Global navigation satellite systems (GNSSs) have revolutionised positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is now an established atmospheric observing system, which can accurately sense water vapour, the most abundant greenhouse gas, accounting for 60–70 % of atmospheric warming. In Europe, the application of GNSS in meteorology started roughly two decades ago, and today it is a well-established field in both research and operation. This review covers the state of the art in GNSS meteorology in Europe. The advances in GNSS processing for derivation of tropospheric products, application of GNSS tropospheric products in operational weather prediction and application of GNSS tropospheric products for climate monitoring are discussed. The GNSS processing techniques and tropospheric products are reviewed. A summary of the use of the products for validation and impact studies with operational numerical weather prediction (NWP) models as well as very short weather prediction (nowcasting) case studies is given. Climate research with GNSSs is an emerging field of research, but the studies so far have been limited to comparison with climate models and derivation of trends. More than 15 years of GNSS meteorology in Europe has already achieved outstanding cooperation between the atmospheric and geodetic communities. It is now feasible to develop next-generation GNSS tropospheric products and applications that can enhance the quality of weather forecasts and climate monitoring. This work is carried out within COST Action ES1206 advanced global navigation satellite systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC, http://gnss4swec.knmi.nl).

This paper reports on the transport of ozone (O₃) and related species over the North Atlantic ocean and its impact on Europe. Measurements of nitrogen dioxide (NO₂) and carbon monoxide (CO) columns from the GOME and MOPITT satellite instruments, respectively, are used in conjunction with the GEOS-CHEM global model of transport and tropospheric chemistry to identify the major events of long range transport that reach Europe over the course of summer 2000. Sensitivity model simulations are used to analyse observed O₃ distributions with respect to the impact of long range transport events. For that purpose, we used in-situ O₃ observations taken at the mountain site of Jungfraujoch as well as O₃ vertical profiles taken in the vicinity of central European cities. Over the course of summer 2000, we identified 9 major episodes of transatlantic pollution transport; 7 events are associated with transient cyclones while 2 events occur through zonal transport (e.g. by advection in the strong low-level westerly winds established in summer between the Azores anticyclone and transient cyclones). We find that on average three episodes occur per month with the strongest ones being in June. The number and frequency of long range transport events that reach Europe are driven by the position and strength of the Azores anticyclone. Model sensitivity simulations indicate that the summer mean North American O₃ contribution ranges from 3 to 5 ppb (7–11%) in the planetary boundary layer and 10 to 13 ppb (18–23%) in the middle and upper troposphere. During particular episodes, North American sources can result in O₃ enhancements up to 25–28 ppb in the layer between 800–600 hPa and 10–12 ppb in the boundary layer. The impact of the zonal transport events on O₃ distribution over Europe is more clearly seen below 700 hPa as they tend to transport pollution at lower levels while the events associated with transient cyclones are more likely to have an impact on the middle and upper troposphere (i.e. above 600 hPa). The air mass origins found in the GEOS-CHEM model are clearly confirmed by back trajectory analyses. During most of the 9 events, a strong contribution in North American O₃ is in general associated with only little European O₃ and vice-versa (in particular at the Jungfraujoch). A substantial North American contribution (e.g., 30% or higher) to O₃ over Europe does not always result in pronounced O₃ enhancements in the observations during our period of study.

The importance of water vapor for the hydrological cycle of the earth and the atmosphere is well known but difficult to study and sample. In this respect, the vertically integrated water vapor (IWV) derived from global positioning system (GPS) delay is a potential source of valuable weather information. Because of the relatively dense station distribution, both the temporal and horizontal variability of water vapor are well captured by the GPS. This makes GPS data well suited for numerical weather prediction (NWP) models. In this paper the automated GPS network of Switzerland (AGNES) is used for calculation of IWV and for comparison with radiosonde data and two NWP mesoscale models from MeteoSwiss named Local Model (LM) and Swiss Model (SM). Reasonably good agreement between GPS and radiosonde data is reported. It has been identified that in some particular weather situations with low stratus clouds and temperature inversion, the radiosonde significantly overestimates the water vapor amount. The ... Abstract The importance of water vapor for the hydrological cycle of the earth and the atmosphere is well known but difficult to study and sample. In this respect, the vertically integrated water vapor (IWV) derived from global positioning system (GPS) delay is a potential source of valuable weather information. Because of the relatively dense station distribution, both the temporal and horizontal variability of water vapor are well captured by the GPS. This makes GPS data well suited for numerical weather prediction (NWP) models. In this paper the automated GPS network of Switzerland (AGNES) is used for calculation of IWV and for comparison with radiosonde data and two NWP mesoscale models from MeteoSwiss named Local Model (LM) and Swiss Model (SM). Reasonably good agreement between GPS and radiosonde data is reported. It has been identified that in some particular weather situations with low stratus clouds and temperature inversion, the radiosonde significantly overestimates the water vapor amount. The ...

Nowcasting of severe weather events and summer storms, in particular, are intensively studied as they have great potential for large economic and societal losses. Use of Global Navigation Satellite Systems (GNSS) observations for weather nowcasting has been investigated in various regions. However, combining the vertically integrated water vapour (IWV) with vertical profiles of wet refractivity derived from GNSS tomography has not been exploited for short-range forecasts of storms. In this study, we introduce a methodology to use the synergy of IWV and tomography-based vertical profiles to predict 0–2 h of storms using a machine learning approach for Poland. Moreover, we present an analysis of the importance of features that take part in the prediction process. The accuracy of the model reached over 87%, and the precision of prediction was about 30%. The results show that wet refractivity below 6 km and IWV on the west of the storm are among the significant parameters with potential for predicting storm location. The analysis of IWV demonstrated a correlation between IWV changes and storm occurrence.

In this paper an integrated assessment of the vertically integrated water vapor (IWV) measured by radiosonde, microwave radiometer (MWR), and GPS and modeled by the limited-area mesoscale model of MeteoSwiss is presented. The different IWV measurement techniques are evaluated through intercomparisons of GPS to radiosonde in Payerne, Switzerland, and to the MWR operated at the Institute of Applied Physics at the University of Bern in Switzerland. The validation of the IWV field of the nonhydrostatic mesoscale Alpine Model (aLMo) of MeteoSwiss is performed against 14 GPS sites from the Automated GPS Network of Switzerland (AGNES) in the period of 2001–03. The model forecast and the nudging analysis are evaluated, with special attention paid to the diurnal cycle. The results from the GPS–radiosonde intercomparison are in agreement, but with a bimodal distribution of the day-to-night basis. At 0000 UTC, the bias is negative (−0.4 kg m−2); at 1200 UTC, it is positive (0.9 kg m−2) and the variability increases. The intercomparison of GPS to MWR shows better agreement (0.4 kg m−2), with a small increase of the daytime bias with 0.3 kg m−2. The intercomparison of MWR to the radiosonde gives a bimodal distribution of the bias, with an increase in the standard deviation at the daytime measurement. The relative bias is negative (−3%) at 0000 UTC and is positive (3%) at 1200 UTC. Based on this cross correlation, it can be concluded that the bimodal distribution is a result of radiosonde humidity measurements. Possible reasons are the solar-heating correction or sensor errors. The monthly bias and standard deviation of aLMo exhibit a strong seasonal dependence with a pronounced dry bias during the warm months of May–October 2002. The diurnal IWV cycle in 2001 shows good model performance between 0000 and 0900 UTC but IWV underestimation by up to 1.5 kg m−2 for the rest of the day. In 2002 the diurnal cycle shows a systematic dry bias in both the analysis and forecast that is more pronounced in the analysis. This substantial underestimation of IWV was found to correlate with overestimation of aLMo precipitation, especially light precipitation up to 0.1 mm (6 h)−1 in 2002. There is strong evidence that this underestimation can be related to the dry radiosonde bias in midday summer observations. The aLMo dry bias is about 1.0–1.5 kg m−2 greater in the nudging analysis as compared with the forecast initialized at 0000 UTC.

Bulgaria is a country with a high frequency of hail and thunderstorms from May to September. For the May–September 2010–2015 period, statistical regression analysis was applied to identify predictors/classification functions that contribute skills to thunderstorm forecasting in the Sofia plain. The functions are based on (1) instability indices computed from radiosonde data from Sofia station F1, and (2) combination of instability indices and Integrated Water Vapor (IWV), derived from the Global Navigation Satellite System (GNSS) station Sofia-Plana, F2. Analysis of the probability of detection and the false alarm ratio scores showed the superiority of the F2 classification function, with the best performance in May, followed by June and September. F1 and F2 scores were computed for independent data samples in the period 2017–2018 and confirmed the findings for the 2010–2015 period. Analysis of IWV and lightning flash rates for a multicell and supercell thunderstorm in June and July 2014 showed that the monthly IWV thresholds are reached 14.5 and 3.5 hours before the thunderstorm, respectively. The supercell IWV peak registered 40 min before the thunderstorm, followed by a local IWV minimum corresponding to a peak in the flash rate. In both cases, an increase of IWV during severe hail was registered, which is likely related to the hydrometeor contribution to GNSS path delay. The results of this study will be integrated into the Bulgarian Integrated NowCAsting tool for thunderstorm forecasting in the warm/convective season.