dataesr/scientific-paragraphs-categorization huggingface dataset:
Despite the systematic biases that were identified, the correlation with the other high frequency datasets is encouraging and suggests that simple corrections could be applied in order to make the products more comparable, e. g. the orographic correction proposed by  Bock et al. (2007). There is still room for fine-tuning of the product, namely through proper identification of outliers, development of a quality flag for the retrievals, reducing timeliness and increasing station availability. Given the high annual variability of the PWV over Iberia, using a more extended period, as well as comparison to more data sources could also provide a more complete validationthis is ongoing work. A measure of the product uncertainty is also key and it is envisaged to the near future so that the product usage can be expanded for applications such as data assimilation by limited area numerical weather forecast models.
Another dataset stems from microwave radiometry by remote sensing satellites as, e. g. , combined and provided by the GlobVapour product by the European Space Agency (ESA). In GlobVapour, data of several Earth observation missions (MERIS, GOME, SSM,) was combined with the aim of providing global and consistent time series of IWV, which, in simplified terms, can be equated with PW. In this study, we analyse the correlation between all these datasets and compare them with NWM data from the European Centre for Medium-Range Weather Forecasts (ECWMF). Eventually we draw conclusions about possible synergies and the dataset's ability and performance in describing the amount and variation of water vapour in the troposphere. As to derive more information from the datasets, all observations are plugged into a least squares fitting for Eq.
5. 11 below, the so-called seasonal fit. This outputs information about seasonal variations as well as long-term changes in the data. b mjd ð Þ ¼ A0 þ A1 cos mjd365:252π ð Þ þ B1 sin mjd365:252π ð Þ þ A2 cos mjd365:254π ð Þ þB2 sin mjd365:254π ð Þ þ k mjd ð5: 11Þ In Eq. 5. 11, b(mjd) denotes the respective output quantity dependent on the modified Julian date mjd, A 0 the mean value, A 1 and B 1 the annual terms, A 2 and B 2 the semi-annual terms, and k the linear trend. Figure  5. 31 shows an example of how the fitting affects the data. The seasonal fits are determined for the whole data at all 18 globally distributed VLBI/GNSS stations which are considered in this study. The temporal resolution of the datasets is slightly different:  : 1995 : -2007 : (6-hourly) • GlobVapour: 1996 : -2008 (weekly) (weekly)  In Fig.
5. 32, the comparison of IWV from all four datasets is shown for station MATERA in Italy. • NWM: 1995-2016 (6-hourly) • VLBI: 1995-2014 (6-hourly) • GNSS Figure  5. 32 proves that the datasets in general fit quite well to each other. Averaging the IWV from all 18 considered stations yields the main results of this study (Table  5. 7). Highest correlation is reached between IWVs from VLBI and GNSS, with the NWM data yielding very high correlation coefficients with VLBI and GNSS as well. The GlobVapour data, on the other hand, does not correlate that well with the other datasets, although a correlation coefficient between 0. 6 and 0. 7 is not bad either. A comparison between GlobVapour and VLBI is not possible, as they have too few identical epochs. The biases of the dataset pairs are generally very small, which means that none of the datasets is systematically higher or lower than the others.
The standard deviation is inversely proportional to the correlation coefficient; a high correlation coefficient is therefore accompanied by a low standard deviation, and vice versa. Furthermore, we also took a look at the linear trends k, that is, the longterm changes of the respective IWVs (Fig. 5. 33). Unfortunately, no clear systematics can be derived whether the IWV increases or decreases at the considered stations, as all datasets provide contradicting results. Only at one single station, SESHAN25 in China, the datasets agree about the algebraic sign of the gradient. As a result, no safe statements can be made about  long-term changes of IWV from this study. The reason is most likely that the considered time spans are too short. In summary we can conclude that the considered datasets agree very well with each other.
VLBI turns out to be a very appropriate technique for deriving long time series of IWV as well. The GlobVapour data is highly beneficial, in particular because it is available globally and not bound to terrestrial stations such as GNSS and VLBI, however its performance suffers from the fairly low temporal resolution compared to the other techniques. In further research we will consider longer time spans, more sample stations and more remote sensing products, as to derive even more meaningful results. ASIC), were utilized in the present analysis. To derive ZTD from GPS the AS0 solution characterized by a 5-min sampling was applied and GPS data were averaged over 15 min. A scatter plot of GPS and RAOB-derived ZTD for the entire dataset is shown in Fig. 5. 34. A relatively small bias and standard deviation (SD) of À0.
From 1999 to 2013, the SD decreases from 0. 671 to 0. 435 cm with an average value of 0. 575 cm. The bias has a long-term average value of À0. 092 cm. Using the same database, yearly bias and SD between GPS and ZTD RAOB were computed for all corresponding values at 00:00 UTC and 12:00 UTC and are shown As part of the analysis the GPS data collected at Sodankylä from 2008 to 2013 were processed using the ASIC and ASO solutions. The ZTD GPS values from the ASIC solution have a sampling time of 15 min, while the corresponding values referred to the AS0 solution have a sampling time of 5 min. Fig. 5. 37 shows the bias and SD of the ZTD obtained with the two methodologies with respect to the RAOB. The GPS ASIC solution produces a considerably higher positive bias than the AS0 solution. On the contrary, the AS0 solution lead to smaller bias values that however show a slight seasonal dependence: slightly negative in summer and almost zero during colder months.
g. MAW1, in Antarctica). At such sites, drifts in the reanalysis, due to changes in the data assimilated by the reanalysis over time, are plausible. Therefore, a second reanalysis, MERRA-2, was used to complement ERA-Interim and GPS, namely in regions of high uncertainty in these datasets (e. g. Antarctica) or in regions where few or no GPS data are available (e. g. Africa, Asia, the global oceans). The monthly IWV trends computed for MERRA-2 (Fig. 5. 38b) are in good agreement with ERA-Interim for most regions. They describe consistent global moistening/drying dipoles along the inter-tropical Pacific Ocean, across Australia, South America and between eastern and western USA, and general moistening over the Arctic and Europe. However, there appears to be also significant differences over several parts of the globe, in particular over Indonesia and the Indian Ocean, central Africa, western (coastal) and northern Africa, Central Asia and Antarctica.
Seasonal Variability Now we study how the seasonal cycle is represented (geographically) by the different IWV datasets. We therefore plot harmonic functions through the IWV time series and compare the amplitudes and phases of those functions, see Fig. 5. 41. Overall, we found that the seasonal cycle and its geographical variability are similarly depicted by all datasets. GOMESCIA underestimates the number of sites with small amplitudes of the seasonal cycle (< 4 mm), especially with respect to ERA-interim, and the phase of the maximum in the seasonal cycle also peaks one month later in the Northern Hemisphere in the GOMESCIA dataset. Additionally, the GOMESCIA time series also more often contain higher order periodic signals (e. g. 6 months) than the IGS Repro 1 and ERA-interim datasets.
Linear Trends From the IWV monthly anomalies, we calculated linear trends for the different datasets. These are shown in Fig. 5. 42 for IGS Repro 1 and GOMESCIA. Overall, we found that IGS Repro 1 has the highest number of sites with (statistically significant) positive trends in IWV: about two third of the sites have positive trends. GOMESCIA on the other hand, has the highest number of sites with negative trends in IWV. The sign of the trend is hence dependent on the dataset used, and only above Europe (moistening) and West Australia (drying) a consistent geographical pattern is achieved among the three considered datasets here (ERA-interim not shown). We also want to note that the ERA-interim IWV trends can, at least from a qualitative point of view, nicely be explained by the ERA-interim surface temperature trends.
Stepwise Multiple Linear Regression To gain insight in the processes responsible for the IWV time variability present in the 3 datasets, we tried to fit the monthly mean time series with a linear regression that uses explanatory variables as the surface temperature, surface pressure, tropopause pressure, but also teleconnection patterns like ENSO, NAO, etc. The approach is stepwise: first, we include all the explanatory variables in the linear regression and rank them according to the explained variability; then we include the explanatory variables one by one, according to this ranking, and only the explanatory variables that explain a statistically significant part of the variability are retained in the regression. An example of a fit is given in Fig. 5. 43. One important outcome of the analysis is that a large fraction of the variability, and for the majority of the sites, can be explained by the surface temperature, of course after accounting for the seasonal cycle by the use of long term means or harmonic functions.
Conclusions We examined three completely different IWV datasets (GNSS, ERA-interim and GOMESCIA satellite retrievals) to study the IWV variability of a set of 120 global IGS Repro 1 sites. Although every used dataset might have its own remaining issues like homogeneity, spatial representativeness, etc. ), we can conclude that combining the analysis results of the three of them, has the potential to characterize and understand the IWV time variability from a geographical consistent point of view.
6. 6). 4. ZTD to IWV conversion methods have been reviewed, including the uncertainty due to refractivity constants and various sources of auxiliary data  (Parracho 2017). Recommendations are provided in the Final Report (WG3). 5. A reference long-term reprocessed GNSS IWV dataset was prepared for homogenization activities in WG3  (Bock 2016a; Klos et al. 2017a, b; Van Malderen et al. 2017a, b, c). It is based on IGS repro1 ZTD data, screened and converted to IWV using the aforementioned methods and ERA-Interim as auxiliary data (for surface pressure and Tm). 6. Global IWV trends and variability have been analysed based on the aforementioned GNSS IWV dataset and on reanalysis data from ERA-Interim and MERRA  (Parracho et al. 2018)  as well as from global climate model simulations  (Parracho 2017 ) and regional climate model simulations Med-CORDEX project  (Bastin et al.
If a proper stochastic model is not employed, one will obtain results that should not be interpreted in terms of climate change as the trend uncertainties may be underestimated by a factor of 5-14 compared to the white noise only assumption. Moreover, a global comparison was performed between the ZTD derived from the ERA-Interim and that derived from ground-based GPS. It was found that the ability of ERA-Interim to predict the ZTD degrades with the increase in the amount of atmospheric water vapour, and vice versa. This comparison was based on a 5-years long dataset comprising of 406 globally distributed GPS stations. The ERA-Interim data was provided by Jan Douša of GOP. ZTD/IWV estimation in Poland: Analysis centre held at Institute of Geodesy and Geoinformatics GNSS&Meteo Working Group of Wrocław University of Environmental and Life Sciences (WUELS) is participating in ZTD/IWV estimation from GNSS data in Poland, Lithuania (in cooperation with Vilnius University) as well as in Victoria state in Australia (in cooperation with RMIT University in Melbourne).
ZTD estimates including horizontal gradients from Polish and Lithuanian stations are submitted hourly to the E-GVAP database in COST-716 format. Except of near real-time GNSS data processing, WUELS is also developing ultra-fast ZTD estimation services, estimating troposphere products each 15 min. The pilot implementation of processing engine at Wroclaw Centre of Networking and Supercomputing (WCSS) was developed in 2017. The ultra-fast processing is now optimized to process GNSS data from 50 stations (15 EPN + 35 selected from Leica SmartNet, Fig. 6. 17). The details of WUELS GNSS data processing activities for troposphere study are as follows: • WUEL network (submitted to E-GVAP): since 2012, 225 stations (EPN + ASG-EUPOS+SmartNet), • WLIT network (submitted to EGVAP): since 2016, 50 stations (EPN + LitPOS), • VICNET network (Australia): since 2015, 149 stations (IGS + APREF+GPSNet).
WG3 tasks: The research on noise characteristics in ZTD from homogeneously reprocessed GPS time series was performed at MUT as well as the investigation of the influence of adopted GNSS processing strategy on ZTD parameter, including e. g. various mapping functions, elevation mask, troposphere alignment or software. The same group was working on the influence of incorrectly modelled vertical position on tropospheric delay parameters and uncertainties of ZTD linear trend estimation process. MUT contributed significantly with synthetic benchmark datasets for homogenization of IWV time series retrieved by GPS delivered to the GNSS4SWEC community. WUELS group investigated on refractivity coefficients obtained from ERA-Interim data. Research group at UWM analysed the influence of troposphere modelling on the realization of the ETRS89 by the reference stations of the Polish national Ground Based Augmentation System (GBAS) network -ASG-EUPOS, and also on the tropospheric parameters.
Options include obtaining data from UCAR, working with Natural Resources Canada geodetic division to provide ZTD for sites that they have access to, and producing ZTD data ourselves from raw GNSS receiver data. ZTD (and IWV) estimates from UL01 for a period of approximately 1 month (July 17-August 20, 2013) was prepared in the form of 1-hourly COST-716 format files and was then converted into the BUFR format. Please note that the term "GNSS" will imply only the Global Positioning System (GPS) in this summary. Followed by the generation of the GNSS-derived NRT ZTD dataset, forecast experiments were conducted using the 3D-variational (3D-VAR) data assimilation system of the AROME NWP model. For the period of July 17-August 20, 2013, three forecast experiments were conducted using the AROME 3D-VAR NWP model: i.
The GNSS derived water vapour proved to have very high correlation with the WRF model (above 0. 9) for the whole period for the complete dataset. GNSS IWV from this processing has a positive systematic bias, compared to the model simulations. The results of this work are included in Sect. 3. 7. 2 "Supporting new analysis centres, new networks, transfer of knowledge" of the WG1 report for the COST action. The work from this STSM will be continued with the establishment of autonomous near real-time processing of the regional ground-based GNSS network in Southeast Europe in support of the EUMETNET E-GVAP. STSM Applicant: Mr Peter Szabo, Climate Modelling Group, Hungarian Meteorological Service, Budapest (HU), szabo. p@met. hu STSM Topic: Tropospheric products from GNSS and ALADIN-Climate regional climate model for East-Southeast Europe Host: Assoc.
The collocation algorithms were used to calculate the total refractivity field over a western part of Switzerland. The grantee has also implemented the ZTD horizontal gradients models into the software to investigate if they bring any improvements for the interpolation of the refractivity. The tropospheric parameters were calculated from two main data sources: ground-based meteorological measurements (air pressure, temperature and water vapour used to calculate total refractivity) and GNSS (ZTD and horizontal gradients). From those data sources, different datasets of input data were constructed: ZTD/Ntot, ZTD/Ntot/GRAD and Ntot only. Using the particular input dataset, the total refractivity profiles over Payerne were calculated to assess which dataset has the best agreement with the reference radiosonde measurements.
The dataset with the best performance of total refractivity interpolation was ZTD/Ntot with the absolute biases from 0 to 6 ppm and standard deviations 3-10 ppm. Excluding the ZTDs from the collocation results in worsening the interpolation above the 2 km by over 10 ppm. Introducing the horizontal gradients was presumed to improve the interpolation, but for the vertical interpolation at Payerne the refractivity field from the dataset with gradients was worse by about 0. 5 ppm than the interpolation without gradients. Another way to assess the refractivity models obtained using COMEDIE is to compare them with ground-based data from all meteorological stations at the surface level. Except for two stations, the absolute biases of the residuals were at the level of 0-4 ppm with 7-8 ppm standard deviations for both datasets -including and excluding the gradients.
The short term objective is to initialize and validate the IWV tomography by using co-located RO profiles. The long term objective is to relate the IWV trend before the rain and the storm's thermal structure to the storm intensity. The GPS RO profiles were downloaded from two different data centres (UCAR and Wegener Centre) which provide water vapour in different units (water vapour pressure in mbar and water vapour in Kg/Kg respectively) and with different tangent point reference. The first step was to create a homogeneous dataset for being compared to the IWV in terms of units, vertical and horizontal resolution.
Firstly, two tools were developed by the recipient. A set of scripts called WVR_POTS_STD, which serves for (pre-) processing of observations from WVR operated by GFZ Potsdam. Its main purpose is a conversion of original slant integrated water vapour (SIWV) values to STDs needed for the validation with an effort to minimally distort the original SIWV observations. The second tool suits for an automatic validation of STDs delivered in a new TRO-SINEX format including generation outputs in a form of tables and figures. Secondly, an initial validation of STDs was realized. There was a logical decision to use the Benchmark dataset prepared within COST ES1206 Action  (Douša et al. 2016)  for this purpose. It covers a 56-day long period in May and June 2013 which included severe weather events. In total, a subset of ten GNSS reference stations located at six different places were selected for the validation.
Pottiaux@oma. be The short-term scientific mission I attended was hosted by the Royal Observatory of Belgium and the Royal Meteorological Institute of Belgium, under the supervision of Dr. Eric Pottiaux and Dr. Roeland Van Malderen. We examined the properties of the IWV data retrieved from GPS observations and compared these with the IWV values calculated with the ERA-Interim model. The entire analysis was performed for the IGS repro1 dataset of 120 stations, prepared by Dr. Olivier Bock for the purpose of the data homogenisation activities of the sub-WG3 'data Homogenisation'. The differences of IWVs were examined with the Maximum Likelihood Estimation (MLE) approach to estimate the parameters which characterize the dataset, as trend, seasonal signals, breaks and noise character. Thereafter, three synthetic datasets were created basing on the parameters we estimated for real IWV records: 'Easy', 'Less-complicated' and 'Fully-complicated', each containing 120 stations.
Conclusions The synthetic datasets were used to benchmark the performance of various statistical homogenization tools. These three synthetic datasets were subjected to homogenisation task and shared with the homogenisation activity leaving the epochs of breaks blinded. The synthetic datasets we created within this STSM, will be used to assess the ability of different homogenisation tools to report epochs of breaks when different parameters are found in the series. The tool which performs the best will then be used to homogenise the real IWV dataset before trend is being estimated.
WP3. 1 Processing: aimed at making an inventory of available reprocessed GNSS datasets, which included DORIS and VLBI as well. Sensitivity studies, tests and evaluations were also conducted on processing options and products. WP3. 2 Post-processing: aimed at developing ZTD screening methods, standardize the ZTD to IWV conversion procedure, and assess the homogeneity of existing GNSS datasets and existing homogenisation methods. WP3. 3 IWV intercomparisons: aimed at making a literature review from previous multitechnique campaigns and stimulate new intercomparison with well qualified IWV data (from GNSS and other techniques).
Acknowledgments Engagement de chercheurs supplémentaires dans le cadre de la loi du 18 juillet 1997 et de l'A. R. du 19 août 1997. MO/34/021-Report: 2014-2015 « Développement du système international de prévision probabiliste à méso-échelle GLAMEPS ». The contribution of ROB to E-GVAP is supported by the  Solar-Terrestrial Centre of Excellence (STCE).
Acknowledgments Furqan Ahmed and Wenwu Ding were funded by the  Fonds National de la Recherche, Luxembourg  through projects PWVLUX (Reference No. 1090247 ) and POSILUX (Reference No. 6823109 ). Addisu Hunegnaw  was funded through the competitive ULX research projects GSCG and SGSL. Besides the IGS and EUREF special acknowledgment is given to the GNSS data provides Walcors, SPSLux and BIGF, who provided real-time streams and hourly RINEX files for the various processing systems at ULX. The  Administration des Services Techniques de l'Agriculture (ASTA) and MétéoLux of the Administration de la Navigation Aérienne (ANA)  for the provision of meteorological data from Luxembourg and the latter for advise on meteorological aspects and its continued support of ULX GNSS meteorology activities. MétéoFrance is acknowledged for collaboration and the hosting of the  STSM of Furqan Ahmed.
Methodology The hourly NRT ZTD solution generated at the UL, namely UL01, contains ZTD estimates from a European network of GNSS stations, which include stations belonging to IGS, the EPN and the Reseau GNSS Permanent (RGP). To densify the network over Luxembourg and the Greater Region, six stations from SPSLux (Luxembourg) and 22 from WALCORS (Wallonie, Belgium) were also processed. In total the GNSS network approaches 200 stations. A dataset containing.
WP3. 1 Processing: aimed at making an inventory of available reprocessed GNSS datasets, which included DORIS and VLBI as well. Sensitivity studies, tests and evaluations were also conducted on processing options and products. WP3. 2 Post-processing: aimed at developing ZTD screening methods, standardize the ZTD to IWV conversion procedure, and assess the homogeneity of existing GNSS datasets and existing homogenisation methods. WP3. 3 IWV intercomparisons: aimed at making a literature review from previous multitechnique campaigns and stimulate new intercomparison with well qualified IWV data (from GNSS and other techniques). R. Pacione e-GEOS/Centro di Geodesia Spaziale-Agenzia Spaziale Italiana, Matera, MT, Italy e-mail: rosa. pacione@e-geos. it O. Bock IGN Institut national de l'information géographique et forestière, Paris, France e-mail: olivier.