dataesr/scientific-paragraphs-categorization huggingface dataset:
Notwithstanding exceptions, the division of labor outlined above still holds. For example, pre-determiner quite is found with positively connoted modalities (e. g. 'value_desirable', 'psych_stim_good', or 'physical_property_good') whereas preadjectival rather is found with their negative counterparts ('value_undesirable', 'psych_stim_bad', 'physical_property_bad'). Admittedly, not all the oppositions listed in Table  6  are found in the first two dimensions of the MCA plot in Figure  4  (for example, the 'luck_good' vs. 'luck_bad' is absent from this second dataset). It suggests that the behavior of the alternation differs slightly from the more general behavior of quite and rather. Coming back to Allerton's claim, we may concede that the choice of predeterminer position over the default pre-adjectival position is sensitive to context or register.
At this stage, however, we should sound a voice of caution against an absolute interpretation of typicality. For example, the results showed that the choice of an attributive adjective was less typical than the choice of a predicative adjective, based on dimensions 1 and 2 in Figure  3  vi. Yet, at a finer grained level of analysis, we could also interpret the choice of an attributive adjective as more typical than the choice of a predicative adjective in the intensification of the following meanings: negatively connoted energy, social/psychological properties, dimension/position, intensity, and superiority. Because typicality is relative and constructional profiles are context-dependent, quite constructions are not always more typical than rather constructions. In contrast to confirmatory statistical models, which provide accuracy scores of explanatory power, CA and MCA are exploratory: these methods merely tell us that the distributions in our dataset are statistically significant and not due to chance.
Therefore, we should not infer any causal link between the variables. Concerning CA specifically, it could be argued that the dataset is a sparse matrix (containing many cells with small or null values), as mentioned above. Although this could be a problem for confirmatory statistics, the fact that the conditions of validity of the χ 2 statistics are not met does not much affect exploratory methods. Indeed, CA does not so much prove the existence of a relationship between columns and rows as the capacity of a limited set of data to make that relationship visible  (Husson et al. 2011). Finally, one anonymous reviewer considers that quite and rather are genuine intensifiers only when these adverbs express high degree, as opposed to when they express a moderate degree, as in well, she's quite beautiful.
Conclusion I have combined several statistical methods to compare the constructional profiles of quite and rather, taking into account both semantic and syntactic factors. First, I asserted the need for better statistics in the collocation-based study of intensifiers. Regardless of the specific quantitative method that one adopts, it is particularly important to filter away co-occurring pairs that are unrealistically too frequent or too rare. It is also important to compute collocation strengths with statistics that do not violate the distributional assumptions that are specific to language data. Next, I augmented the collocation data with syntactic and semantic variables that license the use of quite and rather in context. Finally, I used two multifactorial methods to explore and visualize two datasets: one illustrating the differences between quite and rather, another one focusing on the pre-determiner vs.
Nucleation refers to the formation of ultra-fine particles from gaseous molecules. SCRAM uses the parametrisation of  Vehkamäki et al. (2002)  for the homogeneous binary nucleation of sulfate and water. It was adopted from the existing SIREAM code  (Debry et al. , 2007). It may be replaced by a better parametrisation in future versions, because it may lead to unrealistic results under some extreme conditions  (Zhang et al. , 2010). For coagulation, SCRAM uses the code of  Dergaoui et al. (2013)  to simulate the collisions of particles caused by Brownian motion. Condensation/evaporation describe the mass transfer process between the gas and particle phases. It is essential to include condensation/evaporation, because this process not only largely influences the size distribution of aerosols, but may also change the composition of particles significantly.
Simulation set-up Data were extracted from one grid cell of the 3-D simulation performed by  Couvidat et al. (2013)  over Greater Paris. This surface grid cell was chosen because black carbon (BC) emissions are high in that location, due to high traffic emissions. Figure  5  shows the BC emission map at 02:00 UT,  on 1 July 2009. The highest emission rate is located at the grid cell centre of longitude and latitude (2. 28 • E, 48. 88 • N), which was selected here to extract the SCRAM simulation input data for emissions, background gas and aerosol concentrations, and initial meteorological conditions (temperature and pressure). In the absence of specific information on individual particle composition, all initial aerosol concentrations extracted from the database were assumed to be 100 % mixed (i.
Acknowledgements. The authors gratefully acknowledge  H. Dergaoui  ( INRA ) for providing his original code of coagulation process and  E. Debry  ( INERIS ) for optimising the computation of coagulation distribution coefficients. We also would like to thank  F. Couvidat  ( INERIS ) for his support on the implementation of his H 2 O model into SCRAM as well as S. Deschamps (CEREA), who helped to improve the size redistribution algorithm. Edited by:  G. A. Folberth.
2014) , continually supplying them with the fuel for star formation, and increasing the chance of minor mergers. Examining the void galaxy population is impossible without deep, wide-field redshift surveys. Such surveys are vital not only to provide a statistically significant sample of void galaxies, but also to identify the void regions themselves via accurate positional and distance measurements. In this paper, we examine void galaxies in the Galaxy and Mass Assembly (GAMA) survey  (Driver et al. 2009) , utilising optical and infrared data. In addition to data at optical wavelengths provided by the GAMA and SDSS surveys, we furthermore use mid-IR data to search the GAMA void galaxy population for obscured star formation. Sensitive to the emission from dust-reprocessed star formation, data from the mid-IR is crucial in establishing if a population of truly passive void galaxies exists (intrinsic vs.
WISE photometry GAMA sources with WISE photometry are identified by crossmatching the WISE All-Sky Catalogue to the GAMA II observed sources catalogue using a 3 arcsec cone search radius. 86 per cent of GAMA sources in G09 were detected in the WISE All-Sky survey, with 82 per cent of G12 sources and 89 per cent of G15 sources detected. The data product and its reduction is described in full detail in  Cluver et al. (2014). Here, we match the optically selected void galaxy sample defined in Section 3 to the GAMA-WISE catalogue using the galaxies GAMA catalogue IDs. 527 out of 577 void galaxies with masses > 10 9 M ⊙ are matched to the GAMA-WISE photometry catalogue. For the comparison sample, 7233 out of 7718 matches are found. The majority (70 per cent) of non-matches in both the void and comparison samples have masses < 5 × 10 9 M ⊙ -10 per cent of all galaxies below this mass limit.
For both samples, 30 per cent have W3 magnitudes (and therefore [4. 6] -[12] colours) that are flagged as upper limits or null photometry. Where available, upper limits on their W3 photometry from the profile-fitted measurements from the WISE All-Sky Catalogue are provided. We include these upper limits in our examination of the WISE colour-colour diagram in Figures  8, 9 , and 10 as upper limit arrows. In this paper, we present WISE colours in Vega magnitudes, the native magnitude system of the WISE dataset. The Vega magnitudes can be transformed to the AB-System using the transformations of  Jarrett et al. (2011). However, care must be taken when applying these transformations due to uncertainty in the W3 and W4 filter response curves (see  Wright et al. 2010, and Brown et al. 2014a  for more information).
ACKNOWLEDGEMENTS SJP acknowledges the support of an  Australian Research Council   Super Science Postdoctoral Fellowship  grant  FS110200047 , and postdoctoral funding from the  University of Portsmouth. MB acknowledges financial support from the  Australian Research Council  ( FT100100280 ) and the  Monash Research Accelerator Program  (MRA). We thank the reviewer for their comments which have improved this paper. GAMA is a joint European- Australasian  project based around a spectroscopic campaign using the  Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the  Sloan Digital Sky Survey  and the  UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programs including GALEX MIS,  VST KiDS , VISTA VIKING, WISE, Herschel-ATLAS, GMRT and ASKAP providing UV to radio coverage.
A review of remote sensing technology for lower tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer-usually characterized by an inversion-and the lower troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs.
g. ,  Sherwood et al. , 2010; Wulfmeyer et al. , 2011]. CI results in the formation and organization of clouds and precipitation in dependence of the forcing mechanisms and the preconvective distribution of T and WV, the latter constrained by the Clausius-Clapeyron equation. All these processes are interwoven in a nonlinear way leading to fascinating but challenging studies of the Earth's weather and climate as a chaotic system. Observations of WV and T profiles-hereafter, for the sake of brevity called thermodynamic (TD) profiling-are at the heart of the understanding of these key processes. Furthermore, the knowledge of TD fields is crucial for the improvement of forecasts by means of data assimilation. Generally, it is essential to determine WV and T fields with high resolution simultaneously, particularly in the lower troposphere.
The current and expected positive contributions of these systems to the understanding of the Earth system are assessed. The sections in this work are organized to answer the following questions: What insight into key Earth system processes can be gathered from the current TD profiling capabilities? Several examples are presented in section 2 confirming the importance of high-resolution and high-quality TD observations. What is the status of WV and T observing systems and what are their current resolution, accuracy, and coverage? This question is answered in section 3. Section 4 deals with the question of what are the key requirements for the next generation TD profiling systems. In our discussion, we distinguish and analyze the needs from the perspective of monitoring, verification and calibration, data assimilation, and process studies.
g. , http://hdcp2. zmaw. de) [Wulfmeyer et al. , 2014b [Wulfmeyer et al. , , 2014c;; D. D. Turner et al. , submitted manuscript, 2014]. From the observational standpoint, data are needed for studying the energy balance closure and assumptions of Monin-Obukhov theory in complex terrain, entrainment fluxes, and intermittent turbulence at the Reviews of Geophysics 10. 1002/2014RG000476 top of the ABL as well as the formation of clouds and precipitation, simultaneously. This requires that the TD profiles in the lower troposphere resolve the gradient functions in the stable, neutral, and unstable surface layer as well as gradients and entrainment fluxes at the top of the ABL. Unfortunately, long-term data sets of this kind are entirely missing. Currently, the resolution, the coverage, and the accuracy of operational TD observations is neither sufficient for processes studies nor for model verification.
Highresolution TD data over land and the oceans would also be very useful for developing and verifying surface flux databases such as GEWEX SeaFlux and LandFlux, the Water Cycle Multi-mission Observation Strategy (WACMOS) Evapotranspiration (WACMOS-ET) of the European Space Agency (ESA), and the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) databases  [Andersson et al. , 2010]. New technologies for addressing these pressing observational needs are emerging. They are based mainly on active remote sensing technologies such as temperature rotational Raman lidar (TRRL)  [Di Girolamo et al. , 2004a; Radlach et al. , 2008; Newsom et al. , 2013] , WVRL  [Turner et al. , 2002; Wulfmeyer et al. , 2010; Turner et al. , 2014b] , and water vapor differential absorption lidar (WVDIAL)  [Wulfmeyer, 1999a [Wulfmeyer, , 1999b;; Behrendt et al.
Reviews of Geophysics 10. 1002/2014RG000476 remote sensing systems for measuring WV and T distributions, the success was limited. This was mainly due to the limited spatial coverage of high-performance TD remote sensing systems, even when combined during field campaigns, and due to constraints with respect to operation times. Obviously, it is necessary to continue and to intensify CI studies using networks of operational TD profilers with very high vertical resolution and accuracy in the lower troposphere over various types of land cover (urban areas and vegetation) and over the oceans combined with a hierarchy of model simulations down to the grey zone. 2. 5. Data Assimilation for Reanalyses, Advanced Process Studies, and Forecasting Data assimilation (DA) is the key methodology for accurate reanalyses, numerical weather prediction (NWP), seasonal forecasting, and parameter estimation.
10. 1002/2014RG000476 In the near IR, sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are carried on the NASA's Earth Observing System (EOS) Terra and Aqua satellites and the MEdium Resolution Imaging Spectrometer (MERIS), which is an instrument on ESA's Environmental Satellite (ENVISAT) platform. These instruments provide unique IWV fields over land surfaces, where IR and MW soundings close to the surface are not much exploited. The assimilation of IWV over land, as derived from MERIS showed neutral to positive impact  [Bauer, 2009]. Another source of signals containing WV and T information are the Global Navigation Satellite System (GNSS) Zenith Total Delays (ZTDs), Slant Total Delays (STDs), and radio occultation (RO) bending angle (refractivity) profiles. Particularly, RO data have been successfully applied to global DA  [Cucurull et al.
It would be necessary to increase considerably the density of the radiosonde network and the frequency of launches in order to get insight into the mesoscale variability of the regional water cycle and to increase the Reviews of Geophysics 10. 1002/2014RG000476 representativeness of the sounding of WV and T on this scale. This is considered not feasible due to resource limitations. 3. 2. 2. Passive Remote Sensing Data gaps can be reduced with networks of operational passive remote sensing of TD profiles using IR spectrometers or MWRs. Several ground-based FTIRs  [Feltz et al. , 2003; Knuteson et al. , 2004a Knuteson et al. , , 2004b] ]  and MWR networks  [Cadeddu et al. , 2013]  are already operating within the ARM. Also, in Europe networks of MWRs are operated [e. g. ,  Crewell et al. , 2004] , which are currently being combined in a global network of passive remote sensing systems called MWRnet  [Cimini et al.
Global Navigation Satellite System During recent years, the coverage of ground-based GNSS receivers has been considerably increased. Furthermore, algorithms for deriving ZTD and STD have been developed and improved. Networks of GNSS receivers with harmonized retrieval algorithms are operated in Europe such as E-GVAP and in the U. S. Details of GNSS methodology are presented in section 5. 2. 2. When GNSS signals are received by a ground-based station, it is, in general, not possible to derive a refractivity profile or separate TD profiles. However, the estimated ZTD, which represents the integrated refractivity above the station, is closely linked to IWV. For this reason ZTD data are becoming increasingly useful for weather and climate research  [Wickert and Gendt, 2006]. The tropospheric WV estimates retrieved from GNSS postprocessed data are used not only for the analysis of meteorologically significant events  [Bock et al.
However, lidar systems are more complex with respect to system setup, maintenance, and routine operation, which concern all components such as the laser transmitter, the receiver, and the data acquisition system. Lidar systems perform measurements with high SNR in the cloud-free atmosphere and through optically thin clouds. In the presence of optically thick clouds, range-resolved measurements are possible within clouds for an optical thickness typically not exceeding 2; otherwise, measurements are possible up to the cloud base. Measurements during rain are hardly possible due to the large extinction of the transmitted radiation. Therefore, similar operation times can be achieved as for IR spectrometers. The high resolution and the accuracy of ground-based WV lidar measurements have been demonstrated with WVRL [e.
Figures  4  and 5  demonstrate that huge data gaps remain over all regions of the Earth with respect to TD profiling in the lower troposphere. A variety of additional WVRL research systems have been developed and are operated by different international scientific institutions. Some of these systems do not have an exclusive dedication to WV, as, in fact, they have multipurpose atmospheric applications; however, they provide high performance in terms of WV measurement Reviews of Geophysics 10. 1002/2014RG000476 capability. These include the Scanning Raman lidar (SRL) of the Goddard Space Flight Center (GSFC) in Greenbelt, USA  [Whiteman, 2003a [Whiteman, , 2003b]] ; the Backscatter, Extinction, lidar Ratio, Temperature, and Humidity profiling Apparatus (BERTHA) of the Leibniz Institute for Tropospheric Research (IfT) in Leipzig, Germany  [Althausen et al.
Monitoring 4. 1. 1. Importance and Applications Monitoring is the continuous observation of a variable of the Earth system. The requirements are strongly dependent on the user demands and the application but there is usually no limit of operation time; the data set should be collected as long as possible. The goal of monitoring is the derivation of climatologies of specific variables with suitable resolution and accuracy with respect to the application. This is getting more and more attention in the era of anthropogenic climate change and is important in all climate regions. Monitoring does not necessarily imply a high temporal and/or spatial resolution. In many cases, an observation twice a day or even once a day may be sufficient. A key aspect is the reproducibility of the measurement and the maintenance of the performance characteristics of the remote sensing system.
The analysis of these climatologies is generally model independent and may lead to improved process understanding of the Earth's water and energy cycles. In contrast, verification is the process of assessing the quality of a model simulation. The JWGFVR strongly recommends the gridding of observational data to the resolution of the models in order to reduce sampling and representativeness errors. As pointed out above, this requires the setup of denser networks of TD profilers in order to reduce these errors and to provide gridded data sets on the meso-gamma scale for the new generation of weather and climate models. The verification of observing systems can be achieved through the comparisons of their calibration and retrieval methodologies. If the WMO acknowledges that an observing system has a particularly high accuracy, it is recommended as a WV or T reference standard.
Reviews of Geophysics 10. 1002/2014RG000476 The systematic error (bias) in the WV profiles should be as low as requested for monitoring in order to avoid systematic errors in the derivation of skill scores and for instrument intercomparisons and calibration. A systematic error of < 5% is considered very beneficial, a bias down to 2% would be even more interesting because then the WV data would become a reference standard. Again, as for monitoring, this low systematic error is expected to be a very desirable feature for weather forecast centers and space agencies, as bias corrections can be avoided. At the specified vertical and temporal resolution, the noise error should be < 5% because this should still be sufficient to identify atmospheric processes such as the development of convergence lines, CI, and land surface heterogeneity.
Data Assimilation 4. 3. 1. Importance and Methodology In order to specify the state of the Earth system as accurately as possible, the information content of the model system and of the observations must be used and merged in a consistent manner. Consequently, two sources of information must be considered: (a) The a priori state of the system and its PDF with its temporal development according to the model operator as well as (b) the observations with their PDF and their error covariance matrices. The merging of their information content is called DA and leads to the analysis of the initial state of the system. The information content of observations is maintained in the system by the operation of an analysis cycle or a rapid update cycle (RUC). This cycle consists of a forecast step used to assess the next expected a priori state of the system.
Reviews of Geophysics 10. 1002/2014RG000476 DA is not only essential for weather forecasting but it is a key methodology in Earth system sciences. DA can also be applied for the initialization of seasonal prediction and climate models depending on the temporal range of the simulations. Particularly, it is expected that DA of land surface and oceanic data will lead to advanced probabilistic predictive skill on decadal time scales. Another essential application is the consistent and continuous DA of observing systems in a frozen model system from the past to today. This reanalysissuch as ERA-40 and ERA-Interim-can be considered the best estimate of water and energy cycles and other climate statistics (see sections 2. 2 and 2. 5). Further applications include parameter estimations in land surface-vegetation models, OSSEs, Observing System Experiments (OSEs), uncertainty studies of model systems, and predictability studies.
However, it should also be mentioned that similar results can be achieved with an ensemble of variational analyses (En3-DVAR or En4-DVAR). In any case, an update of B in each analysis step is considered very important due to the chaotic nature of the Earth's weather and climate system. Both 3-DVAR and EnKF are intermittent DA techniques where the observations are assimilated at well-defined analysis times. Typically, a RUC of 1-3 h is applied on the mesoscale but in connection with radar DA even higher update rates have been proposed up to 5 min  [Sun, 2005; Stensrud et al. , 2009]. In contrast, 4-DVAR, which continuously assimilates observations during a DA window, is a smoother. This is an advantage for assimilating data with high temporal resolution such as lidar and radar. Furthermore, model spin up or inconsistencies at initial time are reduced, which is important for improving forecast skills from nowcasting to the short range  [Kawabata et al.