dataesr/scientific-paragraphs-categorization huggingface dataset:
A night-time turbulence regime classification, the so-called "HOckey-Stick Transition " (HOST) theory, proposed by  Sun et al. (2012)  from the Cooperative Atmosphere-Surface Exchange Study-1999 (CASES-99) is explored using data from the Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign which took place during summer 2011 in the central French Pyrenean foothills. Results show that the HOST turbulence relationships for the BLLAST field campaign data are strongly dependent on both the meteorological and orographic features. The HOST pattern only appears for nights when a stably stratified boundary layer can be developed, corresponding to fairweather and clear-sky nights, when the flow is generated by the nearby orography, from the south and the south-east. Those flows strongly influenced by the orography may generate intermittent or enhanced turbulence.
When considering the whole nocturnal dataset for these flow directions, several enhanced turbulence points are found to be associated with sudden wind speed and directional shear transitions. In contrast, flows from other directions do not reproduce the HOST relationships and the turbulence relationship is almost linear, independent of vertical temperature gradients, corresponding to flows driven by synoptic scales. In addition we identify examples of gravity waves and top-down turbulent events that lead to transitions between the turbulence regimes.
Introduction With the aim to investigate and categorize turbulence patterns generated by wind shear in the atmospheric stable boundary layer (SBL),  Sun et al. (2012)  analysed a month-long dataset collected in Kansas from the Cooperative Atmosphere-Surface Exchange  Study-1999 (CASES-99)  in October 1999  (Poulos et al. , 2002). They found that, depending on the relationship between the turbulence intensity of the flow and the wind velocity, turbulent mixing from the surface to the last level of measurement (55 m) can be categorized in three regimes (Fig. 1 ). A regime (regime 1) occurring when the wind speed (V ) is less than a threshold value (V T ), characterized by low turbulence intensity increasing slightly with the velocity. This is the weak turbulence regime, which is generated by local shear and modulated by the vertical temperature gradients of the SBL.
, 2012) , the aim of this study is to explore the validity of the HOST theory in an area with complex terrain, for flows that are influenced by the nearby orography. This study considers nocturnal boundary layers with special emphasis on the very stable boundary layer. The data analysed herein are from the Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) campaign  (Lothon et al. , 2014) , which took place during early summer 2011 in France, on the "Plateau de Lannemezan", north of the central French Pyrenean foothills. The structure of the paper is as follows. The site, the in situ observations of near-surface turbulence obtained from the array of instruments, and the data processing method applied are described in Sect. 2. The wind flow and wind regimes affecting the site during the BLLAST field experiment are analysed in Sect.
From the Valimev tower we use data from the sensors located at 29. 4, 45. 8, and 61. 4 m (hereafter z 30m , z 45m , and z 60m , respectively), see Table  1. As the analysis is restricted to night-time, we only consider data measured from 19:42 to 04:20 UTC  (Lothon et al. , 2014; Blay-Carreras et al. , 2014)  which are representative of the sunrise and sunset times during this period and in this area. An alternative method to select the study periods would be to consider the time when the sensible heat flux changes its sign instead of the sunset and sunrise; however, the first approach was followed for consistency with previous studies. Two datasets with different sampling frequencies were used from Skinflow mast levels between z 2m and z 8m : thermocouples (1 Hz) and sonic anemometers (10 Hz) (Table  1 ).
In this paper we use the term "intermittency", to specifically describe a temporal variation of the turbulence intensity or turbulence strength, represented by the V TKE value calculated at a fixed location, as in  Sun et al. (2012). Reasons for increased turbulence are diverse and may include the intrusion of coherent structures such as gravity waves, density currents  (Terradellas et al. , 2005; Udina et al. , 2013; Ferreres et al. , 2013; Soler et al. , 2014) , and low-level jets (LLJs)  (Mahrt, 1999; Newsom and Banta, 2003; Cuxart and Jiménez, 2007). From the Valimev 60 m tower we used the measurements from the sonic anemometers located at the z 30m , z 45m , and z 60m levels, as shown in Table  1. The dataset for this tower starts on 14 June 2011 for the z 30m and z 45m levels, and slightly later for the z 60m level, between 10:00 and 11:00 UTC on 15 June 2011, and finishes on 8 July 2011 for the three levels.
The same data processing method used for the Skinflow mast levels measurements is applied to the Valimev levels, using temporal averages of 5 min in addition to averages ranging between 10 s and 1 min. We follow the corrections and filters indicated in  Said et al. (2011a, b) and De Coster et al. (2011) , where the postprocessing method applied to the BLLAST data is explained in detail (e. g. applying a planar-fit rotation to the wind direction dataset). Additionally, sonic measurements were not considered when anomalous V TKE values were observed during rainfall periods. We calculate several parameters in order to study the turbulence regime relationships. The first of these was the wind speed, V : V = u 2 + v 2 , ( 1 ) where u and v are the horizontal wind components. The second was the turbulence intensity or turbulence strength, V TKE : V TKE = 1 2 (σ 2 V + σ 2 w ), (2) where σ represents the standard deviation of the wind components, and σ 2 V = σ 2 u + σ 2 v represents the potential virtual temperature: θ v = T v + d z.
As a consequence the north-west winds may prevail at high levels during nIOPs despite their anabatic origin. Although the main focus of this paper is the study of the HOST theory for the SBL, we also aim to analyse the behaviour of such theory for non-nIOP conditions, and how the peculiarities of the Plateau de Lannemezan site affect its structure. Bearing this in mind we represent the wind rose for the whole night-time dataset from the same levels of both towers (Fig. 4 ). One of the most relevant results for the Skinflow tower is the increasing frequency of winds coming from the westerly sector when all data are considered, unlike in nIOPs where westerly winds are almost negligible. The reason for this may be the fact that winds from the west and west-north-west, linked to larger-scale synoptic  (Barneoud et al.
, 2010)  flow, are very frequent in this part of Europe, with additional channelizing to westerlies due to the mountain range south of the site. The same result is observed for the Valimev tower, although winds from the south-east sector become slightly less dominant. For both datasets, southeast is clearly predominant for the Skinflow tower heights vs. Valimev; this is because Skinflow tower has measurements at very low levels and, therefore, it is able to measure small-scale phenomena as SDFs which are generated by small-scale features such as local slopes. As we have seen throughout the flow characterization, the presence of shallow drainage flows, mountain-plain flows, and the Aura Valley jet favours the formation of LLJ with maximum winds at different heights. In addition, taking into account the results of previous work carried out by  Nilsson et al.
(2013). It is also noticeable that the slope for the regime 2 for all of the levels is quite similar, which indicates that, as expected, once the layer is neutral, the relationship between the turbulence intensity and the wind speed tends to be independent of the layer depth. Although the three turbulent regimes of the HOST pattern are visible at all levels, at the highest level, z 60m , regime 2 does not seem to behave as expected for the highest wind speed values, showing a decrease in the turbu- z 3m z 5m z 8m z 30m z 45m z 60m V T (m s -1 ) 1. 3 1. 7 2. 1 3. 7 6. 1 6. 9 lence intensity as the V increases. This is due to the small amount of data for these wind speed bins; therefore, no statistical conclusion can be drawn from these results. The relationships of σ V and σ W with V (not shown) also present the HOST dependence seen for V TKE.
Turbulence regime analysis: whole BLLAST dataset Herein we aim to explore how well the HOST behaves when we use the whole night-time dataset from the BLLAST field campaign without any restriction. To achieve this objective, we carried out an analysis similar to the one outlined in Sect. 4; we first analysed the turbulence relationship, then performed a deeper analysis using box plots for each measurement level, and finally examined turbulence relationships with the thermal stratification. Therefore, the first step was to analyse the differences between all of the night-time data wind roses (Fig. 4 ) and those corresponding to nIOPs (Fig. 3 ). As we have seen in Sect. 3 the most remarkable feature is the high frequency of the western flows when the whole nocturnal dataset is considered. Taking into account that many storms and frontal systems were from this direction  (Barneoud et al.
Turbulence relationships As in Sect. 4, we analyse the turbulence intensity relationships, but herein we focus on the whole night-time dataset without differentiating the fair-weather nights from the nonfair-weather nights. The analysis is carried out by splitting the data into two main directions, flows coming from the NWs and the SEs directions (Fig. 7 ). For the NWs the relationship between the V TKE and V is linear, so the HOST pattern is not observed (Fig. 7a ). Contrary to this, for the SEs the V TKE vs. V does resemble a hockey stick; we can also distinguish a V T for each height (Fig. 7b ), which are similar to those found for the nIOPs but not equal, as the Figure  6. Relationship between the bin-averaged turbulence strength V TKE and the wind speed V during the BLLAST campaign nIOPs as a function of potential temperature difference intervals, defined as, θ v = θ v (z) -θ v z 0 , where θ v z 0 is the virtual potential temperature of reference at 2 m a.
g. l. , and θ v (z) is the virtual potential temperature at each height z: (a) z 3m , (b) z 5m , and (c) z 8m. The wind speed threshold V T is marked using a black triangle for each height (a) z 3m , (b) z 5m , and (c) z 8m. dataset herein includes all of the night-time data. In fact, most of the SEs situations correspond to nIOPs (between 63 % and 70 % of them), so the hockey-stick shape reproduced in Fig. 7b  is very similar to that shown in Fig. 5. Indeed, this plot mostly corresponds to fair-weather nights when stably stratified and near-neutral stratifications occurred and both regimes (regimes 1 and 2) could be distinguished. In contrast, situations corresponding to NWs are tied to frontal systems, cloud skies, or cyclonic situations, which substantially modify the boundary layer ideal structure.
Turbulence regimes and thermal stratification To further understand the HOST pattern, we explore the effect of stratification on the turbulence relationship for the whole BLLAST dataset, as previously carried out in Sect. 4 for the nIOPs. We also separate the data depending on the two wind directions considered, NWs (Fig. 10a ) and SEs (Fig. 10b ). We only use data from the Skinflow mast, and because the results for the different levels (z 3m , z 5m , and z 8m ) are very similar, we only undertake the analysis and the discussion for the z 5m level. For the NWs flows (Fig. 10a ), turbulent mixing leads to a neutral stratification (red and green lines) and also to unstable stratification (turquoise lines), indicating thermal instability in the flow. Stable stratification (purple line) only appears to be associated with very low velocities and with relatively large V TKE values.
Conclusions In this study we have analysed the HOST theory proposed by  Sun et al. (2012)  using the nocturnal dataset of the BLLAST campaign, which took place on the "Plateau de Lannemezan", an area located north of the central French Pyrenees. The main objective was to explore the influence of the orography and the weather conditions during the BLLAST campaign on the HOST pattern that was originally defined for a relatively flat area and for fair-weather conditions, the predominant conditions of the CASES-99 field campaign. Here, the analysis was carried out by separating nights into two types: nocturnal IOPs (nIOPs) and the whole nocturnal dataset. For the nIOPs of the BLLAST campaign, the HOST theory is found to be valid and the turbulence relationships show the different turbulence regimes with a wind speed threshold for each height that locates the transition between the weak wind and weak turbulence intensity generated by local shear (regime 1) and the strong turbulence intensity generated by bulk shear (regime 2).
These wind speed thresholds are lower than in  Sun et al. (2012) ; therefore, the bulk shear needed to achieve regime 2 is also lower, due to the greater roughness length of the mountainous terrain linked with the incoming southern flow. Similar results were found by  Mahrt et al. (2013)  and  Bonin et al. (2015)  for other locations. These nights are characterized by clear skies and intense radiative cooling where the predominant flow comes from the south-east quadrant corresponding to the arrival of shallow drainage flows to the lower levels and mountain-plain flows later on. These flow circulations generated by the orogra- phy lead to turbulence enhancement and transitions between regimes, modifying the ideal HOST pattern. Instead, when the turbulence relationship is analysed for the whole night dataset, results are significantly different, and they become wind direction dependent.
The HOST pattern is observed for thermally driven flows, from wind directions between 45 and 235 • (SEs), but it cannot be seen for winds coming from the other directions (NWs). For the NWs flows, the turbulence relationship is almost linear and independent of the vertical temperature gradients. Indeed, when considering the whole night-time dataset the frequency of winds coming from westerly and northerly directions increases, representing the synoptic-scale meteorological situations, in which the HOST pattern is erased mainly due to the fact that the stable boundary layer cannot be developed. To further investigate the turbulence relationship, we used box plots to provide additional information. Comparing the nIOPs and the SEs flows from the whole nocturnal dataset there are interesting differences: (i) the turbulence intensity tends to increase when we use the whole nocturnal SEs dataset; (ii) the number of outliers increases for the whole nocturnal SEs dataset, as do the number of turbulent events associated with transitions between regimes 1, 2, and 3.
Data and Products To evaluate model performance and reliability of the simulations, we make use of several observational products. Observations for Sea Surface Salinity (hereafter SSS) are from Soil Moisture and Ocean Salinity (SMOS) satellite mission  [29]. Data are available for the period 2010-2017 on a regular and global grid with 1/2°horizontal resolution at a monthly basis. Salinity field are corrected from land-sea contamination and latitudinal bias. Systematic errors are reduced and variability is improved compared to previous versions. For sea level anomalies, the Climate Change Initiative (CCI) product released in 2017 for the period 1993-2015  [30]  is used for its accuracy and its homogeneous long-term satellite-based record. The goal of the CCI data product is to provide an accurate and homogeneous long-term altimetry-based sea level record with two main objectives.
First, the CCI product considers all available satellite altimeters including the ESA missions (ERS-1/2-Envisat) and the Topex/Poseidon and Jason reference missions. Second, geophysical corrections and all processing steps are applied with coherent data sets over the whole period of the CCI product. This is done to meet the Global Climate Observing Systems requirements. The CCI data set consists of monthly sea level anomalies on a global ¼°grid  [30]  from January 1993 to December 2015. We are also considering the mean dynamic topography data from CMEMS (http://marine. copernicus. eu/documents/QUID/CMEMS-SL-QUID-008-056-058. pdf) to evaluate its counterpart from the regional NEMO configuration over the period 1993-2018.
2006). This leads to one of the major current challenges for conservation, which is to obtain reliable and accurate information on a large scale to monitor biodiversity, resources, ecosystem services as well as the human impact on natural ecosystems. Remote sensing is considered as being a key to this effort, mainly because of spatial resolution and temporal resolution of the datasets, which currently enable tracking elements of biodiversity, and also because of the increase in available data and computational capacity  (Pettorelli et al. 2014; Turner 2014; He et al. 2015; Kwok 2018). The species occurrence measurement by satellite is among the 10 proposed biodiversity metrics to monitor the progress toward the Aichi Biodiversity Targets  (Skidmore et al. 2015)  as well as a recommended action to reach millennium goal 7: 'Ensure environmental sustainability'  (United Nations, 2005).
Forest and land cover data To test if our forest-type classification was consistent with independent datasets of land cover/use maps of the region, we compared our results to the Global Forest Change Map 2000-2016 version 1. 4  (Hansen et al. 2013)    maps using automatic classification processes applied to satellite images. A complete description of the project can be found at http://mapbiomas. org. The MapBiomas map was produced based on the Landsat Data Archive available in the Google Earth Engine platform, encompassing the years from 2000 through the present days. The MapBiomas map results from a pixels-based classification based on random forest machine learning to overcome empirical calibration of the input parameters for image classification; details of the methods are given in the manual of the product.
Algorithm The model was coded in R language (R Core Team, 2016) with Rstudio interface to Keras  (Chollet et al. 2015; Allaire and Chollet 2016)  and Tensorflow backend  (Abadi et al. 2015). R code is available upon request. The training of the models took ~2-20 h using Graphics Processing Unit (GPU) on a Nvidia Quadro K6000 with a 12 GB dedicated memory. Prediction using GPU of a single tile of 16 384 9 16 384 pixels (4. 9152 km 2 ) took approximately 35 min.
Acknowledgments The research leading to these results received funding from the project  BIO-RED 'Biomes of Brazil -Resilience, Recovery, and Diversity ', which is supported by the  São Paulo Research Foundation  ( FAPESP ,  2015/50484-0 ) and the  U. K. Natural Environment Research Council  ( NERC ,  NE/N012542/1 ). F. H. W. has been funded by  FAPESP  (grant  2016/17652-9 ). A. S. acknowledges the support of the  FAPESP  (grant  2016/03397-7 ). Y. T. has been funded by the project  EPITOME ANR-17-CE23-0009  of the  French National Research Agency (ANR). R. G. L. acknowledges the support of the  Coordination for the Improvement of Higher Education Personnel (CAPES)  (grant  PDSE 88881. 132115/2016-01 ). M. P. F. thank the support of the  FAPESP  (grant  2016/24977-1 ). M. P. M. A. has been funded by  ECOFOR Project, BIOTA-FAPESP  (grant number  2012/51872-5 ).
Funding Information The research leading to these results received funding from the project  BIO-RED 'Biomes of Brazil -Resilience, Recovery, and Diversity ', which is supported by the  São Paulo Research Foundation  ( FAPESP ,  2015/50484-0 ) and the  UK Natural Environment Research Council  ( NERC ,  NE/N012542/1 ). F. H. W. has been funded by  FAPESP  (grant  2016/17652-9 ). A. S. acknowledges the support of the  FAPESP  (grant  2016/03397-7 ). Y. T. has been funded by the project  EPITOME ANR-17-CE23-0009  of the  French National Research Agency (ANR). R. G. L. acknowledges the support of the  Coordination for the Improvement of Higher Education Personnel (CAPES)  (grant  PDSE 88881. 132115/2016-01 ). M. P. F. thank the support of the  FAPESP  (grant  2016/24977-1 ). M. P. M. A. has been funded by  ECOFOR Project, BIOTA-FAPESP  (grant number  2012/51872-5 ).
Recent events in Western Attica in Greece (24 deaths in November 2017), in the Balearic Islands (13 deaths in October 2018), and in southern France (15 deaths in October 2018) show that flood-related mortality remains a major concern in Mediterranean countries facing flash floods. Over the past several years, many initiatives have arisen to create databases on flood-related mortality. An international initiative started in 2011 pooling regional and national databases on flood mortality from region and/or countries bordering the Mediterranean Sea. The MEditerranean Flood Fatality Database (MEFF DB) brings together, in 2018, six Mediterranean regions/countries: Catalonia (Spain), Balearic Islands (Spain), Southern France, Calabria (Italy), Greece, and Turkey, and covers the period 1980-2018.
MEFF DB is on progress and, every year, new data are included, but for this study, we kept only the preliminary data that were geolocated and validated on 31st of December 2018. This research introduces a new step in the analysis of flood-related mortality and follows the statistical description of the MEFF DB already published. The goals of this paper are to draw the spatial distribution of flood mortality through a geographical information system (GIS) at different spatial scales: country, NUTS 3 (Nomenclature of Territorial Units for Statistics. Level 3) regions, catchment areas, and grid. A fatality rate (F: number of deaths/year/million of inhabitants) is created to help this analysis. Then, we try to relate mortality to basic (human or physical) drivers such as population density, rainfall seasonality, or rainfall frequency across the Mediterranean Basin.
There are several degrees of analysis of flood-related deaths according to the selected spatial scale. • Globally, we find studies based on international databases, mainly the Emergency Events Database (EM-DAT) of the Center for Research on the Epidemiology of Disaster (CRED) (https://www. emdat. be/emdat_db/), as well as the Darmouth flood observatory DB (http://www. dartmouth. edu/%7Efloods/Archives/index. html), Desinventar (https://www. desinventar. org/) or Reliefweb (https://reliefweb. int/) [4] [5] [6]. Those databases are well-known and frequently used by researchers to get a global overview of mortality as they provide general data by event and, when available, the number of fatalities  [7] [8] [9]. The approach is by event and at the scale of countries  [10]. However, the location of disaster-related fatalities is not accurate enough to bring outcomes for decision makers at the regional and local level  [11].
• Faced with the shortcomings of these international databases, many initiatives came up in Europe and/or in the Mediterranean Basin to build a database on disaster impacts in the frame of research projects  [3, [14] [15] [16]  (http://riskam. ul. pt/disaster/) or by national weather services  [17, 18]. The main concern of these efforts is the verification, the geographical location, and the collection of accurate information for each fatality.