dataesr/scientific-paragraphs-categorization huggingface dataset:
Model CCATT-BRAMS data structure extends the original BRAMS data structure in a natural way. Derived data type chem contains history-carrying variables introduced by the chemistry. Multiple grids are represented by an array of type chem. But since each species requires its set of history carrying variables at each grid, the array has to be indexed by species number and grid number. nspecie at grid ngrid. This organization keeps at nearby memory addresses all fields of one species of one grid. The (z, x, y) memory organization eases code development. Modelers can develop new code components for a single atmospheric column (the z direction) dealing with rank one arrays. Then, a wrapper is written to connect the new code component to the existing code. The wrapper loops over columns, invoking the code component with one column at a time.
Based on the fire data input used by 3BEM for Africa, one should expect it to be more comparable to GFED, which, according  Andreae et al. (2012) , yield a large underestimation of a factor four and seven for GFEDv2 and GFEDv3, respectively. For the BARCA B period, in May 2009, the events of air mass inflow from the Northern Hemisphere were mostly captured on a case-by-case basis, but smoothed in the monthly mean for both model and Aura data (Fig. 5 , bottom). During this period ozone was measured primarily in the northern and eastern regions (not shown). Typically the model also captured the general vertical structure in both regions. A more detailed discussion of BARCA CO and O 3 measurements and comparison with CCATT-BRAMS and WRF-CHEM results is given in  Andreae et al. (2012)  and  Bela et al.
Acknowledgements. Longo and Freitas acknowledge partial support of this work by  CNPq  ( 309922/2007-0 ,  306340/2011-9 ). This work was partially supported by the  European  integrated project  SCOUT-O3  ( GOCE-CT-2004-505390 ), by the program LEFE/INSU in France (projects UTLS-tropicale and Tropopause 2009), and by the  Inter-American Institute for Global Change Research (IAI)   CNR II 2017 , which is supported by the  US National Science Foundation  (Grant  GEO-0452325 ). The computer resources were provided by  CINES  ( Centre Informatique National de l'Enseignement Sup érieur ), project pce  2227 , and by the  Brazilian Institute for Space Research (INPE). The authors thanks to CETESB and LBA/BARCA team for data availability, especially  Andi Andreae  and  Paulo Artaxo  for CO and O 3 data, respectively.
Hereto, we will make use of the FTIR data, model simulations and the in situ surface data at both stations. Moreover, a Trimble NetR9 Global Navigation Satellite System (GNSS) reference receiver coupled to a Zephyr Geodetic 2 Antenna has been set up since March 2013. Using the latest generation of Trimble receiver technology, this reference receiver offers 440 channels for unmatched GNSS multi-constellation tracking performance at 1 Hz frequency. The choice of this additional instrument for water vapor monitoring comes from many reasons. First, the high acquisition frequency will provide the opportunity to document the temporal variability of water vapor above the Maïdo observatory with a very high resolution. Secondly, the accuracy in GPS integrated water vapor (IWV) has been assessed by many authors, using intercomparisons with radiosondes, microwave radiometers, sun photometers, lidars, and very long baseline interferometry  (Foelsche and Kirchengast, 2001; Niell et al.
, 2001; Bock et al. , 2004). The agreement between these techniques is about 1-2 kg m -2 and leads to make the GPS as a reference for total columns. In addition, being independent from solar radiations, this GPS will be devoted to the nighttime Raman water vapor lidar system calibration. Indeed, lidar calibration by comparison with other collocated sensors has become the standard  (Ferrare et al. , 1995; Turner et al. , 2002; Whiteman et al. , 2006). The basic GPS atmospheric product is the tropospheric delay of the GPS signal that has traveled between a GPS satellite and a ground-based receiver. The standard procedure for GPS data analysis assumes that the delay in any direction can be mapped from the delay at zenith to which a horizontal gradient is added. Three sets of parameters are then estimated during the analysis: zenithal tropospheric delays (ZTDs), gradients, and postfit residuals, which are the difference between the modeled atmosphere and the measurements.
, 2011). In addition, chemical composition of bulk (PM 10 ) aerosol is currently monitoring using filter sampling. Collection is performed between 22:00 and 05:00 local time in order to capture only free tropospheric aerosols without local (Reunion Island) contamination. Aerosol number concentration measurements performed in parallel are used to ensure that collections were performed within free tropospheric conditions. Filter samples are collected on a weekly basis for the quantitative determination of the particulate mass (gravimetry), the major ion species (Cl -, NO 3 -, SO 4 2-, Na+, NH 4 + , K + , Mg 2+ , Ca 2+ ), light organics (oxalate, methanesulfonate), light absorption (at 370 and 880 nm), elemental and organic carbon (EC, OC). This large data set will be further compared against similar observations performed in the southern Indian Ocean at Amsterdam and Crozet Islands  (Sciare et al.
Acknowledgements. The project to build an altitude station at Maïdo has been imagined and initiated by  Gérard Mégie ,  Jean Leveau  and  Serge Baldy , and politically strongly supported by  Jean-Marie Flaud. The  UMS 3365 of OSU-Reunion  and the Maïdo facility have been supported by  Reunion Island Council , the  French Ministry of Research and European Community  for building and equipment funding. They are now supported by  CNRS  and  Reunion Island University  for the operational phase. The  research laboratory LACy  ( UMR8105 ) is supported by the  CNRS ,  Météo-France  and  Reunion Island University. Water vapor and aerosols instrumentations are supported by  French national SOERE  programs, respectively ROSEA and ORAURE. Doppler lidar instrumentation is supported by the  French agency CNES.
Remerciements Les auteurs, membres du groupe ELSA (www. elsa-lca. org),remercient la région  Languedoc-Roussillon  pour son support. Les auteurs sont également reconnaissants envers les organisateurs de l'animation scientifique sur l'agroécologie soutenue par Agropolis pour l'opportunité offerte de présenter cette analyse lors d'un séminaire. Les auteurs remercient l'ADEME, l'IRD et la FAO pour leur soutien à ce travail dans le cadre du projet  11-60-C0003. Enfin, les auteurs remercient les relecteurs pour leurs corrections et commentaires pertinents qui ont contribué à améliorer la qualité de l'article.
L'HVE est un processus volontaire dans son ensemble. L'affichage environnemental en revanche basé sur l'ACV devrait devenir obligatoire bien que la forme définitive reste à définir. En termes de périmètre géographique, la méthode ACV a l'avantage d'être applicable quel que soit le pays. Ainsi, des produits importés peuvent être évalués, ce qui est en cours au CIRAD pour la mangue du Brésil par exemple. Cependant l'ACV se base sur des bases de données qui doivent être construites progressivement et qui sont encore très incomplètes pour la majorité des pays. La majorité des pays européens, les USA, ou encore la Thaïlande et le Japon avancent actuellement dans la collecte de données ACV dans la perspective éventuelle de développer l'affichage environnemental. En Thaïlande, un programme national, Thai National LCI Database, a été lancé en 2007 notamment pour établir et afficher les bilans de gaz à effet de serre de 611 produits (agricoles comme industriels).
En France, dans le cadre de l'utilisation de l'ACV pour l'affichage environnemental, un projet d'envergure national, Agri-BALYSE, financé par l'ADEME, a été lancée en 2010 pour une durée de trois ans afin de constituer une base de données nationale sur les inventaires d'ACV de 46 produits agricoles(Koch et al. , 2011). L'investissement pour construire ces bases de données est considérable et nécessitera la collaboration des filières étrangères, ce qui prendra plusieurs années. Pour l'HVE par contre, le cahier des charges n'est Concernant la construction de la démarche, l'ACV et l'HVE correspondent à des approches très différentes. Pour l'ACV, le projet a été porté par une volonté politique forte, de type top-down. La méthodologie est en cours de construction avec une forte implication des scientifiques du public et du privé et des instituts techniques représentant les filières agricoles.
Acknowledgments The authors gratefully thank  Nadia Crini ,  Brice Mourier  and  Dominique Rieffel  for their technical help and Cecile Grand from the  Agence De l'Environnement et de la Maı ˆtrise de l'Energie (ADEME)  for fruitful scientific discussions. Jordi Peig  is gratefully acknowledged for his help with calculation of the scaled mass index.
Field experiments and chemical analyses were financially funded by the  Agence Nationale de la Recherche  ( ANR , contract nuANR- 05-ECCO-004 ) and the  ADEME  (contract nu0572C0058) within the framework of the  STARTT program. Nicolas Tete was financially supported by the  Conseil Regional de Franche-Comte  ánd the ADEME. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The feedback between soil moisture and precipitation has long been a topic of interest due to its potential for improving weather and seasonal forecasts. The generally proposed mechanism assumes a control of soil moisture on precipitation via the partitioning of the surface turbulent heat fluxes, as assessed via the Evaporative Fraction, EF, i. e. the ratio of latent heat to the sum of latent and sensible heat, in particular under convective conditions. Our study investigates the poorly understood link between EF and precipitation by investigating the impact of before-noon EF on the frequency of afternoon precipitation over the contiguous US, using a statistical analysis of the relationship between multiple datasets of EF and precipitation. We analyze remote sensing data products (EF from GLEAM, Global Land Evaporation: the Amsterdam Methodology, based on satellite observations; and radar precipitation from NEXRAD, the NEXt generation weather RADar system), FLUXNET station data, and the North American Regional Reanalysis (NARR).
While most datasets agree on the existence of regions of positive relationship between between EF and precipitation in the Eastern and Southwestern US, observation-based estimates (GLEAM, NEXRAD and to some extent FLUXNET) also indicate a strong relationship in the Central US which is not found in NARR. Investigating these differences, we find that much of these relationships can be explained by precipitation persistence alone, with ambiguous results on the additional role of EF in causing afternoon precipitation. Regional analyses reveal contrasting mechanisms over different regions. Over the Eastern US, our analyses suggest that the apparent EF-precipitation coupling takes place on a short day-to-day time scale and is either atmospherically controlled (from precipitation persistence and potential evaporation) or driven by vegetation interception and subsequent re-evaporation (rather than soil moisture and related plant transpiration/bare soil evaporation), in line with the high forest cover and the wet regime of that region.
This is often not representative of deeper layers and, thus, of EF, especially in vegetated areas. Second, we note that one of the most challenging tasks in assessing soil moisture-precipitation coupling (i. e. , A-B) from observational data is to establish causal rather than mere statistical links between soil moisture (or EF) and precipitation (see also  Salvucci et al. , 2002; Orlowsky and Seneviratne, 2010). The difficulty of causal inferences from observational data arises from two main confounding effects. First, given the influence of precipitation on soil moisture (process C) it can be difficult to assess whether a detected relationship between soil moisture and precipitation is due to A-B, C or both. In particular, persistence in precipitation at various time scales (from synoptic to interannual scales, including seasonal-scale) can induce apparent causal links, for which even lagged correlations between e.
Finally, they suffer from the similar difficulties in isolating causal relationships as the studies based on observational data, although they provide a more comprehensive data basis. In this study, we investigate soil moisture-EF-precipitation coupling (i. e. , processes A and B) over North America, addressing the aforementioned issues. We use direct observations of EF and precipitation from FLUXNET sites, remote-sensing-derived products (satellite-based EF from GLEAM and precipitation from the US radar network NEXRAD), and the North American Regional Reanalysis, NARR (see Sect. 2). Specifically, we quantify the effect of before-noon EF (and soil moisture) on afternoon convective rainfall occurrence via the Triggering Feedback Strength (TFS, see  Findell et al. , 2011 and Sect. 3 ). This metric suggests, when applied to NARR, a region of coupling over the Eastern US  (Findell et al.
, 2011). Here, we first compare TFS estimates derived from observation-based datasets with that from NARR (Sect. 4). We then analyze the potential confounding effect of precipitation persistence on TFS (Sect. 5), and further investigate the role of soil moisture and vegetation interception storage on land evaporation and the inferred EF-precipitation coupling (Sect. 6). Finally, results from these sections and their implications are discussed in Sect. 7. Figures.
Datasets We provide here a description of the datasets used in this study. The analysis is restricted to North America for consistency with  Findell et al. (2011). The datasets considered here include a reanalysis product (the North American Regional Reanalysis, hereafter referred to as NARR), ground-based point-scale observations from FLUXNET, and remote-sensing-derived products: the NEXt generation weather RADar system (NEXRAD) and Global Land Evaporation: the Amsterdam Methodology (GLEAM). They are summarized in Table  1. For three-hourly datasets (NARR and GLEAM), values closest to local 3 h (in standard local time based on longitude) are used, as in  Findell et al. (2011). Thus, a lag of up to one hour between datasets can occur in either direction, depending on the longitude.
The root zone is defined for each grid cell as a function of vegetation type -at the analyzed sites, it includes the top 3 to 4 layers, depending on the site. In this study, we use NARR data from the years 1995-2007, and most of the analyses are restricted to days when data are available from other datasets (NEXRAD and GLEAM, see  Sects. 2. 3 and 2. 4, respectively). This removes possible impacts of different time periods or time series lengths. Analyses of the longer 1979-2007 period are included in the  Supplement (1979 Supplement ( -2007) )  for comparison, yielding similar results. All data is adjusted to local time by taking the 3 h period closest to the standard local time. Thus, for e. g. afternoon values (12-6 p. m. ), data from 3:00-9:00 UTC are used West from 247. 5 • E while 6:00-12:00 UTC data are used for the rest of the continent.
Back Close Full area, possible change in footprint depending on e. g. wind direction), provides largely model-independent data and is therefore a direct estimate pertinent to our analyses. In this study, we use data from the FLUXNET LaThuile dataset, a global standardized database of eddy covariance measurements which includes a large number of sites. Measurements of sensible (H) and latent (λE) heat fluxes are used to compute EF, while global radiation (i. e. , incoming shortwave, R g ) and potential global radiation (i. e. extraterrestrial radiation, R pot g ) are used to get a proxy for cloud cover (see Sect. 3. 2). One of the main issues with eddy-covariance measurements is that the energy balance is not closed, likely due to an underestimation of H and λE (e. g. Wilson et al. , 2002; Foken, 2008; Hendricks Franssen et al.
, 2010). However, as we do not use H and λE directly but only through EF, we note that the commonly used "fixed Bowen ratio" correction for the energy balance closure (i. e. attributing the missing energy to latent and sensible heat fluxes while keeping the Bowen ratio B w = H λE constant, e. g. Blanken et al. , 1997)  does not affect EF. Hence, we can expect that EF is only marginally affected by the mentioned measurement error at the sites. A total of 39 sites, listed in Table  2 , are used in this study, all of them located in the US and Canada. The selection of the sites is based on several criteria: first, coverage by precipitation radars from NEXRAD (see Sect. 2. 3) as well as R g measurements are requirements for use in our study. Second, summers with many gaps in any of the required variables are removed, and only sites with a reasonable amount of remaining data are kept for the analysis ( 100 days).
(2010) ; β is a constant to account for declines in transpiration when the canopy is wet -for more details see  Miralles et al. (2010 Miralles et al. ( , 2011b)). We use a version of GLEAM that is driven by the input datasets noted in Table  3 ; importantly, precipitation from NEXRAD (see Sect. to assumptions underlying the conductivity of water in the soil module and Gash's analytical model of interception. Here, to estimate before-noon EF  (9) (10) (11) (12) i. e. EF i, (9) (10) (11) (12)  ) several modifications to the original methodology are undertaken. As shown in Eq. (  1 ), the computation of E i requires daily estimates of potential evaporation, E pot i , evaporative stress, S i , and interception, I i. GLEAM daily input variables are aggregated to represent a diurnal cycle beginning/ending at around 9 a.
m. standard local time. Therefore, the estimates of root-zone soil moisture (θ i-1 ) used to derive S i-1 , roughly correspond to 9 a. m. on day i, as they are derived using the cumulative precipitation up to 9 a. m. and instantaneous observations of surface soil moisture from the early morning hours (between 1. 30 a. m. and 6 a. m. depending on the satellite platform -see  Owe et al. , 2008 , for details on the soil moisture remote-sensing product). Thereby, for our calculations of GLEAM's before-noon EF at day i (i. e. , EF i,9-12 ), we use S i-1 as a proxy for the evaporative stress conditions. Since days with morning-time precipitation are not included in the computations of the TFS, I i,9-12 is assumed to be zero. EF i,9-12 is therefore calculated as: EF i,9-12 = λE pot i,9-12 S i-1 R net i,9-12 -G i,9-12 (2) where R net is net radiation from the GEWEX SRB dataset (satellite-based product, see  Stackhouse et al.
S * i-1 is used to calculate before-noon EF (i. e. EF i,9-12 ) using Eq. (2) (except in the left panel of Fig. 11 , where S i-1 is used instead of S * i-1 to isolate the effect of soil moisture). Note that the timing of the input datasets for the S and S * computation is crucial to this application, in particular for precipitation. First, we do not want to include any information about afternoon precipitation for the estimated before-noon EF on the same day. Second, rainfall occuring in the night preceding the estimated EF must be included in order to get an EF reflecting the conditions in the early morning. Unfortunately, the definition of "days" in many standard daily precipitation products varies, as shown in Table  S1  in the Supplement, and is sometimes unclear: for instance, the use of data from the Global Precipitation Climatology Project (GPCP, see  Huffman et al.
, 2001)   S1 ). Also noteworthy, for the CPC Unified gauge product  (Chen et al. , 2008)  days are defined differently depending on the country. For most of the USA, the defined window is 12:00 to 12:00 (UTC, i. e. 4-4 a. m. in the West Coast/8-8 a. m. in the East Coast), which in principle suits our requirements although uncertainties remain due to differing reporting times between contributing rain gauge stations. NEXRAD is not affected by this issue given its higher temporal resolution. Given the large range of precipitation products, the sensitivity of GLEAM to the precipitation dataset used as an input to derive EF time series has been investigated in the Supplement (see Supplement Discussion S2). Datasets used for this sensitivity test are NEXRAD, CPC-Unified  (Chen et al. , 2008)  and PERSIANN  (Hsu et al.
, 1997). These three datasets either suit the required daily time window (like in the case of CPC-Unified) or have a sub-daily temporal resolution and therefore appropriate daily aggregates can be constructed (like in the case of NEXRAD and PERSIANN). With the exception of three sites in the middle of the Western US, where NEXRAD data displays suspect features, results obtained from these three independent precipitation datasets are qualitatively similar (see Fig. S3  and text in the Supplement for more details).
(2011). Thus, we compute here a simplified version of TFS, where EF QX is the Xth percentile of EF. The variable σ EF and the percentiles of EF are determined for each location and dataset independently. The definition of the bins ensures clearly distinct bins (i. e. no possible overlap even if EF Q60 = EF Q40 ) while retaining most of the available data. Considering quantiles also partly accounts for different shapes of the EF distributions when comparing different EF datasets. EF values outside of the 0-1 range are excluded from the analysis. Although TFS * is an approximation of the original TFS defined by  Findell et al. (2011) , the two different computations show close agreement when applied to NARR (see Supplement). TFS * = σ EF Γ(r |EF > EF Q60 ) -Γ(r |EF ≤ EF Q40 ) EF Q80 -EF Q20 , (5).
Cutoff ratios between 0. 5 and 0. 8 do not lead to different results (not shown). -R g /R pot g > 0. 67max(R g /R In this study, the dataset combinations use these criteria computed on the following datasets, chosen according to data availability: -NARR: precipitation and R g from NARR -FLUXNET-NEXRAD: precipitation from NEXRAD and R g from FLUXNET -GLEAM-NEXRAD: precipitation from NEXRAD and R g from NARR The impact of the criteria for the selection of potentially convective days on TFS * , in particular with respect to the NARR analysis and the different set of criteria used in our study compared to  Findell et al. (2011) , is small, as discussed in the Supplement (Fig. S2 ).
TFS from different datasets The impact of before-noon EF on precipitation occurrence is quantified using the modified Triggering Feedback Strength TFS * (see Sect. 3). TFS * is computed at FLUXNET sites from three dataset combinations: (i) a reanalysis product (NARR), (ii) direct measurements of surface turbulent heat fluxes at FLUXNET sites for EF in combination with radar precipitation from NEXRAD, and (iii) a satellite-based estimation of EF (GLEAM) in combination with NEXRAD precipitation. We compare estimates of the Triggering Feedback Strength (TFS * ) from these datasets (Sect. 4. 1), complemented by comparing general characteristics of the EF datasets (Sect. 4. 2). soil moisture-precipitation coupling "hot spot" expected from other studies (e. g. Koster et al. , 2004) , while the Eastern US displays strong EF-precipitation positive relationship in NARR  (Findell et al.
Over both the Central US and Southwestern US, GLEAM-NEXRAD and to some extent FLUXNET show larger TFS * values compared to NARR (Figs. 2  and 3 ). Results from GLEAM-NEXRAD for the three sites in the middle of the Western region indicated by empty dots on Fig. 3  (map), however, should be interpreted with caution, as inspection of the NEXRAD time series reveals suspect features in some years (not shown). Note that GLEAM data generated with input from three independent precipitation datasets lead to similar results, albeit with less consistent TFS * in the Eastern US (Sect. 2. 4 and Supplement and Fig. S3 ), except for the three mentioned sites in the middle of the Western US with issues in NEXRAD and for which only GLEAM generated with NEXRAD displays strong coupling in the Western US. ii.