Global Navigation Satellite Systems (GNSS) have not only revolutionised positioning, navigation and timing, but also provided an accurate sensor of the most abundant greenhouse gas, water vapour. In Europe, the application of GNSS in meteorology started over two decades ago and today it has evolved into a well established research field with operational assimilation in many European National meteorological services Numerical Weather Prediction (NWP) models.
Even so, water vapour is still under-sampled in the current meteorological and climate observing systems, therefore obtaining and exploiting more high-quality humidity observations is essential to weather forecasting and climate research.

This session welcomes contributions in the following fields: derivation of atmospheric parameters from GNSS, InSAR, VLBI, GNSS-RO and SAR; production and application of advanced GNSS tropospheric products (multi-GNSS, real-time, gradients, slant delays, tomography); multi-instrument retrievals and inter-comparisons of tropospheric parameters; the use of real-time tropospheric products in high-resolution forecasting; detection of precipitating environments; weather, climate and hydrology research and GNSS-based soil moisture research

Convener: Jonathan Jones | Co-convener: Guergana Guerova
Lightning talks
| Tue, 07 Sep, 09:00–10:30 (CEST)

Lightning talks: Tue, 07 Sep

Chairperson: Jonathan Jones
Pierre Bosser, Joël Van Ballen, and Olivier Bousquet

In the framework of the research project “Marion Dufresne Atmospheric Program – Indian Ocean” (MAP-IO), which is aiming at collecting long-term atmospheric and marine biology observations in the under-instrumented Indian and Austral Oceans, a Global Navigation Satellite System (GNSS) receiver was installed on the research vessel (RV) Marion Dufresne in October 2020 to describe, and monitor, global moisture changes in these areas. GNSS raw data are recorded continuously and used to retrieve integrated water vapor contents (IWV) along the RV route.

After a data quality check that confirmed that a wise choice of location of the GNSS antenna on the RV is crucial to avoid mask, signal reflection and interference from other instruments that may degrade IWV retrieval, a first assessment of the GNSS analysis performances was carried out by comparing the vertical component of the estimated positions to sea surface height model. The differences are on the order of 20 to 30 cm; they are consistent with both the error budget for sea surface height determination using GNSS and the sea surface height model formal errors.

An evaluation of GNSS-derived IWV was conducted using IWV estimates from the ECMWF fifth ReAnalysis (ERA5) and ground-based GNSS reference stations located nearby the tracks of RV Marion Dufresne. Preliminary analyses show encouraging results with a mean root mean square error of ~2-3 kg m-2 between ERA5 and GNSS-derived IWV. The use of ultra-rapid GNSS orbit and clock product was also investigated to assess the performance of near real-time GNSS-derived IWV estimation for numerical weather prediction purposes.

How to cite: Bosser, P., Van Ballen, J., and Bousquet, O.: IWV retrieval from ship-borne GNSS receiver in the framework of the MAP-IO project, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-172, https://doi.org/10.5194/ems2021-172, 2021.

Tzvetan Simeonov, Markus Ramatschi, Sibylle Vey, and Jens Wickert

The permanent and seasonal snow covers are an important element of the global hydrological cycle and have substantial influence on global climate. Currently around 10% of the Earth’s land surface is covered by glaciers, ice caps and snow cover. Snow and ice cover play important role in the Earth’s climate by reflecting solar radiation and thus decreasing the average Earth temperature. Glaciers and ice caps participate in a positive feedback loop in the Earth’s climate. By contracting due to increasing temperatures, they reflect less solar radiation, further contributing to the global temperatures increase.

Using the single antenna ground-based GNSS Reflectometry (GNSS-R) method for snow depth estimation is an emerging application. A new technique for snow depth measurement using the phase changes in the observed SNR data, rather than the height estimates, is validated in a GNSS-R setup in Antarctic station Neumayer III. The new technique shows improved characteristics to the classical single antenna ground-based GNSS-R snow depth determination method. The validation is done in an environment of constant snow accumulation. The results from new technique show high correlation of the de-trended datasets between the GNSS-R and in-situ snow buoy measurements of 0.85. The de-trended classical height estimations of the SNR show lower correlation to the snow buoys of 0.60.

A screening of the International GNSS Service (IGS) global network shows, that snow depth observations are possible in only 7 of the 506 available stations. The main limitations on the stations are the local topography and climate. The snow depth observations from these seven stations are compared with the ERA5 snow depth estimations, local measurements and climate normals. The analysis of the data for station Visby, following the new GNSS-R analysis technique, shows very high correlation of 0.91 and low RMSE of 2.26cm, while the classical GNSS-R estimation has RMSE of 2.48cm and ERA5 shows RMSE of 4.2cm when compared to local meteorological observations.

How to cite: Simeonov, T., Ramatschi, M., Vey, S., and Wickert, J.: Snow depth monitoring with GNSS reflectometry: Results from Antarctica and selected geodetic ground stations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-187, https://doi.org/10.5194/ems2021-187, 2021.

Guergana Guerova, Jan Dousa, Tsvetelina Dimitrova, and Pavel Václavovic

GNSS is an established atmospheric monitoring technique delivering water vapour data in near-real time with latency 90 minutes for operational Numerical Weather Prediction in Europe within the EGVAP service. However, nowadays with advancement of GNSS processing the quality of real-time GNSS tropospheric products is well comparable to near-real time solution and in addition they can be provided in a temporal resolution of 5 minutes and low latency, suitable for severe weather nowcasting. The aim of the project is to exploit the added value of GNSS tropospheric product for nowcasting of convective storm by building demonstrators in support of public weather and hail suppression services in Bulgaria. In Bulgaria  thunderstorms and hail events are  occur between May and September with a peak in July. The convective Storm Demonstrator (Storm Demo) is based on GNSS tropospheric products and Instability Indices to derive site specific threshold values integrated and updated in real-time on a publicly accessible geoportal. The demonstrator targets development of service centered at GNSS products for two regions with hail suppression operations namely Northwestern and Central Bulgaria.  As a part of the Storm Demo real-time PPP processing will be conducted with G-Nut software for the first time in Southeast Europe for the hail suppression season May-September 2021. Evaluation of the real-time products will be performed using reprocessed GNSS tropospheric products.  The added value of the high temporal resolution of the GNSS tropospheric products will be investigated for selected storm cases.  This service will be unique in Europe and will serve as a prototype for real-time provision of GNSS products for storm nowcasting. 

How to cite: Guerova, G., Dousa, J., Dimitrova, T., and Václavovic, P.: GNSS storm nowcasting demonstrator for Bulgaria, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-349, https://doi.org/10.5194/ems2021-349, 2021.

Galina Dick, Jonathan Jones, Junhong Wang, Kalev Rannat, Jens Wickert, Florian Zus, Benjamin Männel, Kyriakos Balidakis, and Karina Wilgan

The Global Climate Observing System (GCOS), supported by a number of international partners and the World Meteorological Organization (WMO), is establishing a reference climate observation network, the GCOS Reference Upper Air Network (GRUAN). Currently, this network comprises 30 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity in the upper atmosphere, thus providing a cornerstone to more reliable monitoring of signals of global and regional climate change. GRUAN observations are required to be of reference quality, with known biases removed and with an associated uncertainty value, based on thorough characterization of all sources of measurement.

In support of these goals, GNSS precipitable water (GNSS-PW) measurement has been included as a priority one measurement of the essential climate variable water vapor. In addition, a minimum of twice daily measurements (ideally hourly measurement) of PW are required as entrance to the GRUAN program. GNSS-PW is the primary means to accomplish this entrance requirement. The GNSS-PW program produces a nearly continuous reference measurement of PW and is therefore a substantial part of GRUAN.

GFZ contributes to GRUAN with its expertise in processing of ground-based GNSS network data to generate precise PW products. Since 2013, GFZ hosts a GRUAN central processing facility for the GNSS-PW. GFZ is responsible for the installation of GNSS hardware, data transfer, processing and archiving, derivation of GNSS PW products according to GRUAN requirements including PW uncertainty estimation, as well as for quality check and archiving of the GNSS-PW products. Currently half of the GRUAN sites are equipped with GNSS receivers. At the beginning of this year the data processing of GNSS-PW and its associated documentation has been GRUAN certified. GNSS-PW products including uncertainty estimation and results of selected validation studies are presented.


How to cite: Dick, G., Jones, J., Wang, J., Rannat, K., Wickert, J., Zus, F., Männel, B., Balidakis, K., and Wilgan, K.: GNSS-based Precipitable Water Vapor for the Global Climate Observing System, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-428, https://doi.org/10.5194/ems2021-428, 2021.

Florian Zus, Galina Dick, and Jens Wickert

Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life.  A geophysical key application is atmospheric water vapor monitoring using GNSS ground station data. GNSS water vapor data, derived from regional ground networks hereby close gaps in the established meteorological observing systems. No other observing system provides data with such high temporal and spatial resolution. The data from European GNSS networks are therefore already widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the limited atmospheric information content, which is provided by the currently used Zenith Total Delay (ZTD) data.

In this talk we introduce the new project EGMAP (Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction). This project will pioneer the development and usage of next generation data products; tropospheric gradients. The new data products, developed and provided within the project, are expected to improve the impact of the currently provided GNSS data to weather forecast systems. The main innovations, which will be addressed by the project are: (1) Developments to provide high quality ZTDs and tropospheric gradients in near-real-time for the German SAPOS network; (2) Developments to make use of ZTDs and tropospheric gradients in numerical weather prediction, i.e., implement operators in the variational/ensemble data assimilation system of the Weather Research and Forecasting (WRF) model; (3) Impact studies with the state of the art numerical weather model. In this talk we provide an overview and the current status of the project.

How to cite: Zus, F., Dick, G., and Wickert, J.: Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction (EGMAP): project overview and status, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-442, https://doi.org/10.5194/ems2021-442, 2021.

Sharon Jewell, Dawn Harrison, and Gareth Dow

Zenith Total Delay (ZTD) from networks of ground based GNSS receivers has been assimilated in both the Met Office global and high-resolution regional numerical weather predication (NWP) models for over a decade. It is useful to be able to quantify the impact of assimilating these data and compare this with the impact of assimilating other observation types. This helps inform observing network evolution.

Forecast Sensitivity to Observation Impact (FSOI) analysis is an established method for monitoring the collective impact of an observing network on the quality of an NWP forecast. FSOI uses an adjoint-based method to compute the observation sensitivity for each assimilated observation in a global model forecast simultaneously. The sensitivity value represents the change in forecast error for a unit observation innovation; this information is combined with innovation data from the model forecast to provide an estimate for the resulting change in error for the total 24-hour energy norm. The computational efficiency of the FSOI process makes it a good alternative to traditional Observation System Experiments (OSE) when considering the benefits of an observing network.

Unfortunately, the underpinning assumptions in the FSOI methodology mean that the method does not easily translate to higher resolution regional NWP models. For these models, observation impacts can still be determined through OSEs through which observations of specific type or types are denied to the model and the results compared with a Control model run with all types of observations included.

FSOI analysis has previously been used to compare and contrast the benefits associated with different observing method types. This study uses the original FSOI methodology but refines the output to collate impact data at the observing-site level (in relation to a specified observing network).  This enables geographic variations in the impact associated with individual sites within an observing network to be visualised and the tool can be used to assist with decisions on network design and performance.

Results are presented that illustrate the impact of GNSS ZTD data on global NWP model forecasts on a seasonal and annual basis. A number of metrics are used to assess the benefits associated with a GNSS site, including the total impact over a fixed period of time as well as the mean impact per observation on both a global and country scale. The results highlight the significant impact that individual sites in areas of low data density (such as the southern hemisphere) have on model forecasts.

The impact of GNSS ZTD data on the 1.5 km resolution UKV model are also presented. The contrasting impacts in summer and winter, and variation of forecast impact as lead-time increases are explored.

How to cite: Jewell, S., Harrison, D., and Dow, G.: Quantifying the Impact of Assimilating Ground-based GNSS in Operational NWP Models, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-450, https://doi.org/10.5194/ems2021-450, 2021.

Matthias Aichinger-Rosenberger, Elmar Brockmann, and Gregor Möller

The atmospheric delay experienced by a signal of the Global Navigation Satellite System (GNSS) is proportional to the water vapour content along the signal path. This fact is typically exploited in GNSS Meteorology by introducing GNSS derived atmospheric parameters like the Zenith Wet Delay (ZWD) in data assimilation schemes. In numerous studies, the positive impact on the (especially precipitation) forecast has been demonstrated. However, while mostly precipitation-related studies represent the current focus of research, other meteorological phenomena might also be investigated by means of GNSS.

The present study represents an initial investigation on the detection of another important meteorological phenomena using GNSS time series: Foehn winds. Foehn denotes a gusty, warm fall wind occurring in mountainous regions worldwide, leading to a relatively mild climate in affected areas. On the other hand, Foehn can also be characterized as severe weather leading to disasters, due to the high wind speeds frequently encountered.

The proposed detection method of Foehn in ZWD time series is based on the significant drying/wetting effects on the lee/luv side of an affected mountain range associated with Foehn. The comparison of ZWD from stations on both sides of the main Alpine ridge reveals characteristic features like distinctive ZWD minima/maxima and significant decrease in correlation between the stations.

In this study we investigate a number of well-documented Foehn events in the Swiss Alps (therefore called Alpine Foehn) using ZWD time series from the Automated GNSS Network Switzerland (AGNES) station network, operated by the Swiss Federal Office of Topography (swisstopo). Based on these case studies, an assessment of the usability of GNSS-ZWD for Foehn detection is presented and possible strengths and weaknesses will be analysed. Finally, an outlook on possible improvements and innovative extensions to the presented approach is given. These range from embedment of ZWD data in operational Foehn classification and the application of Machine-Learning techniques for detection, to the establishment of collocated GNSS/weather stations, which come with a number of scientific benefits - not only for Foehn investigations but GNSS Meteorology in general.

How to cite: Aichinger-Rosenberger, M., Brockmann, E., and Möller, G.: Detection of Alpine Foehn in GNSS-ZWD time series: An innovative application of GNSS Meteorology, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-453, https://doi.org/10.5194/ems2021-453, 2021.

Grzegorz Nykiel, Zofia Baldysz, Beata Latos, and Mariusz Figurski

Among various greenhouse gases, water vapour is characterized by the single highest positive feedback on the surface temperature and dominates increasing of the Earth’s surface temperature. Hence, long-term changes in its concentration in the atmosphere are one of the indicators for the assessment of the global warming rate. Consequently, monitoring of water vapour interannual variability is an important element in climate observing system, especially considering limitations of the surface technology that is traditionally used for this purpose. In this work, we have used 18 years of global navigation satellite system (GNSS) observations derived from 43 International GNSS Service (IGS) stations located across the global tropics. Based on them, we have estimated zenith tropospheric delay (ZTD) time series by precise point positioning (PPP) approach, and in next step converted them to long-term and homogenous precipitable water vapour (PWV) time series. We have investigated their interannual variability through estimation of non-linear trends and assessment which climate phenomena affect GNSS PWV long-term variability the most. Results have shown that for most of the analysed stations, GNSS PWV time series present distinct analogies to the global and regional climate phenomena such as El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) or North Pacific Gyro Oscillation (NPGO). Comparative analysis between GNSS PWV non-linear trends and selected climate indices showed strong cross-correlation, that amounted to 0.78. Moreover small-scale weather phenomena, such as local droughts, were clearly distinguishable, thus showing how GNSS PWV time series are sensitive to the combined effect of various weather and climate patterns. 

How to cite: Nykiel, G., Baldysz, Z., Latos, B., and Figurski, M.: Interannual variability of the GNSS derived precipitable water vapour in the light of tropical climate patterns, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-470, https://doi.org/10.5194/ems2021-470, 2021.

Steffen Schön and Gaël Kermarrec

Long-term variations of the tropospheric refractive index delay the carrier phase measurements from Global Navigation Satellite System (GNSS). This information is now operationally integrated in Weather prediction models. Random fluctuations of the refractive index correlate the phase measurements and induces non-stationary noise processes. The correlation structure and spectral properties of observation residuals from GNSS relative positioning provide a unique opportunity to study specific properties of the turbulent atmosphere. In this contribution, we will give a short overview on turbulent processes and their impact on GNSS carrier phase measurements. We will discuss our data analysis concepts to separate the tropospheric fluctuations from other temporally varying error sources such as GNSS receiver clock errors or multipath. The analysis is based on the power spectrum of single or double differences of carrier phase measurements. This approach enables a determination of the cut-off frequencies of the atmospheric noise and the associated power law processes with their typical slopes. The obtained values are compared with theoretical expectations. We will show results for GPS from the Seewinkel network (Austria), as well as from a small network at Physikalisch-Technische Bundesanstalt (PTB, Germany) where all receivers are connected to a common highly stable atomic clock. We show that (i) a two slopes power spectrum can be reliably determined and (ii) that the outer scale length can be taken to a constant value, close to the physically expected one and in relation with the size of the eddies at tropospheric height. The study of their dependencies with the satellite geometry, the Day of the Year (DOY) or the time of the day provides a new insight on the two- and three-dimensional atmospheric turbulence in the free atmosphere.

How to cite: Schön, S. and Kermarrec, G.: Studying tropospheric turbulence with GNSS, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-486, https://doi.org/10.5194/ems2021-486, 2021.


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