows a decrease of up to 30% in the RMS value of the one-way post-fit carrier phase residuals. We have also tested our MPS for other constellations such as GLONASS, Galileo and BeiDou, and combinations of these .

Flooding is the number one natural disaster in terms of insured and uninsured losses annually. The development of reliable methods for flood simulation have greatly improved our ability to predict floods thereby reducing damages and loss of life in flood-prone regions. However, there is still a lot of room for improvement and innovation to provide better predictions, especially for flash floods, particularly in urban areas This is addressed in the present study, the goal of which it is to improve simulation and prediction of flash floods and to develop a spatial decision-making model for implementing flood protection measures. In this regard, different approaches for flood simulation and flood protection should be applied. The proposed methodology links flood hazard modeling, remote sensing and machine learning methods. Combining these physical models and data driven methods will result in a more reliable hybrid model that can be employed for prediction of (flash) floods and event analysis. In order to achieve the research goal of present study we: i) add more functionality to a hydrodynamic model code; ii) complement the latter with data driven methods ;iii) develop a spatial decision-making model framework for defining flood protection measures, iv) validate process-based and data driven methods, and finally v) cross-evaluate Light Detection And Radar (LiDAR) topography with available local super-resolution drone data to assess the ability to incorporate local flood defenses into the models. The most important outcome is the creation of valuable flood maps in areas where it matters - while accounting for effects of land use and climate change. This will serve scientists as well as land and risk management authorities with better actionable flood risk information in locations where people and assets are located and in danger. It also develops innovative methodologies for estimating the changing risk from flash floods based on land use scenarios and climate change projections. Moreover, developing spatial multi-criteria decision making (SMCDM) can help decision makers to determine suitable locations and methods for flood protection measures. These methods will be particularly valuable in the context of solving current challenges of accounting for and mitigating flash floods and the effects of climate change.

Water vapor is an important medium for the transmission moisture and latent heat in the atmosphere. It is one of the most abundant and dominant greenhouse gases in the atmosphere, which is crucial for global warming. With higher temperatures, the specific humidity will also increase as predicted by the nonlinear Clausius-Clapeyron relationship, indicating a positive feedback loop. Hence, estimation of the trend of Integrated Water Vapor (IWV) in the atmosphere is of great importance for global warming research. However, previous studies have shown that the trends of IWV are usually rather small. Therefore, it is important to estimate the IWV trend and its associated uncertainty with a reasonable mathematical model for the homogenized time series from homogenously reprocessed GPS data sets. Since the 1990s, the Global Positioning System (GPS) has successfully been employed to retrieve IWV with a high temporal resolution, all-weather condition and with global coverage. In this work, we used the hourly GPS Zenith Total Delay (ZTD) time series for 1995.0-2017.0 at 21 European GPS stations derived from a homogeneous data reprocessing. For the conversion of ZTD to IWV, we employed the meteorological variables from ERA5, a state-of-the-art atmosphere reanalysis product newly released by the European Centre for Medium-Range Weather Forecasts (ECMWF). Then, we investigated the influence of noise model assumptions within the mathematical model on the uncertainties of IWV trend estimates. As expected, the results confirmed that the assumption of a white noise only model tends to underestimate the trend uncertainty. A first-order autoregressive process is the preferred mathematical model for a more realistic estimation of the IWV trend uncertainty.

Zenith Total Delay (ZTD) time series, derived from the re-processing of Global Positioning System (GPS) data, provide valuable information for the evaluation of global atmospheric reanalysis products such as ERA-Interim. Identifying the correct noise characteristics in the ZTD time series is an important step to assess the "true" magnitude of ZTD trend uncertainties. The ZTD residual time series for 1995–2015 are generated from our homogeneously re-processed and homogenized GPS time series from over 700 globally distributed stations classified into five major climate zones. The annual peak of ZTD data ranges between 10 and 150 mm with the smallest values for the polar and Alpine zone. The amplitudes of daily curve fall between 0 and 12 mm with the greatest variations for the dry zone. The autoregressive process of fourth order plus white noise model were found to be optimal for ZTD series. The tropical zone has the largest amplitude of autoregressive noise (9.59 mm) and the greatest amplitudes of white noise (13.00 mm). All climate zones have similar median coefficients of AR(1) (0.80 ± 0.05) with a minimum for polar and Alpine, which has the highest coefficients of AR(2) (0.27 ± 0.01) and AR(3) (0.11 ± 0.01) and clearly different from the other zones considered. We show that 53 of 120 examined trends became insignificant, when the optimum noise model was employed, compared to 11 insignificant trends for pure white noise. The uncertainty of the ZTD trends may be underestimated by a factor of 3 to 12 compared to the white noise only assumption.

Global Navigation Satellite Systems (GNSS) meteorology is an established operational service providing hourly updated GNSS tropospheric products to the National Meteorologic Services (NMS) in Europe. In the last decade through the ground-based GNSS network densification and new processing strategies like Precise Point Positioning (PPP) it has become possible to obtain sub-hourly tropospheric products for monitoring severe weather events. In this work one year (January–December 2013) of sub-hourly GNSS tropospheric products (Zenith Total Delay) are computed using the PPP strategy for seven stations in Bulgaria. In order to take advantage of the sub-hourly GNSS data to derive Integrated Water Vapour (IWV) surface pressure and temperature with similar temporal resolution is required. As the surface observations are on 3 hourly basis the first step is to compare the surface pressure and temperature from numerical weather prediction model Weather Forecasting and Research (WRF) with observations at three synoptic stations in Bulgaria. The mean difference between the two data-sets for 1) surface pressure is less than 0.5 hPa and the correlation is over 0.989, 2) temperature the largest mean difference is 1.1 °C and the correlation coefficient is over 0.957 and 3) IWV mean difference is in range 0.1–1.1 mm. The evaluation of WRF on annual bases shows IWV underestimation between 0.5 and 1.5 mm at five stations and overestimation at Varna and Rozhen. Varna and Rozhen have also much smaller correlation 0.9 and 0.76. The study of the monthly IWV variation shows that at those locations the GNSS IWV has unexpected drop in April and March respectively. The reason for this drop is likely problems with station raw data. At the remaining 5 stations a very good agreement between GNSS and WRF is observed with high correlation during the cold part of 2013 i.e. March, October and December (0.95) and low correlation during the warm part of 2013 i.e. April to August (below 0.9). The diurnal cycle of the WRF model shows a dry bias in the range of 0.5-1.5 mm. Between 00 and 01 UTC the GNSS IWV tends to be underestimate IWV which is likely due to the processing window used. The precipitation efficiency from GNSS and WRF show very good agreement on monthly bases with a maximum in May-June and minimum in August–September. The annual precipitation efficiency in 2013 at Lovech and Burgas is about 6 %.