dataesr/scientific-paragraphs-categorization huggingface dataset:
The materials used are: • Data of WB2, LMBM3, WB5 systems available online  [9] , • Intel(R) Xeon(TM) MP CPU 2. 70 GHz 7. 00 Go RAM, • MATLAB version 7. 7 2008b, • MATLAB-package MATPOWER version 3. 2  [37] , • SeDuMi 1. 02  [33]  with tolerance parameter pars. eps set to 10 -12 for all computations, • MATLAB-based toolbox "YALMIP"  [24]  to compute Optimization 4 (Dual OPF) in  [22]  that yields the solution to the rank relaxation, • MATLAB-package GloptiPoly version 3. 6. 1  [14]  to compute solutions to a hierarchy of SDP relaxations (  26 )-(  29 ). The same precision is used as in the solutions of the test archives  [9]. In other words, results are precise up to 10 -2 p. u. for voltage phase, 10 -2 degree for angles, 10 -2 MW for active power, 10 -2 MVA for reactive power, and cent per hour for costs.
Datasets of the observed weather and climate on a regular grid are an important requisite for environmental and climate change research. In this paper we evaluate several European-scale regional reanalyses, developed in the EU project EURO4M, and compare them to existing datasets popularly used in applications today. The evaluation focuses on precipitation in the region of the European Alps, which, due to the marked topographic imprints, poses a challenging test bed. Utilizing a set of statistical indicators, we examine how the datasets represent the spatial pattern, annual cycle, frequency distribution and interannual variations. The evaluated datasets encompass new model-based regional reanalyses (UKMO and HIRLAM) and downscaling datasets (MESAN and MESCAN), one global reanalysis (ERA-Interim), and three station-based interpolation datasets (E-Obs, CRU, GPCC).
The reference (APGD) is a gridded interpolation derived from very dense rain-gauge observations. The evaluation provides insight into the relative strengths and limitations of the various datasets and construction concepts. The new model-based regional reanalyses show richer spatial variations in the climatology of daily precipitation compared to the driving global reanalysis (ERA-Interim), and they correct for several unrealistic spatial features seen in global and continental interpolation datasets. But they also show biases, shifts in regional anomalies and inaccuracies in mountain-valley contrasts. Common to both regional reanalyses are overestimates of mean precipitation and wet day frequency, and an underestimate of the frequency of heavy precipitation. The 4DVar UKMO reanalysis shows a better space-time coherency with APGD compared to the 3DVar HIRLAM reanalysis.
The accuracy of datasets that explicitly use rain-gauge observations (interpolation and downscaling) is found to be very heterogeneous, depending on the density of available station time series. In regions of high station density, downscaling procedures effectively correct for biases in the underlying reanalysis and reduce RMSE and SEEPS errors by a factor of 2 to 5. In areas where E-Obs and the downscaling datasets have similar input of station data the skill of the two methodologies is comparable. Interannual variations of monthly mean precipitation are highly correlated (> 0. 9) with those of the reference for all European-scale datasets and even for meso-beta scale subregions. Our evaluations illustrate that the new regional reanalyses provide a valuable data resource also in a region with complex topography, but many applications will have to involve bias correction and downscaling procedures based on direct observations, ideally at high spatial resolution.
Introduction Datasets that provide spatially comprehensive information of the observed weather and climate at the earth's surface are a key input for modelling natural processes (e. g. Monestiez et al. , 2001; Zappa, 2008; Machguth et al. , 2009) , for planning tasks concerned with natural resources and natural hazards (e. g. Greminger, 2003; Tveito et al. , 2005; Weingartner et al. , 2007; Holzkämper et al. , 2012) , for the monitoring of climate variations of the past (e. g. Brunetti et al. , 2006; Auer et al. , 2007; Schrier et al. , 2013)  and for the study of potential impacts of future climate change (e. g. Blenk-insop et al. , 2008; Ott et al. , 2013; Köplin et al. , 2014; Bosshard et al. , 2014). There is an increasing resource of high-resolution datasets suitable for such applications.
Many of these rely on the spatial interpolation of observations at weather stations onto a regular grid, eventually supplemented by satellite and radar data (e. g. Paulat et al. , 2008; Erdin et al. , 2012; Frei et al. , submitted). Due to restrictions in national data policies, in Europe, many of these interpolation datasets are constrained onto national territories (e. g. Perry and Hollis, 2005; Dolinar, 2006; Perčec Tadić, 2010; Brunetti et al. , 2012) , or larger geographic entities from which data was collected for specific projects (e. g. Rubel and Hantel, 2001; Rauthe et al. , 2013; Isotta et al. , 2014). There exist datasets based on station data only that extend over the entire European continent or even the globe  (Haylock 16  F. A. Isotta et al. :  Evaluation of European regional reanalyses and downscalings  Meteorol.
Z. , 24, 2015 Meteorol. Z. , 24, et al. , 2008;; Becker et al. , 2013; Harris et al. , 2014). Large improvements have been made in the volume of data accessible for these datasets (e. g. Klok and Klein Tank, 2008; Schneider et al. , 2014) , but the density of station series is partly still limited, and measuring conventions are heterogeneous. This compromises the effective resolution and regional consistency in these datasets, especially in areas of complex physiography (e. g. Hijmans et al. , 2005; Hofstra et al. , 2009). Also the highly variable density of stations prevents from using versatile interpolation methods, which exploit the observational data extensively where it is abundant. Global reanalysis datasets such as those from the NOAA/NCEP  (Saha et al. , 2010) , the ECMWF  (Dee et al.
, 2012)  or by combination with high-resolution surface observations through objective analysis (e. g. Jansson et al. , 2007; Vidal et al. , 2010). More recently, techniques of regional model-based data assimilation are being pursued at climate time scales (e. g. Mesinger et al. , 2006; Hahmann et al. , 2010; Luhamaa et al. , 2010). There is a high potential of these systems to represent mesoscale patterns and extreme events more accurately than the global reanalyses or downscaling procedures mentioned above. This is because advanced data assimilation techniques allow for the assimilation of observation types not integrated into global reanalyses, and because the much finer model resolutions allow better exploitation of observations where they are abundant. Within the EU project EURO4M (European Reanalysis and Observations for Monitoring) the needs in highresolution datasets for climate monitoring and environmental research were addressed specifically for the domain of Europe.
For this purpose, the project has developed and brought together continental scale datasets from all the mentioned categories. In particular, existing climate datasets from interpolation of station data have been extended and improved, and new regional reanalyses were developed using regional model-based data assimilation. The present study is part of EURO4M and aims at comparing and evaluating existing and new datasets from the above categories with regard to the representation of the daily precipitation climate in the re-gion of the European Alps. Datasets of particular interest here are two new model-based mesoscale reanalyses based on, respectively, a 3DVar data assimilation with the HIRLAM model  (Undén et al. , 2002)  and a 4DVar data assimilation with the UK Met Office North Atlantic and European model  (Bush et al.
, 2006). Furthermore, two datasets will be examined that were derived by further downscaling the HIRLAM reanalysis with an objective analysis of high-resolution surface observations  (MESAN and MESCAN, Häggmark et al. , 2000). For comparison, our evaluation also includes the global reanalysis that was used to drive the regional reanalyses (ERA-Interim,  Dee et al. , 2011)  as well as two global and one continental interpolation dataset (GPCC,  Becker et al. , 2013; CRU, Harris et al. , 2014; E-Obs, Haylock et al. , 2008). The general purpose of this evaluation is to provide insight into the relative strengths and limitations of the various datasets in a region of complex topography. Specific consideration will be given to the meso-beta scale patterns and the statistics of daily precipitation, including the frequency of intense precipitation.
These characteristics are essential for several applications of the new regional reanalyses and improvement over existing coarser resolution datasets may be expected particularly in these elements (e. g. Bukovsky and Karoly, 2007). The global datasets (ERA-Interim, GPCC, CRU) are included here primarily to illustrate the added value of the new reanalyses. Moreover, comparison of the downscaling procedures MESAN and MESCAN against the driving regional reanalysis (HIRLAM) will point to the added value of integrating additional surface observations in a posteriori downscaling. Interestingly, our study domain encompasses regions with many and regions with little additional observations used for downscaling, so that the benefit can be examined in its dependence on data density. Finally, our evaluation also examines the reproduction of intra-seasonal to interannual variations in order to assess the suitability of the datasets for climate monitoring.
The various datasets span a range of spatial resolutions, from 50-80 km for the global datasets down to 5 km for the downscaling datasets. The present evaluation is conducted at three distinct resolutions (5, 22 and 55 kilometers), in order to ensure scale consistency and to portray the performance of the datasets over a range of spatial scales. Our study region extends over a 1200 × 700 km 2 domain  (2-17. 5 °E, 43-49 °N)  and comprises the entire mountain range of the European Alps, adjacent flatland and several smaller-scale hill ranges (see Figure  1 ). This is a challenging test area for regional reanalyses. Its precipitation climate is influenced by mid-latitude disturbances, local convection, the moisture source of the Mediterranean and several orographic dynamical phenomena  (Schär et al.
, 1997). As a result, the region exhibits strong topographic imprints on precipitation and pronounced regional variations in the seasonal cycle and the distribution of heavy precipitation (see e. g. Isotta et al. , 2014). Even though the present evaluation covers only a small portion of the area, for which the evaluated datasets have been constructed (Europe), the results may give valuable insights into their potential and limitations, considering that complex physiography and shortrange climate variations are met in many other parts of the continent. Compared to other high-mountain areas, the Alpine region is one with particularly dense in-situ rain-gauge observations. This data resource is distributed between many different national and regional services. While only a limited portion of it was available for the construction of the evaluated datasets, this resource is extensively exploited in the Alpine Precipitation Grid Dataset (APGD,  Isotta et al.
, 2014) , the reference dataset used in this evaluation. APGD is a spatial interpolation derived from all available high-resolution networks of the Alpine countries, incorporates 5500 quality-controlled rain-gauge measurements on average per day and is constructed on a grid with 5 km spacing. Both the Alpine region and earlier versions of APGD have been considered in several previous evaluation studies with observation datasets and numerical models. These show, for example, that many of the large-scale interpolation datasets lack mesoscale variations even at scales nominally resolved by the grid spacing  (Rubel and Rudolf, 2001; Hofstra et al. , 2009). Also, systematic underestimates in the frequency of intense precipitation were observed as a result of the smoothing inherent to interpolation with coarse station data  (Hofstra et al.
It will be interesting to learn how these challenges affect the accuracy of current large-scale observation datasets in a direct comparison of both categories, interpolation datasets and regional reanalyses. The outline of the paper is as follows. The evaluated datasets and the reference dataset are introduced in sections 2 and 3 respectively. Section 4 details the evaluation procedure. Section 5 describes and discusses the results in subsections devoted to the different aspects of the evaluation. Finally, the results are summarized in Section 6.
Evaluated datasets The present evaluation examines eight different datasets from three different categories. Three datasets (GPCC, CRU and E-Obs) were generated by statistical interpolation of in-situ rain-gauge observations and will hereafter be denoted simply as interpolation datasets. The station data available over the Alpine region varies   Taillefer, 2002  considerably between them, with CRU building on coarser coverage compared to GPCC and E-Obs. The second category encompasses three datasets produced by model-based data assimilation systems, one of which covers the globe (ERA-Interim) and two that are regional and cover Europe (HIRLAM and UKMO). These will be denoted as global and regional reanalyses in the following. The regional reanalyses are both driven by boundary conditions from ERA-Interim.
As a third group we consider two datasets (MESAN and MES-CAN) that were produced by downscaling a regional reanalysis and objective assimilation of additional raingauge observations. The HIRLAM reanalysis served as the common basis for these downscaling datasets. MESAN and MESCAN are methodologically very similar and use station data from the ECA&D archive  (Klok and Klein Tank, 2008) , which also serves as input for E-Obs. Over France and some other countries (outside the Alps), MESAN and MESCAN also use additional high-resolution rain-gauge data. All the evaluated datasets extend over the European continent and North Africa or over the entire globe. Table  1  gives an overview of the main characteristics of the datasets, including spatial and temporal resolutions. Additional detail and references on each of the datasets is provided in the following subsections.
E-Obs The E-Obs dataset is a daily European land-only gridded dataset  (Haylock et al. , 2008)  that was established in the ENSEMBLES project and was extended in EURO4M. Here we use version 7 on the 0. 22 °rotated pole grid, i. e. with a grid spacing of about 25 km over the Alps. E-Obs covers several surface climate variables, extends over the period 1950-2012 and is widely used for climate model evaluation and climate monitoring at European scale (e. g. Lenderink, 2010; Nikulin et al. , 2011; Schrier et al. , 2013). E-Obs builds on qualitycontrolled rain-gauge data of an updated and extended version of the European Climate Assessment Dataset ECA&D  (Klok and Klein Tank, 2008). The precipitation data utilized is not corrected for measurement bias  (Neff, 1977). The interpolation was done in a first step for the monthly precipitation totals with a threedimensional thin-plate splines algorithm  (Hutchinson, 1995) , then for the daily proportions by a combination of indicator and ordinary kriging  (Haylock et al.
GPCC The Global Precipitation Climatology Centre (GPCC, http://gpcc. dwd. de), operated by the German Meteorological Service (DWD), has established and regularly updates a suite of gridded precipitation datasets at monthly time resolution over global land areas  (Becker et al. , 2013). In this study, we use the Full Data Reanalysis V6, provided on a regular longitude-latitude grid with a spacing of 0. 5 °degrees, i. e. approximately 55 km. The dataset covers the full study period  considered here. Data sources for the GPCC dataset are various GTS and CLIMATE archives and additional data supplied bilaterally by national meteorological services  (Schneider et al. , 2014). A semiautomatic and source-specific quality control system is applied to the data before analysis  (Schneider et al. , 2014).
CRU This monthly gridded global land-only dataset constructed by the Climatic Research Unit (CRU, http:// www. cru. uea. ac. uk) at the University of East Anglia aims at providing information on long-term climate variations at continental space scales since 1901  (Mitchell and Jones, 2005; Harris et al. , 2014). In this study we use version CRU TS 3. 20 for the period 1971-2008. The dataset is provided on a 0. 5 °longitude-latitude grid (approximately 55 km grid spacing). The spatial interpolation builds on a climatology  derived by three-dimensional thin-plate splines  (Hutchinson, 1995; New et al. , 1999)  and a triangular linear interpolation of monthly anomalies  (Harris et al. , 2014). The number of stations integrated in CRU is smaller than in GPCC (see also  Schneider et al. , 2014).
ERA-Interim (ERA-INT) The global reanalysis ERA-Interim of the European Centre for Medium-Range Weather Forecasts (ECMWF, www. ecmwf. int) is documented in  Dee et al. (2011). It has a resolution of approximately 75 km (0. 7 °) and, hence, is the nominally coarsest of all datasets considered. ERA-Interim is based on a 12-hourly 4DVAR cycle. The dataset extends over the full period considered in this study. Clearly, we are not expecting ERA-Interim to reproduce meso-beta scale climate patterns in the Alpine region. It is still interesting to consider it here for illustrating the added value of higher resolution datasets. Evaluations of ERA-Interim (and its predecessor ERA-40) for precipitation and the hydrological cycle at larger space scales have been conducted, for example, by  Zolina et al.
MESCAN MESCAN is the name given to the developments of the Météo-France surface analysis system CANARI  (Taillefer, 2002)  during the EURO4M project. Like MESAN, MESCAN is based on the OI algorithm and both systems use the same observational datasets and downscaled HIRLAM reanalysis, but with different features of the host system, such as a different model orography, the usage of the observational data base (Saarinen, 2004), the selection of the observations and the spatial quality control. Moreover it has a modified statistical model with, among others, a different background error correlation function (structural function, implemented as in the Canadian precipitation analysis Project,  Mahfouf et al. , 2007)  and shorter correlation length compared to MESAN. For the present study, MESCAN data was available for one year (2008) only.
Reference dataset The dataset used as reference in this evaluation is itself an interpolation dataset, derived by statistical interpolation of rain-gauge observations. In comparison to the evaluated datasets, the Alpine Precipitation Grid Dataset APGD  (Isotta et al. , 2014)  is based on much denser insitu data. It integrates data from all available national and regional high-resolution networks of the Alpine region (i. e. the domain depicted in Fig. 1 ), with more than 8500 time series in total and around 5500 observations on average per day over the period 1971-2008. The rain-gauge density varies between 0. 4 and 1. 4 stations per 100 km 2 , with denser coverage over Austria, Germany, Slovenia and Switzerland and coarser coverage over Croatia and Italy (see Fig. 3  of  Isotta et al. , 2014).
A large proportion of the stations dispose of long records. APGD is a gridded analysis of Alpine daily precipitation on a 5-km grid (see Table  1 ). The spatial analysis was produced with a combination of PRISM  (Daly et al. , 1994 and 2002)  for the long-term monthly mean (see also  Schwarb et al. , 2001)  and a modified version of SYMAP  (Shepard, 1984 ; see also  Frei and Schär, 1998)  for the relative daily deviations from climatology. All observations underwent extensive quality testing, manual and automatic, checking for coding problems and spatial consistency. APGD is an updated, improved and higher resolution version of a previous Alpine precipitation dataset  (Frei and Schär, 1998)  that was used in various earlier evaluation studies (e. g. Frei et al. , 2003; Hofstra et al. , 2008; Suklitsch et al.
, 2008). The APGD is used here as reference because of the amount of observational information integrated. But, like the other datasets, it is affected by errors and uncertainties (for a detailed discussion see Section 4 of  Isotta et al. , 2014). In particular, the following error sources shall be born in mind when interpreting the results of this evaluation: The rain-gauge data used here are subject to systematic errors due to the measurement undercatch  (Neff, 1977; Yang et al. , 1999). This is not corrected for and it likely results in an underestimate of true precipitation by APGD. Sevruk (1985)  and  Richter (1995)  estimate the systematic measurement error in the Alps to range from about 7 % (5 %) over the flatland regions in winter (summer) to 30 % (10 %) above 1500 m MSL in winter (summer).
Note that all other interpolation and downscaling datasets considered in this study, except GPCC, are affected by measurement biases too. The interpolation method used in APGD is designed to estimate an area average precipitation value in the neighbourhood of the grid points. In general this estimation also involves measurements outside the pixels of the underlying grid and, hence, the spatial precipitation fields of APGD represent area averages of a larger domain than the nominal 5-km grid pixels. The fine grid spacing is of advantage for climatological mean fields where topographic effects on the distribution are eminent and can be estimated using physiographic predictors  (Schwarb, 2000). But for daily precipitation fields the effective resolution of APGD is coarser than the grid spacing, in the order of 10-20 km (typical inter-station distance).
It can be even larger in areas with coarser networks such as in Northern Italy  (Isotta et al. , 2014). As a result, there is a tendency for APGD to overestimate low quantiles of daily precipitation (and the frequency of wet days), and to underestimate high quantiles (and the frequency of intense precipitation), compared to an analysis with a true 5-km resolution. The magnitude of bias from this resolution mismatch is difficult to estimate, but an upper limit can be estimated when grid-point estimates are compared against independent point measurements. Isotta et al. (2014)  conducted such a cross-validation and found that biases at high quantiles are about 8 % in data dense regions and about 20 % in a region with very coarse station coverage. At low quantiles the bias is small (a few percent) and only evident in summer.
Parameters, scores and domains The main focus of the present evaluation is on statistics of daily precipitation and their meso-beta scale patterns. To this end we have calculated and compared a large number of the precipitation indices proposed by the ETCCDI  (Klein Tank et al. , 2009). For simplicity we present and discuss a selection only. The choice is made to convey the most characteristic features of and differences between the datasets. The statistics to be presented include mean annual precipitation, the frequency of wet days and the 95 % quantile of daily precipitation. A threshold of 1 mm was chosen to separate dry and wet days. Furthermore, a large number of empirical quantiles were estimated to describe the frequency distribution of daily precipitation. For this purpose the daily grid-point values within pre-defined sub-domains were pooled.