dataesr/scientific-paragraphs-categorization huggingface dataset:
Observed changes of offset a and slope b between a postflight and the next preflight calibration are most likely caused by the cleaning procedure of the sensor in the laboratory prior to the preflight calibration  (Helten et al. , 1998). However, it should be mentioned that despite the consistency of the long-term sensor behaviour, only current calibration functions are used for the data analysis. In a final assessment, the uncertainty of RH liquid data was analysed as a function of altitude or temperature, respectively. As is explained in detail by Helten and co-workers Introduction.
Back Close Full Figure  11  show the variations of uncertainties of the RH measurements, the mean plus standard deviation of the individual total uncertainties over all  MOZAIC data of 1994 MOZAIC data of -1999 MOZAIC data of and 2000 MOZAIC data of -2009 period. period. In the middle and upper troposphere the total uncertainties obtained are very similar for both periods. In the lower troposphere the total uncertainties for the first period are slightly higher compared to the second period due to the missing calibrations at temperatures larger than -20 • C. For measurements of stratospheric humidity, where RH values below 5 % prevail, the uncertainty of the MOZAIC humidity device is insufficient for quantitative water vapour measurements, since sensor response time is too slow to equilibrate at the low relative humidity and low temperatures.
In total 6 flights have been conducted in the period between 23 and 29 November 2006 at mid-latitudes (  45 • -70 • N) and at flight altitudes between 7-12 km. These flights in the upper troposphere and lowermost stratosphere (UT/LS) were launched from Hohn Airforce Base in northern Germany with the Learjet 35A operated by enviscope GmbH. CIRRUS-III provided a dataset with approx. 14 flight hours in air masses colder than -40 • C, approx. 4 flight hours in cirrus clouds and 10 flight hours out of cloud. Furthermore, stratospherically influenced air masses have been sampled for 20 min with ozone volume mixing ratios (VMR) above 125 ppmv and 35 min with ozone VMR above 100 ppmv, respectively. Part of the scientific payload of CIRRUS-III was dedicated to the measurement of water vapour and total water by one MCH for measuring relative humidity and one open path tuneable diode laser system (OJSTER; MayComm Instruments;  May and Webster, 1993; Krämer et al.
, 2009)  which delivered the water vapour VMR. Simultaneously total water, i. e. gas phase and ice water, was measured by the reference instrument FISH (Fast In-Situ Hygrometer). This closed-cell Lyman(α) fluorescence hygrometer  (Zöger et al. , 1999)  was equipped with a forward facing inlet to sample also the ice particles. To determine whether a data point was inside a cirrus cloud or not, the difference between total water and water vapour was used to define a cloud index; see  Krämer et al. (2009)  for the detailed data analysis procedure. For the sensor intercomparison study, data for H 2 O VMR > 1000 ppm were excluded because the FISH instrument becomes optically thick and thus insensitive in this VMR regime  (Zöger et al. , 1999). Furthermore data with sensor temperature TAT < -40 • C Introduction.
Back Close Full were beyond the MCH calibration limits. In order to neglect effects of warm clouds the maximum ambient air temperature of accepted data was set to the level of instantaneous freezing of -40 • C. For a complete validation of the MCH the data set was split into a clear sky-set and a cirrus cloud-set by means of the above-described cloud index. Finally, flight sequences of the Learjet 35A with strong ascents and descents were excluded, since these flight conditions are not suitable for instrument intercomparison, because already small time shifts between instruments with different response times lead to large differences due to the rapidly changing H 2 O VMR. For the instrument intercomparison we analysed the sensors with respect to RH liquid since this is the measured quantity the MCH is calibrated against.
Back Close Full The identification of a bias of UTH data from the MCH towards more humid conditions (e. g. ,  Lamquin et al. , 2012)  sparked an in-depth reanalysis of the entire MOZAIC UTH data set from year 2000 onwards, whereas MOZAIC MCH data from the pre-2000 period  (Gierens et al. , 1999)  were found to be unbiased. The reanalysis identified an error in the analysis of the instrument calibration as the source for this bias. The entire calibration data set since year 2000 was reanalysed and the MOZAIC data set was reprocessed using the corrected calibration functions. The annually averaged PDF of reprocessed UTH data from the MCH operated aboard the MOZAIC fleet is shown in Fig. 14. The reprocessed MOZAIC MCH data set exhibits the key features of physically sound UTH data, i. e. , only a statistically insignificant fraction of the observations (< 10 -4 ) is above the limit of 100 % RH liquid (Fig.
In conclusion, the reanalysis of MOZAIC RH data should be considered for studies which have focused on the investigation of ice supersaturation in the UT and used mainly MOZAIC data from year 2000 and later. The reprocessed UTH data set from measurements aboard MOZAIC aircraft will become available at the IAGOS/MOZAIC Database website http://www. iagos. fr/web/ for scientific exploration as Version No. 1. Figures.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | caused by the temperature uncertainty. The MOZAIC database contains estimates of the total uncertainty of RH for each individual humidity data point. Since at the beginning the MOZAIC program focused on the middle and upper troposphere, the pre-flight and post-flight calibrations of the humidity sensors above -20 • C were not performed before the year 2000. This means that then the coefficients a and b of the MOZAIC humidity sensors for measurements in the lower troposphere are based on the interpolation between pre-flight and post-flight calibrations at around -20 • C and the manufacturer's calibration at +20 • C. Also, estimates of calibration uncertainties, based on pre-flight and post-flight analyses cannot be given for the lower troposphere for the period 1994-1999.
ACPD  ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD Acknowledgements. The authors gratefully acknowledge substantial contributions by  Peter Spichtinger  ( Mainz Univ. ) and  Klaus Gierens  (DLR) to the discussion of potential explanations for the observed bias in MOZAIC RH data. The support by  Manfred Helten  during the reanalysis of the sensor calibrations and by  Lukas Alteköster  ( RWTH Aachen/FZ Jülich ) in the preparation of the global data set is also appreciated. Part of this work was funded by the  German Federal Ministry for Research and Education (BMBF)  in the framework of the joint programme  IAGOS-D  under Grant No. 01LK1223A. The service charges for this open access publication have been covered by a  Research Centre of the Helmholtz Association. ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD.
MODELING STRATEGY The solar panel exchanges energy with the other components of the system. Very few parameterizations taking these exchanges into account exist in the literature. The level of detail depends strongly on the objectives of the authors. On the one hand, when looking at the building scale, it is possible to consider some implementation characteristics of the panels, as in  Scherba et al. (2011) , who modified the Energy+ software (software dedicated to building energetics) to improve its previous solar panel model (which only computed the energy production). Their solar panel model considers the tilting of the panels and associated sky-view factors. They then perform an analysis of the impact of several types of roofs on sensible heat fluxes toward the atmosphere, but are unable to link these fluxes to the UHI, which needs to take all the buildings of the entire city into account.
RADIATIVE CHARACTERISTICS OF SOLAR PANELS To establish the energy balance of the equivalent urban canyon, the TEB model needs the albedo (integrated between 0. 4 and 2. 5 µm) and the emissivity in the thermal infrared (integrated between 5 and 12 µm) for the following main areas: road, roofs, facades, glazing. The French Center for Aerospace Research (ONERA) laboratory maintains a current database of optical properties of urban materials. Specific measurements were made for emerging materials: rough white paints, photovoltaic solar panels, metal cladding, and glass (including low emissivity). The measurements for large samples of materials, e. g. , for solar panels, were made using a goniometer (Figure  2 , left). The measurement process is fully automated in the 0. 4-2. 5 µm spectral domain.
SIMULATION CONFIGURATION AND SCENARIOS We are now able to simulate the impact of the implantation of solar panels in a city on the UHI. The simulations are performed on the Paris metropolitan area, with TEB, coupled with the vegetation scheme ISBA  (Noilhan and Planton, 1989)  for rural areas, within the SURFEX modeling software  (Masson et al. , 2013b). The simulation domain is 100 km by 100 km, with a resolution of 1 km. At such a resolution, only the main characteristics of the buildings within the blocks in the grid mesh are kept. Geometric parameters are averaged in order to conserve the surface areas (for walls, roofs, gardens, roads, water, rural areas), while a majority rule applies for the architectural characteristics of buildings (age, materials, equipment) and the use to which they are put (residential, offices, commercial or industrial).
These urban data are provided by a database at 250 m resolution (Figure  3  of  Masson et al. , 2014) , which contains block types as well as 60 urban indicators. Some parameters needed by TEB, such as albedos, thermal characteristics or equipment within 1 http://www. insee. fr/fr/themes/document. asp?ref_id=ip1396 buildings, are deduced for each 1-km-by-1-km grid mesh from urban block types and from the use and age of the majority of buildings. Countryside parameters, such as land use and vegetation characteristics are deduced from the ecoclimap database at 1 km resolution  (Masson et al. , 2003). The methodology presented in  Masson et al. (2014) , based on a simplified Urban Boundary Layer generator  (Bueno et al. , 2013; Le Bras, 2014)  is chosen, in order to be able to perform a simulation over an entire year.
Meteorological, air pollution and SES data: Hourly data for air pollutants were obtained from the Bogota Air Quality Monitoring Network (BAQMN). Temperature and relative humidity (RH) registries were obtained from the BAQMN and the Institute of Hydrology, Meteorology and Environmental Studies (IHMES) stations. Information for constructing the SES stratification of the city was obtained from the 2005 census and the 2007 Bogota Quality of Life Survey conducted by the National Administrative Department of Statistics. Exposure assessment: Daily (24-hr) averages of PM 10 were calculated for all monitoring stations, considering a sufficiency criterion of 75% of data. The same procedure was carried out for temperature and RH. In order to assign exposure for each SES, daily average PM 10 was calculated using the daily registries of the stations located within the localities of each SES.
Acknowledgments The authors would like to thank the IHMES, the District Secretary for the Environment and the  Bogota District Secretary of Health  for supplying the information to carry out this study, and  CIaudia Vargas  and  Alexander Orejuela  for their translation help. The principal author dedicates this work to  Orlando Blanco Casteñeda (R. I. P)  and  Ana de Jesús Becerra de Blanco , loving parents, exemplary and dedicated.
Acknowledgements. The authors wish to thank the  Institut Universitaire de France  for funding T. C. MSc  stipend and  Dimitri Lague  for constructive comments on an earlier version of this manuscript. We are grateful to  G. R. Roberts  and  A. Howard , whose comments helped to improve the manuscript. M. Sambridge  is thanked for making the neighborhood algorithm code available. Edited by:  S. Castelltort.
, 2008; Ercanoglu et al. , 2008). These studies are usually conducted at the 1 / 25 000 or 1 / 50 000 scale using the directly available data sets  (van Westen et al. , 2006; Fell et al. , 2008). Even if these spatial statistical approaches (bivariate or multivariate) give good results, a problem of compatibility of the scale of analysis can easily arise between the 1 / 10 000 scale (mapping objective) and the 1 / 25 000 or 1 / 50 000 scale (most accurate data sets available). Some studies have shown the possibility of applying these methods at the 1 / 10 000 scale with an adapted procedure and particular attention to the model calibration  (Thiery et al. , 2007). Nevertheless, two main steps increase the cost of these approaches and limit their use in operational contexts: (1) the construction of an accurate database and (2) the calibration and validation of the models that are performed using an iterative procedure, since no real standardised methodology exists  (Ercanoglu et al.
Some attempts to map susceptibility with indirect methods were conducted in this region  (Fressard et al. , 2010) , but remain in the exploratory research framework. However, there is a demand from the stakeholders for obtaining tools that could help in landslide hazard management  (CARIP, 2005). It is then necessary to assess the possibility of defining an adapted and operational procedure to map landslide susceptibility using statistical methods in this region. Instead of comparing different quantification methods of the susceptibility, we propose a comparison of the results obtained with various sets of data with different quality. The variations in the quality of the data sets refer to the resolution and accuracy of the data, but also to the cost (both economical and time spent on the database construction).
Landslide typology and activity Due to the difficulty of obtaining accurate data and landslide inventories, this study concentrates on a relatively small area (i. e. 24 km 2 , Fig. 1 ). This area is considered to be a test study site that aims to calibrate the methodology and identify the necessary data to expect going further into susceptibility mapping over larger areas. The selection of this study area was guided by the amount of available data, its accessibility and its important landslide activity. The following descriptions of the landslide typology, activity and data collection apply to the 24 km 2 selected study area. A detailed landslide inventory map was performed during the winters of 2009 and 2010 over the study area  (Fressard et al. , 2010; Fressard, 2013). Morphology, nature, freshness and size of the scarps and fractures of the landslides were described to estimate the type and relative age of the events.
Data sources and data preparation Different sets of spatial data representing the landslide location and the environmental factors controlling slope stability were acquired on the selected 24 km 2 study area. For the landslide susceptibility analysis, four main types of data are usually employed: (1) landslide inventory, (2) topographic data (e. g. slope angle, slope aspect, slope curvature; extracted from digital elevation models, DEMs), (3) materials data (geology and/or surficial formations) and (4) landuse data. These data can be provided by different institutes and commercial companies or specifically created for the study. The source and the methodology employed in the production of these data sets have a significant impact on the quality/accuracy and on the cost of the data  (Glade and Crozier, 2005).
For this study, several data set combinations were tested, but only the ones showing a substantial contrast are presented. Five sets of data were finally retained, noted DS in the text. These five data assemblages were constituted within a constant improvement in the data quality and related cost of acquisition. The data constituting each assemblage are considered homogenous in terms of human and economic investment and can be considered to be used for preliminary (DS 1 and DS 2), intermediate (DS 3) and advanced zoning (DS 4 and 5). The first two sets (DS 1 and DS 2) are composed of free or rapidly produced data. The third set (DS 3) is considered intermediate in terms of cost and accuracy, as only limited field investigations are included (landslide filed mapping). The fourth and fifth sets (DS 4 and DS 5) are composed of data with a high geomorphological significance (surficial formations map) and accurate topographic data (detailed DEM).
Landslide data Three landslide inventories were acquired using different methods. These inventories show large differences regarding the accuracy and number of identified landslides (Figs. 4  and 5 ). 1. The first landslide inventory was obtained using the directly available BDMvt (French geological survey landslide database,  Couëffé et al. , 2005). This is a free database that can be directly downloaded from the BRGM website. It was compiled using the landsliderelated incident records, and questionnaires sent to city councils  (Couëffé et al. , 2005). Only landslides referenced with a high spatial accuracy were used for the analysis. Finally, 61 landslides are identified in the area without any distinction of type and activity. 2. The second landslide inventory was obtained using only the air photo-interpretation (API inventory) on the 2006 and 2009 orthorectified aerial images from the French geographic institute with 50 cm pixel resolution  (Fressard et al.
, 2007). Morphological parameters, landslide type and state of activity were stored in a GIS database. This inventory is considered substantially complete  (Malamud et al. , 2004 ) and contains 52 mass movement phenomena: 12 solifluction processes, 13 deep-seated landslides and 27 shallow landslides (Figs. 4 and 5, Table  3 ). Only the shallow landslides were used to prepare the susceptibility map as they are the only type still triggering in the current environmental conditions. The solifluction processes are relatively stable or very slow-moving phenomena. The deep-seated landslides are dormant phenomena. Their study would also rather refer to reactivation hazard mapping  (Dewitte et al. , 2006).
Topographic data Three DEMs were obtained from different providers and methods. These DEMs allow extraction of the various topographic parameters that would be integrated into the models (e. g. slope angle, slope curvature, slope aspect, etc. ). 1. The first DEM is the BDAlti ® , provided for free by the French Geographic Institute (I. G. N. ) with a grid cell resolution of 25 m (Figs. 6  and 7 ). 2. The second DEM was extracted from the digitalised contour lines of the I. G. N. topographic maps at the 1 / 25 000 scale (Figs. 6 and 7) using the modified spline algorithm proposed by the ANUDEM software  (Hutchinson, 1996; Hutchinson and Gallant, 2000). Different interpolations were realised and compared. The best DEM was selected following the procedures of  Carrara et al. (1995)  and  Hutchinson and Gallant (2000).
Geologic materials data Two maps representing the geologic materials of the study area were acquired (Fig. 6 ). 1. The 1 / 50 000 scale geological maps (BRGM, French geological survey) were digitalised and classified according to the lithology. 2. For the shallow landslide susceptibility mapping, the surficial formations map was considered more relevant than the traditional ground geology map. This surficial formations map was created using extensive field survey and 108 boreholes and augurings of various depths in the study area (81 boreholes available from the BRGM database and 27 specific augurings). These boreholes were interpreted along representative cross sections to identify the rules of deposition and dynamics of the surficial formations. The map was then depicted manually over the study area following the geomorphological interpretation (Fig.
Landuse data Two different landuse maps were tested in this research and show large differences regarding their quality (Fig. 6 ). 1. The landuse data were obtained throughout the Corine Landcover database provided free of charge by the European Environment Agency (EEA). 2. Because the Corine landcover data is not very accurate  (Thiery et al, 2003) , interpretation and digitalising on the 2009 orthorectified images of the French Geographic Institute (I. G. N. ) were performed to obtain detailed landuse data. Six landuse classes were mapped based on visual interpretation and field checks: forest, grassland, cropland, orchards, fallow fields and urban areas.
Landslide data preparation The landslide susceptibility modelling necessitates using the landslide data as points feature in the GIS software  (Bonham-Carter, 1994; Kemp et al. , 2001). BDMvt was directly used to prepare the landslide susceptibility map. The original point format of the database permits direct introduction into the GIS software. For the other landslide inventories (API and field inventory), the original data is obtained in a polygon format. Several strategies to transform the polygon into points are proposed in the literature  (Poli and Sterlaccini, 2007; Thiery, 2007). This can be performed by selecting only one point for each landslide (generally the geometric centre) or selecting multiple points per landslide. In this case study, the number of landslide events is relatively low in the API and field inventory (15 to 27 shallow landslides according to the different databases).
Modelling strategy The statistical model is implemented in ArcGIS 10 ® through the ArcSDM extension  (Kemp et al. , 2001; Sawatzky et al. , 2009a Sawatzky et al. , , 2009b)). ArcSDM is a "black box" model that does not permit all logistic regression parameters to be set (e. g. sampling of the "no events" stepwise logistic regression), but has the advantage of being GIS integrated. This was preferred to specific statistical software in a transfer of knowledge perspective. The proposed methodology consists in four major steps (Fig. 8 ).
Selection of the explanatory variables and landslide cell sampling The explanatory variables of each model were selected according to their accuracy, availability and cost. All collected data were split into the five data sets detailed in Table  2. In order to preserve a set of landslide data for the validation step, only 75 % of the triggering zone cells were used for the model calibration. The other 25 % were used for the validation step  (Chung and Fabbri, 2003). This sampling was performed using a random selection of the landslide polygons.
Description of the model results The DS 1 model has an area under the ROC curve for the calibration data set (AUC-cal) of 0. 73 and 0. 66 for the validation data set (AUC-val). These results indicate a fair to poor classification accuracy  (Metz, 1978). The map is very difficult to classify as the susceptibility scores are clustered on a limited number of value ranges. The CAPP curve is then characterised by brutal changes and few thresholds (Fig. 9 ). Then the relative error is very high: 0. 88 for the calibration data (ξ -cal) and 0. 92 for the validation data (ξ -val) (Table 4). This means that only 12 and 8 % of the analysed landslides are identified in the high susceptibility class. Some flat (i. e. < 5 %) and valley bottom sectors are classified in the high and moderate susceptibility class, which is not in accordance with the field observations  (Fressard, 2013).