dataesr/scientific-paragraphs-categorization huggingface dataset:
7 ). Unclassified days were those with measurement coverage less than 12 h or with intermittent sampling periods. The observed frequency of NPF events is very low in comparison to other forest environments worldwide (e. g. ,  Boy et al. , 2008; Han et al. , 2013; Held et al. , 2004; Mäkelä et al. , 2000). In a boreal forest site in Finland, which hold one of the longest time series of particle size distribution observations, NPF event days reach 24 %, being most frequent during spring and autumn  (Dal Maso et al. , 2005). In this dataset, the percentage of days with events classified as undefined (28 %) is similar to what has been reported in Finland (37 %, according to Dal  Maso et al. , 2005). Most NPF events started at daytime (Fig. S13 ) and during the wet season (19 out of 24 events).
This is expected, as worldwide NPF event days are typically characterized by relatively low condensation sinks (e. g. ,  Hyvönen et al. , 2005). The median particle surface area, which is approximately proportional to the condensation sink of non-volatile vapors, was 18 µm 2 cm -3 (on average) at the onset of NPF events. This can be compared with the total dataset median values of 24 and 102 µm 2 cm -3 during the respective wet and dry seasons (Table  1 ). Growth rates for the NPF events were calculated by fitting a first-order polynomial to the geometric mean diameter of the nucleation and Aitken modes, following growth between 10 and 50 nm. The median growth rate was 5. 5 nm h -1 , ranging from 1 to 17 nm h -1. For comparison, particle growth rates at a boreal forest site in Finland during the summer typically range from 8 to 12 nm -1  (Kulmala et al.
3 ), are associated with surface emissions and are likely of biogenic origin. Based on the available dataset, there is not enough evidence to confirm whether these particles are primary or secondary; therefore, the mechanism behind the undefined events remains unclear. NPF and growth events may be connected with the occurrence of convective downdrafts, since precipitation and negative θ e were frequent 2 h before such events (Fig. S14 ). Moreover, cluster 1 (Fig. 5a ), associated with the transport of Aitken mode particles from above the BL (Sect. 3. 3), was characteristic of NPF events (Fig. S10 ). Conversely, cluster 2 was uncommon during non-event days, and very frequent during both NPF and undefined events (Fig. S10 ), which meant that particle size distributions within this cluster could be used as tracers for both events.
Conclusions This work reports a multi-year dataset of submicrometer particle size spectra in Amazonia. In the dry season, the accumulation mode dominates the particle number size spectra, due to the continuous input of aged biomass burning particles from regional emissions. During the wet season, the Aitken and accumulation modes, as well as the Hoppel minimum, are present, and the relative importance of the modes changes during the day. Cluster analysis, applied to normalized particle number size distributions, was useful in distinguishing particle size spectra shape typical of the influence of fresh biomass burning aerosols, vertical transport of particles associated with convective downdrafts and nucleation events. One of the major scientific questions supporting the continuous observation of particle number size distributions in Amazonia is "Was the lack of surface level NPF events observed during short-term experiments representative over all seasons?".
This dataset confirms that NPF and subsequent particle growth is rare in Amazonia, such that 24 events were detected in 749 days of observations (3 %). This frequency is very low in comparison to other forest environments worldwide (e. g. ,  Boy et al. , 2008; Han et al. , 2013; Held et al. , 2004; Mäkelä et al. , 2000). The majority of the few NPF events observed occurred after precipitation and were concurrent with decreasing surface θ e , which links NPF events to the occurrence of convective downdrafts. Conversely, bursts of Aitken and/or nucleation mode particles without subsequent growth (undefined events) were relatively frequent (28 % of the measurement days). Undefined events did not show a clear diurnal pattern or a relationship with downdraft occurrence, which means that the observed particle may be a combination of different processes.
Since the measurement site is in a forest reserve where local pollution sources are not significant, this source is likely biogenic and either from primary emissions or secondary aerosol formation. Primary biological emissions could be trigged by fast RH variations in the late afternoon, through the rupture and wet discharge of fungal spores  (China et al. , 2016; Elbert et al. , 2007). The mechanisms behind nighttime surface particle emissions require further investigation. Data availability. The datasets used in this publication can be downloaded from the file repository created and maintained by the Laboratory of Atmospheric Physics at the University of São Paulo (LFA/USP): http://lfa. if. usp. br/ftp/public/LFA_Processed_ Data/ (LFA/USP, 2018). Supplement. The supplement related to this article is available online at: https://doi.
Acknowledgements. This work was supported by  Fundação de Amparo à Pesquisa do Estado de São Paulo  ( FAPESP grant  nos. 08/58100-2  and  13/05014-0 ), the  Conselho Nacional de Desenvolvimento Científico (CNPq)  and the European Integrated  FP6  project  on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI - project no. 34684 ), under the scope of the LBA experiment. We thank the  INPA  ( Instituto Nacional de Pesquisas da Amazônia ) for the coordination of the LBA Experiment and meteorological data. The authors gratefully acknowledge financial support from the  Academy of Finland Centre of Excellence program  (project no. 1118615 ), the  Swedish Research Council FORMAS  (project no. 2014-1445 ) and  STINT  (project  BR2013-5210 ). The authors would also like to thank the following key people for support with aerosol sampling and analysis:  Alcides Ribeiro ,  Ana Lucia Loureiro ,  Fernando Morais ,  Fábio Jorge ,  Glauber Cirino ,  Kenia T.
Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-1213 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 4 December 2018 c Author(s) 2018. CC BY 4. 0 License. altitude. The geometric altitude is approximated by the mean altitude grid from GROMOS, which is for each retrieved profile determined from operational model data of the European Centre for Medium-Range Weather Forecasts (ECMWF).
Data assimilation is considered as a problem in Bayesian estimation, viz. determine the probability distribution for the state of the observed system, conditioned by the available data. In the linear and additive Gaussian case, a Monte Carlo sample of the Bayesian probability distribution (which is Gaussian and known explicitly) can be obtained by a simple procedure: perturb the data according to the probability distribution of their own errors, and perform an assimilation on the perturbed data. The performance of that approach, called here ensemble variational assimilation (EnsVAR), also known as ensemble of data assimilations (EDA), is studied in this two-part paper on the non-linear low-dimensional Lorenz-96 chaotic system, with the assimilation being performed by the standard variational procedure.
, 2014; Liu et al. , 2017). Bardsley et al. (2014)  have extended it in to what they call the randomize-then-optimize (RTO) algorithm. These works have shown that EnsVAR is not in general Bayesian in the non-linear case, but can nevertheless lead to a useful estimate. EnsVAR is also used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF)  (Isaksen et al. , 2010)  in the definition of the initial conditions of ensemble forecasts. It is also used, both at ECMWF and at Météo-France (see respectively  Bonavita et al. , 2016, and Berre et al. , 2015) , under the name ensemble of data assimilations (EDA) for defining the background error covariance matrix of the variational assimilation system. And ECMWF, in its latest reanalysis project ERA5  (Hersbach and Dee, 2016)  uses a low-resolution ensemble of data assimilations system in order to estimate the uncertainty in the analysis.
2). This defines a simple algorithm for obtaining a Monte Carlo sample of that posterior distribution. Namely, perturb the data vector z according to its own error probability distribution, compute the corresponding estimate (Eq. 5), and repeat the same process with independent perturbations on z. That is the ensemble variational assimilation, or EnsVAR, algorithm that is implemented below in non-linear and non-Gaussian situations, with the analogue of the estimate x a being computed by minimization of Eq. (3). In general, this procedure, as already mentioned in the introduction, does not achieve Bayesian estimation, but it is interesting to study the properties of the ensembles thus obtained. Remark. In the case when, the data operator being linear, the error ζ in Eq. (  1 ) is not Gaussian, the quantity x a defined by Eq.
The validation procedure We recall the general result that, among all deterministic functions from data space into state space, the conditional expectation z → E(x|z) minimizes the variance of the estimation error on x. What should ideally be done here for the validation of results is objectively assess (if not on a case-by-case basis, at least in a statistical sense) whether the ensembles produced by EnsVAR are samples of the corresponding Bayesian probability distributions. In the present setting, where the probability distribution of the errors k in Eq. (  7 ) is known, and where a prior probability distribution is also known, through the observation y 0 , for the state vector x 0 , one could in principle obtain a sample of the exact Bayesian probability distribution by proceeding as follows.
Acknowledgements. This work has been supported by  Agence Nationale de la Recherche, France , through the  Prevassemble and Geo-Fluids projects , as well as by the programme Les enveloppes fluides et l'environnement of  Institut national des sciences de l'Univers, Centre national de la recherche scientifique, Paris. The authors acknowledge fruitful discussions during the preparation of the paper with  Julien Brajard  and  Marc Bocquet. The latter also acted as a referee along with  Massimo Bonavita. Both  of them made further suggestions which significantly improved the paper. Edited by: Alberto Carrassi Reviewed by:  Marc Bocquet  and  Massimo Bonavita.
ACKNOWLEDGMENTS This work was supported by the A* MIDEX  project (Nr. ANR-11-IDEX-0001-02 ) funded by the  French Government "Investissements d'Avenir" program  managed by the  French National Research Agency (ANR). Structure and dynamical data analysis was supported by the  Russian Science Foundation , Project  RSF 14-50-00124. We thank  Roman Kompaneets  for reading the manuscript and suggesting improvements.
However, in the model's initial and boundary conditions the concentrations of mineral dust and sea salt are included. For the calculation of the biogenic emissions (isoprene, monoterpenes, sesquiterpenes and soil NO) we followed the methods described by Andreani-Aksoyoglu and Keller (1995) using temperature and SSR data from the WRF output as well as land use data from the GlobCover 2005-06 land use inventory (http://due. esrin. esa. int/page_globcover. php) and the United States Geological Survey (USGS). All emissions were injected in the first model layer and were treated as area emissions. A detailed discussion and values of the emissions used in this study are given in  Oikonomakis et al. (2017).
Observations The European Air Quality Database v7 (AirBase;  Mol and de Leeuw, 2005)  provided observational surface data for the air pollutant concentrations (http://acm. eionet. europa. eu/databases/) with an hourly time resolution, which were used for chemical model evaluation. The details of observational data treatment and the statistical methods were described in the model evaluation part of  Oikonomakis et al. (2017). Furthermore, PM 10 (particles with an aerodynamic diameter, d < 10 μm) and PM 2. 5 (d < 2. 5 μm) data from the AirBase database as well as from the Swiss National Air Pollution Monitoring Network (NABEL; Empa, 2010) were used for trend analysis. Switzerland and the Netherlands were chosen for the PM trend analysis as they have PM 10 data going back to 1990 and 1992, respectively.
Acknowledgments. We would like to thank the following agencies for preparing the datasets used in this study:  TNO  for the anthropogenic emission inventory; the  European Environmental Agency (EEA)  and the  Swiss National Air Pollution Monitoring Network (NABEL)  for the air quality data; the  European Centre for Medium-Range Weather Forecasts (ECMWF)  and  Baseline Surface Radiation Network (BSRN)  for the meteorological data; the  National Aeronautics and Space Administration (NASA)  and its data-contributing agencies ( NCAR, UCAR, AERONET ) for the TOMS, MODIS and AOD data, the global air quality model data and the TUV model. Calculations of meteorological data were performed with the  Swiss National Supercomputing Centre (CSCS). Our thanks extend to RAMBOLL ENVIRON and especially  Cristopher Emery  for their continuous support of the CAMx model.
, 2016). At variance with the Cyclades that dominantly outcrop undersea, the SRCC largely outcrops inland allowing easy observation of faulting and related sedimentary basins associated to the core complex segmentation. The present paper, after a review of the SRCC, focuses on the deformation mechanisms involved in the segmentation of the SRCC by the superposed event of wide rifting during Neogene. Our field study of the SRCC is documented by (i) maps of the deformation zones that segment the core complex and of the associated Neogene basins, (ii) deformation patterns and structures that characterize these deformation zones, and (iii) geochronological data that provide constraints on their timing of development together with stratigraphy of basin infilling. It is shown that the development of several Neogene basins in the region was controlled by ramp-flat extensional systems.
A3. Relation Between Field Structural Evidence and Thermochronology Data On the basis of low-temperature dating,  Kounov et al. (2015)  summarizes the double detachment interpretation (Figure  20d ) in three steps: "The Kerdylion detachment initiated the exhumation of the SRCC at the latest at 42 Ma and controlled it until about 24 Ma. Between 24 and 12 Ma, the Strymon Valley detachment accommodated the exhumation. Since 12 Ma brittle normal faults, some of them cutting the Strymon Valley detachment were responsible for the final cooling of the basement rocks in the studied area and the formation of syn-tectonic sedimentary basins. " Besides the fact that the Kerdylion-Pangeon contact is primarily a thrust and not a detachment, the ages of 24 and 12 Ma proposed for the onset and end of the detachment activity are weakly supported by data.
Acknowledgments Since we started to work in the Rhodope in the early 1990s we benefited from discussions with C. Burchfiel, J. -P. Burg, G. Clauson, C. Faccenna, D. Gapais, P. Gautier, F. Gueydan, L. Husson, L. Jolivet, D. Kostopoulos, L. Royden, L. E. Ricou, J. -P. Suc, and J. Van Den Driessche. Whereas some of them do not necessarily agree with some aspects of our interpretations, we are grateful to all of them for exchanges that helped us strengthen our analysis. We are also grateful to our students in PhD (K. Kydonakis and C. Tirel)  and in MSc (C. Briend, M. Corver, and G. Estournes) who participated in the field and/or in the laboratory to some aspects of this work. We warmly thank the two reviewers J. Dewey and K. Soukis for their very positive comments and useful suggestions of improvement.
Acknowledgments This research was funded by the  National Natural Science Foundation of China  (major program  51490651  and  51774305 ) and by the  European Research Council  grant  ERC-2015-CoG: 681346 -REALISM. We are grateful to  Damien Deldicque  for microscopy imaging support at  ENS  and to IRSN for technical support providing samples. The data produced during our experiments are available in the supporting information.
Acknowledgements We appreciate support from the  Office of Naval Research  ( ONR N00014-12-1-0939 ), the  National Science Foundation  ( OCE-1419450 ) and the  California Ocean Protection Council  grant (Integrated modeling assessments and projections for the California Current System). J. G. acknowledges the support of  LEFE/IMAGO  through the Project  AO2017-994457-RADII. This work used the  Extreme Science and Engineering Discovery Environment (XSEDE)  and the HPC resources of [TGCC/CINES/IDRIS] under the allocation  A0010107298  attributed by  GENCI  ( Grand Equipement National de Calcul Intensif ) computers. The authors thank  Kaushik Srinivasan  and  Julien Jouanno  for useful discussions.
The co-location of various instruments in the same station is favored to enable a better understanding of the causes of observed changes in the measured parameters. Fig. 7  shows the actual geographical coverage of the network. More information on the NDACC sites and archived data can be found on the NDACC website: http:// www. ndsc. ncep. noaa. gov/. The network is organized in working groups based on the instrumental techniques selected within NDACC, e. g. , lidar remote sensing, UV/Visible spectrometers, Fourier Transform InfraRed spectrometers (FTIR), microwave spectrometers, ozone sondes, Dobson and Brewer spectrophotometers, UV spectroradiometers. It is important to note that NDACC coordinates the worldwide measurements, but the instruments are operated and supported by national agencies and organizations.
Data quality issues Strict quality-control procedures have been implemented within the NDACC by the various instrument-working groups. They include comparisons of retrieval algorithms, as well as field intercomparison campaigns in order to verify the correct operation of the instruments at the various sites (see the NDACC website for a compilation of results of the various intercomparison campaigns). Such campaigns generally include a ''blind'' period, during which exchange and discussion of measurements are prohibited. They are supervised by a neutral referee, who is Fig. 9. Annual mean ozone anomalies near 2 hPa or 42 km, as recorded by merged satellite data sets and ground-based stations (see text). Grey lines show the multimodel mean ozone anomalies from CCMVal-2 simulations  (Eyring et al.
Ozone measurements in Antarctica There are currently multiple ground-based stations (i. e. Dobson, Brewer, UV-VIS spectrophotometers, lidars and ozonesondes) regularly monitoring long-term ozone and the concentration changes of the compounds causing the Ozone Hole over Antarctica (see map in Fig. 11 ). Many stations contribute with near-real-time data to the NDACC database and to the WMO Antarctic ozone bulletins (see, e. g. were crucial to discover the Ozone Hole and to detect the anthropogenic ozone depletion in the 1980s  (Chubachi and Kajiwara, 1986; Farman et al. , 1985; Komhyr et al. , 1986). The depletion in the ozone column appears during the austral spring after enhanced levels of chlorine and bromine compounds initiate ozone-depleting reactions. The reactions occur on the surface of polar stratosphere clouds that form in extremely cold temperature within the Fig.
, http://www. wmo. int/pages/prog/arep/ WMOAntarcticOzoneBulletins2016. html). The quality-assured ozonesonde, Dobson and Brewer data are regularly archived in the NDACC archives or in WOUDC. Long-term series such as Dobson measurements (stored in WOUDC including Halley Bay (1957-present) and Syowa (1961present)), and NDACC database (Dobson data of the South Pole (1963-present) and Arrival Heights (1988-present)).
Acknowledgments The authors want to acknowledge the invaluable contributions of all national and international agencies and authorities for their support of ground-based monitoring of the atmospheric composition and of ozone in particular (for example NASA,  BAS  ( British Antarctic Survey ), and ESA). We furthermore acknowledge the contributions of the  European Commission  (for example, the Arctic campaigns EASOE, SESAME. and  EU  projects related to atmospheric monitoring such as REVUE and CANDIDOZ). We also thank  WMO/GAW  and  NDACC  for support in archiving data and making sure that quality assurance is implemented through the responsible working groups as well as all the researchers and operators who have devoted part of their life to atmospheric research.
We have compared 2433 nights of Rayleigh lidar temperatures measured at L'Observatoire de Haute Provence (OHP) with co-located temperature measurements from the Microwave Limb Sounder (MLS) and the Sounding of the Atmosphere by Broadband Emission Radiometry instrument (SABER). The comparisons were conducted using data from January 2002 to March 2018 in the geographic region around the observatory (43. 93 • N, 5. 71 • E). We have found systematic differences between the temperatures measured from the ground-based lidar and those measured from the satellites, which suggest non-linear distortions in the satellite altitude retrievals. We see a winter stratopause cold bias in the satellite measurements with respect to the lidar (-6 K for SABER and -17 K for MLS), a summer mesospheric warm bias (6 K near 60 km), and a vertically structured bias for MLS (-4 to 4 K).
The objective of our study is similar to that of  Yue et al. (2014)  insofar as we are interested in the time evolution of lidar-satellite temperature comparisons and identifying potential seasonal or decadal trends. However, we are seeking to make nightly temperature comparisons be-tween lidar and two satellites, SABER and MLS, over multiple years without assuming large contributions from the annual oscillation (AO) or its harmonics. Our study uses more than 9 times as many coincident measurements and spans the entire SABER data record. Further study of seasonal temperature anomalies between ground-based lidar and SABER was done by  (Dou et al. , 2009)  comparing 2332 nights of lidar data from six different sites in the Network for the Detection of Composition Change (NDACC) to zonally averaged temperature profiles from SABER.
14a  and b. This technique does not seem to work well when the stratopause is highly disturbed, as can be seen in the two wintertime examples in Fig. 9. The implications of satellite underestimation of sudden stratospheric warming events is of particular concern for reanalysis projects attempting to model middle atmosphere dynamics. However, using lidar data to supplement the satellite record, these fast dynamical processes can be better resolved. An additional point about vertical resolutions should be made before further interpreting the results of our lidarsatellite temperature comparison. As was noted in Sect. 2 both MLS and SABER measure radiances on a pressure grid with reported vertical averaging kernels, whereas the lidar measures temperature on a fixed geometric altitude grid. SABER has a relatively small and uniform vertical resolution with a FWHM of 2 km, and MLS has a somewhat lower vertical resolution in the middle atmosphere, which increases with height, reaching a FWHM of 15 km at the top of our comparison region at 80 km.