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Monitoring atmospheric conditions that lead to severe weather events is critical to their timely and accurate prediction and can help prevent of large economic losses. Bulgaria, located in southeastern Europe, has the highest mean number of thunderstorms and hailstorms. These events generally occur between April and September with a peak in July. In this study, both radio occultation (RO) and ground-based observations from the Global Navigation Satellite Systems (GNSS) were used to study two severe hailstorms that occurred in 2014 and 2019. In both storms, a cold upper-air pool was detected in addition to a large specific humidity anomaly between 2 and 6 km. In the hailstorm that occurred in July 2014, there was an RO temperature anomaly between 10 and 14 km as well as a positive specific humidity anomaly between 4 and 6 km. The integrated vapor transport (IVT) reanalysis from ERA5, indicated that the high specific humidity over the Mediterranean could be tracked to an atmospheric river over the North Atlantic, which was connected to a tropical cyclone. In the hailstorm that occurred in May 2019, elevated IVT values were observed before the storm. During this storm, a negative temperature anomaly peak was observed in the RO profile at 11.3 km as well as a positive specific humidity anomaly between 2 and 4.5 km. The WRF Model and the ERA5 dataset could reproduce the temperature profiles for both storms relatively well; however, they tended to underestimate specific humidity. The RO profiles were complemented by ground-based GNSS tropospheric delays with high temporal resolution. The evaluation of the WRF with ground-based GNSS tropospheric products revealed a time delay between the modeled and observed developments of both hailstorms.

One of the indispensable elements of high-resolution weather forecast systems is the provision of reliable initial conditions using observations. Among the methods for collecting meteorological data, besides the quality of measurements, their time and space variability play a crucial role. Hence, GNSS observations stand out as stable, bias-free alternatives for weather stations, radiosondes, or microwave satellites.Current studies of GNSS observations in weather forecasting give promising results. However, the observations themselves are subject to errors due to their geometry, mainly caused by insufficient vertical and horizontal resolution. Therefore, applying them in an operational forecasting model is challenging. A possible way to solve this is to integrate space and ground-based observations into one tomography model.The solution should be able to detect local, extreme weather phenomena with repeatable uncertainty and high numerical stability. Hence, we propose a precise 3D ray tracing solution for effective simulations of the ray path between the GNSS satellite and the GNSS receiver (Low Earth Orbiting LEO satellite), along with the ground receiver. Although, the combination of these results in one computationally efficient and stable model is a complex task.The following step is the 3D ray tracing simulation integration into a modified TOMO2 operator dedicated to the tomography of 3D wet refractivity fields. The ray tracing module collects information on ray points’ refractivity and distance traversed in models’ voxels along the ray path. Then delivers it to mutual observational matrices for ground- and space-based simulations. This study focuses on the methodology of integrated tomography modeling. Results are compared to the ground-based only GNSS tomography solution and validated with radiosondes profiles. The case studies are based on severe weather events in Poland with RO data delivered by SPIRE company and GNSS ground-based observations produced by UPWr. Numerical Weather Model input comes from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5.

Detection of volcanic hazards are important for public health and aircraft safety. In this study, we investigate the ability to detect volcanic plume using a simple analytic approach based on Global Navigate Satellite System (GNSS) observations, numerical weather model (NWM) fields and ground-based automatic weather stations. The GNSS data obtained from the network located around the Sakurajima volcano in Japan was used to estimate zenith tropospheric delays (ZTD) in October 2014, when the volcano showed an increase in activity. For our analysis, the ZTDs reconstructed in the ray tracing approach were applied to ERA5 reanalysis, and then compared with the ZTDs estimated by the Natural Resources Canada web GNSS processing software. Four analytical techniques were employed: target, space, time, and eventually, time and space differentiation. Meteorological data from nearby weather stations, as well as ERA5 reanalysis, helped separate the ZTD increments connected to weather conditions and volcanic activity. The results revealed that our approach correctly demonstrates Sakurajima activity in four cases out of five. We also noted a correlation between the results of our calculations and plume spreading direction data in 25% of cases.

Water vapour is a highly variable constituent of the troposphere; thus, its high-resolution measurements are of great importance to weather prediction systems. The Global Navigation Satellite Systems (GNSS) are operationally used in the estimation of the tropospheric state and assimilation of the results into the weather models. One of the GNSS techniques of troposphere sensing is tomography which provides 3-D fields of wet refractivity. The tomographic results have been successfully assimilated into the numerical weather models, showing the great potential of this technique. The GNSS tomography can be based on two different approaches to the parameterisation of the model’s domain, i.e. block (voxel-based) or grid (node-based) approach. Regardless of the parameterisation approach, the tomographic domain should be discretised, which is usually performed in a regular manner, with a grid resolution depending on the mean distance between the GNSS receivers. In this work, we propose a new parameterisation approach based on the optimisation of the tomographic nodes’ location, taking into account the non-uniform distribution of the GNSS information in the troposphere. The experiment was performed using a dense network of 16 low-cost multi-GNSS receivers located in Wrocław and its suburbs, with a mean distance of 3 km. Cross-validation of four different parameterisation approaches is presented. The validation is performed based on the Weather Research and Forecasting model as well as radiosonde observations. The new approach improves the results of wet refractivity estimation by 0.5–2 ppm in terms of RMSE, especially for altitudes of 0.5–2.0 km.

Nowadays, weather forecast is an important factor of everyday life that we should be well prepared for. Especially the amount of rainfall can positively or negatively influence our lifestyle. While a moderate rainfall is supportive for agriculture or provision of potable water, too much rainfall can cause disasters like floods. Therefore, accessing the rain information in near-real-time is beneficial in all aspects. In recent years, GNSS meteorology has been widely utilized as a valuable tool to better interact with the weather conditions in the now-casting and forecasting applications. Nevertheless, rainfall cannot be estimated directly from the GNSS measurements, and therefore some other methods like Artificial Intelligence (AI) are employed to do so. One of the well-known methods in AI is Machine Learning (ML) which focuses on data in order to model or classify various cases such as anomaly detection, earthquake prediction, and rainfall classification. The main objective of this research is to develop a predictive model for accumulated rain every 3 hours for an area populated with 21 GNSS stations of the EUREF Permanent GNSS Network (EPN). For this purpose, we applied different ML methods. The period of interest ranges from 2017 January to 2021 October. The years 2017 to 2020 are used for training, and 2021 is utilized to evaluate the rain model. The temperature, atmospheric pressure, wind speed, wind direction, relative humidity, Zenith Wet Delay (ZWD), Gradients (GN-S, GE-W), Total Electron Contents (TEC) are selected as input parameters in ML. Besides, the rain product from Global Satellite Mapping of Precipitation (GSMaP) is considered as the reference of the model. Finally, the accumulated rain prediction models are derived every 3 hours over the area of interest.

The number of tropospheric observations that are assimilated in current numerical weather forecasting systems, is large. From automatic weather stations, to geostationary satellites, from radiosondes to polar orbiting microwave satellites. Amid the currently available data sources, GNSS stands out as a bias free, self calibrating, high fidelity temperature and water vapour measurements. Until recently GNSS was used weather forecasting only in two ways: as a ground-based point, high-frequency observation of integrated water vapour (IWV) or zenithal integrated observation of temperature and water vapour, or as a sparse space-based profile observation of temperature and water vapour content (provided as a refractivity or bending angle profile). In the last couple of years GNSS tomography, a 3D imaging technique, is gaining attention as a weather model data source. However, low space resolution combined with large uncertainty of the tomography reconstruction makes this technique difficult to apply in operational forecasting. Therefore this technique, to be considered as a valuable data source in weather models, has to be numerically stable with known repeatable uncertainty. We believe that a way forward is to combine space-based and ground-based observations using the tomography principle. A way forward is to effectively simulate the signal trajectory between the GNSS transmitter and GNSS receiver (Low Earth Orbiting LEO satellite). 3D ray-tracing modelling of the radio occultation (RO) event based on Numerical Weather Model is performed. The challenge here is to make these ray-tracing results comparable with excess phase observations at the LEO satellite. Modelling by 3D ray-tracing is performed by the modified Atmospheric TOMography (ATOM) software with the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 model. This module uses the position of the GNSS satellites as starting point and iteratively propagates the signal path by collecting information on refractive parameters along its path based on nodal points. This study is based on the ten selected RO events from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) report “Optimising tracking strategies for Radio Occultation. Task 1 - the profile dataset.”. Modelling was performed by varying the grid resolution of the ERA5 model and the length of a propagator step size segment to obtain total excess phase delay values. Additionally, Radio Occultation Processing Package (ROPP) 2D ray-tracing multiple phase screen simulation was run to confront obtained from ATOM phase delays. The COSMIC Data Analysis and Archive Center (CDAAC) observed excess phase was used as a reference data source.

Tropical cyclone (TCs) prediction has improved in the last decade, but studying their structure and dynamics remains a challenging task due to limited in situ observations. The global navigation satellite system (GNSS) technique enables retrieving the state of the troposphere with high accuracy in all weather conditions. In this article, we use GNSS slant total delays (STDs) to verify the quality of the weather models during the passage of the TC Meranti, which was the strongest TC in 2016. STDs observed from 28 GNSS stations in Taiwan are compared with the STDs reconstructed from three meteorological data sets: the weather research and forecasting (WRF) model, the global forecast system (GFS), and the ERA-Interim (ERA) using a 2-D ray-tracing technique. Furthermore, two strategies were tested—including and excluding the delays due to liquid and ice water content. The results reveal a good consistency between the GNSS and the ray-traced STDs. The best agreement was found for the WRF, with a mean difference of −0.5 mm and a standard deviation of 29.4 mm when the hydrometeor delay was included. The mean hydrometeor contribution reached up to 2.8 mm for the WRF, while for the ERA and GFS, the contribution to the total delay was negligible. The recorded absolute percent differences were greater for the northern HENC station (mostly within 0.8%) than for the southern GOLI station (on average below 0.5%) due to a closer location to the passing Meranti. The most significant biases were seen in the ERA (around 7%) and the GFS (exceeding −3%) for the PLIM station, located in the central mountain range valley.

From Global Navigation Satellite Systems (GNSS) signals, accurate and high-frequency atmospheric parameters can be determined in all-weather conditions. GNSS tomography is a technique that takes advantage of these parameters, especially of slant troposphere observations between GNSS receivers and satellites, traces these signals through a 3-D grid of voxels, and estimates by an inversion process the refractivity of the water vapour content within each voxel. In the last years, the GNSS tomography development focused on numerical methods to stabilize the solution, which has been achieved to a great extent. Currently, we are facing new challenges and possibilities in the application of GNSS tomography in numerical weather forecasting, the main research objective of this paper. In the first instance, refractivity fields were estimated using two different GNSS tomography models (TUW, WUELS), which cover the area of central Europe during the period of 29 May–14 June 2013, when heavy-precipitation events were observed. For both models, slant wet delays (SWDs) were calculated based on estimates of zenith total delay (ZTD) and horizontal gradients, provided for 88 GNSS sites by Geodetic Observatory Pecny (GOP). In total, three sets of SWD observations were tested (set0 without compensation for hydrostatic anisotropic effects, set1 with compensation of this effect, set2 cleaned by wet delays outside the inner voxel model), in order to assess the impact of different factors on the tomographic solution. The GNSS tomography outputs have been assimilated into the nested (12 and 36 km horizontal resolution) Weather Research and Forecasting (WRF) model, using its three-dimensional variational data assimilation (WRFDA 3D-Var) system, in particular, its radio occultation observation operator (GPSREF). As only total refractivity is assimilated in GPSREF, it was calculated as the sum of the hydrostatic part derived from the ALADIN-CZ model and the wet part from the GNSS tomography. We compared the results of the GNSS tomography data assimilation to the radiosonde (RS) observations. The validation shows the improvement in the weather forecasting of relative humidity (bias, standard deviation) and temperature (standard deviation) during heavy-precipitation events. Future improvements to the assimilation method are also discussed.

Inversion of radio occultation (RO) measurements to atmospheric parameters in the neutral atmosphere utilizes the assumption of spherical symmetry by implementation of the Abel transform. The main contribution to the retrieved refractional angle and other geophysical parameters comes from gaseous properties of the atmosphere. The atmospheric refraction is expressed by a function of air pressure, air temperature, and water vapor pressure. Such commonly adopted methodology results in highly comparable RO retrievals with background models. However, in the lowermost troposphere referred to as planetary boundary layer, inversion in spherically symmetric atmosphere is an ill‐conditioned problem. The presence of superrefractions introduces negative errors in the RO‐retrieved refractivity (N‐bias). We show that significant refractivity gradients are frequently collocated with clouds over oceans in tropical and subtropical regions. Based on gridded monthly means we show that superrefractions usually occur at altitudes up to 2 km and the largest cloud fractions tend to suspend at underlying layers. The magnitude of clouds expressed in terms of refractivity units can exceed 1.5, which corresponds to 0.5% in terms of fractional differences. We use both geometrical optics and wave optics techniques to illustrate propagation mechanisms in RO retrievals. Simulation experiments suggest that RO inversions in cloudy planetary boundary layer lead to larger negative N‐biases. Low‐level clouds retrieved from numerical weather prediction model could therefore be used as an indicator of erroneous RO observations. A better agreement with RO refractivity could be achieved by incorporating cloud variables into background fields especially over the Pacific and Atlantic Oceans.

Predictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies.

The COVID-19 pandemic has highlighted the importance of public health policies and crisis management. The spread of diseases is a complex phenomenon with many time-dependent variables, which hampers an accurate prediction of epidemic evolution. Models of epidemic spread play an important role in guiding in designing public health policies, enabling hypothetical scenarios simulation and rapid analyses of ongoing epidemics.Over the last century disease spread models evolved from deterministic compartmental models into complex metapopulation and agent-based simulations. Today’s solutions consider many factors, not limiting to the disease itself but also simulating socio-demographic structure and population flows. In the era of globalisation, human mobility became the major factor of rapid disease spread. Although current models consider international and regional travels, used mobility models are simplistic. This limits the accuracy and spatio-temporal resolution of these simulations, providing daily cases updates aggregated to large regions.We propose an agent-based mobility model, offering a simulation of hourly temporal resolution depicting mobility with less than a few hundreds of meters spatial precision. Agent-based models allow each simulation agent to assign different characteristics, e.g. susceptibility to infection, mobility behaviour.We integrate our mobility model with disease spread simulation, using an agent’s interaction to detect virus transmission. In every time step of the model, the interaction between the agents, their current state and localisation of interaction are used to determine the probability of infection. Social interactions in the context of the spread of the disease are a fundamental element influencing the temporal and spatial extent of the disease. An essential aspect of our model is the integration of the simulation environment with the points-of-interests (POIs), which represent the destination of the majority of non-home-work related activities. We validate the accuracy of mobility replication and present hypothetical scenarios of disease spread in one of the large European cities, presenting capabilities of our solution.

The dynamics of water vapor distribution in the troposphere, measured with Global Navigation Satellite Systems (GNSS), is a subject of weather research and climate studies. With GNSS, remote sensing of the troposphere in Europe is performed continuously and operationally under the E-GVAP (http://egvap.dmi.dk/) program with more than 2000 permanent stations. These data are one of the assimilation system component of mesoscale weather prediction models (10 km scale) for many nations across Europe. However, advancing precise local forecasts for severe weather requires high resolution models and observing system. Further densification of the tracking network, e.g. in urban or mountain areas, will be costly when considering geodetic-grade equipment. However, the rapid development of GNSS-based applications results in a dynamic release of mass-market GNSS receivers. It has been demonstrated that post-processing of GPS-data from a dual-frequency low-cost receiver allows retrieving ZTD with high accuracy. Although low-cost receivers are a promising solution to the problem of densifying GNSS networks for water vapor monitoring, there are still some technological limitations and they require further development and calibration.We have developed a low-cost GNSS station, dedicated to real-time GNSS meteorology, which provides GPS, GLONASS and Galileo dual-frequency observations either in RINEX v3.04 format or via RTCM v3.3 stream, with either Ethernet or GSM data transmission. The first two units are deployed in a close vicinity of permanent station WROC, which belongs to the International GNSS Service (IGS) network. Therefore, we compare results from real-time and near real-time processing of GNSS observations from a low-cost unit with IGS Final products. We also investigate the impact of replacing a standard patch antenna with an inexpensive survey-grade antenna. Finally, we deploy a local network of low-cost receivers in and around the city of Wroclaw, Poland, in order to analyze the dynamics of troposphere delay at a very high spatial resolution.As a measure of accuracy, we use the standard deviation of ZTD differences between estimated ZTD and IGS Final product. For the near real-time mode, that accuracy is 5 mm and 6 mm, for single- (L1) and dual-frequency (L1/L5,E5b) solution, respectively. Lower accuracy of the dual-frequency relative solution we justify by the missing antenna phase center correction model for L5 and E5b frequencies. With the real-time Precise Point Positioning technique, we estimate ZTD with the accuracy of 7.5 – 8.6 mm. After antenna replacement, the accuracy is improved almost by a factor of 2 (to 4.1 mm), which is close to the 3.1 mm accuracy which we obtain in real-time using data from the WROC station.

The numerical weather model starts from the initial state of the Earth's atmosphere in a given place and time. The initial state is created by blending the previous forecast runs (first-guess), together with observations from different platforms. The better the initial state, the better the forecast; hence, it is worthy to combine new observation types. The GNSS tomography technique, developed in recent years, provides a 3-D field of humidity in the troposphere. This technique shows positive results in the monitoring of severe weather events. However, to assimilate the tomographic outputs to the numerical weather model, the proper observation operator needs to be built.This study demonstrates the TOMOREF operator dedicated to the assimilation of the GNSS tomography‐derived 3‐D fields of wet refractivity in a Weather Research and Forecasting (WRF) Data Assimilation (DA) system. The new tool has been tested based on wet refractivity fields derived during a very intense precipitation event. The results were validated using radiosonde observations, synoptic data, ERA5 reanalysis, and radar data. In the presented experiment, a positive impact of the GNSS tomography data assimilation on the forecast of relative humidity (RH) was noticed (an improvement of root‐mean‐square error up to 0.5%). Moreover, within 1 hour after assimilation, the GNSS data reduced the bias of precipitation up to 0.1 mm. Additionally, the assimilation of GNSS tomography data had more influence on the WRF model than the Zenith Total Delay (ZTD) observations, which confirms the potential of the GNSS tomography data for weather forecasting.

The determination of ground deformation may be carried out by applying various measurement methods such as levelling, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR) and many others. In this work, we focus on the comparison of the deformation effects measured by Global Navigation Satellite Systems (GNSS) and satellite Interferometric SAR (InSAR) methods in the Upper-Silesian coal mining region (SW Poland).An unquestionable advantage of GNSS technology is the possibility of continuous monitoring of deformations in three-dimensional space. Moreover, the evolution of real-time (RT) techniques such as: near real-time (NRT), ultra-fast NRT or RT allows to obtain a high precise position determination with a relatively slight latency (ranging from a few seconds to less than one hour). The limitation of the satellite navigation technology is the spatial range of the measurements. The deformation can only be observed at the point where the GNSS antenna is located. Furthermore, the acquisition, installation and maintenance of the equipment may also involve high costs.In contrast to the GNSS technique, the InSAR methods enable measurement of the large-scale subsidence areas with possibility to use free products and software (e.g. Sentinel-1 and SNAP). The large-scale InSAR investigations provide a better overview of local terrain changes. Unfortunately, InSAR methods also have some limitations related to data acquisition technology: a few days latency in acquiring a new image, insensitivity to changes in the northern component, acquiring deformation only in the LOS direction. The main goal of this research is to analyse the deformation results obtained using GNSS and InSAR methods with respect to the capabilities and limitations of these two techniques. We investigated the level of agreement of eight GNSS and InSAR time series in areas with no subsidence, together with results acquired on seven regions of mining deformation where the maximum subsidence velocity exceeds 50 cm/year. The mean RMS time series fitting error obtained in subsidence basin is more than 5 cm and in non-deformed areas is equal to 2 cm. The study also investigated the effect of coherence threshold levels (0.3 ÷ 0.6) with introduction of the northern GNSS component on the InSAR decomposition process. Assuming the same GNSS deformation value in each directions (north, east, and up), the impact of the northern component was estimated as 10% of the total LOS subsidence.

Understanding what impacts the predictability of human movement is a key element for the further improvement of mobility prediction models. Up to this day, such analyses have been conducted using the upper bound of predictability of human mobility. However, later works indicated discrepancies between the upper bound of predictability and accuracy of actual predictions suggesting that the predictability estimation is not accurate. In this work, we confirm these discrepancies and, instead of predictability measure, we focus on explaining what impacts the actual accuracy of human mobility predictions. We show that the accuracy of predictions is dependent on the similarity of transitions observed in the training and test sets derived from the mobility data. We propose and evaluate five pattern matching based-measures, which allow us to quickly estimate the potential prediction accuracy of human mobility. As a result, we find that our metrics can explain up to 90% of its variability. We also find that measures that were proved to explain the variability of predictability measure, fail to explain the variability of predictions accuracy. This suggests that predictability measure and accuracy of predictions should not be compared. Our metrics can be used to quickly assess how predictable the data will be for prediction algorithms. We share developed metrics as a part of HuMobi, the open-source Python library.

Tropospheric wet delay, the main source of which is water vapor, is one of the major factors affecting the accuracy of positioning techniques using microwave. Tropospheric tomography is a powerful method to reconstruct the water vapor content in four-dimensional (4D) space. This paper studies the effect of using function-based and voxel-based tropospheric tomography methods on the positioning accuracy. This examination is performed on the static and kinematic positioning modes using Global Navigation Satellite Systems (GNSS) stations under different weather conditions. After validating the results of tomography methods using radiosonde observations, the tomography-based positioning solutions, including function-based and voxel-based approaches, are compared with the positions obtained using tropospheric models. The results of two GPS stations show that the accuracy increases when applying tomography approaches. The function-based tomography is able to increase the accuracy of the up component of the static and kinematic modes by about 0.42 and 0.79 cm, respectively, compared to the voxel-based method. In addition, the use of the function-based tropospheric tomography can decrease the convergence time of the kinematic Precise Point Positioning (PPP) solution.

Global Navigation Satellite Systems (GNSS) sense the atmosphere remotely and provide low-cost, high-quality information about its state. Nowadays, radio occultation (RO) profiles from space platforms and tropospheric delays from ground-based stations are routinely assimilated in Numerical Weather Models (NWM).In spite of provision of valuable information for weather forecasting, both space- and ground-based data have significant limitations. The RO technique has low horizontal resolution and does not provide reliable profiles in the first 3-5km of the troposphere. Whereas, the station-specific integrated value of troposphere are sparse and pose a problem to NWM adjoint operator for correcting model fields at different heights. These deficiencies could be resolved by the GNSS tomography technique that utilizes an inverse Radon transform to derive the 3D refractivity distribution over certain troposphere space. The combination of space-based and ground-based observations in the tomographic model will enable us to increase the number of intersections of GNSS signals and improve the refractivity solution within individual model locations. The aim of this research is to harness the full potential of Space 4.0 era, rapidly growing numbers of RO and GNSS satellite constellations as well as low-cost GNSS ground-based networks worldwide. We will not only use current infrastructure but also examine impact of future constellations on model performance. 3D model of refractivity from dense observations should be an excellent tool in weather prediction. Our previous research proves that the assimilation of the GNSS tomography outputs into the NWM improves relative humidity and the short-term weather forecasts. Therefore, the research goal of this project is to assess the benefit of integrated tomography model on the severe weather prediction and urban scale weather models.

The microwave signals passing through the troposphere are delayed by refraction. Its high variations, both in time and space, are caused mainly by water vapor. The tropospheric delay used to be considered only as a source of error that needed to be removed. Nowadays, these delays are also a source of interest. The tropospheric delays or integrated water vapor are being assimilated into nowcasting or numerical weather prediction (NWP) models. Moreover, long time series of tropospheric observations have become an important source of information for climate studies. On the other hand, the meteorological data support the space-geodetic community by providing models that can be used to reduce the troposphere impact on the signal propagation. Furthermore, the delays calculated by one microwave technique can be used to mitigate the errors in others.There are several ways of observing the troposphere, especially considering water vapor. The classical meteorological are: in-situ measurements, radiosondes or radiometers, which allow to sense the amount of water vapor directly. Another, indirect way of observing the water vapor distribution is by using the Global Navigation Satellite Systems (GNSS). This method is called GNSS meteorology. Other microwave techniques such as Very Long Baseline Interferometry (VLBI) or Interferometric Synthetic Aperture Radar (InSAR) are also capable to retrieve the atmospheric information from their signals.This contribution shows an overview of the troposphere sensing techniques and their applications. We present multi-comparisons of the tropospheric parameters, i.e. refractivity, tropospheric delays in zenith and slant directions and integrated water vapor. The integration of the different data sources often leads to an improved accuracy of the tropospheric products but requires a careful preparation of data. The combination of the data sources allows for using the techniques of complementary properties, for example InSAR with very high spatial resolution with GNSS observations of high temporal resolution. With the emergence of new technologies, some traditional ways of tropospheric measurements can be augmented with the new methods. For example, we have tested meteo-drones as an alternative to radiosondes. The comparisons with GNSS data shows a good agreement of the drone and microwave data, even better than with radiosonde. Moreover, we present the results of the GNSS data assimilation into NWP models and the developments towards multi-GNSS, real-time assimilation of advanced products such as slant delays and horizontal tropospheric gradients.

Within the International Association of Geodesy (IAG), a new working group was formed with the intention to bring together researchers and professionals working on tomography-based concepts for sensing the neutral atmosphere with space-geodetic techniques. Hereby the focus lies on Global Navigation Satellite Systems (GNSS) but also on complementary observation techniques, like Interferometric Synthetic Aperature Radar (InSAR) or microwave radiometers, sensitive to the water vapor distribution in the lower atmosphere. In the next four years (2019-2023), we will address current challenges in tropospheric tomography with focus on ground-based and space-based measurements, the combination of measurement techniques and the design of new observation concepts using tomographic principles. While geodetic GNSS networks are nowadays the backbone for troposphere tomography studies, further local densifications, e.g. at airports, cities or fundamental stations are necessary to achieve very fine spatial and temporal resolution. Besides, the combination of ground-based GNSS with other microwave techniques like radio occultation or InSAR seems to be beneficial due their complementary nature. Therefore, several further developments in the field of tropospheric tomography are required. This includes more dynamical tomography models - adaptable to varying input data, advanced ray-tracing algorithms for the reconstruction of space-based observations and the coordination of a benchmark campaign. In this presentation, we will give an overview about the current challenges in tropospheric tomography and the objectives of working group. The latter will also include standards for data exchange and therefore, make tomographic products available for the assimilation into numerical weather prediction models but also for various other disciplines, which rely on accurate wet refractivities or derived products like tropospheric signal delays., EGUsphere

The water vapor content in the atmosphere can be reconstructed using the all-weather condition troposphere tomography technique. In common troposphere tomography, the water vapor of each voxel is represented by an unknown parameter. This means that when the desired spatial resolution is high or study area is large, there will be a huge number of unknown parameters in the problem that need to be solved. This defect can reduce the accuracy of troposphere tomography results. In order to overcome this problem, an optimal voxel-based troposphere tomography using the Weather Research and Forecasting (WRF) model is proposed. The new approach reduces the number of unknown parameters, the number of empty voxels and the role of constraints required to enhance the spatial resolution of tomography results in required areas. Furthermore, the effect of considering the topography of the study area in the tomography model is examined. The obtained water vapor is validated by radiosonde observations and Global Positioning System (GPS) positioning results. Comparison of the results with the radiosonde observations shows that using the WRF model outputs and topography of the area can reduce the Root Mean Square Error (RMSE) by 0.803 gr/m3. Validation using positioning shows that in wet weather conditions, the WRF model outputs and topography reduce the RMSE of the east, north and up components by about 17.42, 10.46 and 20.03 mm, which are equivalent to 46.01%, 35.78% and 53.93%, respectively.

GPS tomography has been investigated since 2000 as an attractive tool for retrieving the 3D field of water vapour and wet refractivity. However, this observational technique still remains a challenging task that requires improvement of its methodology. This was the purpose of this study, and for this, GPS data from the Australian Continuously Operating Research Station (CORS) network during a severe weather event were used. Sensitivity tests and statistical cross-comparisons of tomography retrievals with independent observations from radiosonde and radio-occultation profiles showed improved results using the presented methodology. The initial conditions, which were associated with different time-convergence of tomography inversion, play a critical role in GPS tomography. The best strategy can reduce the normalised root mean square (RMS) of the tomography solution by more than 3 with respect to radiosonde estimates. Data stacking and pseudo-slant observations can also significantly improve tomography retrievals with respect to non-stacked solutions. A normalised RMS improvement up to 17% in the 0-8 km layer was found by using 30 min data stacking, and RMS values were divided by 5 for all the layers by using pseudo-observations. This result was due to a better geometrical distribution of mid- and low-tropospheric parts (a 30% coverage improvement). Our study of the impact of the uncertainty of GPS observations shows that there is an interest in evaluating tomography retrievals in comparison to independent external measurements and in estimating simultaneously the quality of weather forecasts. Finally, a comparison of multi-model tomography with numerical weather prediction shows the relevant use of tomography retrievals to improving the understanding of such severe weather conditions. Web of Science 2 1 art. no. 30

For more than a decade, GNSS-meteorology has been increasingly used operationally in Europe particularly for data assimilation in Numerical Weather Prediction (NWP) models, mainly thanks to the EIG EUMETNET GNSS Water Vapour Program (E-GVAP, 2005-today). As such, GNSS has become a well-established, mature observing technique for data assimilation applications. Over this period however, scientists and specialists in GNSS-meteorology noted the clear potential for enhancements and novelties in the domain. The work carried out by the COST Action ES1206 Working Group 2 members addressed these potential enhancements and novelties from the meteorological point of view, in collaboration with WG1. This included the establishment of discussion channels with forecasters in order to determine which GNSS products would be best suited for their day-to-day operational requirements. Particular areas of interest include engaging more operational forecasters (e.g. use of meteorological case studies), especially for non-numerical nowcasting of severe weather, and getting more meteorological agencies to assimilate GNSS products in regions of Europe where they were not yet/well exploited. It also included the development of the techniques and tools necessary to benefit from the brand new products developed by the Action WG1 and WG2 members, namely real-time GNSS tropospheric products for rapid-cycle NWP and non-numerical nowcasting, data assimilation of horizontal tropospheric gradients and tropospheric slant delays as well as tomographic products. Finally, the work carried out by the WG2 members brought operational improvements through dialog, transfer of knowledge, and standardisation (e.g. the new standardized tropo-SINEX format or the development of assimilation operators). The major WG2 outcomes are discussed in this Chapter.

Over the last decade, near real-time analysis of GPS data has become a well-established atmospheric observing tool, primarily coordinated by the EIG EUMETNET GPS Water Vapour Programme (E-GVAP) in Europe. In the near future, four operational GNSS will be available for commercial and scientific applications with atmospheric science benefiting from new signals from up to 60 satellites observed at any one place and time, however, many challenges remain regarding their optimal combined utilization. Besides raw data streaming, recent availability of precise real-time orbit and clock corrections enable wide utilization of autonomous Precise Point Positioning (PPP), which is particularly efficient for high-rate, real-time and multi-GNSS analyses.