dataesr/scientific-paragraphs-categorization huggingface dataset:
15 One of the key points about the Lidar1200 is its capacity to detect and monitor water vapor structures. Such structures may be signatures of transport and dynamical processes that might be interesting to study in the tropical UT/LS. It is a challenge for the Lidar1200 to be able to retrieve profiles with a high resolution because such structures might be very thin, i. e. around a few hundred of meters, with a short lifetime. Before the MORGANE campaign, a first CFH sonde has been launched from the Maïdo Observatory on 18 November 20 2014. This dataset was not included in the previous validation test because both CFH and lidar profiles were not valid in the upper troposphere. Nevertheless, the comparison of the water vapor mixing ratio profiles on 18 November 2014 shows a good agreement in the troposphere (Fig.
12 , red line), while, on the contrary, it is better detected and reproduced with 10-min 30 session lidar data (Fig. 12 ). At 17:10 UTC, the lidar already detects a thin wet layer of 400 m and a maximum of 133 ppmv at 9. 05 km (Fig. 12 , in yellow). This layer is monitored 100 m further down 10 minutes later, then becomes thinner, gets wetter until shaping the peak observed by the CFH between 17:20 and 17:30 (Fig. 12 , in green). Magnitudes of the peak are very similar, 158 ppmv by the lidar versus 180 ppmv by the CFH. The CFH dataset does not give any information of what the structure becomes, alternatively the lidar dataset shows that this peak stays at the same altitude with the same thickness and 35 gets drier (Fig. 12 , in cyan), dries further, then disappears (Fig. 12 , blue curve). It is an example of the opportunity of fine scale monitoring of atmospheric structures that the Lidar1200 may provide for geophysical studies.
Summary The benefit of the hindsight on the 2-years dataset allowed us to define empirically a user's guide for the time integration of the data leading to a good compromise between the uncertainty and the vertical resolution (Table  3 ). The detection of fine-an instrumental comparison in the troposphere with, for example, a radiosounding, an integration time of 40 minutes is necessary. To reach the troposphere above 14 km asl, the time integration should cover at least 4 hours. Around 10-hours of integration are required to reach altitudes above the tropopause and a coarse vertical resolution is implied as a consequence. Depending on the scientific investigations, specific filter points and integration times are chosen (Table  3 ). The vertical resolution is coarser in the lower stratosphere but does not exceed 1.
3 km. The vertical resolution is the finest for profiles of 5 few minutes (with a number of points of the filter reaching 21) and is greater than 100 m (Fig. 1 ). 5. Overview of the database and preliminary characterization of seasonal variations of water vapor campaigns ensured that 40% of the measurement periods in the second year were greater or equal to 240 min. The ratio of 120-240 min sessions compared to >240 min sessions, respectively 44% and 36% to the whole dataset (Fig. 13 ), and the most frequent measurements sessions around 240 min shows that the time slot of routine operations (i. e. around 19:00 to 01:00 +1 LT) is well covered. The measurements are subject to instrumental problems and depend on the availability of the technical staff and, in addition, to meteorological conditions (clear sky nights only).
Conclusions The spatio-temporal variability of the water vapor is not well documented through direct observations. To improve its understanding, it is necessary to establish climatologies and trends at global and regional scales in the troposphere and in the stratosphere, to characterize the fine scale structures and to document the associated atmospheric processes. The difficulty in performing these investigations can be found in collecting homogeneous water vapor observations covering the entire 15 atmospheric column (i. e. up to the UT/LS) with an acceptable error. These data should be obtained regularly over decades with a vertical resolution sufficient to discern the major dynamical processes, and extend to the UT/LS. The establishment of such a dataset on several sites worldwide is an important priority for the international networks such as NDACC and GRUAN.
These networks require stable instrumental systems supplying data with a high accuracy, the characterization of the sources of uncertainty, and well documented data processing. Such efforts have been deployed here to produce water vapor profiles using 20 the Lidar1200. In operation at the Maïdo Observatory (Reunion Island) since 2013, this instrument delivered its first two years of data. The different steps of the process of evaluation of the performances of the Lidar1200 that have been realized as follows.  The establishment of a calibration methodology based on GNSS IWV for a long-term stable data processing has been made. At the Maïdo Observatory, the GNSS technique is successfully used on a routine basis. A methodology based on the 25 identification of quasi-stationary period of the hourly calibration coefficients has been applied and tested on the 2013-2015 dataset, allowing a robust calibration of the Lidar1200.
The average standard error on the calibration coefficient is 2. 7% illustrating the efficiency of this method. This is the first time, to our knowledge, that a Raman water vapor lidar is demonstrated to be successfully calibrated through GNSS IWV measurements on a routine basis.  The comparison between the CFH sondes launched during MORGANE and the lidar profiles led to the conclusion 30 that the performance of the Lidar1200 is nominal in the whole troposphere. The relative difference between both techniques is less than the water vapor variability in the troposphere during this period. In the UT and in the LS, the absolute difference is lower than 0. 8 ppmv. With respect to the CFH and lidar uncertainties, the instruments are in agreement up to the UT/LS and the CFH profiles validated the lidar data up to 22 km asl.
(2010). As a matter of fact, the water vapor Lidar1200 NDACC application based on the results shown here has recently been positively reviewed by the NDACC steering committee. The Lidar1200 is now able to provide international networks with various routine water vapor data profiles Level 2 products (Table  4 ), whose use and relevance depend on the user's scientific objectives. This regular 20 instrumental comparison effort is secured through the recurrent CFH soundings performed at the Maïdo Observatory in the GRUAN framework. Aside from its quality, the exceptional character of this dataset resides in its location in the Southern Hemisphere, subject to dynamical processes associated to tropical and subtropical areas. The data of the Lidar1200 on their own or with others measurements performed in Reunion Island bring or will bring new results on the characterization of the water vapor in the troposphere, the monitoring of the water vapor in the UT/LS, the study of the exchanges processes between 25 the stratosphere and the troposphere and the validation of satellites measurements.
Acknowledgments. The lidar data used in this publication were obtained as part of the Network for the Detection of Atmospheric Composition Change (NDACC) and level 2 product as daily vertical water vapor profiles will be publicly available through the NDACC portal (http://www. ndacc. org) and the French atmospheric data portal (http://www. pole-30 ether. fr/). Raw data and other products will be available on demand and available at the OPAR web portal. We       Atmos. Meas. Tech. Discuss. , doi:10. 5194/amt-2017 -32, 2017  Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 4 April 2017 c Author(s) 2017. CC-BY 3. 0 License. Table  2. Main characteristics of the water vapor profiles of Figure  9. The duration of the campaign corresponds to the number of nights between the beginning and the end of the campaign (including the nights with no lidar shot).
Raman lidar calibration has been designed with GNSS measurements. The very low values of the standard calibration uncertainty guarantee a high reliability of the coefficient calibration. The methodology 40 minimizes the calibration uncertainty and its use has been successfully tested on the 2-years dataset of the Lidar1200. The Atmos. Meas. Tech. Discuss. , doi:10. 5194/amt-2017-32, 2017 Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 4 April 2017 c Author(s) 2017. CC-BY 3. 0 License. methodology will be further validated thanks to instrumental comparisons presented in Sect. 3. 3. Validation of the Lidar1200 dataset: comparison with CFH sondes data during measurement campaigns 3. 1 Description of the measurement campaigns The Lidar1200 was involved in 5 campaigns (Fig.
Presentation of the 2-years water vapor database The 2-years validated water vapor dataset extends from November 2013 to October 2015 (note that the measurements made during the test phase, October 2012 -October 2013, are internally used for specific technical studies). The functioning 10 of the instrument became more stable on the period from November 2013 on. The number of nights of measurement has increased from 66 to 84 nights per year. The number of hours of operation increased almost 60% year-over-year from 185 h during November 2013 -October 2014 to 293 h during November 2014 -October 2015. There was an average of 1 to 2 measurement nights per week. This dataset includes the routine measurement periods but also Intensive Observing Periods (IOP) during campaigns described in Sect. 3.
is the first-time series of monthly water vapor profiles of the Lidar1200 over the 2-years database. In spite of the difficulty of the lack of data in January and August, the water vapor seasonal cycle is well documented with the peak of the wet (dry) season in DJF (JJA), which clearly offers hope to further investigating of inter-annual variability and trends with an extended coverage 30 of the database. Hoareau et al. (2012) had analyzed the profiles obtained between 2002 and 2005 by the instrument in operation in Saint-Denis, before its upgrading and transfer to the Maïdo Observatory, and found a water vapor seasonal cycle from the ground to 9 km characterized by a ratio (wet seasonal mixing ratio/dry season mixing ratio) between 0. 5 and 3. No seasonal cycle was found between 9 and 15 km.
The total integration time of 47 hours of the MORGANE campaign allowed measurements to reach 22 km asl with an absolute error less than 0. 8 ppmv, a relative error lower than 20% and a vertical resolution of 1. 3 km. This work of evaluation led to the creation of a user's guide: the definition of criteria of integration times 5 and filtering adjusted to the process studied in terms of altitude, thickness and temporal scale. Some preliminary results have been obtained from analyzing the new dataset at Maïdo in 2013-2015. This analysis confirms a clear seasonal cycle in the lower and middle troposphere. In addition, values in the TTL and in the lower stratosphere show some variability, but no seasonal cycle. Some differences with a climatology established by a previous instrument configuration in operation atSaint-Denis in 2002 -2005 (Hoareau et al.
5. 1 1 Presentation of the 2-years water vapor database The 2-years validated water vapor dataset extends from November 2013 to October 2015 (note that the measurements made during the test phase, October 2012 -October 2013, are internally used for specific technical studies). The functioning 10 of the instrument became more stable on the period from November 2013 on. The number of nights of measurement has increased from 66 to 84 nights per year. The number of hours of operation increased almost 60% year-over-year from 185 h during November 2013 -October 2014 to 293 h during November 2014 -October 2015. There was an average of 1 to 2 measurement nights per week. This dataset includes the routine measurement periods but also Intensive Observing Periods (IOP) during campaigns described in Sect.
0 a reality. Concepts such as CPPS (Cyber-Physical Production Systems), and IIoT (Industrial Internet of Things) have emerged and represent challenges that need to be tackled to answer the requirements of a continuously changing environment. With roots on CPS, CPPS are systems of collaborating and autonomous across all levels of production, from processes through machines up to production and logistics networks  [8]. They are capable of acessing, providing and using production data from real world at real-time. Aspects such as sovereignty, collaboration, optimization and responsiveness need to be especially addressed by CPPS. In addition to that, simulation, sensor networks, big data and security issues represent an important part to deal with challenge of CPPS implementation. The recent advances on smart sensors, wireless networks and embedded systems, together with the consistent decrease of technology costs have contributed for the rapid development of industrially oriented IoT.
Thus, IIoT appears as the main driver for the implementation of Industry 4. 0 and is directly related with the successful development of CPPS. Making use of technologies such as data acquisition and data integration, capabilities to capture and fuse information from various sensors/objects and cloudbased data centers, IIoT facilitates the adjustement of production parameters, opening new perspectives in easy reconfiguration of production lines, effective detection of failures, autonomous maintenance triggering and prompt reaction to unexpected changes in production. Although the possibilities for their application are immense there are still some technological issues that need to be addressed to ensure the full implementation of IIoT (e. g. semantic integration and analysis for which additional developments on standards and protocols are needed).
, 2011). Several weather forecast centres produce these global atmospheric reanalyses, including the European Centre for Medium-Range Weather Forecasts (ECMWF). The ECMWF Re-Analyses (ERA) include different products that have various date ranges, spatial resolutions and assimilated datasets (Table  1 ,  Uppala et al. , 2005; Dee et al. , 2011; Poli et al. , 2016). Although we can use the finer-scale reanalysis as initial conditions for a given period, dynamical downscaling of the global reanalyses is also necessary, since they are too coarse to force the regional wave and storm surge models. Furthermore, certain mesoscale processes related to the formation of strong surface winds, such as sting jets  (Hewson and Neu, 2015) , are absent even in ERA-Interim, one of the higher-resolution reanalyses available from the ECMWF.
Therefore, in order to better resolve mesoscale features associated with mid-latitude cyclone development and their interaction with locally complex coastal topography, dynamical downscaling can be applied to these reanalyses using a high-resolution numerical model (e. g. ,  Reistad et al. , 2011; Li et al. , 2016). In this study, we apply two different downscaling methods on ERA datasets. The first one is a simple dynamical downscaling approach beyond the reanalysis truncation, whereas the second is more complex. We evaluate to what extent the mesoscale features resolved by the first downscaling technique impact our surge and wave reconstruction over the French and Bulgarian coasts, followed by an examination of the added value of the second downscaling method against the first, simpler one.
Dynamical downscaling of reanalyses The general method of dynamical downscaling uses a coarseresolution dataset, like global atmospheric reanalysis data, as initial conditions for a numerical atmospheric model. Three ECMWF reanalyses are selected for this study: ERA-20C, ERA-40 and ERA-Interim (Table  1 ). They are all produced by older versions of the Integrated Forecasting System (IFS), the ECMWF's operational forecasting coupled model system. ERA-40 includes conventional observations (e. g. , surface stations, buoys, radiosondes), polar satellites and geostationary satellites. ERA-Interim datasets benefit from improvements in assimilation methods and a large expansion of available data, with observation quantity and quality increasing over time. In order to mitigate this inhomogeneity in the 20th century reanalysis, only observations of surface pressure and surface marine winds are assimilated in the ERA-20C dataset.
In order to provide the best possible atmospheric conditions for wave and storm surge hindcast, the following ERA datasets are downscaled for each event: ERA-20C for cases before 1957, ERA-40 for the 1957-1978 period, and ERA-Interim for storms occurring in 1979 and thereafter (Table 2). The designator "ERA-x" is used in this manuscript to describe a group of cases where more than one ERA reanalysis product is applied. Hereafter, this study focuses on the advantages of downscaling global atmospheric reanalysis for the development of wave and storm surge hindcasts. Over both the French and Bulgarian domains, numerical weather prediction (NWP) models require high horizontal and temporal resolution, especially for the storm surge model hindcast. For French events, the selected model, ARPEGE (Action de Recherche Petite Echelle Grande Echelle), is the operational global primitive-equation NWP system used at Météo-France and is based on the ARPEGE-IFS software developed in collaboration with ECMWF (Table  3 ;  Courtier et al.
, 1991). A stretched grid allows for a finer horizontal resolution over France (around 10 km). The version used here has 70 hybrid vertical levels from 17 m to 70 km height. The Bulgarian events are hindcast from ALADIN (Aire Limitée, Adaptation dynamique, Développement InterNational) model, which is a limited-area model based on the ARPEGE system  (Radnóti et al. , 1995). The model's core characteristics are the same as for ARPEGE. Two dynamical downscaling methods are examined here, hereafter referred to as D1 and D2, where D2 represents an Then we apply the Schmidt transformation, which is well defined in spectral space to project the fields into the ARPEGE stretched grid. The land-surface initialization is not straightforward, since there are many differences between the ERA reanalyses and the NWP models in terms of the applied land- surface parameterizations and physiographic databases.
For instance, the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) scheme of ERA-x uses four soil layers with fixed thicknesses, each layer having its own water content. The land-surface scheme of ARPEGE, however, only uses two layers in our experiments; the top layer has a fixed size of 1 cm, and the second layer overlaps the first one and has a variable depth. Furthermore, for a given grid point, soil types are often very different in the two land-surface schemes. Therefore, using the raw land-surface datasets from ERA-x as initial conditions would be troublesome, since the water saturation fraction depends on the soil type. Thus, we interpolate the surface fields so as to preserve as much as possible the ERA-x surface heat and momentum fluxes (Boisserie et al. , 2016). The procedure is based on the conservation of the soil wetness index (a relevant indicator for soil water availability) during the interpolation process, since soil water availability is supposed to regulate the partition of latent and sensible heat fluxes, which, in turn, influence energy and water exchanges between the atmosphere and the land surface.
Wave hindcasts For the wave reconstruction evaluation, simulated significant wave heights (SWHs) are compared against observations from satellite altimeter data and in situ observations. Several satellites operated over the French and Bulgarian coasts during the storms: (1992-2005), ERS2  (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) ,  ENVISAT (2002 ENVISAT ( -2012) )  and  Jason-1 (2002 Jason-1 ( -2013)). In addition, buoys and acoustic Doppler current profiler (ADCP) provide in situ SWH information. The limited scope of each of these observational datasets, together with the coarse resolution of altimeter measurements, preclude a comprehensive validation for all the selected cases. For an initial evaluation of our modelling approach, the results from the wave model driven by ERA-x and D1 data are compared to available altimeter data.
As satellite altimeters provide data along a track, these observations can be useful for mapping the spatial distribution of the SWH. For further examination, we present the 2012 Bulgarian storm as an example of a more detailed evaluation of the reconstruction against observations. The wave model Nice buoy observations ERA-interim forcing D1 forcing 1 9 /0 2 -0 0 :0 0 U T C 1 9 /0 2 -1 2 :0 0 U T C 2 0 /0 2 -0 0 :0 0 U T C 2 0 /0 2 -1 2 :0 0 U T C 2 1 /0 2 -0 0 :0 0 U T C 2 1 /0 2 -1 2 :0 0 U T C Figure 7. Time series of significant wave heights (SWHs) for the storm on February 2010 near Nice (43 • 24 0 N, 7 • 48 0 E) in the Mediterranean Sea. Purple and green stand for ERA-Interim and D1 forcing, respectively. The red line shows the time series of the Nice buoy observations. outputs using ERA-Interim or D1 initial conditions are first compared to the 214 along-track data points measured by the Jason-1 and ENVISAT satellite altimeters on 7 and 8 February 2012.
The overall improvement of the SWH reconstruction by using D1 is confirmed by the statistical analysis in Table  5. The temporal evolution of a storm can also be evaluated with in situ buoys. For example, for the 2010 Mediterranean storm, we compare the time series of SWHs from model and buoy data (43. 4 • N and 7. 8 • E) off the coast of Nice, France, at the peak of the storm (Fig. 7 ). The results show that the SWH induced by using D1 data more closely matches the buoy observations when compared to the ERA-Interim data forcing. Given our validation of the D2 approach discussed in Sect. 3. 1, the D2-driven SWH hindcasts of the  2004, 2007, 2008 and 2010  French Atlantic storms are also compared to satellite altimeter data. The statistical analysis (bias and NRMSE) reveals that the use of D2 winds leads   to better results than the use of D1 winds (Fig.
Storm surge hindcasts Storm surge hindcasts can be evaluated by tide gauge measurements. A network of 25 tide gauges along the French coasts is maintained to validate the surge model implemented at Météo-France. Furthermore, an additional 12 hydro-meteorological stations are located along the Bulgarian coasts for validation purposes. Depending on the storm extent and instrument condition, the number of available data points is different for each storm (Table  2 ). For a global hindcast evaluation of tide gauges, all available measurements with a peak in storm surge are selected. Weighted normalized observation error (WNOE) is calculated to highlight the overestimation and underestimation of the simulated maximum storm surges with respect to available measurements, and it is defined in Eq. (  1 ).
Conclusions ECMWF reanalyses data are widely used for many climatological studies. However, due to the coarse spatial resolution and the limited temporal resolution of reanalysis model output, there is significant bias for high wind speeds associated with extreme mid-latitude cyclones. To overcome this problem, dynamical downscaling techniques are implemented and applied to reproduce high-resolution historical atmospheric fields. ERA-20C, ERA-40 and ERA-Interim data are used to encompass the studied period of 1924-2012. Very short range forecasts using 10 km resolution and hydrostatic models initialized with ERA-x analyses provide the downscaled data, which are used in turn to force wave and storm surge numerical models. This approach was already tested for the North Sea coast for a long period using only ERA-  40 data.