dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, our objective in this study is to develop a new map at 1 km resolution based on recent land cover products, suitable for Earth system modeling, which could be further refined if new PFTs are developed. For that purpose, different remote sensing land cover products are available. They have been developed from multispectral and multitemporal imagery, in order to separate the various ecosystems presenting different spectral properties and seasonal variations. At medium resolution (hundreds of meters to kilometers), the most popular and most recent products are the GLC 2000 land cover database  (Bartholomé and Belward, 2005)  based on SPOT-4 VEGETATION instrument, the GlobCover land cover products  (Arino et al. , 2005 (Arino et al. , , 2012) )  derived from Envisat/MERIS radiometer and the MODIS land cover data sets  (Friedl et al.
Sulla-Meneshe et al. (2011)  developed the Northern Eurasia Land Cover (NELC) database from supervised classification of MODIS data, which allows the separation of 15 land cover classes including land use (urban, agriculture), wetlands and tundra classes. Meanwhile,  Pflugmacher et al. (2011)  cross-compared GLC 2000, Glob-Cover and MODIS products as well as Landsat-based reference maps in northern Eurasia. The map legends were converted to a common classification on the basis of dominant life-form type (LFT). The results show regional disagreements among products and the difficulties to map shrubs and herbaceous vegetation types. More recently, Shepaschenko et al. (  2011 ) produced a highly detailed land cover/land use data set for Russia essentially based on GLC 2000 data set at 1 km resolution combined with VCFs from MODIS, soil and vegetation databases and different inventories and statistics.
All these studies found significant differences between data sets and highlighted strengths and weaknesses of each product, but none concluded on the superiority of one compared to the other. Moreover, since for most of these works, the final objective was not PFT mapping, the methodology developed for the cross-comparison and the final mapping could not be used directly for our study. Further, no comparison to date has included the MODIS 5. 1 product, which benefits from a reprocessing of the complete MODIS archive, with an up-to-date training database, and an extension of the land cover data to 2011. Therefore, it was necessary to develop a new comparison of the most recent land cover products available for Siberia, to build dedicated aggregation rules for ORCHIDEE PFT mapping and to generate a new PFT map over Siberia at 1 km scale.
Methods We acquired recent land cover satellite products available at medium spatial resolution (300 to 1000 m) and focused our comparison on Siberia. The data sets are presented in Table 1 with their specifications in terms of spatial resolution, time of acquisition, geographic projection, and thematic information, including the number of land cover classes with the respective classification legends in Table  2. The global products include the GLC 2000 product  (Bartholomé and Belward, 2005)  developed in northern Eurasia by  Bartalev et al. (2003) , the GlobCover 2005 and 2009 products  (Bicheron et al. , 2006; Arino et al. , 2005 Arino et al. , , 2012) )  and the MODIS land cover type collection 5. 0 and 5. 1  (Friedl et al. , 2002 (Friedl et al. , , 2010)). The first four products have been already compared and evaluated in various regions and at different scales, using ground truth measurements and have shown strengths and weaknesses  (See and Fritz, 2006; Jung et al.
, 2006; Frey and Smith, 2007; Kaptué Tchuente et al. , 2010 , 2011; Pflugmacher et al. , 2011; Schepaschenko et al. , 2011). Our first goal was to identify the most suitable product for further PFT mapping. To achieve this, we assessed the land cover classification methodology and land cover class definition in terms of their capacity to represent the spatial heterogeneity and in terms of the spatial agreement between products. To assess these criteria, we compared various data sets at different temporal and spatial scales, including highresolution optical images like Landsat-TM (Thematic Mapper) products.
GlobCover uses a fully automated unsupervised classification approach using GLC 2000 as the main auxiliary data set for the class interpretation. Finally, the MODIS land cover products developed by the Boston University Department of Geography and Center for Remote Sensing (https://lpdaac. usgs. gov) are based on NASA MODIS instruments on board Aqua and Terra platforms. They are available at annual time step, from 2001 until 2007 for the C5. 0 product  (Friedl et al. , 2002 (Friedl et al. , , 2010) )  with a spatial resolution of 500 m (1/240 • GSD). The most recent product (C5. 1) is available for the 2001-2010 period with the same spatial resolution of 500 m. The two products are available on an Integerized Sinusoidal Grid (ISG) projection, with a legend based on the IGBP classification system that separates 17 classes.
Auxiliary data sets Several auxiliary products representing different features of the land surface were used to assist in the evaluation of the global products (see Table  1 ). These products first helped us in the interpretation of the different legends and later assisted in the merging process, which permitted us to build the harmonized legend. Among them, two land cover maps based on aerial photointerpretation and ground truth data have been used to understand better the class significance and evaluate product accuracy and spatial variability representation. For these two products, the digital database was not accessible and only graphical maps were used. The first one is the Circumpolar Arctic Vegetation Map (CAVM,  Walker et al. , 2005) , which was developed within the National Science Foundation Arctic Transitions in the Land-Atmosphere System (ATLAS) project, and is presently the most precise mapping of the Arctic tundra.
It is available at 1 : 7 500 000 scale at http://www. geobotany. uaf. edu/cavm, in Lambert azimuthal projection, and separates 18 classes describing very precisely the various tundra ecosystems. This data set is based on photointerpretation by vegetation experts of nine Arctic regions, which allowed delineating the various biomes into a NOAA-AVHRR images database. The second one is the Yakutsk region land cover mapping provided by  A. Fedorov (personal communication, 2012) , which was derived from Landsat images acquired in 2002 combined with ground truth data. In this map, 12 land cover classes were separated including six different types of forest like larch and birch in different states and three types of grasslands (alas, wet and dry valley meadows). These 2 maps have mostly been used in the following to evaluate the ability of the various land cover products to separate the shrubs/herbaceous classes and the broadleaf/needleleaf forests.
Lastly, the MODIS global VCFs  (Hansen et al. , 2003 (Hansen et al. , , 2006) )  and the forest canopy height map proposed by  Simard et al. (2011)  complemented all these data sets. The VCF product derived from MODIS sensors is provided at 500 m spatial resolution on an annual basis  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010)  and at global scales. The VCF is a proportional estimate of vegetative cover types: woody vegetation, herbaceous vegetation, and bare ground. Collection 4 (version 3) was downloaded at http://glcf. umd. edu/data/vcf, where it is available  in the same projection as the land cover product. Finally, the forest height product based on 2005 data from the Geoscience Laser Altimeter System (GLAS) on board ICEsat (Ice, Cloud, and land Elevation Satellite) is available globally at 1 km spatial resolution and provides an estimation of the canopy height.
In its standard version, the PFTs are defined from two databases: the AVHRR IGBP 1 km global land cover map  (Belward et al. , 1999)  and  Olson et al. (1983)  biome classification including 96 land types  (Vérant et al. , 2004). The final map prescribes the fraction of each vegetation type over a resolution cell of 5 km. Therefore, different PFTs can coexist in every grid element, and their fraction can vary when the dynamic vegetation submodule is activated. Figure  5  presents the standard PFT maps used in ORCHIDEE. In Siberia, nine PFTs are present in the standard land cover map. They include four types of forests (temperate and boreal needleleaf evergreen, needleleaf summergreen, broadleaf summergreen forests), C3 grass, C3 crops, unlikely (but very few) C4 crops and bare soils.
Acknowledgements. The authors are grateful to the  French ANR CLASSIQUE  and  EU-FP7 PAGE21  projects for funding this study (internship of  J. Lescure ). They acknowledge also the MODIS team,  Mark Friedl  and  Damien Sulla-Menasche , the  JPL (M. Simard) , the  Melnikov Permafrost Institute SB RAS (A. Fedorov) ,  ESA  and  JRC  for making their data sets available for this work. The anonymous reviewers are also acknowledged for their constructive comments, which helped to improve the paper. Edited by:  R. Key.
The presented work addresses the topic of energy savings in existing public buildings, when no significant retrofits on building envelope or plants can be done and savings can be achieved by designing intelligent ICT-based service to monitor and control environmental conditions, energy loads and plants operation. At the end of 2010 the European Commission, within the Seventh Framework Program, has founded a project entitled ''Smart Energy Efficient Middleware for Public Spaces'' (SEEMPubS). To achieve this goal the project will implement, in a set of demonstrator buildings, an interoperable web-based software and hardware solution for real-time monitoring and control of lighting, heating, ventilation and air conditioning services, through both wired and wireless sensor networks. In this paper the first phase of the project, concerning the selection of the environments to be used as demonstrator and the definition of the control and monitoring strategies to reduce energy consumptions for lighting and air conditioning, are presented.
The project will use the LinkSmart middleware potential to create services and applications across heterogeneous devices to develop an energy-aware platform. The SEEMPubS platform will provide functions and tools to add energy efficiency features to the Building Management System (BMS), by monitoring environmental and energy data in real time, and by controlling the operation of active systems like lighting and HVAC services, taking advantage of natural resources (daylight and solar energy) to ensure the best possible comfort conditions with the most efficient use of energy. A real-time energy-awareness service for all users of the Public Spaces will be also created and it will combine awareness services with a community portal to actively involve people in energy saving issues. The functionality of the designed platform will be demonstrated in existing buildings at the Politecnico di Torino Campuses (Italy).
Climate-based daylight modelling allows the prediction of various lighting quantities using sun and sky conditions that are derived from standard meteorological datasets. At present there is quite few software that allows a climate-based daylight simulation. Among them Radiance and Daysim are the most well-known and used in the lighting field. Daysim calculates the annual daylight availability in arbitrary buildings based on the Radiance backward ray tracer, using external daylighting conditions derived from standard meteorological local datasets. It includes specific occupant behaviour model algorithms to mimic occupant use of personal controls such as light switches and venetian blinds and to predict the electric lighting use due to automated lighting controls such as occupancy sensors and photocell controlled dimming systems.
Model validation To start the lighting simulation with Radiance or Daysim a 3D model of the selected rooms and their external environment were prepared (Figure  10 ). The rooms' models were prepared using the parametric software Revit and then imported in Radiance and Daysim through the software Ecotect. To correctly characterise the model's opaque and transparent surfaces, in field measurements of luminous reflectances and transmittances were carried out. The models' validation was done comparing the output of the Radiance simulations with the quantities measured inside the rooms, with same sun position and sky condition  (Mardaljevic, 1995; Reinhart and Herkel, 2000; Reinhart and Walkenhorst, 2001; Reinhart and Breton, 2009). As an example, Table  1  presents some first results obtained for a clear and an overcast sky condition.
HVAC simulations 3. 2. 1. Software presentation TRNSYS is a internationally recognized software dedicated to the simulation of dynamic systems, developed by the Solar Energy Laboratory (SEL), University of Madison in the United States (TRNSYS official website). It allows performing dynamic simulations of the thermal behaviour of a mono or multi-zone building. It can also follow hourly consumption (heating, air conditioning) or production (solar panels) annual energy facilities of a building. TRNSYS is in fact a software which compiles different modules (called ''types'') and allows to link them one to the other to create a realistic model of the building (or the room) tested. Each type has its own part in the simulation. For example, three types are used to create meteorological data of the simulated place, and a special type to model the building  (walls and their compositions, windows, etc.
This paper studies seismo-electromagnetic anomalies observed by the French satellite DEMETER (Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions) during the 27 February 2010 M = 8. 8 Chile earthquake. The nighttime electron density (N e ), electron temperature (T e ), ion density (N i ), ion temperature (T i ) and whistler counts (C w ) are investigated. A statistical analysis of the box-and-whisker method is applied to see if data of two or more groups under study are significantly different. A cross-examination of temporal variations before and after shows that N e and N i (C w ) increases (decreases) appear 10-20 days before the earthquake. A comparison of data over the epicenter and those over its reference area can be employed to discriminate the earthquake-related anomalies from global effects.
Three summer data sets have been used and applied to our methodology. We present successively the surface measurements of PM10 which are the basic surface measurements and then AOT observations used in this study. Then the different model inputs are described. In Section 3, the methodology is described and applied to the dataset. Section 4 presents results of PM10 retrieval from AOT and hence the relevance and the precision of the method Section 5 is a discussion of PM10 measurements and PM10 chemical model estimates. Particularly as we were only users of CHIMERE output, we analyze the difference between PM10 observations and model output. For that purpose 2003 pollution event is considered in relation with CHIMERE forecast modelling results. We also consider the interest to combine our retrieval method with chemical model forecast in case of high particles pollution level probability.
Large-Scale Data: Output of the Atmospheric and Chemical Transport Models and Auxiliary Meteorological Parameters In our study we used the vertical profiles of meteorological variables simulated by MM5 (http://www. mmm. ucar. edu/mm5/) and the surface values of PM10 simulated by the chemical transport model CHIMERE. The hourly gridded meteorological data analysis (vertical profiles of temperature, zonal and meridional wind components) from the regional atmospheric model MM5 (2003 to 2007) forced at the lateral boundaries by the operational NCEP (National Center for Environmental Prediction)/NCAR (The National Center for Atmospheric Research) model, were used to determine the weather types affecting the Lille area. The atmospheric aerosol concentration was simulated by the chemistry-transport model CHIMERE.
This model was forced with data and observations provided from different sources: 1) Biogenic and anthropogenic emission data by the data base EMEP (European Monitoring and Evaluation Programme). 2) Meteorological observations by the European Range Weather (ECMWF) analysis. 3) Land topography by a terrain data base (MNT). The output of the atmospheric model (MM5) and that of the chemical transport model CHIMERE were obtained by the runs made in the framework of the GEOMON program (http://www. geomon. eu/) over a domain covering a large part of Europe (35˚N -70˚N, 10˚W -35˚E). The MM5 model used twenty vertical sigma-pressure levels with equivalent Z height levels (in metres): from 46 m up to 11,725 m with a variable vertical resolution from typically 50 m to 100 m in the lower part of the atmosphere up 1800 m at the atmosphere top.
Analysis Methods PM10 concentration retrieval from AOT measurements is a difficult task, since the physical relationship between AOT and PM10 both depends on intrinsic PM10 properties which are surface measurements, on particles which are integrated inside the photometer beam, and on meteorological variables. [4]  showed that a quite simple relationship between sun photometer observations and groundlevel PM10-Obs concentration can however be used with a minimum of meteorological weather types. As in  [4]  we clustered here the 11040 meteorological situations provided by the MM5 meteorological model output for the summers of 2003-2007 by using a selforganizing map (SOM). Since the size of the data set we have is much larger than the one used by  [4] , we adopted a more systematic approach. We determined a huge number of prototypical meteorological situations using SOM and then reduced this number using a hierarchical ascendant classification (HAC).
For a given training pattern, p L  , presented to the network, the Euclidian distance for all the reference vectors is computed and the closest reference vector, Wj, is selected. This reference vector is called the best matching unit (BMU) and its associated neuron is denoted the winning neuron. After finding one BMU, all the reference vectors, Wj, of the SOM are updated: the BMU and its topological neighbours are moved in order to better match the input vector. At the end of the training process, the SOM map provides topological (neighborhood) relationships among all the different neurons (or classes). A classification can be thus applied to analyze new meteorological situations. Analysis of a new situation is done by introducing it into the input layer and computing its BMU. The large number of subsets provided by the SOM map allowed us to take into account the complexity of the dataset, but may have prevented us from synthesizing some meteorological information embedded in the learning set.
The 720 (20 × 3 × 12) components of the input vector of SOM are outputs of MM5. At the end of the learning phase, the 10 × 10 SOM map (denoted SOM1) provides 100 typical meteorological situations representative of the 2003-2007 summer periods. These typical meteorological situations were reduced into eight groups by using a hierarchical ascendant classification. A summary of the Open Access OJAP 67 methodology we used is given in Figure  4. and PM10 values. In the following, we suggest to take these mean AOT values as pollution indices: We then retrieved the PM10-Obs from the corresponding AOT measurement by applying a dedicated SOM for each weather type. Index 1: AOT < 0. 11, corresponds to a low pollution level (weather type 1). In the next section we describe more precisely the different data which were used to apply our method for the five summers analyzed.
In order to obtain a good accuracy in the PM10 retrieval, we used 6 × 6 neurons SOMW-N maps. For each SOMW-N, Table  2  shows the number of collocated data pairs (PM10-Obs and AOT) used for the learning and the test phases for each weather type. The performances of the inversion computed on the test datasets are satisfactory for most of the classes. In Figure  7 , we show the scatter plot for the mass concentration PM10 retrieved with respect to PM10-Obs, for weather types n°2, n°4 and n°7, corresponding to the three weather types presented in Figure  5. In order to check the overall performance of the methodology, we show the scatter plots for the learning (Figure  8(a) ) and test set (Figure  8(b) ) obtained by pooling the eight inversions we processed. Table  3  shows some statistical estimators (see Annex 1 for the definition of the statistical estimators) computed on the learning and the test sets, allowing us to estimate the accuracy of the AOT -PM10 relationship.
CHIMERE Model PM10 Output In order to build an air-quality modelling system with confidence for forecasting pollution events, we evaluated the CHIMERE model performance against direct measurements. For that purpose we used the 836 PM10-Obs (mean hourly concentrations measured at the five urban ground stations in the Lille region) collocated with the AERONET observations. This data set was also collocated with the CHIMERE output. Figure  9  presents the histogram of the PM10-Obs (in blue) concentration collocated with this of the PM10 modelled by CHIMERE (hourly gridded surface CHIMERE data with a spatial resolution of 0. 5˚ × 0. 5˚ covering the Lille area-in black). Clearly, the CHIMERE overestimates the small concentration values of PM10 and underestimates the large ones though histogram modes are close each others.
CHIMERE mainly underestimates the high PM10 concentrations for 34 situations, and overestimates the low PM10 values for 19 situations only. The model data set used in this analysis, has therefore a tendency to underestimate high PM10 concentration values and consequently should be used with some caution, at least for pollution threshold alerts. This underestimation of the PM10 modelled by CHI-MERE was also mentioned by  [16]. They analyzed measurements made at ground level between April 2003 and March 2004  [7, 16, 17] ). We also note that other models underestimate the PM 10 ground-level concentration, as observed by  [18]  who compared the results of several aerosol numerical models for the atmosphere over Los Angeles. Regarding the 34 high PM10 values of the validation data set, which were underestimated by CHIMERE, we note that they correspond to pollution peaks and must be accurately predicted to activate pollution alerts for end-users.
This case corresponds to the heat wave of August 2003 and it is a weather type for which an alert threshold can be dispatched. Examining the time-series of PM10 concentration e associated weather types, we find that 25% of the data belonging to weather type 7 were measured during the first two weeks of August 2003. This period was characterized by an exceptional heat wave associated with persistent high pressure conditions characterized by very high temperatures (  [19] ). Figure  13  presents a sketch of the time-series for the summer of 2003. During that period (of weather type 7), pollution level was very strong. Indeed the meteorological conditions were favourable to the development of a large-scale photo chemical pollution episode, (  [11] ). The stagnation of the air mass also led to the accumulation of primary emitted particulate matter (PM) and the development of secondary aerosols.
Acknowledgements We thank  L. Menut  for fruitful discus gratehe ful for the support we receive  ADEME agency ("Agence de Maitrisse de l'Energie"). We thank the  INERIS agency  for providing the CHIMERE and MM5 model output database,  P. Goloub  principal investigator of the  Aeronet nload site of "Lille ", for their effort in establishing and maintaining the site. We also thank  ATMO France  ( Fédération des Associations Agréées de Surveillance de la qualité de l'Air ) for providing the database of direct measurements of mass concentration at the Lille stations.