dataesr/scientific-paragraphs-categorization huggingface dataset:
Fortunately, at least in Europe, North America, Japan, and Korea, the density of stations is high enough that unusual patterns emerge during data analysis such as the mixing ratio and trend maps that were generated for TOAR-Health, TOAR-Vegetation, and TOAR-Climate. Indeed, in a few dozen cases such anomalies were seen on preliminary versions of the TOAR plots and they prompted us to perform a closer inspection of the respective time series, which in turn resulted in some unit corrections and the flagging of several outlier values. For some regions with a limited number of sites but suspicious features in individual time series, we plotted the data from neighbouring sites together in order to check their consistency and identify the most suitable time series. One example of such analysis is shown in Figure  4  for three Romanian stations from the EEA Airbase.
The apparent inconsistencies in the ozone data from DJ-4 are likely caused by NO x emissions from the nearby power plant (see map). DOI: https://doi. org/10. 1525/elementa. 244. f4 during at least a third of the depicted period. Ozone variations at the third site (DJ-4) initially also follow those from the other two sites but then change their behaviour. Ozone mole fractions at DJ-4 are then substantially lower than at the other two sites and only rarely correlate with them. We excluded all data from DJ-4, because this station was clearly visible as an outlier in all TOAR analyses. In retrospect, after re-investigating this case for the preparation of this manuscript, this may have been a mistake, because the observed ozone variations and lower mole fractions at DJ-4 could be real as there is a thermal power plant about 700 m away from the station.
However, even so, it is questionable if such data with very strong local influence are suitable for TOAR analyses. Future analyses of this kind could greatly benefit from the inclusion of ozone precursor data, i. e. NO 2 or NO x measurements, and from more precise NO x emission data at finer resolution. The TOAR database contains several "duplicate" records where data from one station had been submitted to different data archives. One would expect these time series to overlap indistinguishably on top of each other. In most cases this is indeed the case. However, we did find instances with obvious discrepancies from periods missing in one time series but not the other, outliers flagged in one data set but not the other, to different concentration values due to subtle differences in the application of data coverage rules.
For example, in one network hourly values were considered valid only if two 30-minute average values were valid, whereas the presence of only one valid 30-minute value sufficed in the other network. In the case of a European mountain station (Jungfraujoch, Switzerland) the comparison of data from the different archives also led to the discovery of a unit conversion error in one of the databases. Here, different reference temperature and pressure values for the conversion from ppbv to µg m -3 were assumed by the data provider and the data center, and for a high-altitude site this has a significant effect. Note that instead of removing duplicate records from the database we established a selection and merging table (see Supplemental Material 1). In summary, this review showed that large uncertainties remain in the reported surface ozone data from many networks and data centers.
In spite of substantial efforts to identify and flag erroneous and suspicious parts of the data, there may be many time series with anomalies or inconsistencies which escaped our attention. Conversely, as demonstrated by one example above, we may have erroneously flagged legitimate data in some cases, because of insufficient information. Fortunately, as evidenced by the analyses presented in TOAR-Health, TOAR-Vegetation, and TOAR-Climate, the impacts of remaining data errors appear to be minor, because the concentration and trend maps show largely consistent features across wide regions. Where trends vary within a region, this may be a combination of local impacts (changes of local emission sources) and data quality issues. As discussed with the example of three Romanian stations in the context of data consistency above, detailed knowledge about individual stations and instrument performance is required in order to avoid misinterpretation of ozone trends.
Metrics data sets Precompiled data sets with extensive sets of statistical quantities, including metrics defined for assessing ozone impacts on health, vegetation, and climate, and ozone trend statistics, have been made available for the TOAR report and wider research use at the PANGAEA data portal (https://doi. org/10. 1594/PANGAEA. 876108). These data products are provided in the form of easily readable ASCII files (csv format), or NetCDF (http://www. unidata. ucar. edu/software/netcdf/) files in case of gridded products for the evaluation of numerical models. A detailed description of the available surface ozone data products can be found in Supplemental Material 1. The primary TOAR data products which are also used extensively in the analyses of TOAR-Health, TOAR-Vegetation, and TOAR-Climate, are the 5-year aggregate data sets and the trend statistics.
This makes it possible to relate metrics and trends to site characteristics such as population density, nighttime light intensity, etc. Figures 5-8 demonstrate the value of the TOAR surface ozone data products and the possibility to distinguish sites by their metadata. Figure  5  shows annual median ozone mixing ratios versus station altitude compiled from more than 5000 data sets measured between 2008 and 2015. The data show a large spread in low altitudes ranging from around zero to above 50 nmol mol -1. Ozone mole fractions generally increase with altitude (e. g. Staehelin et al. , 1994; Chevalier et al. , 2007) , although the maximum values above 3000 m are only slightly larger than those in the lowest 500 m. Rural sites tend to exhibit larger ozone mole fractions than urban sites and there is no tendency of increasing values with altitude in urban environments.
This indicates that ozone in the urban environment is primarily controlled by local chemical processes and less influenced by large scale advection. Figure  6  shows maps of monthly mean gridded daytime ozone averages in January and July between 2010 and 2014. The data represented in these maps are intended for the evaluation of global chemistry models. The grid size of 5° × 5° longitude-latitude was chosen in order to find a compromise between maximizing the data coverage and preserving regional-scale features. Data sets with other grid resolutions are also available on the TOAR data portal. While the maximum daytime average mole fractions are seen over Europe and North America during July, the largest values actually occur over East Asia in April and May (see Figure  7  and map plots on the TOAR data portal for details).
Conclusions The TOAR database constitutes the world's largest collection of surface ozone data achieved so far and is the primary source of information for the TOAR exploratory analyses of the global distribution and trends of tropospheric ozone using metrics relevant to the impact of ozone on human health, vegetation, and climate. Data from more than 9,600 sites around the world have been brought together in one single repository and thereby coverage in many world regions has substantially increased compared to previous global ozone data collections. However, important gaps still remain in several parts of the world as there are few measurements in Africa, Central and South America, Central and Southern Asia and the Middle Eastern region (or data from these regions were not accessible). Centralized extractions of aggregated metrics data sets applying consistent filtering and statistics routines to the entire data set for all TOAR analyses are made available through the TOAR data portal at the PANGAEA publishing site.
Additional methods to access TOAR data are the JOIN web interface and the associated REST services. The TOAR database allows for novel ways of analysing surface ozone data and trends as a result of the extensive data collection, the data quality control efforts, the multi-faceted metadata, and the consistent processing of all surface ozone observations. One particular example is the first objective, globally consistent station characterisation through combination of various global, gridded data sets. Demonstrations of new surface ozone analyses are given in this paper. An important lesson from the TOAR database building effort was the recognition of the value of intensive communication with ozone data centers and other data providers and the appreciation of their work, expressed also by the long author list of this paper.
As there is no global standardization of atmospheric composition metadata, data formats and data access methods, the data collection effort is rather complex and error-prone (see discussion in  Sofen et al. , 2016b). Only by intensive communication with the original data providers has it been possible to resolve errors and ambiguities, and we hope that the feedback they have received from the TOAR database curators will help to improve the quality and consistency of the original ozone data repositories. To enhance the usability and value of surface ozone and air quality observations worldwide, the community must make further progress on the data and metadata harmonization and quality control. We recommend to managers of air quality data that: (i) data centers improve the documentation of their metadata and include more information on the measurement sites, the instruments and calibration techniques in their repositories; (ii) station coordinates be provided with better accuracy and station locations be verified in order to allow analysis of ozone data together with high resolution geographical information, thereby enabling new applications such as automatic search for nearby pollution sources; (iii) more automated quality control tools be developed and harmonized among data centers.
Automated quality control tools should be based on rigorous statistical methods and may benefit from new methods of big data analytics (e. g. "deep learning") in order to test data sets for consistency in space and time and use additional information such as meteorological fields or ozone precursor data to test the plausibility of reported ozone concentrations. Finally, we express our hope that access to quality controlled hourly observations of air quality will be facilitated through the implementation of state-of-the-art web services. At present a lot of manual intervention is needed in order to collect all available observations. Over the coming years we aim to further expand and continue updates of the TOAR database. Recent additions include ozone precursor and meteorological data from European stations.
While the existence and accessibility of the TOAR database is guaranteed for several years, its further development will depend on the availability of funding. The database and tools that have been developed in the context of TOAR constitute an important step towards a global data architecture (see for example https://www. rd-alliance. org/about-rda/who-rda. html) and are used as examples in presentations and discussions on this topic. This work shows a lot of potential but also reveals many issues with respect to building the fully interoperable, federated data repositories (https://en. wikipedia. org/ wiki/Federated_database_system) of the future. In particular, it clearly demonstrates that the cooperative involvement of many people is needed in order to bring such systems to life and to make sure that the underlying information is of known quality, robust, documented, and traceable.
Data Accessibility Statement General access to TOAR data is free and unrestricted through the JOIN web interface (https://join. fz-juelich. de/) and its associated REST service (see documentation in Supplemental Material). This applies to all data products on daily and coarser time resolution. The hourly ozone data in the TOAR database are not publically available due to restrictions imposed by individual data providers. For use of the interactive JOIN web interface, a registration is required. Many of the original hourly ozone observations are, however, available from the original data center websites (see Table  1  of this article). The TOAR data portal on PANGAEA (https://doi. org/10. 1594/ PANGAEA. 876108) contains ozone statistics (including metrics for assessing health, vegetation, and climate impacts), trend estimates, and graphical material.
Supplemental Files The supplemental files for this article can be found as follows: • Supplement 1. Documentation of TOAR surface ozone data products. DOI: https://doi. org/10. 1525/ elementa. 244. s1 • Supplement 2. Documentation of the Jülich Open Web Interface for accessing TOAR surface ozone data. DOI: https://doi. org/10. 1525/elementa. 244. s2 Note 1 In most cases the results provided through TOAR will be very close to official statistics, but there are subtle differences in the implementation of various TOAR metrics which may change the results of such analysis. Furthermore, as described in section 5, we performed extensive additional quality control on the TOAR database records and even though we reported data flagging changes or unit errors back to the data providers, these may not always be adopted in the original archives.
Ozone monitoring networks as defined in the TOAR database a. DOI: https://doi. org/10. 1525/elementa. 244. t1Note that not all stations of a given network have measured ozone continuously. More information on the number of available data records during specific periods can be obtained from Figure1. For detailed information on data availability from individual stations the TOAR database should be accessed directly via the JOIN web interface as explained in Supplemental Material 2).
The microwave radiometers (MWRs) on board the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new total column water vapour (TCWV) and wet tropospheric correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time.
However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely ERA-Interim-derived WTC for all satellites and for the entire time series. Even compared to the European Space Agency's (ESA) operational WTC retrievals, which incorporate in addition to MWR additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites, ERS-1 and ERS-2.
Introduction ESA's altimetry missions are at the heart of significant progress on oceanography. The combined coverage of highquality observations by the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat spans over more than 20 years from 1991 to 2012. During this period, improvements in instrument data processing as well as orbit and geophysical corrections allowed reaching an accuracy/sensitivity of 1 cm on instantaneous sea surface height (SSH) measurements and demonstrated the capability to observe a 3 mm yr -1 sea level rise  (Ablain et al. , 2009). Published by Copernicus Publications on behalf of the European Geosciences Union. A major source of uncertainty for radar altimetry is the wet tropospheric correction (WTC). The spatial and temporal variability of water vapour is such that an instantaneous estimation of its impact is needed.
, 1990; Forster et al. , 2007). Various groups have reported trends in the amount of columnar water vapour. In particular, over the oceans, a strong trend in TCWV has been observed  (Trenberth et al. , 2005). TCWV also appears to be a key factor regulating tropical precipitation  (Bretherton et al. , 2004). The importance of water vapour in the climate system is recognized by the Global Energy and Water Cycle Experiment (GEWEX), which is currently performing an assessment of long-term water vapour products (the GEWEX Water Vapour Assessment, G-VAP). Of particular importance for the current study is the recent publication by  Schroeder et al. (2016) , who provide an overview on existing TCWV datasets and assess their long-term stability as well as issues caused, for example, by changes in the observation systems.
The MWR instruments on board ERS-1, ERS-2, and Envisat are based on very similar architectures and have measured water vapour over the ocean between 1991 and 2012 and therefore provide a dataset of 20+ years of water vapour observations. Data continuity into the future is ensured with ESA's Sentinel-3 series of satellites, the first of which was launched in February 2016. MWR's two channels located at 23. 8 and 36. 5 GHz allow for the simultaneous retrieval of TCWV and cloud liquid water path (LWP) as outlined later in this publication. Through the REAPER (REprocessing of Altimeter Products for ERS) project  (Gilbert, 2014)  1 , ESA has provided significant efforts to produce an up-to-date data record of well-calibrated MWR observations. The efforts described herein build on the REAPER dataset and address three interconnected issues.
Firstly, the homogenization and intercalibration of the MWR data record is studied and an improved intercalibration is developed. Secondly, using state-of-the-art one-dimensional variational (1D-VAR) retrievals, a TCWV data product is developed and made 1 https://earth. esa. int/web/sppa/activities/ multi-sensors-timeseries/reaper/ available to the community. A revised WTC product accompanies this dataset also. Thirdly, the dataset is validated both against Global Navigation Satellite System (GNSS) observations of TCWV as well as in terms of its mesoscale stability with respect to WTC. The so-derived dataset is made available to the community. This publication is organized as follows: in Sect. 2 we describe the MWR brightness temperature time series as well as the methods used for retrieving TCWV and WTC.
The MWR dataset The MWR flown on board the ERS-1, ERS-2 and Envisat missions are two-channel nadir-pointing passive microwave instruments measuring top-of-atmosphere (TOA) brightness temperatures (see Table  1  for more details). A similar instrument is also flown on ESA's current Sentinel-3 mission. The MWR is nadir-pointing only as its main use to support wet tropospheric correction for the collocated radar altimeter. ERS-1, ERS-2, and Envisat were flown in 98. 4-98. 5 • inclination orbits at altitudes around 780 km, providing nearglobal MWR coverage with the exception of a narrow region near the poles. The Envisat MWR brightness temperatures underlying the have been generated by Collecte Localisation Satellite (CLS) in 2014 in the framework of the Envisat MWR L1B Expert Support Laboratory (ESL) activities funded by ESA.
It consists of a corrected dataset that removes the anomaly that has been observed in version 2. 1  (Ollivier and Guibbaud, 2014). The ERS-1 and ERS-2 MWR brightness temperatures used herein are based on the REAPER project  (Gilbert, 2014)  but have been entirely reprocessed in the framework of the EMiR (ERS/Envisat MWR Recalibration and Water Vapour Thematic Data Record Generation) project. The so-called "first run" REAPER L1B data have been the basis for this reprocessing. Land measurements are discarded and no specific processing is applied in coastal areas so that contamination from land may occur above coastal waters at distances of less than ca. 50 km from land. Such potentially land-contaminated pixels are excluded from the analysis presented here.
GNSS dataset MWR estimates of TCWV are evaluated against a 2-hourly dataset of TCWV measured by ground-based GPS  (Wang et al. , 2007) , composed of the International GNSS Service (IGS), SuomiNet, and GPS Earth Observation Network (GEONET), and hosted by NCEP/NCAR. While the focus of SuomiNet (see e. g. Ware et al. , 2000)  is the US and Central America and GEONET (see e. g. Shoji, 2009)  is based on Japanese stations, IGS (see e. g. Byun and Bar-Sever, 2009)  includes about 500 stations distributed globally 2. The methodology of how TCWV is derived from the measured GPS zenith path delay is described in detail in  Wang et al. (2007). They also found the individual errors to be less than 1 mm and total errors to be less than 1. 44 mm. The GNSS database version 721. 1 includes data from 1995 to 2014.
In a first step, the grid was set up according to the considered spatial resolution (2 • × 2 • or 3 • × 3 • lat/long). For each MWR footprint, the corresponding indices of the global fields were calculated from the latitude-longitude information. If all conditions were met (see above), the retrieved and auxiliary values were added to the corresponding grid box. The daily averages were then calculated as the arithmetic mean of all observations within that grid box within 1 day. If, for a given month and grid box, more than 20 days had valid observations, the arithmetic mean of those was assigned to be the monthly-mean value. The so-derived monthly-mean fields, along with the individual retrievals, are part of the published dataset. All analysis reported within this publication was, however, performed on individual retrievals.
Method Because of the low sensitivity of the retrieval to the first guess as well as to the background state (see Appendix B), the bias correction proposed here relies on two main assumptions. First, the globally averaged TCWV from ERA-Interim is considered reasonably accurate in terms of its absolute value to provide a reference against which to gauge the average brightness temperature biases of the MWR time series. Note that we do not make any claims about the long-term stability of the ERA-Interim TCWV or any trends and discontinuities of the dataset. The only assumption we make is that ERA-Interim TCWV on a globally and monthly averaged basis is accurate to within ±2 kg m -2. This assumption is justified from intercomparison efforts such as  Schröder et al. (2013). The second assumption is that histograms of instantaneous retrieved LWP must show a significant fraction of negative retrieved LWP, which corresponds to measurement noise around zero LWP for cloud-free situations.
TCWV MWR-retrieved TCWV was validated against TCWV derived from coastal GNSS stations. Validation was performed in terms of biases and root-mean-square errors (RMSE) as well as in terms of long-term stability of the dataset. A total of 30 712 valid collocations were found. The temporal collocation difference was chosen to be at maximum 1 h. Note that our dataset excludes observations close to land, so that the distance between a coastal GNSS station and the nearest MWR observation is somewhere around 100 km. The maximum collocation distance was chosen to be 150 km. Figure  4  shows the coastal GNSS stations used. Figure  5  and Table  1  summarize the validation results. For the entire dataset, we find a relatively small bias of 0. 63 kg m -2 and an RMSE of 4. 68 kg m -2. If the comparisons were restricted to exclude MWR retrievals with high LWP (> 200 g m -2 ) and GNSS stations with altitudes higher than 50 m above mean sea level (MSL) were also excluded, the RMSE was reduced to 3.