dataesr/scientific-paragraphs-categorization huggingface dataset:
Standard deviation The absolute standard deviations of H 2 O as measured by HAMSTRAD, radiosonde, in situ, AIRS (ascending and descending nodes) and IASI, and analysed by ECMWF are presented in Fig. 12. The general shape is a vertical profile decreasing with height with an amplitude being much greater in summer (0. 10-0. 20 g m -3 ) than in winter (0. 03-0. 07 g m -3 ). Superimposed on that, some fine structures appear with local maxima at 200-500 m in HAMSTRAD, radiosonde, ECMWF and AIRS datasets in autumn/spring and winter, whilst in summer, the local maximum is close to the surface in radiosonde, AIRS and ECMWF datasets. The HAMSTRAD standard deviation is systematically much greater than all the other datasets, whatever the season considered, whilst. In summer AIRS standard deviation is systematically less than all the other datasets.
Correlation Figure  13  shows the H 2 O correlation with respect to height of the radiosonde, IASI, AIRS (ascending and descending nodes), in situ and ECMWF datasets against the HAMSTRAD dataset, calculated in temporal coincidence within a 60-min window. On average, the correlation ranges between -0. 2 in the upper troposphere-lower stratosphere to 10. 9 in the lower troposphere, that is to say the H 2 O datasets are less consistent than the temperature datasets. The highest correlation coefficients (0. 7-0. 9) are usually obtained close to the surface and at , 1 km for radiosonde, ECMWF and AIRS, decreasing with height up to the upper troposphere-lower stratosphere where it can reach values close to zero or even negative (-0. 2). We also can note a systematic minimum at 500 m whatever the season considered.
The other datasets, namely AIRS, radiosondes, and ECMWF, are rather consistent with each other. This mainly confirms the fact that the HAMSTRAD H 2 Oda ta se t is of a poorer quality compared to radiosonde, ECMWF and AIRS datasets. In summer, the IASI correlation departs considerably from all the other datasets but in winter IASI correlation below 1 km is consistent with AIRS, radiosondes, and ECMWF.
Discussion: H 2 O vs temperature correlation We have taken the opportunity of having access to a large dataset including both temperature and H 2 O measured and analysed simultaneously over one year and over a wide atmospheric layer (0-10 km) to check the correlation between these two parameters, and infer some conclusions about: 1) the quality of the datasets, and 2) the processes that could explain the correlation between temperature and H 2 O. Figure  14  shows the seasonally-averaged H 2 Ov s temperature correlation calculated considering HAMSTRAD, radiosonde, in situ, AIRS (ascending and descending nodes), IASI and ECMWF databases. Whatever the season considered, the correlation coefficient is generally. 0. 7 from the surface to about 4-5 km for in situ, radiosonde and ECMWF datasets.
It is much less for the HAMSTRAD dataset (0. 4-0. 8, even 0. 0 at 500 m in winter), and IASI (0. 4-0. 8, even -0. 2 below 1 km in summer). The best correlation coefficients are usually found close to the surface for in situ, radiosonde and ECMWF (0. 9). We can observe an obvious transition in summer at 5 km with a correlation of almost null at 6 km, although in winter, the correlation is not negligible (, 0. 5) in the upper troposphere-lower stratosphere (except for HAMSTRAD for which the correlation is almost null). As discussed in the previous sections, the quality of the HAMSTRAD H 2 O measurements strongly impacts the weak values of the temperature vs H 2 O correlation whatever the altitude considered. In the lowermost troposphere, the space-borne IR sensors AIRS and IASI have little sensitivity due to their poor vertical resolution and this prevents high H 2 O vs temperature correlations.
In the upper tropospherelower stratosphere, HAMSTRAD measurements of H 2 Oand temperature start losing sensitivity at , 5 km above surface, consequently the H 2 O vs temperature correlation is rather low, oscillating around zero. Considering the other datasets in the upper troposphere-lower stratosphere, namely AIRS, IASI, radiosonde and ECMWF, we have checked (not shown) that, despite the fact that: 1) the amount of H 2 Oi s strongly decreasing, and 2) the vertical resolution of the space-borne sensors is rather poor, the time evolutions of H 2 O were consistent within all these datasets. This means that, on average, all these H 2 O measurements and analyses (except HAMSTRAD) are sensitive to the upper troposphere-lower stratosphere. Consequently, the H 2 O vs temperature correlation deduced from these datasets is meaningful in this altitude layer.
If we now concentrate on the datasets that give consistent results both in temperature and H 2 O, namely radiosonde, in situ, AIRS (excluding the lowermost troposphere) and ECMWF, the vertical distribution of H 2 O vs temperature can be represented in a schematic form (Fig. 15 ) by the season: summer, autumn/spring and winter. In general, we conclude that, in the troposphere, whatever the season considered, H 2 O and temperature are highly correlated (0. 7-0. 9), whilst, in the upper troposphere-lower stratosphere, the correlation is lost in summer (0. 0-0. 5) but still persists in winter (0. 4-0. 6) with a transition period in autumn/spring (0. 2-0. 6). Since the measurements of H 2 O and temperature can be considered as independent, we can investigate the processes that produce such a high correlation.
Conclusions Measurements of tropospheric temperature and water vapour together with the IWV performed in 2010 at the Dome C station, Antarctica, are statistically analysed to assess their quality and to study the yearly correlation between temperature and H 2 O over the entire troposphere. The datasets are made of measurements from the groundbased microwave radiometer HAMSTRAD, radiosonde, in situ sensors, the space-borne IR sensors IASI on the MetOp-A platform and AIRS on the Aqua platform, and the analyses from the ECMWF. First of all, two important points need to be stated for both temperature and H 2 O. T h efi r s tp o i n ti st h ea b s e n c eo fI A S I measurements above Dome C, mainly after 14 September 2010. On that particular date, the retrieval software developed at EUMETSAT has been updated to v5.
0. 6 and pixels over icy surfaces were systematically but erroneously flagged as cloud-contaminated pixels, and consequently, not processed. Secondly, radiosonde and ECMWF temperature fields are usually very similar due to the assimilation of radiosondes launched at Dome C into the ECMWF analysis and forecast system. In general, all the temperature datasets behave consistently with each other, except for two main differences: 1) considering the HAMSTRAD dataset, the mid-to-upper troposphere is much warmer by about 10 K on average and the tropopause height is much lower by about 1-3 km than in all the other datasets, and 2) the lowermost troposphere as measured by AIRS in autumn/ spring and winter is much colder by about 10-20 K than in all the other datasets. The strong temperature inversion observed in the lowermost troposphere by HAMSTRAD, radiosonde, in situ and ECMWF is also detected by AIRS (but higher and less intense) but not by IASI.
Whilst the poor vertical resolution of the space-borne IR sensors (1000-2000 m) in the planetary boundary layer prevents the detection of such an intense gradient, IASI also seems to lack sensitivity within the surface layer. In the lowermost and free troposphere, whatever the season considered, the radiosonde, in situ, AIRS (in summer) and ECMWF datasets show a very high correlation (. 0. 8) with the HAMSTRAD dataset, except for IASI for which the correlation can be very low (0. 2-0. 7). In the upper troposphere-lower stratosphere, the correlation with HAMSTRAD is low (0. 6-0. 8). This reinforces the fact that the HAMSTRAD radiometer loses sensitivity in the upper troposphere-lower stratosphere. All the IWV datasets are very consistent with each other showing a dry season from May-August with values ranging from 0.
05-0. 6 kg m -2 and a wet (or less dry) season from November-February with values ranging between 0. 4 and 1. 5 kg m -2 , and some intense spikes in February and December with values. 2. 1 kg m -2. A systematic negative bias of all the datasets against HAMSTRAD dataset is observed in winter of about -0. 03 ± 0. 01 kg m -2 (-15 ± 5%) whilst, in summer, the negative bias reaches -0. 1 ± 0. 05 kg m -2 (-10 ± 5%). The correlation with HAMSTRAD is very high when considering the radiosondes (. 0. 9) and the ECMWF (. 0. 8), high when considering AIRS ascending and descending nodes (. 0. 7), but low when considering IASI (, 0. 7). A wet season in summer followed by a dry season in winter is observed considering all the datasets with no obvious difference between autumn and spring patterns. In summer, the amount of H 2 Ois.
0. 1 g m -3 from the surface to a height of about 2 km for HAMSTRAD and AIRS, reaching , 3 km for radiosonde and ECMWF up to , 4km in the IASI dataset. In winter, HAMSTRAD and AIRS tend to measure H 2 O amounts (, 0. 3 g m -3 ) much greater than all the H 2 O amounts from the other datasets (0. 06 g m -3 ). Whatever the season considered, the H 2 O profiles from HAMSTRAD, radiosonde, in situ and ECMWF datasets show a positive gradient from the surface to about 250 m, decreasing with altitude above 250 m. This sharp surface gradient is missed by the two space-borne IR sensors IASI and AIRS due to the poor vertical resolution of 1000-2000 m. The correlation between HAMSTRAD and all the other datasets ranged between -0. 2 in the upper tropospherelower stratosphere to 10. 9 in the lower troposphere, which means that the H 2 O datasets are less consistent with each other than the temperature datasets.
The highest correlation coefficients (0. 7-0. 9) are usually obtained close to the surface and at , 1 km for radiosonde, ECMWF and AIRS, decreasing with height up to the upper troposphere-lower stratosphere where it can reach values close to zero or even negative (-0. 2). Our study underlines the great potential of HAMSTRAD measurements to validate both meteorological analyses and satellite measurements in such a remote place where very few independent data are available. Finally, we investigated the temperature vs H 2 O correlation within all the datasets. Whatever the season considered, the correlation coefficient is in general. 0. 7 from the surface to c. 4-5 km for in situ, radiosonde and ECMWF datasets. It is much less for HAMSTRAD and IASI datasets (0. 4-0. 8). The highest correlation coefficients are usually found close to the surface for in situ, radiosonde and ECMWF (0.
Acknowledgements The HAMSTRAD programme (910) has been funded by the  Institut National des Sciences de l'Univers (INSU) /  Centre National de la Recherche Scientifique (CNRS) , the  Institut Polaire Francais Paul-Emile Victor (IPEV)  and the  Centre National d'Etudes Spatiales (CNES). The permanently manned Concordia Station is jointly operated by  IPEV  and the  Italian Programma Nazionale Ricerche in Antartide (PNRA). IASI has been developed and built under the responsibility of  CNES. It is onboard the MetOp-A satellite as part of the EUMETSAT polar System. IASI vertical profiles of water vapour and temperature were extracted from the  Ether French atmospheric database  (http://ether. ipsl. jussieu. fr,accessed July 2013). AIRS has been developed and built under the responsibility of the  National Aeronautics and Space Administration (NASA).
It is built using a combination of satellite data including passive microwave observations and a linear mixture model to account for vegetation. Three wetland classes, differing by the value of the water table depth, are simulated for each grid cell and at each time step: saturated wetland and wetland with a mean water table at 3 and 9 cm below the soil surface. The wetland emissions are driven by the CRU-NCEP climate forcing data set  (Viovy and Ciais, 2009). In this study, four scenarios of wetland emissions are given based on: -accounting or not for the non-saturated wetlands (i. e. all classes of wetlands or saturated wetlands only) -multiplying or not the simulated wetland area by peat-land map in boreal regions. This map is obtained by using soil organic carbon data from IGBP DIS at high resolution (5 ′ × 5 ′ ), by dividing each pixel of this database by 130 kg m -3 (which is the maximum soil carbon density of peat), and then by regridding the result at 1 • × 1 • resolution  (Lawrence and Slater, 2007).
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Anthropogenic deforestation in tropical countries is responsible for a significant part of global carbon dioxide emissions in the atmosphere. To plan efficient climate change mitigation programs (such as REDD+, Reducing Emissions from Deforestation and forest Degradation), reliable forecasts of deforestation and carbon dioxide emissions are necessary. Although population density has been recognized as a key factor in tropical deforestation, current methods of prediction do not allow the population explosion that is occurring in many tropical developing countries to be taken into account. Here, we propose an innovative approach using novel computational and statistical tools, including R/GRASS scripts and the new phcfM R package, to model the intensity and location of deforestation including the effect of population density.
Inversely, when deforestation occurs, the progressive increase in the mean elevation measurement can lead to a decrease in the deforestation rate at the landscape scale, even though the population density continues to increase. One possible way of overcoming this problem is to separate the process determining the intensity of deforestation (or "quantity" census  Pontius and Millones [2011] ) from the process determining the location (or "allocation" census  Pontius and Millones [2011] ) of the deforestation. This is the approach chosen by classic software that can be used to model and forecast deforestation, including CLUE-S  (Verburg et al. 2002) , Dinamica EGO  (Soares et al. 2002) , GEOMOD  (Pontius et al. 2001) , and Land Change Modeler (LCM)  (Kim 2010 ). In the first step, these programs compute a "deforestation trend" by comparing land cover maps at two different dates.
2001; Soares et al. 2002; Verburg et al. 2002; Kim 2010). In this study, we present a coherent framework and new statistical tools to overcome these problems and to accurately forecast deforestation and the resulting carbon dioxide emissions while taking population expansion into account. As a case study, we used recent data on land cover changes covering two time periods from five sites in Madagascar's tropical humid and spiny-dry forests. Madagascar is widely known for its exceptional rates of both diversity and endemism in many taxonomic groups  (Goodman and Benstead 2005) , as well as for its low percentage of remaining native forest cover  (Achard et al. 2002; Harper et al. 2007 ) and high level of threat associated with rapid population growth  (Raftery et al. 2012 ). The method we present is simple, flexible, and overcomes the abovementioned problems.
To encourage the use of this method, we provide a new R package  (Ihaka and Gentleman 1996)  named phcfM (for "programme holistique de conservation des forêts a Madagascar"), which includes functions for estimating the parameters of the demographic and deforestation models. We also provide associated R/GRASS scripts  (Ihaka and Gentleman 1996; Neteler and Mitasova 2008) , which outline the necessary steps for the modeling and forecasting procedures.
S1 ) with different time intervals between observations (Figs. 1  and S1 ). Using the satellite images at the three time points, a multi-date supervised classification of the land cover change was carried out following the methodology of  Grinand et al. (C. Grinand, G. Vieilledent, F. Rakotomalala & R. Vaudry, in review). We used the Random Forests classification algorithm  (Breiman 2001 ) available through R software  (Ihaka and Gentleman 1996). Random forests efficiently manages the multi-modal spectral signatures associated with Landsat TM images acquired at several dates and in different seasons. We considered five classes of land cover: forest (class F), nonforest including rocks, crop land, and savanna (class P), wetland (class W), cloud (class C), and shade (class S). These were converted into seven classes of land cover change including Figure  1.
year À1 ). deforestation (i. e. , change in land cover from forest to nonforest) between dates t 0 and t 1 (class FPP), deforestation between dates t 1 and t 2 (class FFP) and unchanged land cover (FFF, PPP, CCC, SSS, and WWW). In the humid forest, the forest class was defined as 10% or more canopy cover for trees of 5 m or more in height. In the spiny-dry forest, the same definition was used except that the minimum height was set at 3 m. To build the classification trees, a training data set representative of the seven classes of land cover change was manually created by visual interpretation of the Landsat images. A number of additional data sets, including freely available QuickBird images from GoogleEarth TM , and expert information were used as reference materials to help interpretation.
Potential spatial factors driving the location of deforestation In a second step, we considered the sub-process determining the location of deforestation. The objective was to identify the factors explaining why deforestation was occurring at particular places. We modeled the probability of deforestation at the pixel level using several spatial factors that can be classified in different categories: (i) landscape factors, (ii) transport factors, (iii) socioeconomic factors, and (iv) land policy factors. The landscape factors included the following: elevation (in m), forest fragmentation index, the shortest distance to forest edge (in m), and the shortest distance to previously deforested pixels (in m). The elevation data were obtained from the SRTM (Shuttle Radar Topography Mission). The fragmentation index was computed following the method of  Riitters et al.
(2000)  who identified five forest classes: patch, transitional, perforated, edge, and interior. To compute the dynamic landscape factors that change with time (i. e. , all landscape factors except elevation), we had to make the plausible assumption that the forest boundary did not significantly change between the dates of acquisition of the satellite images that made up the mosaic (Fig. S1 ). The transport factors included the following: the shortest distance to the main road (in m) and the distance to the nearest main town in the Fivondronana (in m). The Fivondronana is an administrative entity grouping several Firaisana. Generally speaking, the main roads connect the main towns in each Fivondronana. These data were derived from the FTM maps ("Foiben-Taosarintanin'i Madagasikara", Madagascar National Geographic Institute, http://www.
ftm. mg). The socioeconomic factors included the population density (in peop. km 2 ) at the Fokontany level, which was obtained from the demographic model. Using data from the ILO project in Madagascar (Improve Public Information and Dialogue, http://www. ilo. cornell. edu), we also included the following potential explanatory variables defined at the Firaisana level: the percentage of poor, the number of mines, the number of cattle, and the percentage of farmers who use chemical fertilizers. Land policy was described by a logical variable indicating whether the forest pixel i was located in a protected area. To determine this variable, we used the delimitation of the SAPM ("Syst eme d'Aires Naturelles Prot eg ees a Madagascar") available at the Rebioma web-portal (http:// www. rebioma. net/).
Model performance in forecasting deforestation A cross-validation procedure was used to evaluate the performance of our approach in forecasting deforestation. For each study area, we divided the data set made up of the Q 1 observations (from date t 1 to date t 2 ) into two, using 70% of the observations as training data set and 30% of the observations as test data set. We estimated the intensity of deforestation using the training data set in addition to the Q 0 observations from date t 0 to t 1. We fitted the model for estimating the spatial probability of deforestation using the training data set with observations from date t 1 to date t 2 only, as we needed to compute the distance to previously deforested pixels. Models were used to predict deforestation based on the test data set. We computed a confusion table comparing the predictions of our modeling approach with the observations of the test data set.