dataesr/scientific-paragraphs-categorization huggingface dataset:
To quantify the radiative effects of dust, the NODUST simulation was also initiated at 00:00 UTC on 17 October but with all the radiative effects of dust switched off. The assessment of cloud fields was done using the so-called model-to-satellite approach (see  Chaboureau et al. (2008)  and references within). In this approach, brightness temperatures were computed from the model outputs using the radiative transfer code RTTOV (Radiative Transfer for Tiros Operational Vertical Sounder) version 8. 7  (Saunders et al. , 2005)  and compared to satellite observations. Previous studies  (Clark and Chaboureau, 2010; Chaboureau et al. , 2012; Rysman et al. , 2015, among others)  have shown the overall skill of Meso-NH to correctly predict cloud and rain fields over the Mediterranean. The dust effect is not taken into account in RTTOV version 8.
The cloud layers are well represented in the Meso-NH DUST simulation using a cloud condensate mixing ratio of 0. 005 g kg -1. The arrival of the dust plume in Menorca is detected in the WALI extinction coefficient data around 22:00 UTC on 17 October and confirmed by the depolarization ratio data (not shown). High extinction values (> 0. 1 km -1 ) are seen between 0. 2 and 2 km a. m. s. l. until 19 October 00:00 UTC and then between 0. 2 and 4. 5 km until 19 October 12:00 UTC. Later that day, the vertical extent of the dust plume is seen to decrease with time until 00:00 UTC on 20 October. Low-level dust observed on 18 October suggests dust emission at the coast of northern Africa, while elevated dust observed on 19 October suggests transport from more remote locations such as southern Algeria.
Summary, discussion and conclusions The direct radiative impact of a moderately intense dust plume (AOD between 0. 2 and 0. 8 at 550 nm) from northern Africa on precipitation over the southern fringes of the Massif Central was investigated during IOP 14 of the HyMeX SOP 1. A high-resolution mesoscale simulation with prognostic dust, validated against numerous ground-based, airborne and space-borne aerosol data sets, was used to assess the dust radiative impact by comparison with a highresolution simulation that took no dust effects into account. The simulation with prognostic dust was first shown to simulate the most intense dust plume observed during the HyMeX SOP 1, in terms of spatiotemporal variability but also, and more importantly, in terms of vertical distribution and dust aerosol concentration.
Acknowledgements. This work was supported by the  French Agence Nationale de la Recherche (ANR)  via the  IODA-MED  grant  ANR-11-BS56-0005 , the  MUSIC  grant  ANR-14-CE01-014 , the  MISTRALS/HyMeX programme , the  French space agency (CNES)  and the  Commissariat à l'Energie Atomique (CEA). Airborne data was obtained using the ATR-42 Environment Research Aircraft operated and managed by  Service des Avions Français Instrumentés pour la Recherche en Environnement (SAFIRE) , which is a joint entity of  CNRS ,  Météo-France  and  CNES. The SAFIRE staff is thanked for their support during SOP 1. The authors are grateful to  D. Bruneau ,  P. Genau ,  C. Merlet ,  R. Meynadier ,  T. Deleporte ,  S. Bastin ,  C. Kocha ,  C. Lavaysse  (LATMOS), as well as  F. Blouzon  and  A. Abchiche  ( DT/INSU ) for operating the LEANDRE 2 system aboard the ATR-42 during the HyMeX SOP 1.
We use satellite observations from IASI (Infrared Atmospheric Sounding Interferometer) on board the MetOp-A satellite to evaluate the springtime daily variations in lower-tropospheric ozone over east Asia. The availability of semi-independent columns of ozone from the surface up to 12 km simultaneously with CO columns provides a powerful observational data set to diagnose the processes controlling tropospheric ozone enhancement on synoptic scales. By combining IASI observations with meteorological reanalyses from ERA-Interim, we develop an analysis method based only on IASI ozone and CO observations to identify the respective roles of the stratospheric source and the photochemical source in ozone distribution and variations over east Asia. The succession of low-and high-pressure systems drives the day-to-day variations in lower-tropospheric ozone.
Carbon monoxide from IASI The CO data used here are retrieved from the IASI spectra within the 2143-2181. 25 cm -1 spectral range using the FORLI-CO (Fast Optimal Retrievals on Layers for IASI for CO) retrieval code from the Université Libre de Bruxelles (ULB). FORLI-CO retrievals give CO concentration profiles using the optimal estimation method  (Rodgers, 2000)  and a single a priori profile. More details are given in  Hurtmans et al. (2012). The IASI FORLI-CO product used in this study is the total column, publicly available from the Ether website (http://www. pole-ether. fr). Note that only half of the pixels are available for the year 2008. This explains the difference in measurement density between O 3 and CO observations in the different figures. Carbon monoxide is often used as an indicator of biomass burning and anthropogenic pollution (e.
Meteorological data set Meteorological data from the ECMWF ERA-Interim reanalysis are used in our analyses. The reanalysis is based on a 4-D-Var assimilation system with a 12 h analysis window. The spatial resolution of the data set is approximately 80 km on 60 vertical levels from the surface up to 0. 1 hPa  (Dee et al. , 2011). In our analyses, the meteorological parameters are taken at 00:00 UTC, corresponding roughly to the morning overpass time of IASI. The main variables considered in this study are the geopotential height, the potential vorticity (PV), and the horizontal wind field (u and v components), as well as the equivalent potential temperature, the vertical velocity and the convective available potential energy. The geopotential height associated with horizontal wind at 850 hPa gives a proxy for describing the weather situation and horizontal transport in the lower troposphere, whereas the same parameters at 300 hPa describe the situation in the UTLS.
Validation of IASI lower-tropospheric ozone Significant changes in the ozone retrieval procedure compared to the validation exercise reported in  Dufour et al. (2012)  have been made as described in Sect. 2. 2. A new validation exercise was done to evaluate the new version of the ozone product. We use a database of ozonesonde measurements from 2007 to 2012 including 27 stations in the midlatitudinal band (30-60 • ) in both hemispheres and 16 stations in the tropical band (30 • S to 30 • N). Most of the ozonesonde measurements are from the WOUDC (World Ozone and Ultraviolet Radiation Data Centre, http://woudc. org/) and SHADOZ (Southern Hemisphere ADditional OZonesondes, http://croc. gsfc. nasa. gov/shadoz/) databases, except for those at Aquila and Beijing. A list of stations and related information is provided in Table  1.
Acknowledgements. We acknowledge the  Institut für Meteorologie und Klimaforschung (IMK), Karlsruhe, Germany , for a licence to use the KOPRA radiative transfer model. This study was supported by the  French Space Agency -CNES  (project " IASI-TOSCA "). The IASI mission is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The IASI L1 data are distributed in near real time by  EUMETSAT  through the EUMETCast system distribution. We acknowledge the  Ether CNES/CNRS-INSU database  (http://www. pole-ether. fr)for providing access to IASI Level 1 data. We acknowledge the  LATMOS/ULB  for the provision of IASI CO total columns through the  Ether CNES/CNRS-INSU database. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and READY website (http://www.
Rainfall and Atmospheric Data Sets We used daily rainfall observations from 13 S to 18 S and 62 W to 72 W in the southern Andes-Amazon region for the period 1970-2005 (Figure  1 ), covering an altitudinal gradient from 160 to 4700 masl (for more details see supporting information). The data comes from the hydrometeorological services Water Resources Research 10. 1002/2014WR016273 (SENAMHI) in Peru and Bolivia. The quality of the rainfall data was first checked at interannual timescales using the Regional Vector Method  [Espinoza et al. , 2009]. At a daily time step, outliers were identified and compared with values recorded at nearby meteorological stations and those that did not match data from neighboring stations were discarded. Rainfall data recorded at stations with less than 20% of missing values were finally selected giving a total of 95 rainfall stations used in this work.
To complete this information, Tropical Rainfall Measuring Mission satellite Precipitation Radar (TRMM-PR) rainfall estimations were used to describe the spatial distribution of the rainfall over regions where in situ observations are lacking. We used version 7 of the data set for the period 1998-2012. The climatology was constructed from rainfall information taken from the 2A25 product remapped into Cartesian coordinates using bilinear interpolation, with a horizontal resolution of 5 km. Information from TRMM products is available at http://disc. sci. gsfc. nasa. gov/ precipitation/documentation/TRMM_README. Large-scale atmospheric circulation is described using daily values from the 40 year European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis (ERA-40)  [Uppala et al. , 2005]  and ERA-interim reanalysis  [Dee et al.
, 2011]. ERA-40 and ERA-Interim 850 hPa zonal and meridional winds and geopotential height were also analyzed. ERA-40 data are available at a grid resolution of 2. 5 3 2. 5 (http://data-portal. ecmwf. int/ data/d/era40_daily) and are useful to evaluate large-scale atmospheric circulation. ERA-Interim data are available at a grid resolution of 0. 25 3 0. 25 (http://apps. ecmwf. int/datasets/data/interim_full_daily/) and make it possible to describe more details concerning the Andes/Amazon transition. To characterize the intraseasonal variability of the atmospheric circulation, cross-equatorial wind regimes were defined for the 1970-2005 period  [Wang and Fu, 2002]. Northerly and southerly regimes were computed using areaaveraged daily 850 hPa meridional winds from ERA-40 reanalysis (5 S-5 N, 65 -75 W) and from winds recorded at Iquitos meteorological station in the Peruvian Amazon (3.
Using this criterion, 15% of JJA days were characterized by a northerly regime and 15% by a southerly regime. We evaluated the accumulated probability of rainfall distribution in the Peruvian hotspot using both southerly (V1) and northerly regimes (V-) computed from ERA-40. During northerly regimes, 30% of dry days occur, while the 90th percentile reaches 44 mm/d (bold blue line in Figure  10a ). In contrast, during southerly regimes, the probability of dry days doubles (60%) and precipitation > 10 mm/d rarely occurs (bold red line in Figure  10a ). These results show that intense rainfall in the Peruvian hotspot (>30 mm/day) occurs predominantly during northerly regimes. Moreover, using wind regimes similarly computed from data Water Resources Research 10. 1002/2014WR016273 recorded at the Iquitos meteorological station, we observe that differences in rainfall probability in the Peruvian hotspot are almost the same, although not as acute (dashed lines in Figure  10a ).
Acknowledgments We wish to thank the following agencies/organizations for providing access to data: NASA for TRMM data (http://disc. sci. gsfc. nasa. gov/precipitation/documentation/TRMM_ README), the  European Centre for Medium-Range Weather Forecasts (ECMWF)  for the ERA40 (http://dataportal. ecmwf. int/data/d/era40_daily),and the  ERA-Interim  (http://apps. ecmwf. int/datasets/data/interim_full_daily/) reanalysis, and  SENAMHI from Bolivia and Peru  for the historical rainfall observations (www. senamhi. gob. bo and www. senamhi. gob. pe,respectively). We also thank the  PNICP-Peru  for providing funds through the ''contrato  397-PNICP-PIAP-2014 '' contract. The authors are grateful to the reviewers for their contribution to improve this paper and to  Daphne Goodfellow  for correcting accurately the manuscript.
With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as common parameters to consistently combine multi-sensor time series and to exploit them for land/crop cover classification. Artificial neural networks were trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR and FCOVER from Landsat-8 and SPOT-4. The correlation coefficients between field measurements and the retrieved biophysical variables were 0. 83, 0. 85 and 0. 79 for LAI, FAPAR and FCOVER, respectively. The retrieved biophysical variables' time series displayed consistent average temporal trajectories, even though the class variability and signal-to-noise ratio increased compared to NDVI.
Furthermore, they would foster the re-utilization of training data for classification, as they are not sensor-specific. The first part of the work focuses on extracting, integrating and evaluating the consistency of the retrieved high-resolution multi-sensor biophysical time series. Second, the potential of land/crop cover classification with the retrieved bio-physical variables along the season was assessed and compared to accuracies obtained with spectral information or NDVI directly. Particular focus was placed on the evolution of the accuracy of the different classifications along the season as information accumulates. Together, this permits answering the following research questions:  (1)  are biophysical variables more useful compared to traditional spectral and/or vegetation index-based inputs, and if not, what are the reasons for this underperformance; (2) are some biophysical variables more useful than others and why;  (3)  what are the most critical dates for class discrimination; and (4) how many observations are required within a given growing season for optimum classification accuracy.
Datasets The remote sensing dataset is composed of 34 images acquired by two sensors: 16 SPOT-4 and 18 Landsat-8. The Landsat-8 data were obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center (https://lpdaac. usgs. gov/get_data). The SPOT-4 imagery was obtained under the SPOT4/Take5 program. To increase the satellite observation frequency, the site was delimited by the overlap area of the area imaged during the SPOT-4 Take-5 experiment and two Landsat-8 scenes (Paths 198-199 and Row 30). The SPOT-4 Take-5 experiment consisted of lowering SPOT-4's altitude to a 5-day repeat cycle orbit to mimic the revisit cycle that ESA's Sentinel-2 mission will provide. SPOT-4 (20 m, 5-day revisit cycle) surface reflectance data from February 2013-May 2013 (Figure  2 ) were corrected from atmospheric effects, including adjacency effects and the terrain effects  [14].
Percentage Cloud free q q q q 0. 00 0. 25 0. 50 0. 75 Biophysical variable ground observations were collected in elementary sample units (ESU) (20 × 20 m 2 ) over wheat, corn and sunflower from 17 April-23 October 2013 every 10 days with hemispherical photographs (Nikon CoolPix 8400 camera with FC-E8 fisheye lens)  [16]. LAI, FCOVER and FAPAR were derived from valid hemispherical photography inside an ESU using CAN-EYE software  [17]. Its good performance to extract LAI from hemispherical pictures compared to destructive measurements has been demonstrated. Mougin et al. [18]  showed a very good agreement between the two estimates with a correlation coefficient R 2 of 0. 97 and a root mean square error of 0. 26 (n = 99) in Sahelian rangelands. For crops, Demarez et al. [19]  demonstrated a strong correlation between the estimates and the destructive measurements (root mean square error of 0.
63 and correlation coefficient of 0. 95). However, they also highlighted a systematic skew (y = 0. 7810x) that leads to the well-known LAI underestimation in clumped canopies. A reference dataset was built for the training of the classifiers and the validation of the maps based on a field visit for the crop classes and on visual interpretation for the other land cover classes. For the former, 616 fields were visited in 2013 and the five crop types described: winter wheat (164), rapeseed  (49) , corn (277), barley  (26)  and sunflower (100). For the latter, the ESA CCI Land Cover  [20]  and the Google Earth tool were employed to help identify 5 land cover types (water bodies, urban areas, broadleaved forest, needle-leaved forest and grassland). Half of the sample pixels was randomly selected as the training set, and the remaining half was selected as the validation set.
Biophysical Variable Retrieval Biophysical variable retrieval methods fall usually into four categories: parametric and non-parametric empirical relationships, radiative transfer model inversion and hybrid methods  [21]. First, parametric regression methods define relationships between selected spectral information (NDVI, for example) and biophysical variables of interest. This implies choices on the spectral bands to select, on the index formulation and on the fitting functions  [22]. Second, non-parametric regression methods build direct non-parametric algorithms from full optical spectral datasets based, for instance, on machine learning tools. For these two empirical approaches (parametric and non-parametric), a calibration phase relying on local in situ data is necessary, which decreases their generic capabilities  [23].
In this work, neural networks were trained using GeoSAIL, a radiative transfer model  [28] , to retrieve LAI, FAPAR and FCOVER. The retrieval procedure includes three steps  [16] : (1) the generation of a training database with GeoSAIL using canopy biophysical variables-leaf structure parameter, leaf chlorophyll concentration, dry matter content, equivalent water thickness, LAI, average leaf angle and hot spot size parameter-and soil brightness parameters to simulate theoretical reflectances; (2) the calibration of the NNs to inverse GeoSAIL by minimizing the root mean square error (RMSE); and (3) the application of NNs to measured SPOT-4 and Landsat-8 reflectances and geometries of observation in order to derive the biophysical variables of interest (LAI, FAPAR, FCOVER). Due to spectral specificities and geometries of the two different sensors, separate neural networks were generated.
Regarding the SPOT-4, all four bands (green, red, near-infrared (NIR), short-wave infrared (SWIR)) were kept as for Landsat-8; only four bands (blue, green, NIR and SWIR-1) were selected because they are sensitive to the canopy characteristics, such as chlorophyll, water and dry matter. The unique geometrical configuration (VZA: view zenith angle; VAA: view azimuth angle) was used in the training and inversion. For Landsat-8, the view angle is always nadir, so all images have the same geometrical configurations, but for SPOT-4 images, each image has different view angles. Two thirds of the training dataset were used to train the NNs, while the remaining was used to validate the performance. For each biophysical variable, several neural network structures were tested, and the one that minimized the RMSE compared to the validation dataset was selected for each variable.
Land Cover Classification along the Season and Assessment A plethora of classifiers have been developed for land cover mapping based on satellite imagery. Those classification methods range from unsupervised clustering algorithms (ISODATA or K-means  [31] ) to supervised classifiers. The latter encompasses parametric statistical algorithms, such as maximum likelihood  [32] , machine learning algorithms, such as artificial neural networks  [33] , decision trees  [34, 35] , support vector machines  [36, 37]  and ensembles of (machine learning) classifiers  [38]. Several studies have demonstrated the higher accuracy and efficiency of machine learning algorithms especially with high dimensional datasets and large area mapping  [39] [40] [41]. These algorithms are particularly efficient and effective, because they do not assume any data distribution (e.
g. , normality) and yield generally to higher accuracy levels  [42, 43]. However, the number of hyper-parameters to adjust complicates their use and their automation  [44]. In addition, some machine learning algorithms tend to overfit the data  [34]. Machine learning ensemble classifiers have been developed to overcome this drawback: they rely on a community of base machine learning classifiers that generate multiple outputs, which are then combined according to specific decision rules. Ensemble learning circumvents the weaknesses of the base classifiers while keeping their strengths and, therefore, yields higher accuracy levels  [45, 46]. While assessing the effectiveness of random forest  [47]  (an ensemble of decision trees) for land cover classification, Rodriguez-Galiano et al. [48]  demonstrated that random forest does not overfit and offers several advantages, such as: (1) the low number of user-defined hyper-parameters; (2) the estimation of the importance of variables (bands) for the general classification of the land cover categories and for the classification of each category by means of the Gini Index; and (3) its robustness to noise and training dataset size reduction.
The selected output legend in terms of land cover and crop type distribution includes eleven classes: five crop types (barley, wheat, rapeseed, sunflower and corn), needle-leaved and broadleaved forests, grassland, urban areas and water bodies. As it was shown that the random forest out-of-bag error significantly underestimates error and can differ from independent error estimates  [57] , the reference data were randomly split into two parts: half of the samples was selected to train random forest classifiers along the season as the remote sensing data accumulates, while the other half served for independent classification accuracy assessment (Table  1 ). Six classifiers were trained using as input (1) all spectral bands; (2) NDVI; (3) LAI; (4) FAPAR; (5) FCOVER and (6) all three biophysical variables.
For the sake of clarity, these classifications are henceforth referred to as Spectral-C, NDVI-C, LAI-C, FAPAR-C, FCOVER-C and BPV-C, respectively. Table  1. Number of available training and validation pixels per class. The reference data were randomly split into two parts at the object level: half of the samples was selected to train random forest classifiers along the season as the remote sensing data accumulates, while the other half served for independent classification accuracy assessment. The classification accuracy of the classifiers was assessed via quantitative accuracy indicators  [58] , including overall classification accuracy, producer's accuracy, user's accuracy and F 1 -scores. In particular, the overall accuracy and the F 1 -scores were computed for each classifier and at each date to study the evolution of the accuracy.
Acknowledgments This research was carried out within the  ImagineS (Implementing Multi-scale Agricultural INdicators Exploiting Sentinels)  project in the  FP7-SPACE-2012-1  Grant Agreement Number  311766. The Landsat data were obtained through the online Data Pool at the  NASA Land Processes Distributed Active Archive Center  ( LP 440 DAAC ),  USGS/Earth Resources Observation and Science (EROS) Center, 441 Sioux Falls ,  South Dakota  (https://lpdaac. usgs. gov/get_data). The SPOT4 imagery was obtained under the  SPOT4/Take5 program. Imagery is copyrighted to CNES under the mention: "CNES 2013, all rights reserved. Commercial use of the product prohibited".