dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison between GPS dataset and model outputs Each modeling group has provided a file containing the gridded IWV over the 1995-2008 period at daily resolution on its native grid. The IWV is either computed online or offline in the model. The offline computation can introduce some errors due to vertical integration over the discretized vertical grid. For each model, we extracted the value of IWV at the closest grid point of GPS stations. We did it using the native grid, and also after having regridded all the model outputs to ERA-Interim (ERAI) grid, to have a fair comparison with ERAI when necessary. The difference in altitude between the GPS station and the closest grid point is difficult to take into account and can introduce strong bias over complex terrain  (Hagemann et al. , 2003; Wang and Zhang, 2009).
Comparison with SIRTA observations The following comparisons were made with SIRTA observations: -Tropospheric temperature. To be as consistent as possible between model outputs and radiosoundings, the mean tropospheric temperature corresponds here to the daily average value of 2 m temperature, and temperatures at 850, 500 and 200 hPa. So we extracted the temperature values at these pressure levels for each radiosounding launched from Trappes (a few kilometers away from SIRTA) and computed the mean tropospheric temperature as the average of these four values. Then, the daily mean is the average of the two daily radiosoundings. The same method is applied to ERA-Interim reanalysis to compute the mean tropospheric temperature over the other European sites. The impact of using ERA-Interim instead of radiosounding data has been evaluated at SIRTA where both are available.
Due to the width of temperature bins considered in this study, results are not sensitive to the use of one or another. -Precipitation. The first step consists of comparing the precipitation statistics obtained using the single rain gauge located at SIRTA, from the closest grid point (1 km × 1 km) of COMEPHORE over the SIRTA and an average of COMEPHORE over an area centered over the SIRTA and covering 2500 km 2 (i. e. , a 50 km resolution model grid point). Figure  2  shows the mean annual cycle of the frequency of occurrence of different light precipitation regimes. The annual cycle is computed from 30-day means from January to December over the years 2004-2007, which is the common period of all datasets. The SIRTA rain gauge and COMEPHORE product do not indicate the same frequency of occurrence of nonprecipitating days, and very light precipitation, but they show similar frequency of occurrence for light precipitation which corresponds mainly to large-scale precipitation.
Mean bias and standard deviation (SD) in kilograms per meter (kg m -2 ) of the differences in IWV between models and GPS observations using daily time resolution, and using the data obtained with the correction due to altitude difference or without it. Computation has been done using models on their native grid, or bilinearly remapped on the ERA-Interim grid. area is often much weaker as the strongest precipitation rates are generally localized. Table  4  presents an estimate of the frequencies of occurrence of nonprecipitating days, very light precipitation and light precipitation for winter (Julian day 1 to 100) and summer (Julian day 151 to 251) when considering either the common period of the two datasets  (2004) (2005) (2006) (2007)  or the full period of each dataset  (i. e. , 1997 (i.
Method to establish the relationship between IWV and precipitation as a function of tropospheric temperature The objective is to characterize how precipitation depends on IWV for different ranges of mean tropospheric temperature. To do that, we divided our datasets into four different bins of temperature: the first bin is for temperature less than or equal to 254. 5 K, the second bin is for temperature between 254. 5 and 258 K, the third one corresponds to temperature between 258 and 262. 5 K and the fourth one is for temperature higher than 262. 5 K. This choice has been made to en-sure a high number of samples in each bin of temperature to proceed to the next step. In this way, the mean tropospheric temperature of one bin is similar more or less 1 K for all the datasets. Then, in each bin of temperature, the daily mean precipitation rates are sorted according increasing values of daily mean IWV.
4 Humidity biases in the Med-CORDEX simulations 4. 1 Comparison with GPS dataset at regional scale Figure  1  indicates the mean values of IWV retrieved from GPS measurements in winter (Fig. 1a ) and in summer (Fig. 1b ). In winter, higher values are observed along the Atlantic and Mediterranean coasts while central and eastern Europe exhibit very low values of IWV. In summer, there are two different regimes: (i) north of 45  the temperature gradient; (ii) around the Mediterranean, the structure is more patchy with alternating low and high values. Most low values correspond to higher topography but not systematically. Table  3  shows the mean bias and the standard deviation (SD) of ERA-Interim and RCMs' IWV on their native grid or regridded to ERAI grid, in comparison to GPS estimates. Daily datasets are used to compute these statistics and exactly the same sampling is used between models and observations.
The observations are considered for the period 2008-2015 and the different models for the period  2001-2008 (i. e. , the same number of years, despite the time shift). For all these datasets, there exists a critical value of IWV, w c , over which precipitation starts to increase. The values of these critical values are determined from Fig. 7  following the methodology indicated in Sect. 3. 3. The critical value depends on temperature (it increases with temperature) and on the models, as shown in Fig. 8a. UCLM50 presents lower critical values of IWV than observations at all temperatures, and also CNRM50 at high temperatures. This means that in these models the precipitation begins in a drier atmosphere than that which begins to produce rain in the observations. On the contrary, for CMCC50, at high temperature, the atmosphere needs to be as wet or wetter than what is observed to trigger precipitation.
Acknowledgements. This work is a contribution to the  HyMeX program  (HYdrological cycle in The Mediterranean EXperiment) through  INSU-MISTRALS support  and the  MEDCORDEX program  ( Coordinated Regional climate Downscaling EXperiment-Mediterranean region ). This research has received funding from the  French National Research Agency (ANR)  project  REMEMBER  (grant  ANR-12-SENV-001 ) and is a contribution to the  VEGA  project through  LEFE/INSU  support, to the  EECLAT  project through  LEFE-INSU  and CNES supports and to the GNSS4SWEC COST action  ES1206  through  EU  support. It was supported by the IPSL group for regional climate and environmental studies, with granted access to the HPC resources of  GENCI/IDRIS  (under allocation  i2011010227 ), by  SIRTA Working Group "Climate studies " and by the national infrastructure ACTRIS-FR, identified on the  French road map for Research Infrastructures , published by the  Ministry of Research.
The SIRTA-ReOBS effort also benefited from the support of the  L-IPSL  funded by  ANR  under the " Programme d'Investissements d'Avenir " (grant  ANR-10-LABX-0018 ) and by the  EUCLIPSE  project funded by the  European Commission  under the  Seventh Framework Program  (grant no. 244067 ). To process the data, this study benefited from the  IPSL mesocenter ESPRI  facility which is supported by  CNRS ,  UPMC ,  Labex  L-IPSL, CNES and  Ecole Polytechnique. We would like to acknowledge the SIRTA team for collecting data,  Cindy Lebeaupin-Brossier  and  Marc Stefanon  for providing simulation outputs, the  CNES  ( Centre National d'Etudes Spatiales ) for partially funded  Marjolaine Chiriaco  research,  Emmanuele Lombardi  and  ENEA  for the Med-CORDEX database,  Samuel Somot  for his role in Med-CORDEX coordination, and  Thomas Noel  for its help in post-processing data.
Data availability. The SIRTA ReOBS netCDF file is available at https://doi. org/10. 14768/4F63BAD4-E6AF-4101-AD5A-61D4A34620DE (Chiriaco et al. , 2018b). GPS IWV data have been processed by  Bock (2016) : https://doi. org/10. 14768/06337394-73a9-407c-9997-0e380dac559. The simulation datasets can be downloaded at http://www. medcordex. eu (Lombardi, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-1471-2019-supplement.
This study explores the tropical land distribution of precipitation and its extremes focusing on the daily 1°×1°scale. A common period of 5-year over the tropical belt (30°s-30°n) corresponding to more than 39 million data points, is used to highlight the robust (and non-robust) observed features. A set of 10 observational products is analyzed ranging from satellite only to rain gauges only products and various blended intermediates as well as a sub ensemble of satellite-based products relying upon microwave observations. Overall, the various datasets show a small diversity of response as far as tropical land mean precipitation is concerned. When sorted by surface temperature, the spread in mean rainfall is also well below 10% over a large span of the surface temperature regime. The consistency between the surface temperature and the extreme precipitation is further investigated by computing the thermodynamic scaling of daily precipitation extreme with surface temperature.
Compared to the ensemble members, the CHIRPS product appears to overestimate slightly the contribution to total rainfall up to 297 K where it peaks and then shows underestimations in the warmer regime. The TAPEER dataset reveals a distribution close to the ensemble mean but with a slightly underestimated maximum contribution at 299 K. 3. The scaling of the extreme of tropical precipitation with surface temperature The extremes are characterized via the upper wet day percentiles of the distribution for various surface temperature bins (see Supplementary materials: method section). 'Extreme' extremes and the 'moderate' extremes are defined by the 99. 9th and the 99th percentile, respectively. The magnitude of these percentiles are ∼110 and ∼60 mm d -1 (figure  3 ). The use of other metrics or indices to qualify the extremes can limit the considered magnitude of the range of precipitation.
Acknowledgments This study benefited from the  IPSL mesocenter ESPRI facility , which is supported by  CNRS ,  UPMC ,  Labex  L-IPSL, CNES and  Ecole Polytechnique. The author acknowledges the  CNES  and  CNRS  support under the  Megha-Tropiques program. The author thanks an anonymous reviewer and  Dr Richard Allan  for interesting comments that help improve this letter.
For instance, several studies  (Chen et al. , 2018 (Chen et al. , , 2019;; Dubovik et al. , 2008; Henze et al. , 2007)  have shown the ability to retrieve global aerosol sources with inverse models that rely upon satellite observations. Therefore, the practice of satellite data fusion into models provides the possibility to improve aerosol simulations of the pre-and post-satellite eras. However, besides the knowledge of the amounts (concentrations) and locations of aerosol emissions, accurate modeling of atmospheric aerosols and their effects also requires information about particle composition. The lack of comprehensive datasets providing multiple constraints for the key parameters employed in models has hindered the improvement of model simulation. Specifically, improving the ability of aerosol component estimation will require the enhancement of remote sensing capabilities to provide aerosol component information on the global scale.
Refractive indices at other wavelengths are spectrally interpolated utilizing measured data  (Downing and Williams, 1975; Gosse et al. , 1997; Hale and Querry, 1973; Kou et al. , 1993; Palmer and Williams, 1974; Tang, 1996; Tang and Munkelwitz, 1991). A detailed description and FOR-TRAN subroutines for calculating the host complex refractive index is accessible at the GACP (Global Aerosol Climatology Project) website: https://gacp. giss. nasa. gov/data_sets/ (last access: 28 October 2019). Once the refractive index of the host is determined, the refractive index of the mixture is computed using the Maxwell Garnett equations. The Maxwell Garnett effective medium approximation allows for the computation of the average dielectric function based upon the average electric fields and polarizations of a host matrix with embedded inclusions, and can model insoluble particles suspended in a solution  (Bohren and Huffman, 1983; Lesins et al.
Acknowledgements. The authors thank CNES and the  ICARE data distribution center  for the POLDER/PARASOL data and the entire AERONET team, especially the principal investigators of the AERONET sites used in this study, for their long-term efforts to maintain AERONET observations. The authors acknowledge the suggestions from  Mian Chin  ( NASA GSFC ) regarding the connection and comparison with global chemical transport models, and the suggestions from  Lorraine A. Remer  ( UMBC ) regarding the interpretation of the results and the uncertainties. Financial support. This work is supported by the  Labex CaPPA (Laboratory of Excellence -Chemical and Physical Properties of the Atmosphere)  project, which is funded by the  French National Research Agency  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 ".
Omic Approach The identification of EPS producer microalgae and its EPS characterization require a long investment, involving notably time-consuming screening and analysis steps, which are not compatible with industrial requirements. A phylogenic mapping of microalgae correlating both EPS production ability and structure with specific class, genus, or species would be helpful to predict the potential interest of an isolate microalgal strain and their industrial valorization. Although these data are not available, omics-studies may be a relevant approach to establish such data. Notably, genomics and transcriptomics could provide crucial understanding towards EPS production as it was shown for lipids biosynthesis pathways. For example, the exploitation of microalgae genomes database would contribute to the listing and distribution of gene families involved in EPS synthesis (i.
e. , Glycosyl-transferases), among various selected microalgae models. However, the availability of full microalgae genome sequences is restricted to few microalgae models and hence will not give a significant and relevant mapping. Transcriptomics can offer more exploitable data than the genomics approach because it may inform about EPS synthesis related genes activated in EPS production physiological conditions. The characterization of these genes may really contribute to establish defined expression patterns specific to microalgae phyla, genus, or species. From now on, more than 700 microalgae transcriptomes are available on algaedatabase (www. algaedatabase. org/). Analyses of some transcriptomes have shown the expression of glycosyl-transferase potentially involved in membrane or excreted polysaccharides.
Domain of validity FLORSYS was previously evaluated with independent field data on weed short and long-term dynamics at French national scale, over a large range of existing arable cropping systems. It showed that crop yields, daily weed species densities and, particularly, densities averaged over the years were generally well predicted and ranked as long as a corrective function was added to keep weeds from flowering during winter at more southern latitudes  (Colbach et al. , 2016). A further critical analysis of yield loss was carried out in a previous simulation study covering the same regions as and cropping systems that were used here  (Colbach and Cordeau, 2018). They concluded that the model's prediction quality was adequate for the model's purpose, i. e. to predict orders of magnitude and to rank situations in terms of cropping systems and crop species.
The simulated field network A virtual field network was simulated combining (1) a large number of contrasting cropping systems from several regions, (2) different weather series, and (3) presence or absence of weeds. Several sources were used to gather data on contrasting cropping systems from six French regions (Burgundy, Paris region, Aquitaine, Poitou-Charentes, Lorraine, Picardie) and one Spanish region (Catalonia). These systems were all already used in previous simulation studies (find the detailed list of sources and references in  Colbach and Cordeau, 2018)  and were reused here, focusing on different factors and impacts, to tackle our new research questions. In total, 272 arable cropping systems were simulated with FLORSYS (section C online). They included both conventional and organic systems, with a tillage intensity varying from no-till to annual mouldboard ploughing.
Then, we analysed which crop parameters reduce weed-caused crop yield loss and other weed harmfulness indicators. A Principal Component Analysis (PCA) was carried out on annual yield potential (i. e. yield from weed-free simulations), crop yield loss (relative yield difference in weed-free vs. weed-infested simulations) and annual weed seed production as a proxy for the risk of future weed harmfulness. Among the 145 parameters of Table  1 , those most correlated to the PCA axes were projected onto the PCA correlation circle. Analyses were carried out with the FactoMineR package of R. Finally, to evaluate the relative contribution of crop species and cropping systems on weed harmfulness, crop yield loss and weed seed production were both analysed with linear models as a function of crop species, cropping system, region, weather repetition, time since simulation onset as well as interactions between factors, using PROC GLM of SAS.
Acknowledgements This project was supported by  INRA , the  French  project  CoSAC  ( ANR-15-CE18-0007 ), the  Casdar RAID  project funded by the  French Ministry in charge of Agriculture and Food (Ministère de l'Agriculture et de l'Alimentation , avec la contribution financière du compte d'affectation spéciale 'Développement agricole et rural') and the  European Union 's  Horizon 2020 Research and innovation programme  under grant agreement  N. 727217  ( ReMIX  project).
The range of the ultrasound water-level is 0e792 mm in the channel and the resolution is 2 mm. The SSC is estimated using a turbidity probe (Odeon/Aqualabo); its accuracy is lower than 5% NTU. Regular cleaning of the probe head is carried out after each flood event. An automatic water sampler (ISCO 3700; 24 Â 1 L) is used to collect water samples during flood events. The sampler is coupled to the water-level sensor and only operates when water flows through the Venturi channel. All sensors are connected to a data logger (Ijinus Log0500). The measured parameters are recorded every 6 min. Information is downloaded every two weeks on a laptop computer (Software Avelour 6. 0. 4). A tipping-bucket rain gauge (Pr ecis m ecanique; model: 303x; resolution: 0. 2 mm) was also installed in the catchment close to the station (see star symbol in Fig.
1B ). To estimate the SSC over time, 61 water samples were sampled using the automatic water sample. 100 mL of water are filtered in the laboratory using dehydrated The weight difference allows evaluating the concentration of the material in the sample. A correlation curve is defined between T the turbidity data (NTU) measured in the field and SSC corresponding to the suspended sediment concentration (g L À1 ) obtained in the laboratory (Fig. 3 ). Suspended sediment flux (SSF) is then calculated using Eq. (  4 ): SSF ¼ Q Â SSC Â 10 3 ð4Þ With Q the flow discharge in m 3 s À1 and SSF the instant suspended sediment flux in g s À1. The sediment yield (SY; in g) is evaluated for each event as (Eq. 5): SY ¼ Z t f t 0 SSF dt ð5Þ With t 0 and t f corresponding to the beginning and the end of the event considered.
Pearson correlation matrix and principal component analysis (PCA) Statistical analyses were performed using the statistical software R 1 and the following packages: FactoMiner 2 and Corrplot 3. Pearson correlation matrix is 1 R Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org/. 2 https://cran. r-project. org/web/packages/FactoMineR/index. html. 3 https://cran. r-project. org/web/packages/corrplot/index. html. used to evaluate the linear dependency between multiple variables simultaneously. The result is given using the Pearson correlation coefficient r which reflects the linear correlation between two variables. The coefficient is calculated using the covariance of two variables divided by the product of their standard deviations (Eq.
Acknowledgements This work was financially supported by the  Mines-Telecom Institute of Lille-Douai , with additional funding provided by the  Water Agency of Arthois-Picardie  ( QUAS-PER  project). We would also like to acknowledge technical support from the  SYM-CEA  and the regional Chamber of  Agriculture Nord-Pas-de-Calais, France. The authors are grateful to  M. Desmons , the landowner who permitted instrumentation of the catchment.
5 and PM1). It can be used for routine air quality monitoring applications, field work and laboratory research. For example, high-time resolution data from the TC-BC method in combination with different size selective inlets can be used 35 for quality control in aerosol mass spectrometry through comparison of differently derived oxygen to carbon (O-C) and organic aerosol to organic carbon (OA-OC) ratios  (Pieber et al. , 2016). In this study, the new online TC-BC method was tested during a field campaign from 7 February to 10 March 2017 at an urban background air quality monitoring station of the Slovenian Environmental Agency (ARSO). High time resolved data of TC and BC were compared to EUSAAR2 OC-EC analysis of PM2. 5 filter samples that were collected in parallel with a high volume sampler; and to organic aerosol mass measured by 40 ACSM with a PM1 aerodynamic lens.
Summary We present the newly developed Total Carbon Analyzer model TCA08, which offers measurement of the concentrations of total aerosol carbon continuously with high time resolution as rapid as 20 min. Two parallel flow channels provide continuous 5 operation: while one channel analyzes, the other collects the next sample. Thermal analysis by flash-heating of the sample collected on a quartz fiber filter efficiently converts all the particulate carbon to CO2. The increase in CO2 concentration above baseline in a flow of analytic air is measured by an integrated NDIR detector. When the TCA08 is combined with an AE33Aethalometer, the TC-BC method yields OC-EC data with much greater time resolution than that offered by the analysis of filter-based samples. In this study, we show results from these instruments combined on an operational timebase of 1 hour 10 and compare them to conventional 24h filter measurements of EC and OC, and high-time-resolution measurements of organic aerosols with an ACSM.
To the Editor: We thank Dr. Yarandi and Tehrani  [1]  for their interest in our study  [2]  and to challenge us on specific statistical aspects. To avoid misinterpretations, we will try to clarify methodological trade-off we consent, and reveal complementary details and information of our dataset deep analysis. Firstly, to limit overfitting, even if it was not explicitly described in the statistical part of the original paper, we indeed select predictor variables to input our multiple logistic regression model, on the basis of a stringent p-value < 0. 2. Looking at Table  2  of our original research, we can see that the selected variables defending a p-value between 0. 03 and < 0. 0001. Secondarily, we admit that we didn't process any penalization methods to control sparse data biases  [3]  on multiple pregnancy.