dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the different actors of the energy sector who participated to the suite of workshops organized in 2018-2020 in Abidjan. These workshops were organized with the help of  S. Anquetin  and  J. D. Creutin. We especially thank the following organizations and fellows for their cooperation in the collection and expertise of the load data for specific cities:  Prof Rabani Adamou et Prof Koné Daouda  ( WASCAL ),  S. Kam  ( WAPP );  D. E. Essenouwa ,  A. Zekpa  and  K. Apedo  ( CEET );  A. Zan-Bi ,  C. Sounkalo ,  Y. J. Kokora  and  P. K. Koffi  ( CIE );  C. A. T. Diallo ,  M. Sylla ,  S. Coulibaly  and  O. Doumbia  ( EDM-SA );  M. Arzika ,  G. Liman  and  A. Cisse  ( NIGELEC );  F. Yaya ,  C. Hounsounou ,  C. Aïssi  and  M. Hinson  ( SBEE );  A. Ouedraogo ,  P. Hema ,  l. Banao ,  A.
Funding This work is part of the PhD thesis of G. K. A. , funded by  African Centre of Excellence  program supported by the  World Bank and l'Agence Française de Développement , the  French Embassy in Togo  and the  Togolese Government. For the data collection, G. K. A. was supported by  IRD ,  IGE ,  University Grenoble Alpes , the  CNRS 2018  fund for interdisciplinary research CAUSE and  Campus France  for three 3-months visiting periods of in Grenoble.
formation and persistence of clouds and precipitation, and providing an extra constraint in the evaluation of numerical simulations. During the field experiment, researchers deployed seven water vapor isotopic analyzers on two aircraft, on three ships, and at the Barbados Cloud Observatory (BCO). Precipitation was collected for isotopic analysis at the BCO and from aboard four ships. In addition, three ships collected seawater for isotopic analysis. All told, the in situ data span the period 5 January-22 February 2020 and cover the approximate area 6 to 16 • N and 50 to 60 • W, with water vapor isotope ratios measured from a few meters above sea level to the mid-free troposphere and seawater samples spanning the ocean surface to several kilometers depth. This paper describes the full EUREC 4 A isotopic in situ data collection -providing extensive information about sampling strategies and data uncertainties -and also guides readers to complementary remotely sensed water vapor isotope ratios.
All field data have been made publicly available even if they are affected by known biases, as is the case for high-altitude aircraft measurements, one of the two BCO ground-based water vapor time series, and select rain and seawater samples from the ships. Publication of these data reflects a desire to promote dialogue around improving water isotope measurement strategies for the future. The remaining, high-quality data create unprecedented opportunities to close water isotopic budgets and evaluate water fluxes and their influence on cloudiness in the trade-wind environment. The full list of dataset DOIs and notes on data quality flags are provided in Table  3  of Sect. 5 ("Data availability").
This paper describes the collective EUREC 4 A-iso in situ dataset and provides detailed information about the isotopic measurement systems deployed, the ways in which data were processed, the measurement uncertainties, and data formatting and distribution. Particular attention has been paid to quantifying and reporting uncertainties, especially for the isotopic measurements in water vapor. Uncertainties in these data reflect the diverse operating conditions and constraints associated with each platform and the need to tailor postprocessing corrections to individual instrument performance, as is considered best practice  (Aemisegger et al. , 2012; Bailey et al. , 2015). We expect that these reporting efforts will not only promote more accurate cross-platform comparisons but also raise the bar for characterizing uncertainties in fieldbased water vapor isotopic deployments -which have grown in number over the past decade  (Galewsky et al.
, 2016). Extensive efforts have also been made to flag, rather than mask, data whose quality concerns cannot be described by error estimates. These data include high-altitude (free tropospheric) oxygen isotopic measurements from the aircraft, one water vapor isotopic time series from Barbados, rain samples whose collection times were delayed by multiple hours or days, and a group of seawater samples whose storage caps "breathed" during transit. Though many of these samples are not recommended for scientific analysis, they are included in the published datasets so that lessons can be learned and applied to improve instrument performance, sampling installation, and collection and calibration protocols. Such improvements will help promote regular integration of water isotopic measurements in future large-scale field campaigns and regular comparison of water vapor isotopic data with model output.
A summary of all available data and notes on flagged data are provided in Table  3  of Sect. 5. Finally, in addition to describing the in situ data, this paper also guides readers to complementary datasets, including EUREC 4 A-iso remotely sensed isotope ratios. The satellitebased data have been repackaged into custom subsets that cover an extensive region surrounding Barbados for the period in which the 2020 field campaign occurred. Even though the remote sensors exhibit vastly different sensitivities to the atmosphere than the water vapor isotopic analyzers deployed during EUREC 4 A, they provide large-scale spatial and temporal context for the in situ data, which enhances our ability to investigate shallow convective cloud regimes and their role in climate.
Data collection During EUREC 4 A, seven laser-based analyzers measured the concentration of water vapor and its isotopic composition from ground-based, airborne, and ship-based platforms. Two analyzers made collocated ground-based measurements at the Barbados Cloud Observatory (hereafter BCO, https: //barbados. mpimet. mpg. de/, last access: 6 December 2022), airborne vapor measurements were made aboard the French ATR-42 (hereafter ATR) operated by SAFIRE  (Bony et al. , 2022)  and the US NOAA WP-3D Orion (hereafter P-3;  Pincus et al. , 2021) , and ship-based vapor measurements were made aboard the French research vessel L'Atalante (hereafter Atalante), the German research vessel Meteor (hereafter Meteor), and the US NOAA research vessel Ronald H. Brown (hereafter Brown;  Quinn et al. , 2021).
Water vapor isotopic measurements at the BCO The BCO water vapor isotopic measurements were designed to serve as a continuous fixed-point dataset with 1 min time resolution at a location with extensive meteorological in situ and remote sensing observations, including continuous Raman lidar water vapor profiling and passive remote sensing of column water vapor and condensed water. Vapor isotopic measurements at the BCO were made with two laser spectrometric instruments installed side by side (cf. Aemisegger et al. , 2021a) : a Picarro L1115 analyzer, using cavity ring-down spectroscopy (CRDS); and a Los Gatos Research (LGR) analyzer, using off-axis integrated cavity output spectroscopy (OA-ICOS). The two systems operate at different wavelengths in the infrared; consequently, baseline effects due to varying water vapor concentrations can affect the measurements differently  (Johnson and Rella, 2017).
Data post-processing and uncertainties In this section, we provide a detailed report of any corrections, adjustments, masks, or flags applied to the EUREC 4 Aiso data and describe key uncertainties that may affect their quality or interpretation. We also describe any anomalous data points or sampling periods. All datasets provide estimates of both the oxygen and hydrogen isotope ratios of water normalized to the VSMOW-SLAP  (   (Dansgaard, 1964)  -and its uncertainty are provided as well. The water vapor datasets additionally contain estimates of the water vapor concentration, which, unless otherwise specified, is given in the original measurement format as a mole fraction relative to total (moist) air in ppmv (i. e. , n wv /n t ). Several datasets provide more than one expression of the water vapor concentration (e.
4;  Phillips et al. , 1997)  to derive a more conservative total uncertainty estimate. To gap-fill the BCO time series, data users may consider scaling the OA-ICOS isotope ratios to the CRDS isotope ratios, for example, by applying a mean offset or by means of a regression model (Figs. S4  and S5 ). As noted in the OA-ICOS dataset's accompanying README file, data users should also be aware that the OA-ICOS δ 18 O exhibited large oscillations -on the order of 1 ‰ -during some periods of ambient sampling. While the cause of these oscillations has yet to be identified, the fact that they appear only in one isotope ratio and not the other indicates an intermittent problem with the internal spectroscopy. The effect of this oscillation is not included in the OA-ICOS uncertainty estimates, since these estimates are designed to represent trustworthy sampling periods.
Data users should be aware that the oscillations in OA-ICOS δ 18 O also influence the OA-ICOS d time series. Water vapor concentrations were corrected for the CRDS system but not the OA-ICOS system. For the CRDS analyzer, an independent linear scaling was applied (Supplement) to adjust for a high bias in the range 10 000-30 000 ppmvwhich was determined after the campaign using a dew point generator -and to simultaneously convert wet mole fractions (n wv /n t ) to dry mole fractions (n wv /[n t -n wv ]). Given the known small drift of the CRDS system's humidity measurements (< 50 ppmv per month), the bias was assumed constant over the course of the EUREC 4 A deployment. The uncertainty of the corrected CRDS humidity measurements is 223 ppmv (dry mole fraction). Masked (missing) data in either BCO water vapor isotopic dataset represent periods when daily calibration checks or instrument maintenance were performed.
ATR (airborne) The post-processing for the ATR 1 s water vapor isotope data closely follows the procedure presented and applied in previous experiments using the same instrument  (Aemisegger et al. , 2012; Sodemann et al. , 2017; Thurnherr et al. , 2020). Normalization to VSMOW-SLAP was performed by applying the scale and offset derived from fitting a linear regression between the measured and known values of the three standards used during the in-field calibration checks. Standard measurements were deemed of sufficient quality to include in the regression if at least 3 min within the calibration period presented no significant drift and exhibited standard deviations less than 1 ‰ in δ 18 O, less than 2 ‰ in δD, and less than 3000 ppmv in water vapor concentration. Importantly, the in-flight calibration checks did not show a significant difference in either mean or standard deviation compared with the ground-based calibration checks.
8 ‰, 1. 7 ‰, and 1. 9 ‰ for δ 18 O, δD, and d, respectively. These estimates are valid for water vapor concentrations of 25 000 ppmv, which represent near-surface conditions near Barbados. Isotopic uncertainties increase as water vapor concentrations decrease but do not appear to depend on the isotopic composition of the vapor (Fig. 7b ). https://doi. org/10. 5194/essd-15-465-2023 Earth Syst. Sci. Data, 15, 465-495, 2023 Measurements of suspect quality, including those influenced by inlet wetting, are noted in the YAML files that accompany the dataset. See  Bony et al. (2022)  for a general description of these.
Therefore, no humidity-dependence correction was applied. Corrected data are available both at sample rate (∼ 5 Hz) and averaged to 1 s to align with other P-3 airborne datasets  (Pincus et al. , 2021). Uncertainty estimates for the P-3 isotopic data follow guidance from the US National Institute for Standards and Technology (NIST, 2021) and account for all of the following: errors in the correction function used to normalize the data, ambiguities about the stability of the normalization with time (i. e. , drift), and uncertainties associated with an inconsistent humidity-dependent bias. They represent the addition in quadrature of the residual standard deviations from the pre-and post-campaign humidity-dependence tests and the isotopic uncertainties associated with the normalization function and its stability, with the latter given by δ u.
94-2. 51×10 -5 t) to address a drifting offset of 13. 8 to -18. 5 s over the course of the campaign. The 1 min isotopic data files contain a contamination flag equivalent to that found in the Brown meteorological dataset, where a non-zero value marks periods of potential contamination  (Quinn et al. , 2021). Optimal sampling periods occurred when the Brown was pointed into the wind, minimizing contamination by the ship's stack aft of the aerosol container in which the analyzer was housed. A flag value of 2 has been added to the isotopic files to mark time periods when the Brown was near port, when other meteorological data are not reported. An additional flag has been added to mark periods when the blower, pulling air through the sampling mast, into the aerosol container, was reversed.
Masked (missing) measurements in the Atalante vapor isotopic dataset include times during which the analyzer sampled reference gas and the period 00:37-22:12 UTC on 26 January (DOY 26), during which time the analyzer was not functioning properly. Poor data quality periods have been flagged, as have periods when the water vapor isotopic measurements were likely influenced by precipitation or by exhaust or recycled air from the ship (0. 4 % of the data).
Higher-thanexpected d was then used to bias-correct δ 18 O and δD, by assuming a 1 : 2 relationship between the two isotope ratios. Consequently, uncertainties in the isotopic estimates for these samples may be as large as 0. 1 ‰ and 0. 15 ‰ for δ 18 O and δD, respectively. Isotope ratios for the first Atalante rainwater sample are also excessively high, indicating that this particular sample may have undergone evaporation prior to analysis in the laboratory. In contrast, although the BCO and Merian precipitation datasets include flags to mark sample collections that occurred 1 h or more after rain had stopped, close examination of the data suggests post-sampling evaporation is not a concern for these samples. The BCO flag can be used instead to discriminate between event-based samples (collection ≥ 1 h after rain) and those collected every 10 min during the trailing cold front on 22 January (DOY 22).
12 ; Table  2 ). (Correlation between the δ 18 O time series is diminished by the oscillation in the OA-ICOS signal (see Sect. 3. 1). ) Low-frequency coherence is also apparent when comparing the time series from the BCO with those measured by nearby ships. The Meteor, for example, was frequently close enough to Barbados' eastern shores that air masses sampled on the ship would have reached the BCO about 9 h later (assuming easterly wind speeds of about 7 m s -1 ). Shifting the Meteor time series to account for this presumed time difference produces correlations with the CRDS analyzer of 0. 4-0. 5 when 1 h moving averages are applied to both datasets. These results suggest that mixed-layer variability in the trade environment is coherent across hundreds of kilometers. Spikes that appear in the Meteor time series but not the land-based datasets represent measurement periods during and after rainfall, which are flagged in the Meteor dataset (previously described in Sect.
All three remote sensors also provide retrievals of water vapor concentration. The two European isotopic products have been repackaged into custom subsets for the EUREC 4 A-iso effort. One subset provides retrievals within a 10 • × 10 • box defined by 5-15 • N and 50-60 • W. The other subset covers an extended region to support Lagrangian analyses of air mass transport history (i. e. 21 • S-54 • N and 110 • W-22 • E; see  Villiger et al. , 2022). Both subsets cover the period between 10 January and 20 February 2020 (DOY 10-51). The IASI dataset (generated by the latest version of the MUSICA retrieval algorithm; M. Schneider et al. , 2022; Diekmann et al. , 2021c)  is customized for the 10 • × 10 • box over Barbados and consists of H 2 O-δD pairs at all retrieval grid levels between the surface and 56 km, full averaging kernel information, and flag variables indicating the quality of the individual observations.
The quality flag is 1 for clear-sky scenes, 0. 5 for scenes with low clouds (with co-retrieved cloud center height 2 km or less), and 0 for all other scenes. Data with a quality value of 0 should not be used. The median bias -relative to colocated ground-based Fourier transform infrared (FTIR) observations by the Total Carbon Column Observing Network (TCCON) -is 3 % in H 2 O and 17 ‰ in δD for clear-sky scenes and 11 % in H 2 O and 20 ‰ in δD for cloudy scenes. A. Schneider et al. (2022)  describe the retrieval and provide a validation. The full TROPOMI dataset is available from https://tropomi. grid. surfsara. nl/hdo/ (last access: 11 November 2021). As demonstrated in Fig. 14 , the satellites provide rich spatial context for the in situ data. Nevertheless, when using the two in tandem, care must be taken to consider differences in what each type of measurement represents.
Data availability All EUREC 4 A in situ water isotopic data and the repackaged IASI and TROPOMI products are available through the AERIS portal (2022, https://eurec4a. aeris-data. fr/). Data from the P-3 and Brown are also archived at the National Centers for Environmental Information (NOAA, 2022, https: //www. ncei. noaa. gov/). Individual datasets, which have been created for each platform and sample type (e. g. , water vapor, precipitation, seawater), are listed with their DOIs and quality-flag notes in Table  3. We encourage data users to cite individual datasets; however, the full collection may be accessed with one search through https://doi. org/10. 25326/418 (Bailey et al. , 2022).
Concluding perspective on dataset uses The collection of water vapor, rainwater, and seawater isotopic data gathered during EUREC 4 A comprises one of the most extensive cross-platform water isotopic datasets to date. As a result, analyses using datasets specific to many of EUREC 4 A's airborne and ship-based platforms -as well as the BCO -will benefit from the extra observational constraint on water cycle processes in the trade-wind region that water isotopes provide (Fig. 1 ). For instance, combining microphysical data, such as raindrop size distributions, with precipitation isotopic measurements could provide a novel way to independently verify rain evaporation rates (cf. Salamalakis et al. , 2016; Graf et al. , 2019; Sarkar et al. , 2022). Similarly, comparing water isotopic information with moisture flux estimates -derived from eddy covariance or budget techniques -could provide complementary time-integrated and instantaneous perspectives on moisture exchange between the ocean and air.
Water vapor isotope ratios could also constrain mixing processes, such as entrainment into the sub-cloud layer, since mixing between atmospheric layhttps://doi. org/10. 5194/essd-15-465-2023 Earth Syst. Sci. Data, 15, 465-495, 2023 ers produces predictable variations in the isotope ratio as a function of water vapor concentration  (Noone et al. 2011; Noone, 2012). In addition, as pseudo-conserved tracers on larger scales, isotope ratios could provide important context for interpreting anomalies in other atmospheric constituents (trace gases, aerosols) by helping identify the source regions and moisture transport pathways of distinct air masses. The fact that the EUREC 4 A dataset includes isotopic information for different moisture reservoirs also creates opportunities to evaluate scientific questions that have long interested water isotope researchers.
Isotope ratios can provide important additional constraints for such case studies, helping evaluate thermodynamic and microphysical controls on convective development. Typically, the P-3 and Brown sampled the eastern https://doi. org/10. 5194/essd-15-465-2023 Earth Syst. Sci. Data, 15, 465-495, 2023  side of the EUREC 4 A domain, while the ATR and Meteor sampled downwind and to the west. All of these platforms measured upwind of the BCO, potentially creating opportunities to track air masses for multiple hours, if not days, at a time. Such analyses could be especially useful for evaluating numerical simulations at the large-eddy scale. The EUREC 4 A isotopic dataset could also prove useful for evaluating numerical simulations more broadly, such as has been done recently for the eastern subtropical North Atlantic  (Diekmann et al.
, 2021a; Dahinden et al. , 2021). After all, few water vapor datasets provide vertically resolved isotopic information. Moreover, the limited number of airborne isotopic measurements that existed prior to EUREC 4 A primarily represent higher latitude regions (e. g. ,  Ehhalt et al. , 2005; He and Smith, 1999; Herman et al. , 2014; Dryoff et al. , 2015; Sodemann et al. , 2017; Salmon et al. , 2019). EUREC 4 A greatly extends the current small body of observed isotopic profiles from the tropical lower troposphere (cf. Bailey et al. , 2013; Herman et al. , 2020). Similarly, the rain and seawater samples collected during EUREC 4 A help extend the spatial coverage of existing archives (e. g. ,  Schotterer et al. , 1996; Schmidt et al. , 1999) , providing critical observational benchmarks for model evaluation.
All told, EUREC 4 A facilitated the joint deployment of a number of innovative and experimental measurements to address outstanding questions related to convection and cloudiness in the shallow convective environment of the western tropical Atlantic  (Stevens et al. , 2021). The seven in situ water vapor isotopic datasets, five precipitation isotopic datasets, and three seawater isotopic datasets described in this paper helped contribute to EUREC 4 A's visionary approach and are openly available for the community to use in evaluating the processes that regulate the shallow convective hydroclimate state. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-465-2023-supplement. Author contributions. AB, FA, LV, SAL, GR, and EQM coordinated measurements in the field, processed the data, and drafted the manuscript.
SB, JG, DN, PKQ, SS, BS, and EJT designed and directed sampling strategies for the isotopic measurement network and individual platforms. CA, DBB, TB, DC, SPdS, MD, DH, PM, and MR provided critical measurement support and guidance on data quality. TB, CJD, AS, and MS provided the remotely sensed data and their descriptions. All authors helped edit and refine the initial manuscript draft. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Elucidating the role of clouds-circulation coupling in climate: datasets from the 2020 (EUREC 4 A) field campaign".
We are grateful to Mampi Sarkar, Teresa Campos, Mathieu Casado, an anonymous reviewer, and ESSD senior chief editor David Carlson for their valuable comments on early drafts of the manuscript and the pre-print discussion paper. Many funding programs supported the researchers and facilities involved in EUREC 4 A-iso. Financial support. This research has been supported by the National Oceanic and Atmospheric Administration (grant no. Climate Variability and Predictability Program Award NA19OAR4310375), the Narodowym Centrum Nauki (grant no. UMO-2018/30/M/ST10/00674), the Horizon 2020 (TRI-ATLAS; grant no. 817578), the European Research Council, H2020 European Research Council (EUREC 4 A; grant no. 694768), the Deutsche Forschungsgemeinschaft (grant nos. 290612604/GZ:SCHN1126/2-1 and 416767181/GZ:SCHN1126/5-1), the Swiss National Science Foundation (grant no.