dataesr/scientific-paragraphs-categorization huggingface dataset:
Reference data 2: in situ data We compare MDAL v1 and v2 to in situ observations of various stations in Europe and Africa: Cabauw, Evora, Gobabeb and Izaña; additional details for stations are listed in Table  2  and locations within the SEVIRI disk are shown in Fig. 1. Cabauw, Gobabeb and Izaña are part of the BSRN network  (Driemel et al. , 2018) ; Evora is operated by the Karlsruhe Institute of Technology within the framework of the LSA-SAF. All stations measure shortwave downwelling (SWD) and upwelling (SWU) radiation and have a data acquisition frequency of 1 min. For the three BSRN stations we use the same time period as for the comparison to ETAL, i. e. , a full year November 2020 to October 2021, and we obtained the data from the BSRN data portal (https://dataportals. pangaea. de/bsrn/, last access: 1 June 2022).
Reference data 3: MODIS albedo MCD43D51 & MCD43D61 In order to complement the local comparison with in situ albedo we use the MODIS-based albedo products of the MCD43D group (version 6. 1), which are given on the MODIS climate modeling grid and have a spatial resolution of 30 arcsec. In particular, we use the black-sky shortwave broadband (MCD43D51; Schaaf and Wang, 2021a) and white-sky shortwave broadband (MCD43D61;  Schaaf and Wang, 2021b)  products. Both data sets are produced daily using 16 d of MODIS observations, whereas the data are weighted to the central day of this moving window. The constant spatial resolution of the grid in spherical coordinates of 30 arcsec results in a constant Cartesian grid spacing across degrees of latitude of around 0. 9 km, and a varying grid spacing across degrees of longitude that decreases towards the poles, with about 0.
Auxiliary data: MODIS IGBP land cover types For the land-cover-type-based analysis we use the MODIS Land Cover Type Yearly L3 Global 500 m Version 6 (MCD12Q1 v006) product of 2019 (Friedl and Sulla-Menashe, 2019), created using supervised classification of reflectance data (Sulla-Menashe and  Friedl, 2018; Friedl et al. , 2010). The data set was obtained through the NASA Earthdata portal (https://earthdata. nasa. gov, last access: 10 October 2022). We resample the land cover types from the 500 m MODIS grid onto the coarser SEVIRI grid by assigning each SEVIRI pixel the most common cover type of the MODIS pixels that are located within a 1. 5 km radius around that pixel's center (result shown in Fig. 4 ). The results retrieved with the upgraded MDAL results are validated with the main objective of evaluating their performance against albedo values from different data sources, i.
e. , the data sets described in Sect. 3. 1: ETAL, in situ observations and MODIS albedo. The comparison with ETAL, in particular, is of interest to us because an increased alignment between MDAL and ETAL would not only suggest improvement of albedo results but is also beneficial for the LSA-SAF product suite. This is because there are synergies between SEVIRI and AVHRR-based products which can potentially be exploited. The higher the agreement between data sets from both sensors, the better. For this reason, the comparison with ETAL is the main focus of our validation, which we conduct over the full globe, investigating seasonal, regional and land-cover-type-related variations. Additionally, we do pixel-wise comparisons at the locations of several in situ stations, with albedo estimates based on the station's measurements.
Conclusions The proposed upgrade described here of the MDAL retrieval algorithm aims to better account for physical conditions by inclusion of aerosol effects and to keep up with recent scientific developments (update of processing coefficients and sensor bias correction). The comparison of the MDAL albedo values pre-and post-upgrade (v1 and v2) with respect to the reference ETAL and in situ data highlights significant changes in MDAL values. A previously noticeable negative bias in white-sky broadband albedo with respect to ETAL is now strongly reduced in most areas that are part of MSG primary coverage (and on average), while a similar negative bias in black-sky broadband albedo is changed, on average, to a smaller positive bias. The results imply that in regions where a negative bias in MDAL v1 exists (more common in white-sky albedo), MDAL v2 often offers clear improvements, while in regions where agreement between MDAL v1 and reference data was good to begin with (for example in central and eastern Europe and more common in black-sky albedo), there is a risk of obtaining a positive bias with respect to ETAL in MDAL v2.
This general observed improved agreement between MDAL and ETAL is consistent with the nature of the update: now the two products use the same aerosol inputs, narrow-to broadband conversion coefficients based on the same spectral database, and the same base algorithm (6SV1) for calculating SMAC coefficients for atmospheric correction. This shows that (a) the implementation of the update was successful and the new version works as intended and (b) considering the recent favorable validation of ETAL against MODIS that the changes in MDAL results can be seen as an improvement. On top of that, the increased alignment between MDAL and ETAL adds to the potential of exploiting synergies between the two products, e. g. , combining them to obtain increased data coverage for research purposes or for the generation of derived vegetation products (e.
g. , LSA-SAF SEVIRI and https://doi. org/10. 5194/gi-11-389-2022 Geosci. Instrum. Method. Data Syst. , 11, 389-412, 2022 AVHRR-based leaf area index, LAI, and fraction of absorbed photosynthetically active radiation, FAPAR, products). The comparison to in situ stations requires attention to comparability between the footprint of each respective station and the SEVIRI field of view at the station's location. Gobabeb has the most favorable ground situation in this regard, being very homogeneous with a single land cover in both in situ and satellite footprint (although due to the difference in spatial scale the two sensors likely do not see the same level of variation, even within a single land cover type). Here, we see good agreement of both MDAL versions with in situ albedo, albeit with a slight increase in RMSD in v2 compared to v1.
Acknowledgements. MDAL and ETAL albedo data were provided by the  EUMETSAT Satellite Application Facility on Land Surface Analysis  (LSA-SAF: http://lsa-saf. eumetsat. int,last access: 16 November 2022; Trigo et al. , 2011). For help regarding in situ data we thank  Frank Göttsche  ( Karlsruhe Institute of Technology ) for providing data and information for Evora and useful information for Gobabeb and  Wouter Knap  ( Royal Netherlands Meteorological Institute ) for providing additional information on Cabauw. This work was done within the framework of the  LSA-SAF  project, funded by  EUMETSAT. Financial support. This research has been supported by the  European Organization for the Exploitation of Meteorological Satellites  (grant no. LSA-SAF CDOP-3 ). Review statement. This paper was edited by  Ralf Srama  and reviewed by two anonymous referees.
Data availability. All data used in this study are freely available. Operational MDAL and ETAL products are accessible through the EUMETSAT LSA-SAF website (respectively: https://landsaf. ipma. pt/en/products/albedo/albedo-copy/, https://landsaf. ipma. pt/en/products/albedo/etal/, EUMETSAT,  2022a, b). Results of the MDAL v2 experiment are available at https://doi. org/10. 5281/zenodo. 6414693 (Juncu et al. , 2022). In situ data used in this study are provided by third parties: data from Cabauw (https://doi. org/10. 1594/PANGAEA. 940531, Knap, 2021), Gobabeb (https://doi. org/10. 1594/PANGAEA. 938527, Vogt, 2021) and Izana (https://doi. org/10. 1594/PANGAEA. 937907, Cuevas-Agulló, 2021) were obtained from BSRN through the PANGEA data portal (https://dataportals. pangaea. de/bsrn/, last accessed: 20/04/2022); data from Evora were obtained from Frank Göttsche (Karlsruhe Institute of Technology).
g. Oltmans et al 2013) , from approximately weekly ozonesondes (e. g. Logan 1994 , Thompson et al 2021) , from routine commercial aircraft (e. g. Petzold et al 2015) , and retrieved from satellite instruments (e. g. Ziemke et al 2019). We focus here on trends derived from observational datasets that are available for nearly a quarter century and are best suited to comparison with monthly mean ozone from chemistry-climate models: measurements aboard commercial aircraft  (Gaudel et al 2020) ; the Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) and Total Ozone Mapping Spectrometer (TOMS) tropospheric column ozone satellite products  (Ziemke et al 2019) , and remote ground-level stations previously shown to sample a wide range of trends in the boundary layer, though these surface measurements do not necessarily reflect trends in the free troposphere  (Cooper et al 2020).
Observed trends are calculated since 1994 in all regions except for Persian Gulf and Malaysia/Indonesia, which are since 1998 and 1995, respectively. The IAGOS data were analyzed separately for the free and full tropospheric columns  (Gaudel et al 2020) , such that differences between the free and full tropospheric columns are attributable to ozone trends in the boundary layer. We compare trends in median ozone derived from IAGOS measurements (figure 2 of Gaudel et al 2020) to the annual mean trends simulated by each individual ensemble member. The observed trends are calculated as the slope from the GLS applied to the monthly means (accounting for seasonality). For each region and year, model monthly mean ozone fields are sampled for 1994-2014, except for the Persian Gulf and Malaysia/Indonesia regions, which we sample since 1998 and 1995, respectively, for consistency with the observational record.
Gaudel et al (2020)  report trends through 2016, but our simulations end in 2014, so there is a slight mis-match in the time period sampled. We select all vertical grid cells within the bounding latitude-longitude boxes surrounding the IAGOS flight tracks in each region (figure  S1 ), where the top edge pressure ⩾250 hPa, and where the bottom edge pressure is ⩽700 hPa (free tropospheric column; note that there is a mis-match for northern South America for which the free troposphere has a lower boundary of 600 hPa in  Gaudel et al (2020) ) or 950 hPa (full tropospheric column). Capping the tropospheric column at 250 hPa will not capture the full extent of the troposphere in the tropics and may include some of the lower stratosphere in winter at high latitudes. Discrepancies in trends derived from the monthly IAGOS data versus monthly mean model ozone may also result from the different temporal sampling frequency.
Acknowledgments A M F gratefully acknowledges useful discussions with  Clara Deser ,  Dan Horton  and other members of the  U S CLIVAR Large Ensemble Working Group  and participants in the 2019 workshop, and with  Ben Santer. L M P is funded by  NSF  Award # 1914569  from the  US NSF  to  Columbia University. O R C, K L C and A G were supported by the  NOAA Cooperative Agreement  with  CIRES ,  NA17OAR4320101. IAGOS has been funded by the  European Union  projects  IAGOS-DS (Design Study ) and  IAGOS-ERI (European Research Infrastructure ). The IAGOS database is supported in France by  AERIS  (www. aeris-data. fr,last access:  30 June 2022 ). We acknowledge the strong support of the  European Commission, Airbus  and the airlines ( Deutsche Lufthansa ,  Air France ,  Cathay Pacific ,  Iberia ,  China Airlines  and  Hawaiian Airlines ) that carry the IAGOS equipment, partner institutions of the  IAGOS Research Infrastructure (FZJ, DLR ,  MPI , and KIT in Germany; CNRS,  Météo-France , and  Université Toulouse III Paul Sabatier in France ; the  University of Manchester  in the UK), and national agencies in Germany (BMBF), France (MESR), and the  UK (NERC).
This directive has only reinforced an initiative that has been carried out for more than 30 years in France, where the information is stored in a database called "Sise-Eaux", managed by the regional health agencies (https://data. eaufrance. fr/concept/sise-eaux, 20 January 2019 accessed on). The regular updating of this database has generated a volume of data that now enables us to study and spatialize the processes behind the variability in water quality, which offers the possibility of targeted, more effective and less costly management. In this context, and although many parameters are regularly monitored, fecal contamination, detected by the presence of fecal germs, particularly Escherichia Coli and Enterococcus, constitutes the vast majority of cases of non-compliance with water intended for human consumption  [15] [16] [17].
This non-compliance with the drinking water standards, often due to strong contamination, which is, however, rare and punctual, is reflected by extreme values in the databases, i. e. , nonnormal distribution. In Geoscience studies, the normality of the distribution is sometimes verified for aspects related to the physics of the environment, such as geophysical studies or spatial variations in water content, etc. , but more rarely for aspects of chemical quality  [18, 19] , and even less so for the mechanisms responsible for the bacteriological quality of water. The two latter cases present a large amplitude of spatial variability, ranging from several hundred km (spatial variations linked to large geological structures) to very local variations for certain processes affecting, for example, nitrates with millimetric hot spots, or even smaller ones for the denitrification process.
This point is usually only addressed by geostatisticians  [24]. The assumption of data normality is, therefore, a generally forgotten aspect in the study of mechanisms responsible for the variability of water quality, but in the presence of extreme values, these can mask certain processes, and therefore, alter the analysis that is made of the database. The aim of this work is to compare different conditioning options for bacteriological and physicochemical groundwater data, to assess the degree of agreement with a normal distribution model, to evaluate the impact of extreme values on the multivariate processing of the data, in particular by Principal Component Analysis, as well as on the study of the spatial structure of the information. In order to ensure a large disparity of environments on a regional scale, the region chosen for the study is the Occitanie region located in southern France.
Database The data were extracted from the national SISE-EAUX database, managed by the Regional Health Agencies since 1990, according to the procedure described by Tiouiouine et al. [25]. Due to the management of the database by the Regional Health Agencies, these extractions are necessarily carried out according to the administrative division of the French regions, and in the context of this work, on the whole of the Occitanie region. All analyses were performed by laboratories with international accreditation and certification of analytical quality. The extraction concerned the period of about 11 years, more precisely the data acquired between 22 January 2007 and 18 December 2018. Only the analyses of raw water were kept, i. e. , before any treatment. For each water sample taken, several analysis reports could have been realized, namely, complete with several hundreds of parameters, standard with about 30 parameters, or a routine follow-up, with only about ten parameters.
The result is a matrix with some empty cells that need to be conditioned before processing. After the manual correction of errors during data entry in the database and the elimination of empty cells, a matrix consisting of 8110 observations and 15 parameters was used for this study. These samples came from 1972 sampling points, i. e. , an average of 4. 1 samples for each sampling point, and spread over 106 groundwater bodies delimited by the French Geological Survey (BRGM) in the BDLISA database (https://bdlisa. eaufrance. fr/, accessed on 7 February 2022). The selected parameters were Enterococci, E. coli, Electrical Conductivity, Na + , Ca 2+ , Mg 2+ , Cl -, SO 4 2-, HCO 3 -, NO 3 -, Fe, Mn, As and the pH, which was added after transformation into H + concentration to avoid mixing variables with logarithmic and non-logarithmic units.
Mathematical Tools For each parameter, four types of data conditioning were compared, namely, the raw data, a logarithmic transformation, a square root transformation, and a transformation aiming to reinforce the role of the chemical profile independently of the dissolved load. Previous work on a similar database in south-eastern France  [25]  has indeed highlighted a variability of processes responsible for water quality that have been identified using the water chemical profile. For this data conditioning, the bacteriological, minor elements and nitrate data are unchanged, i. e. , expressed in concentration, but the values of the major ion variables have been divided by the electrical conductivity of the solutions, i. e. , by a quantity proportional to the sum of cations and/or anions. This procedure is roughly equivalent to replacing the major ion concentration with the anionic or the cationic profile.
The objective of this transformation further referred to as "chemical profile transformation", is to temper the role of the total concentration, which usually appears as the main factor of variability in principal component analyses on hydrochemical databases  [25] [26] [27] [28] [29]. Multiple correlations were established between E. coli levels, as the explained variable, and the other parameters, as explanatory variables, with the exception of Enterococcus. The objective was to measure the degree of relationship between the bacteriological and physicochemical characteristics according to the data conditioning mode. The results are expressed as the percentage of variance explained (r 2 ) by the multiple regression according to the conditioning mode of the data. A Principal Component Analysis (PCA) was performed in order to reduce the dimension of the data space by losing a minimum of the information contained in the database  [29].
The analysis was conducted using the correlation matrix. Under these Multiple correlations were established between E. coli levels, as the explained variable, and the other parameters, as explanatory variables, with the exception of Enterococcus. The objective was to measure the degree of relationship between the bacteriological and physicochemical characteristics according to the data conditioning mode. The results are expressed as the percentage of variance explained (r 2 ) by the multiple regression according to the conditioning mode of the data. A Principal Component Analysis (PCA) was performed in order to reduce the dimension of the data space by losing a minimum of the information contained in the database  [29]. The analysis was conducted using the correlation matrix. Under these conditions, the factorial axes resulting from this analysis are orthogonal to each other and thus carry information related to independent processes  [25, 27, 30].
Distribution of Water Samples in Factorial Score Plots The distribution of samples in the PC1/PC2 and PC3/PC4 factorial plans were plotted for the three modes of data conditioning in Figure  3. The difference in the distribution of the scatterplots was notable. In the case of raw data, a small number of extreme values monopolized most of the information and thus the variance, regardless of the origin of the variability, i. e. , dissolved load, bacterial load, or other parameters. It resulted in factorial plans with large areas without observations, bounded only by a few rare points, while almost all the observations of the database (more than 7000) were grouped in a small central area. On the other hand, after the logarithmic transformation or to a lesser extent the square root transformation, the extreme values played a much more moderate role plans with large areas without observations, bounded only by a few rare points, while almost all the observations of the database (more than 7000) were grouped in a small central area.
Distribution of Water Samples in Factorial Score Plots The distribution of samples in the PC1/PC2 and PC3/PC4 factorial plans were plotted for the three modes of data conditioning in Figure  3. The difference in the distribution of the scatterplots was notable. In the case of raw data, a small number of extreme values monopolized most of the information and thus the variance, regardless of the origin of the variability, i. e. , dissolved load, bacterial load, or other parameters. It resulted in factorial plans with large areas without observations, bounded only by a few rare points, while almost all the observations of the database (more than 7000) were grouped in a small central area. On the other hand, after the logarithmic transformation or to a lesser extent the square root transformation, the extreme values played a much more moderate role and the observations were spread over the factorial planes, which allowed us to better visualize the diversity of the information contained in the database.
With zero values, the logarithmic transformation is impossible because log (0) does not exist. To get around this problem, we made two choices when preparing the database: On the one hand, observations with missing values were eliminated from the processed database, and on the other hand, the detection limit replaced the values that were below it. Indeed, some analytical laboratories report a value below the detection limit, and others assign a value of 0, but in reality, the only certainty is that the value is between 0 and the detection limit. Feng et al. [35]  mentioned that if the original data follow a log-normal or approximately normal distribution, then the log-transformed data follow a normal or approximately normal distribution and the log transformation removes or reduces the skewness.
The data from many studies, however, do not approximate the lognormal distribution and applying this transformation does not reduce the skewness of the distribution. One of the key criteria is skewness, which is related, for example, to the existence of extremely high or extremely low values relative to the mean in a data set. The larger the database (several thousand or tens of thousands of observations), i. e. , covering a longer period of time or a larger and heterogeneous region, the more likely it is to contain extreme values, thus generating a skewed distribution. It also appears that the use of PCA without prior data conditioning is not an alternative to data conditioning. Indeed, since the PCs are a linear combination of the original parameters they maintain the problems associated with the presence of extreme values and a highly skewed frequency distribution of the data.
Given this observation, what are the options for ensuring that the information carried by these extreme values does not mask a large part of the information contained in the database? The first phase would, therefore, be a diagnostic phase aiming at identifying whether these extreme values are high or low. In the case of high values, the corresponding observations can be eliminated, resulting in a net loss of information in the database. However, this information, although atypical, is often local, with a high impact on a small number of observations, but probably interesting, and is an integral part of the analyzed database. Another solution is to limit the extreme values, which generally concern only one or two parameters (most often bacteriological parameters or metals) out of a few dozen observations.