dataesr/scientific-paragraphs-categorization huggingface dataset:
In consequence, for analysis of satellite or station specific effects in post-fit residuals we recommend undifferenced GNSS data processing strategies, especially in small GNSS networks. Nowadays, multi-GNSS offers new satellites and signals which are expected to strengthen all estimated parameters, in particular the ZTD and horizontal linear tropospheric gradients, or to densify slant tropospheric delays for monitoring the troposphere asymmetry at individual GNSS sites. Currently, data from the US NAVSTAR Global Positioning System (GPS) and the Russian GLONASS constellation are commonly used to produce different products within scientific services. Essential models for these two systems has been already established, and their mutual combination provides better precision then the processing from any standalone system.
Besides others, limitations for the use of GNSS data from other global systems, the European Galileo and the 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 and the IGS Multi-GNSS Experiment (MGEX, http://mgex. igs. org, Montenbruck et al. 2017 ) is continuously filling the gaps in data, metadata, models, formats, standards and products for an optimal exploitation of all global satellite constellations and their regional augmentations.
Evaluation of Results from Collocated GNSS Stations The impact of using multi-constellation data on the tropospheric parameter estimation can be optimally assessed using closely collocated GNSS stations, e. g. within few meters. Although different instrumentation-specific effects, such as phase centre modelling and the quality of a receiver tracking, can affect analyses at both stations, the station should principally observe the same tropospheric delays. For the purpose of our evaluation, we selected two IGS station pairs, ZIM2-ZIMJ (Zimmerwald, Switzerland) and MAT1-MATE (Matera, Italy), all collecting data from GPS, GLONASS and Galileo systems within 10 m and 2. 5 m in horizontal and vertical distances, respectively. We used the GFZ MGEX (GBM) product  (Deng et al. 2017)  and CODE MGEX (COM) product  (Prange et al.
Monitoring and Validation Developing and maintaining such new RT-PPP processing systems and products requires regular assessment, adaptation, and fine-tuning cycles. Therefore, ROB developed its own monitoring/validation system consisting of MySQL databases, statistical assessment programs, and a web-based user-interface to monitor continuously his RT-PPP processing system (graphs, reports. ). The system is capable of monitoring/carrying out: • The campaign setup and its evolution • Inconsistency checks (configuration, equipment, models. ) • The performances of the products at all GNSS stations included in the RT-PPP processing (the monitoring system developed by GOP can only monitor 17% of them), at various time scale and epochs (biases, precision, geographical dependency. ) • Alarm systems in case of problem (dataflow, processing.
Assessment of RT Products in Simulated RT Analysis of Benchmark Data As we said, the RT-PPP demonstration campaign provided promising results but also leaved space for specific studies and improvements. Such kind of studies, like studying the optimal processing settings according to e. g. weather type (severe versus normal condition), studying the influence of the station equipment, or optimizing the convergence period etc. requires to be able to replay the processing several times on the same dataset by changing solely one processing parameter at a time. In other words: simulating the real-time processing in an offline mode for finetunings and assessments. One perfect candidate for this is the WG1 Benchmark campaign, and at the end of the COST Action, we started the real-time benchmark campaign with the following main objectives: • Processing (a core group of) stations of the Benchmark campaign in simulated real-time PPP mode with various real-time orbits and clocks products to produce a final assessment of the capability of each real-time orbit and clock product.
• Develop optimal strategies for the estimation of ZTDs and tropospheric gradients at high (time) resolution (e. g. 5 min), and assess them to reference solutions and/or ZTD/GRD from NWM. • Investigate the capability of dynamically constrain ZTDs and GRDs according to the weather conditions (calm, moderate, turbulent, severe, etc. ). This is very important for natural hazard warnings. • Produce a dataset of simulated real-time tropospheric products (ZTD, horizontal gradients, SPD) using the final fine-tuning of all previous steps that can be re-used to assess the capability/performances of these products in real studies/applications (e. g. a nowcasting case of severe weather). • Production of IWV in almost real-time for non-numerical nowcasting applications. • Standardize methods and format, and provide guidelines towards operational production of real-time tropospheric products (link to E-GVAP).
In that context, ROB processed the complete WG1 benchmark dataset in simulated real-time offline mode with various processing options and various orbit and clock products, focusing on providing the final assessment of the orbit and clock products, on testing different ZTD & GRD (dynamical/optimal) constrains, on studying the dependency of the performance w. r. t. to the equipment (see text of the RT demonstration campaign), and on convergence periods. Similarly as for the regular assessment of the real-time demonstration campaign, we also reprocessed the benchmark campaign with the BSW52 to produce final reference tropospheric products both in PPP and in double-difference approach. In total, 320 flavors of the RT-PPP products are available for inter-comparison (Fig. 3. 50) and assessments.
(2017)  and includes four test cases for optimal method of ZWD combination from GNSS and NWM. Two NWM models were used: (1) global ERA-Interim reanalysis  (Dee et al. 2011)  provided by ECMWF (we used 6-h updates with 1 Â 1 horizontal resolution), and (2) high-resolution regional NWM prediction (European model with 9 Â 9 km horizontal uniform resolution, hourly updated) provided from an operational short-time prediction from the mesoscale WRF model operated by the Institute of Computer Science (ICS), Academy of Sciences of the Czech Republic. The input GNSS ZTDs stems from the analysis of GNSS data of the GNSS4SWEC WG1 Benchmark dataset  (Douša et al. 2016)  provided by GOP as the GNSS reference solution. The concept was implemented in the G-Nut/Shu software for the testing and assessing different variants of ZWD weighting method.
As well as assessing the use of the more traditional GNSS-tropospheric products (i. e. ZTD and IWV), WG2 also looks to assess the impact of more advanced tropospheric products developed by WG1 such as slants and gradients in NWP schemes, and also assesses the benefits and status of GNSS-tomography. A number of work packages were defined to more efficiently organise the activity in the different fields. The main areas of focus were: WP 2. 1 Non-numerical Nowcasting (Sect. 4. 2) • Review and exchange tools and methods for non-numerical GNSS nowcasting. • Assess the benefit of new/enhance GNSS products (real-time, gradients, slants etc. ) for non-numerical nowcasting. • Organize detailed analyses of special case studies by non-numerical nowcasting (fog, foehn, precipitation etc. ), and to establish a database of severe weather case studies that can be re-used.
• Identify tomography products capability in term of requirement for NWP and nowcasting. • Develop operational tomography products and evaluate them. • Define and generate specific benchmark datasets in the form of GNSS observations, alternative water vapour and refractivity observations and NWP products, for test, assessment, and validation • Establish a database with severe weather case studies (data, documentation etc. ). • Establish a database with meteorological data to enhance real-time GNSS positioning. WP 2. 5 ZTD2IWV Conversion (Sect. 4. 6) • Develop, validate and exchange methods to convert between ZTD and IWV. For each case IWV is derived from GNSS ZTD as described in  Stoycheva et al. (2017). In addition, for case 2 the IWV difference is calculated by subtracting IWV from Sredec and Sofia.
Assimilation of E-GVAP ZTD Data into the WRF Model K. Eben • J. Resler • P. Krč Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic e-mail: eben@cs. cas. cz We have performed several experiments, assimilating the ZTD from the E-GVAP database together with other data into the WRF model, using 3DVar assimilation package, WRFDA. In particular, we have investigated the synergy of the OPERA radar composite with E-GVAP data. We have tested different assimilation methods, among others the hybrid ensemble/3DVar method. The OPERA radar composite provides a large source of European-wide data on radar reflectivity. E GVAP and OPERA bear different kind of information on water vapour in the atmosphere and both have a potential for data assimilation. Several simulations have been performed and analysis increments have been compared, using different background covariance models and analysis methods.
Assimilation of Temperature and Specific Humidity For tomography assimilation studies at the Central Institute for Meteorology and Geodynamics (ZAMG), Austria, May and June 2013 were selected as study period. In order to study the atmospheric processes within this period more in detail, a tomography dataset was computed over the entire period of two months with threehour temporal resolution. The resulting wet refractivity fields reflect the atmospheric conditions within the area of Western Austria, parts of Germany and Italy, see Fig. 4. 44. For more details about the tomography processing strategy, the reader is referred to  (Möller 2017). Comparisons with radiometer data at Innsbruck airport reveal that the tomography solution, i. e. the obtained wet refractivity fields are significantly less biased than operational numerical weather model data (ALARO), see Fig.
Assimilation of Wet Refractivity For the described test case, a tomography dataset was derived from slant wet delays, observed at 72 GNSS sites in Eastern Germany and parts of Czech Republic in May and June, 2013. Therefore, the same tomography processing strategy was applied as In total a one-day long assimilation experiment was performed, with (GPS NW) and without (CTRL) GNSS tomography product assimilation. Thereby two solutions were tested: (1) assimilation of relative humidity profiles, (2) assimilation of refractivity observations. The WRF model background (initial and boundary conditions) was derived from NCEP FNL (final) operational global analysis data with 1 Â1 horizontal resolution and 26 vertical layers. In case of assimilation, the model is started at 06:00, 12:00 and 18:00 UTC and is integrated for 6 h (so-called cyclemode).
This sub-WG was established to define and generate specific benchmark datasets in the form of GNSS observations, alternative water vapour and refractivity observations, and NWP products, for test, assessment and validation for each method/ product in conjunction with WG1. Only by validation of traditional and more advanced GNSS tropospheric products (developed in WG1) will the scientific community (specifically the forecasting community) have a better understanding of the benefit of GNSS-tropospheric products to meteorology.
Benchmark Campaign During the first year of the Action, a benchmark area/time period was determined for the testing and validation of advanced GNSS tropospheric products and processing algorithms developed by WG1. After consideration of a number of areas and events, it was determined that the benchmark campaign should be focused on the areas of Germany/Poland (with some data from surrounding countries) which suffered extremely heavy precipitation in the period of May-June 2003, causing severe flooding of the Danube, Moldau and Ebla rivers. A database was established at the Geodetic Observatory, Pecny, Czech Republic in conjunction with the International GNSS Service, which met the requirements of the benchmark database, (http://www. pecny. cz/gop/index. php/gop-tropdb). The database contains the following datasets: • GNSS: ~500 stations, SYNOP: ~200 stations • NWM: regional (Aladin-CZ), global (ERA-Interim, NCEP GFS) • RAOBS: 2 high-resolution • WVR: Potsdam, Lindenberg • RADAR images: Brdy, Skalka • Reference products • GNSS: Bernese (GOP), EPOS (GFZ) • NWP: G-Nut/Shu (GOP), DNS (GFZ) More information on the benchmark campaign can be found in detail in subsection 4.
Severe Weather Case Study Database A database was established on a publicly-facing ftp server at the UK Met Office (ftp. metoffice. gov. uk) for the collection and ditribution of severe weather case studies. The case studies uploaded to the database are those found in Sect. 4. 2 of this Report plus additional severe weather case studies from events in Austria, Czech Republic, Italy, Lithuania, the Netherlands and the UK. The database will be supported long-term for the benefit of scientific research and forecaster use. The database is user/password controlled, so please contact jonathan. jones@metoffice. gov. uk for access.
NWP Database for Input to GNSS Processing This task was created to establish and feed a database with live meteorological data to be used as an input to real-time GNSS positioning. ZTD estimates rely on mapping functions to estimate the atmospheric condition. Advance knowledge of the atmosphere can not only lead to more realistic ZTD and IWV estimates, but also improve GNSS convergence time, thus aiding the positioning community. The creation of a database at the UK Met Office for the provision of live (i. e. realtime) NWP unified model (UM) output to aid real-time positioning was unfortunately not possible due to UM licence restrictions. Whilst providing the GNSS community with live data was not possible, the provision of offline data for postprocessing applications was possible. As is documented in subsection 3.
ZTD to IWV Conversion UK Met Office Operational conversion of ZTD to IWV J. Jones Met Office, Exeter, UK e-mail: jonathan. jones@metoffice. gov. uk At the UK Met Office, IWV is derived from ZTD using surface meteorological observations retrieved from the Met Office database, the MetDB. On an hourly or sub-hourly basis (determined by the frequency of the GNSS processing system), the database is interrogated and meteorological parameters for all 8000+ global surface sites are extracted. The parameters are; site identifier, latitude, longitude, height, date, time, temperature, dew point temperature and MSL pressure. Latitude and longitude are read into a MySQL database and values are compared against the latitude and longitude of the GNSS sites used for that particular processing campaign, and thus meteorological data from the nearest surface-observing site to each GNSS site is chosen.
Further Reading The empirical conversion of ZTD to IWV is covered in subsect. 1. 2. 4. A review of conversion methods with a view of determining a climatological standard are covered in subsect. 5. 4. 2. changes in the stations equipment and environment. Various homogenization algorithms have been evaluated based on a synthetic benchmark dataset. The uncertainty of trends estimated from the homogenized times series is estimated to AE0. 5 kg m À2 per decade. Reprocessed GNSS IWV data are analysed along with satellites data, reanalyses and global and regional climate model simulations. A selection of global and regional reprocessed GNSS datasets and ERA-interim reanalysis are made available through the GOP-TropDB tropospheric database and online service. A new tropo SINEX format, providing new features and simplifications, was developed and it is going to be adopted by all the IAG services.
Model uncertainties, especially regarding the water cycle, are also limiting the quality climate model simulations. Strong interest grew in recent years to assess the benefit of ground-based GNSS measurements for climate research, both as a basic observable of the water cycle, e. g. to evaluate IWV trends and variability, and as a validation data for atmospheric models. Indeed, IWV was recognized as an essential climate variable by GCOS, and global GNSS measurements cover now about 20 years (e. g. , IGS and EPN networks include hundreds of stations) which make them an interesting independent observational dataset for climate model validation. Moreover, several global and regional homogeneously reprocessed GNSS datasets were produced in recent years which were not analysed in this respect so far.
Objectives and Organisation of Activities The general objectives of WG3 were the following: • Review and evaluate existing reprocessed long-term GNSS datasets, processing and post-processing methods. • Establish standards and recommendations for processing and post-processing of GNSS measurements consistent with climate research requirements. • Establish a database of qualified GNSS IWV data for climate research. • Cooperate with climate community on the exploitation of GNSS IWV data. Questions regarding observations (data, metadata, and products), data processing (which software, processing options and products) and dissemination of results (sinex topo format) implied tight cooperation with the geodetic community and WG1. Post-processing (screening, IWV conversion, and homogenization) as well as validation (IWV intercomparisons between GNSS and other techniques) was more at the heart of the activities of WG3 participants and stimulated cooperation with the remote sensing community (e.
g. GRUAN, NDACC). Finally, thematic studies on IWV trends and variability with GNSS data and climate models were conducted as well by participants and stimulated collaborations with WG2 and the climate community. Five work packages were defined at the beginning of the Action to organise more efficiently the activity in the different fields. • GNSS data processing software, • Processing Mode (double differences, precise point positioning) and options (GNSS Product used. ), elevation cut-off angle, handling of site coordinate, mapping function. ). Information was collected for about 24 GNSS datasets as well as global DORIS and VLBI reprocessed datasets. The global GNSS datasets included IGS repro1 (PPP solution) produced by JPL, IGS repro2 solutions from various Analysis Centres, and TIGA solutions from various ACs.
The European GNSS datasets included EPN repro1 (a combined solution) and EPN repro2 from various Analysis Centres. Several regional and national reprocessed datasets are also described (e. g. for Scandinavia, West Africa. ). Some of the datasets have been uploaded to the GOP tropo database (ref. Sect. 5. 1). At the beginning of the Cost Action, several GNSS reprocessed solutions were available. Since a majority of groups had already been using IGS repro1, and this dataset is a global, fully reprocessed dataset, it was adopted by the WG3 participants as a first reference dataset for community activities throughout the course of the Action. This dataset is further described in the next subsection. Other GNSS, VLBI, and DORIS datasets produced and/or used during the course of the Action are described in subsequent subsections, including forthcoming datasets that may be of interest for future studies.
Several long-term (20+ years) reprocessed tropospheric solutions currently exist and are available for climate studies. These time series have been produced using various software and various strategies, and include GNSS stations belonging to various network scale: global (IGS troposphere Repro 1 and TIGA), regional (EPN troposphere Repro 2) and local. International Reprocessing Activities: • EUREF Tropospheric 2nd Reprocessing Campaign http://www. epncb. oma. be/_ productsservices/troposphere/ • TIGA Reprocessing Campaign http://adsc. gfz-potsdam. de/tiga/index_TIGA. html • GRUAN Reprocessing Campaign http://www. gfz-potsdam. de/en/section/spacegeodetic-techniques/projects/gruan/ • IGS 2nd reprocessing campaign (http://acc. igs. org/reprocess2. html) More details about some of the reprocessed datasets can be found in the Chap.
Objectives The motivations for using a common ZTD and/or IWV dataset are the following: • Avoid extra uncertainty due to the use of different datasets when the results from a large community are to be intercompared (e. g. compare results from various post-processing methods: screening, ZTD to IWV conversion, homogenisation, trend estimation. ), • Provide a well-documented and validated IWV dataset to the climate community for model verification.
Description of the ZTD Dataset The tropospheric parameters (ZTD and gradients) were produced by JPL as coordinator of the IGS tropo working group in 2010. The dataset actually used here and referred to as repro1 is composed of two streams: The reason why two batches are available is that the official IGS repro1 campaign (which aim was to reprocess satellite orbits and clocks mainly) covered the period from 1995/01 to 2007/12 only. However, JPL extended the length of the reprocessed tropospheric solution beyond that period using consistent operationally produced satellite orbits and clocks (combined final IGS products). Unfortunately, we noticed that in the 2008 and 2009 archive, a few days of the trop_new dataset were not reprocessed for a number of stations. The impact of this mix of old and new tropospheric estimates is small at most sites except in Antarctica which exhibit small ZTD biases (<5 mm) due to the use of different mapping functions (NMF in the old solution, GMF in the new).
The main processing options used to produce the IGS repro1 dataset are summarised below: More information and validation results are available in  (Byun and Bar-Sever 2009)  and in. The dataset includes tropospheric products for 460 stations over the full period. However, not all stations have been operating since 1995. Figure  5. 2 shows the sites with more than 10 and 15 years of observations. For climate related studies (e. g. analysis of trends) a subset of 120 stations with more than 15 years of data can be selected  (Bock 2016a ). The selected ZTD dataset was screened for outliers and converted to IWV using the following procedures. The screening included a range check and an outlier check for ZTD estimates and their formal errors (σ ZTD ) at the highest temporal resolution (5 min sampling): • range check: ZTD E [1 m, 3 m] and σ ZTD E [0, 6 mm], • outlier check: ZTD E median (ZTD) AE 0.
(2017). The ZTD dataset was screened using range checks and outlier checks were applied to ZTD and formal error estimates (see Sects. 5. 2. 2 and 5. 4. 1). Further quality check and validation was done by comparing DORIS ZTD with ECMWF reanalysis (ERA-Interim) data. The outlier checks rejected 3% of the data, while the ERA-Interim comparison further rejected 1% of data based on a normality test. A linear drift was evidenced in the screened DORIS ZTD data compared to ERA-Interim and to the IGS repro1 GPS ZTD data (Sect. 5. 2. 2), which was associated to the progressive replacement of Alcatel antennas with Starec antennas at the DORIS sites. The DORIS IWV data was homogenized by applying a bias correction computed from comparison with ERA-Interim data, each time station equipment was changed (mostly for antenna replacement).