dataesr/scientific-paragraphs-categorization huggingface dataset:
The homogenized DORIS data showed excellent agreement with the GPS data (correlation coefficient of 0. 98 and standard deviation of differences of 1. 5 kg/m 2 ). Comparison with ERA-Interim and satellite IWV data was also quite good (correlation coefficient > 0. 95 and standard deviation of differences <2. 7 kg/m 2 ). The agreement with radiosonde data was less good, however. Preliminary results of IWV trends and variability at 31 sites with more than 10 years of data showed good consistency between DORIS, ERA-Interim, and GPS. This study demonstrated the high potential of the DORIS IWV data for climate applications. This DORIS IWV dataset is public available at: http://onlinelibrary. wiley. com/doi/10. 1002/2013JD021124/ Later improvements in the DORIS processing procedure are related to zenith tropospheric gradients estimation strategies, for example when estimating such parameters on an hourly basis, instead of once a day as before  (Willis et al.
Therefore, it is still necessary to apply a post-processing screening procedure to provide a clean ZTD dataset. This study shows that many outliers can be avoided using the new baseline strategy. The strategy is well adapted to post-processing when the network can be optimized by successive processing tests, e. g. to get the most stable time series (what is important for climate applications), but also for NRT applications when e. g. national networks are processed in DDthen adopting the star design would help avoiding the disconnections and large outliers. for all solutions, through proper estimation of these corrections. Nowadays, the most common used mapping functions are GMF (Global Mapping Function)  (Boehm et al. 2006a ) and VMF1 (Vienna Mapping Function1)  (Boehm et al. 2006b ). For some time, also an extension of NMF (Niell Mapping Function)  (Niell 1996) , IMF (Isobaric mapping Function)  (Niell 2000) , was used.
pressure and ZHD datasets on the VLBI results: raw in-situ meteorological observations recorded at VLBI stations, the same observations but homogenized, ERA Interim reanalysis, and GFZ-PT. The raw in-situ meteorological observations have been homogenized employing the penalized maximal t-test and series from the model levels of ERA Interim reanalysis (ERAinML) as a reference  (Balidakis et al. 2018) (Fig. 5. 12). B O R 1 D E N T D O U R E B R E H E R S J O Z E L A M A M A S 1 M A T E M E D I M E T S M O P I O N S A R E Y K R I G A S F E R S V T L Z I M M W T Z R W A R E V I L L P O T S P E N C K I R U G R A Z G R A S G O P E D E L F C A G L B O R 1 D E N T D O U R E B R E H E R S J O Z E L A M A M A S 1 M A T E M E D I M E T S M O P I O N S A R E Y K R I G A S F E R S V T L Z I M M W T Z R W A R E V I L L P O T S P E N C K I R U G R A Z G R A S G O P E D E L F C A G [mm/yr] 0 -0.
Using simulated data,  Nahmani et al. (2017)  showed the potential interest of using a Bayesian approach to overcome this problem. For a given dataset, the evidence (probability of a model given the observations) can be computed for different models and used to select the most appropriate modelling. One can thus decide whether ZWD should rather be modelled as a random walk or as a step or piecewise linear function. One can also discriminate between different weighting functions of the GPS observations. Using the Bayesian Variance Component Estimation (VCE) technique, it is also possible to determine optimal random walk parameters for the ZWD and tropospheric gradients, as well as the optimal variance of GPS observations. To apply this approach to real GPS data, it is mandatory to get observation equations from GPS data processing software, which is not possible with the standard version of GIPSY-OASIS software.
5. 2. 2) for year 1996 give a fraction of rejected data between 0. 0065 (mean AE 3σ) and 1. 6 10 À5 (mean AE 5σ) over all available stations. Inspection of results station by station reveals that only a small number of stations contain a significant number of outliers. For these stations, a rejection rate of 0. 01 (i. e. 1%) is achieved when the limits are taken at the mean AE 3. 5σ. We thus recommend to use the latter interval. Because formal errors are very sensitive to changes in the quality of ZTD estimates, we can improve the screening procedure by applying a range check and an outlier check on the formal errors. Inspection of empirical PDFs and percentiles of the formal errors from various reprocessed ZTD datasets showed that the PDFs of stations with no or only few outliers resemble a Chi distribution and that the 95, 98, and 99% percentiles can be used to detect stations with large numbers of outliers  (Bosser and Bock 2016).
However, the results are highly dependent on the processing options, especially the constraints on the temporal evolution of the estimated ZTDs (see  Sect. 5. 3). Hence, the 95% percentile for a global dataset such as the IGS repro1 solution produced with GIPSY OASIS II (one ZTD parameter estimated every 5 min with a random walk model constraint of 3 mm h -1/2 ) ranges between 2. 0 mm (good stations) and 4. 0 mm (bad stations), while for a EPN repro2 solution with Bernese software (one ZTD parameter estimated every 2 h and no constraints) the range is extended to between 1. 2 mm (good stations) and 9. 0 mm (bad stations). We thus recommend to use range check limits matched to the data processing options. An upper limit for the formal error of 10 or 15 mm is adequate to unconstrained tropospheric models (e.
Finally, Rueger (2002)  proposed a new set of 'best average' coefficients after reassessing the dataset used by  Bevis et al. (1994). While there has been a broad consensus on the value of k 1 from previous authors, Rueger's new k 1 coefficient is 0. 115% larger than the standard value. The impact on ZHD computed from Eq. 5. 2a is an increase of about 2. 6 mm at mean sea level (i. e. a bias of IWV ¼0. 4 kg/m 2 ). The impact is also significant on the determination of bending angles from GNSS radio-occultation measurements  (Healy 2011). Healy (2011)  examined the reasons of increase in Rueger's k 1 estimate and identified two obvious reasons: a numerical inconsistency in the value of 0 ¼ 273 K instead of 273. 15 K and the inclusion or not of CO2 in the gas mixture composing the dry air by the previous authors.
Auxiliary Data The computation of ZHD from Eq. 5. 2a requires surface pressure at the GPS station. However, surface pressure is not often observed at GPS stations. Only a small number of IGS stations are equipped with pressure sensors and  Wang et al. (2007a, b, c)  and  Heise et al. (2009)  have pointed out inaccuracies in these data. On the other hand, surface pressure observations are available from the World Meteorological Organization (WMO) surface synoptic network (SYNOP) at about 8500 sites globally, with mostly 1-to 6-hourly reports. The main issues that arise with these data are related to sensor calibration, which has not been performed in a consistent manner across all stations over time, and height correction when reduced to mean sea level pressure  (Ingleby 1995). Parracho (2017) compared ERA-Interim and SYNOP data over Europe and observed that altitude changes larger than 10 m are not uncommon in this dataset and that the update of station altitudes in the WMO and national weather services can be delayed by several months, hence introducing spurious biases and breaks in the observed pressure data.
Recommendations As of the end of this COST Action, the recommended methodology and datasets for the conversion of GNSS ZTD data to IWV are the following: • Use set of conversion equations presented in this subsection: Eq. 5. 1a to 5. 7b • Use the set of 'best average' refractivity constants published by  Rueger (2002)  adjusted for non-ideal gas and updated for a CO 2 concentration of 0. 0375%: Eq. 5. 8 • Use NWP model data for the calculation of surface pressure and Tm at the station, e. g. provided by TUV or, better, recomputed from ERA-Interim pressure-level data • Apply a vertical adjustment for Ps (or ZHD) and Tm if these data are not valid at the altitude of the GNSS station: Eqs. 5. 9a, 5. 9b and 5. 10.
5. 5. 5), to identify the possible mean shifts (change points) in the difference time series. This approach allows for identification of the change points not only in the GPS IWV time series but also in ERA-Interim. After the correction of the mean shifts for the GPS data, an improved consistency in the IWV trends is evident between nearby sites, while a better agreement is seen between the trends from the GPS and ERA-Interim data on a global scale. In addition, the IWV trends estimated for 47 GPS sites were compared to the corresponding IWV trends obtained from nearby homogenized radiosonde data. The correlation coefficient of the trends increases significantly by 38% after using the homogenized GPS data. Within the COST action, a homogenization activity was also set up, targeting the following objectives: (i) select one or two long-term reference datasets, (ii) apply different homogenization algorithms on these reference datasets, and build up a list of commonly identified inhomogeneities based on statistical detection and metadata information, and (iii) come up with an homogenized version of the reference dataset that can be re-used to study climate trends and time variability by the entire community.
As a first reference dataset, we decided to focus on the existing first tropospheric product given by the data reprocessing of the International GNSS Service (IGS) network, named hereafter IGS repro 1. This homogeneous reprocessing (one single strategy) of the data results from a set of 120 GPS stations distributed worldwide providing continuous observations from 1995 until the end of 2010, see Fig. 5. 22. The retrieved ZTDs estimated from the GNSS receiver observations at the stations have been screened, and the outliers have been removed as described in  Bock (2015)  and in Sect. 5. 4. 1. To convert those ZTD measurements in IWV, the surface pressure at the station location and a weighted mean temperature are needed, which are taken or calculated from ERA-interim (or ERAI), see  Bock (2016a, b, c, d)  and Sect.
Most of the inhomogeneities in the GNSS-derived IWV time series due to antenna or radome changes and changes in the observation statistics (¼ events) are characterized by jumps in the IWV time series  (Vey et al. 2009). Therefore, for each site, we calculate differences time series between the IGS repro 1 and ERA-interim IWV datasets, and we will look for the epochs of those events causing offsets in the difference time series.
Assessment of the Homogeneity of ERA-Interim Although ERA-interim is used as reference dataset in our activity, ERA-interim might have inhomogeneities of its own, e. g. when new satellite datasets are introduced in the data assimilation system (see e. g. Ning et al. 2016a, b; Schröder et al. 2016). ERA-interim is also used for the development of tropospheric blind models which includes the temporal modelling of the model parameters. Therefore,  Eliaš et al. (2019)  tried to detect potential change-points that may occur in the ERA-Interim ZHD and ZWD time series. They applied a statistical method that is based on the maximum value of two-sample t-statistics. More than 64. 000 original time series for both products are then analysed, whereas the time span of the series included the years 1990-2015 with a time resolution of 6 h.
As a matter of fact, to test the sensitivity of the performance of the homogenization tools on the complexity of the time series, 3 datasets of 120 synthetic daily IWV differences time series have been created, with increasing complexity: • "easy" dataset: includes seasonal signals (annual, semi-annual, inter-and quarterannual, if present for a particular station) + offsets + white noise (WN) • "less-complicated" dataset: same as "easy" + autoregressive process of the first order (noise model ¼ AR (1) + WN) • "fully-complicated" dataset: same as "less-complicated" + trend + gaps (up to 20% of missing data). This dataset is closest to the real IWV differences. These sets of synthetic time series were made available to the community for a blind testing of homogenization algorithms in use. The inserted offsets of the easy dataset are available to be revealed, if asked for by a participant, for fine-tuning of the algorithm on the use of IWV differences.
HOMOP (Operator: B. Chimani) The homogenization code HOMOP is a combination of PRODIGE (for detection, Caussinus and Mestre 2004), SPLIDHOM (adjustment,  Mestre et al. 2011) , an adapted interpolation  (Vincent et al. 2002) , and improved by some additional plots for facilitating the decision of the homogenisation and extended with some uncertainty information by using different reference stations as well as bootstrapping methods (HOMOP,  Gruber et al. 2009 ). The approach is neighbour-based, and in the particular case of our synthetic datasets, a lower limit of 0. 6 for the correlation coefficients was imposed for selecting potential reference stations. Break detection is done at annual or seasonal base.
CLIMATOL (Operator: J. Guijarro) Another neighbour-based homogenization algorithm is CLIMATOL, which performs a form of orthogonal regression known as Reduced Major Axis  (RMA, Leduc 1987 ) between the standardized anomalies (x-μx)/σx and (y-μy)/σy of the two distributions. Orthogonal regression is adjusted by minimizing the perpendicular distance of the scatter points to the regression line, instead of minimizing the vertical distance to that line as in Ordinary Least Squares regression (OLS). In the case of our synthetic datasets, it was imposed that the only reference time series at the site is the ERA-interim time series. The Standard Normal Homogeneity Test (SNHT,  Alexandersson 1986 ) is applied to find shifts in the mean of the anomaly series in two stages. The code incorporates a filling in of missing data and outlier removal.
g. Venema et al. (2012). To visualize the performance of the different tools in terms of those different statistical scores, we adapted the ternary graph representation from  Gazeaux et al. (2013) , shown in Fig. 5. 26, for the fully complicated dataset. It depicts the ratios of the statistical detection scores (TP + TN, FP, and FN) by their position in an equilateral triangle, highlighting the trade-off between those. A perfect solution would appear on the bottom right corner of the triangle (see blue lines in the figure). From a glance on this figure, it can be directly noted that the involved homogenization tools do not perform very well for the fully complicated dataset: especially the number of false positives are too high. Fortunately, the probabilities of true detection are also high.
Some methods nearly detect all the inserted breakpoints, but at the cost of a high number of false alarms, while other methods are more conservative in detecting breakpoints, resulting in low scores for both true detection and false alarms. So far, we only discussed the results on the breakpoint detection on the fully complicated dataset. It should however be noted that a good performance of the tools is achieved for the majority of the participating methods on the easy and less complicated datasets, especially due to a lower amount of false positives. So, we can conclude that the performance decreases for almost all the tools when adding gaps and a trend in the benchmark time series; adding autoregressive noise of the first order has less impact. Some of the homogenization algorithms also provided the (constant) amplitudes of the detected offsets.
These were compared with the amplitudes of the offsets that were put in the synthetic time series. The result, again for the fully complicated dataset, is shown in Fig. 5. 27. From this figure, it could be seen that some methods tend to underestimate the number of offsets with small amplitudes relatively (e. g. ME1 and ME2), while other methods on the contrary overestimate the amount of those offsets (e. g. RVM 2of3 D), but on the other hand underestimate the number Fig. 5. 26 Ternary graph representing the ratio between three performance measures of the breakpoint detection solutions (TP + TN, FP, and FN). The performance increases with decreasing numbers of false positives and false negatives and increasing numbers of true positives and negatives, so that a perfect solution is located in the lower right corner, marked by the blue area.
Trend Estimation for the Homogenized Datasets Only in the fully complicated dataset, a trend was inserted in the IWV differences series, and the homogenized time series by the different time series should hence reveal the same trend. However, for some stations, trends as large as 0. 1 kg/m 2 (or mm) per year arise after correcting for the detected offset by some methods. Overall, for the fully complicated synthetic datasets, most trends differ within AE0. 05 kg/m 2 per year. As one of the main goals of our homogenization activity is the provision of a homogenized dataset of GNSS IWV time series for use in trend analysis, special care should be taken not to introduce spurious trends in the time series after correction. In this sense, the impact of homogenization on the estimated trend uncertainties should also be further elaborated.
Conclusions and Outlook In this contribution, we described the current activity in homogenizing a world-wide dataset of IWV measurements retrieved from observations made at ground-based GNSS stations. As the distances between those 120 stations are large and correlations are generally low (lower than 0. 6 for distances larger than 1 ), we used the ERA-interim reanalysis IWV fields at those station locations as the reference time series for relative statistical homogenization. Based on the characteristics of the IWV differences series between the GNSS dataset and ERA-interim and the properties of manually checked instrumental change events reported in the metadata of the GNSS sites, we generated three variants of 120 synthetic IWV difference time series with increasing complexity: we first added autoregressive noise of the first order and subsequently trends and gaps.
Those synthetic time series enable us to test the performances of six participating breakpoint detection algorithms and their sensitivity to this increasing dataset complexity. We found that the performances of those algorithms in identifying the epochs of the inserted offsets especially decreases when adding trends and gaps to the synthetic datasets, due to a larger number of false alarms. On the other hand, the hit rates of most tools are rather good, even when applied on daily values instead of on monthly values. Different tools show a different sensitivity for detecting different ranges of amplitudes of offsets, especially for the most complex (fully complicated) synthetic time series: some tools overestimate (underestimate) the number of small-amplitude (large-amplitude) offsets, while the opposite is true for other breakpoint detection algorithms.
). Of course, we highly welcome contributions from other groups running homogenization tools, and in the future, our benchmark will already be extended with few more contributions. After providing solutions for the synthetic time series, the participants will get the opportunity to fine-tune their methods on the specifications of the datasets with the help of the knowledge of the true inserted offsets and their amplitudes. Thereafter, a second round of blind homogenization on a newly generated synthetic dataset of IWV values (probably with simulated metadata information) will be held. Based on the performance of the statistical homogenization tools on these synthetic datasets, we will develop a methodology for combining the results of good performing homogenization tools with metadata information.
Datasets and Methods in Past IWV Intercomparisons We compiled an online inventory of past IWV intercomparisons involving GNSS: http://www. meteo. be/IWVintercomp, which will be regularly updated. As can be seen from this inventory, the GNSS IWV retrievals have been compared with a wide range of other techniques. Each instrument has its own specific sensing properties, which have to be taken into account when giving an overview of different intertechnique analyses.
g. Torres et al. 2010)  or between the site and the satellite ground pixel centre or by demanding that the site lies in the satellite ground pixel. Of course, due to the large differences in horizontal resolutions between the satellite samplings, those site-satellite co-location criteria will impact the IWV agreement to a large extent, even when comparing with satellite instruments that make use of the same wavelength range for the IWV retrieval (e. g. GOME and SCIAMACHY). To prevent the presence of a systematic bias between two datasets of IWV measurements, the vertical separation between sites should be taken into account and corrected for. This can be most easily done when vertical profile measurements of temperature and humidity are present (by radiosonde measurements), e. g. Buehler et al.
Finally, past studies also use different statistical parameters to describe the agreement between two (or more) IWV datasets. The most commonly used variables are the (absolute and relative) bias and the standard deviation, but also the median of the differences, the mean bias error, RMS, RMSE, the linear regression slope and the linear regression offset are widely used. Vaquero-Martinez et al. (2017a) calculated the pseudo median and the interquartile range of the relative differences as estimators for the accuracy and the precision respectively of their satellite data with respect to ground-based GNSS data.
Conclusions Due to its high accuracy and precision, growing coverage, long-term stability and all weather observing capability, the GNSS technique for IWV retrieval is recently been used more frequently as reference device for e. g. the validation of IWV satellite retrievals. Also the validation of the IWV time series from climate models is an emerging field. Assessing the long-term stability and homogeneity of GNSS IWV datasets is therefore of great importance. However, past IWV inter-technique studies including GNSS (http://www. meteo. be/IWVintercomp) concluded that the results of those studies are hard to intercompare, because study and time dependent to a large extent. Here, we summarized some of the most important characteristics of the different instruments and we described major differences between the past studies.
Introduction A team from the Instituto Português do Mar e da Atmosfera (IPMA) and the Space & Earth Geodetic Analysis Laboratory (SEGAL)who already demonstrated the usefulness of GNSS PWV retrievals for weather and climate purposes  (Adams et al. 2011) , developed an operational scheme to produce these estimates for the Iberian Peninsula. The main objective of this work is to validate GNSS PWV by comparison with other standard datasets for this region, which are used routinely at IPMA for nowcasting of extreme precipitation events.
Results In Fig. 5. 28, all the different estimates of PWV are compared for the Lisboa -Gago Coutinho station from December 2016 to March 2017. There is a good overall agreement between the 4 analysed datasets, but the GNSS estimates being wetter than the other techniques for most of the observing period. Part of the mismatch between estimates is likely attributable to errors in colocation. The scatter plots between GNSS and the other data sets are shown in Fig. 5. 29. A Hampel filter was used for outlier removal  (Liu et al. 2004 ). The comparison with the SAF-NWC shows the lowest RMSE, of about 1. 3 mm, and lowest bias (SAF-NWC-GNSS) of about À0. 7 mm. However, this is also the comparison with the lowest Fig. 5. 29 Comparison of GNSS estimates to the remaining data sets (SAF-NWC on the right, ECMWF in the centre and radiosondes in the right) Fig.