dataesr/scientific-paragraphs-categorization huggingface dataset:
U-Pb data of DGP-21, PEN-28 and PEN-22 are taken from  Baresel et al. (2016). Predicted dates (blue horizontal bars) for the onset of the radiolarian decline (RD) and the Permian-Triassic boundary (PTB) in Dongpan and Penglaitan are calculated with their associated uncertainty using the different age-depth models. as reflected by the linear and cubic interpolation models for the interval between DGP-11 and DGP-12, and for the interval between DGP-21 and DGP-18 (Fig. 5a ). In all Bchron models, the thickness of the geologically instantaneously deposited volcanic ash beds was virtually removed and the lithostratigraphy has been rescaled in order to create accurate deposition rate models for the investigated sedimentary successions. This approach has only minor effects in the Bchron age-depth model of Dongpan, where the changes in the calculated age of the PTB and the radiolarian decline (RD) are negligible (see Sect.
parvus (bed 27c) does. Available conodont data from Meishan allow the assignment of bed 24a-e to UAZ1 (UAZ1 might reach further down as indicated by a dashed line in Fig. 6 ), bed 25 to UAZ2, bed 27a-d to UAZ3 and bed 28 to UAZ4  (Brosse et al. , 2016). The stratigraphic thickness comprised between the base of UAZ1 and the top of UAZ4 amounts to 1. 22 m. By using the three-section age-depth models, we attempted to project the respective thickness corresponding to the UAZ1-UAZ4 interval in Meishan onto the two other sections. This projection resulted into a pronounced, artificial lengthening of UAZs in Dongpan and Penglaitan. UAZ1 is the penultimate Permian conodont UAZ in Meishan  (Brosse et al. , 2016). When projected onto the age-depth models of Dongpan and Penglaitan, this UAZ1 is artificially expanded and even crosses the PTB in Penglaitan (Fig.
Acknowledgements. The authors acknowledge financial support by the  Swiss NSF  through projects  200020_137630  (to Urs Schaltegger) and  200021_135446  (to  Hugo Bucher). Aymon Baud (Lausanne)  is thanked for support in the field. Tom Algeo  (Cincinnati) and  Shen Shuzhong  (Nanjing) are thanked for insightful discussions and for providing Excel tables of carbon isotopic data from published sections. Special thanks go to technical and scientific members of the  Geneva and Zurich research groups  who helped during all stages of this study. M. Schmitz  and  S. Burgess  are gratefully acknowledged for comments and suggestions that substantially improved the manuscript. Edited by: M. Heap Reviewed by:  M. Schmitz  and  S. Burgess.
Le  Pichon et al. , 2003)  is based on solving the inverse problem with a celerity model and apparent horizontal phase velocity. A known earthquake's origin time and epicentre coordinates make it possible to refine the localization of the source regions with the measured azimuth and slowness variation in infrasonic signals. The study reported here presents an improved location method for explosions that generate both seismic and infrasonic signals. The method makes full use of the seismic parameters of the explosions and infrasonic estimations of travel times and azimuths. To assess the performance of the method, we collected data on ground-truth events by operating a temporary seismic station inside an open-pit coal mine, one of the most active regions generating both seismic and infrasonic signals in Mongolia.
Microbarographic array and data processing Infrasonic signals are continuously recorded by an eight element array located at Songino, in the East of Ulaanbaatar, Mongolia (47. 80 °N and 106. 41 °E). The Songino (I34MN) array has been set up to characterize infrasonic sources and to improve atmospheric and propagation models. This array is a prototype CTBT 1 station. The CTBT global infrasonic network will permit the detection and location of both ground surface and middle atmosphere explosions. The array, which has an aperture of ~1. 5 km, has been operating continuously since October 2000. Development of infrasound arrays we can divide with two time range. Beginning the I34MN array, a triangular 1. 5 km aperture array located in Tavan Tolgoi valley area, was originally installed in October, 2000 with four infrasound stations.
We have prepared a large volume of pure, concentrated and homogenous zinc standard solution. This new standard solution is intended to be used as a primary reference standard for the zinc isotope community, and to serve as a replacement for the near-exhausted current reference standard, the so-called JMC-Lyon Zn. The isotopic composition of this new zinc standard (AA-ETH Zn) has been determined through an inter-laboratory calibration exercise, calibrated against the existing JMC-Lyon standard, as well as the certified Zn reference standard IRMM-3702. The data show that the new standard is isotopically indistinguishable from the IRMM-3702 zinc standard, with a weighted d 66/64 Zn value of 0. 28±0. 02‰ relative to JMC-Lyon. We suggest that this new standard be assigned a value d 66/64 Zn of +0.
Furthermore, adopting the IRMM-3702 Zn as a primary reference standard would require the IRMM to supply this standard in its current form well into the future, which already seems unlikely given the small quantities in which it is currently distributed. Other potential candidates supplied by international bodies, such as the NIST 3168a Zn standard solution are also unlikely to be useful to the Zn community as a zero-point reference standard, as data suggests that it is isotopically light with a 66 Zn /64 Zn ratio that is approximately 1‰ lower than the JMC-Lyon reference standard (O. Rouxel, unpbl. ). This composition is, therefore, at an extreme end of the natural range thus far reported for Zn isotopes. A second Zn standard from NIST, SRM 682 has an even more extreme composition, which is approximately 2.
Analytical Methodology A summary of the analytical protocols used by the participating laboratories is shown in Table  1. Most of the participating labs employ a Zn double spike for correction of mass fractionation. An underlying assumption of the using double spike techniques is that mass fractionation occurs according to mass, i. e. all isotopic fractionation is mass dependent. This is expected to be the case for elements with Z<80, such as Zn  18. This expectation is confirmed by the data obtained from the Brest laboratory, using a non-double spike approach to correct for mass fractionation (Table  2 ). All participating labs performed measurements of the new AA-ETH Zn standard against both the JMC-Lyon and IRMM-3702 Zn standards. Zn isotopic compositions are reported in the usual d 66 Zn (‰) notation, and are reported relative to both the JMC-Lyon and IRMM-3702 Zn standards (Table  2 ), where d 66 Zn (‰) = ( 66 Zn 64 Zn) sample 66 Zn 64 Zn standard -1 1000 [1] Similarly, d 67 Zn and d 68 Zn are described by the above equation, where  66  Zn is replaced by  67  Zn or 68 Zn respectively.
ETH Zürich All analyses were performed using a Thermo Scientific NeptunePlus MC-ICPMS, using the standard sample and skimmer cones. Prior to analysis, each standard was doped with a mixed 64 Zn-67 Zn double spike to achieve a standard:spike ratio of ~ 1. Instrumental mass bias correction was performed using the double spike techniques described in Bermin et al. 1  Solutions were introduced into the mass spectrometer in a ~0. 3M HNO 3 (2% v/v) solution via a Cetac Aridus II desolvating system or an Elemental Scientific (ESI) Apex-Q desolvating system, both coupled with a PFA nebuliser with a nominal uptake rate of 50 µL min -1. Isobaric interferences from  64  Ni on 64 Zn were monitored using  62  Ni, but any correction here was always negligible. For data collected with the Apex, an addition correction for small amount of hydrides needed to be made, determined by monitoring the 64 ZnH + hydride produced at mass 65.
Results and Recommendation Table  2  and Figure  1  summarises the data collected by the seven participating intercalibration labs, representing a total of 126 separate analyses of the AA-ETH standard. Importantly, the results show a clear agreement between participating labs, with the new AA-ETH Zn standard being isotopically indistinguishable from the already calibrated IRMM-3702  13. Furthermore, both the measured (and implied) offset of IRMM-3702 relative to the JMC-Lyon standard in this dataset are in agreement with previously published values of 0. 29 ± 0. 05‰  13, 15, 16. The data presented in this inter-calibration exercise were collected using a variety of techniques, both analytically and in terms of data reduction and specifically the methods used for correcting instrumental mass bias.
In addition to the Core Observatory, GPM is also a consortium of space agencies who agreed to share all the data arising from PMW radiometers dedicated to rain retrieval in order to provide users with a rain-oriented dataset giving the best possible coverage  (Kidd & Huffman, 2011). Our study focusses on the retrieval of RR from L-band SSS in the intertropical convergence zone (ITCZ) region (between 180 • and 110 • W, 0 • and 15 • N), a region characterized by tropical convective systems for which the spatial resolution of L-band radiometers (∼50 km) is well suited  (Houze, 2004; Waliser & Gautier, 1993). The chosen region is far from coasts and not affected by freshwater water influx from rivers. The study covers the period from August 2014 to July 2016. The year 2015 corresponds to the training phase.
During this year, the Pacific ITCZ was impacted by a rate of precipitation higher than expected, due to El Niño conditions  (Bell, Halpert, & L'Heureux, 2016). The other years (2014 and 2016) are used as the validation period. We use SMOS and SMAP data for salinity measurements and the IMERG product for RR. In Section 2, we present measurements and methods used in this study to estimate salinity anomalies and to relate them to RR. The algorithm developed for deriving RR from satellite salinity, its validation and performance with respect to the RR interpolated product are described in Section 3. Application of the PEALS algorithm to SMAP data is described in Section 4. Finally, results and perspectives of this study are discussed in Section 5.
DATA AND METHODS The shortest time shift between SMOS SSS and PMW RR is observed in 2015 (Figure  1 ). So 2015 datasets are used to develop the algorithm for retrieving SMOS RR from satellite salinity, which will be described in Section 3. Validation is done on instantaneous retrievals in 2014 and 2016. Then instantaneous estimates and monthly averages of retrieved SMOS RR are compared with the interpolated RR (IMERG product) during the whole study period.
SMOS salinity data SMOS is the first satellite mission that carries an L-band microwave radiometer from which SSS can be retrieved. It was launched in November 2009, and since 2010 provides SSS from space  (Kerr et al. , 2010). It crosses the Equator at about 6:00 a. m. (0600 hr local time) on ascending node and 6:00 p. m. (1800 hr) in descending node along a Sun-synchronous orbit. The spatial resolution of individual SMOS SSS is close to 50 km. SMOS SSS are oversampled over an Icosahedral Snyder Equal Area (ISEA) grid at 15 km resolution. In the ITCZ, typical noise on individual SMOS SSS is 0. 5 pss (practical salinity scale)  (Boutin et al. , 2014). When averaged over typically 1 month and 100 km × 100 km, the precision of SMOS SSS in tropical regions is improved to typically 0. 2-0. 3 pss  (Boutin, Martin, Reverdin, Yin, & Gaillard, 2013; Hasson, Delcroix, Boutin, Dussin, & Ballabrera-Poy, 2014).
SMAP salinity data SMAP was launched in January 2015 into a Sun-synchronous orbit. It uses a single radiometer with a 6 m diameter reflector that allows the retrieval of SSS over the ocean at a 40 km resolution. SMAP was also equipped with an L-Band radar which failed in July 2015. It crosses the Equator at the same time as SMOS but in the opposite phase, near 6:00 a. m. for descending orbits and near 6:00 p. m. for ascending orbits, providing numerous collocations between the two satellites. SMAP SSS data are available since April 2015 to date providing a shared period of more than a year between SMOS and SMAP. We use SMAP L2B v3. 0 data from Jet Propulsion Laboratory (JPL) OurOcean Portal (SMAP Salinity and Wind Speed, Data User's Guide). SMAP L2B SSS are provided by the SMAP TB-only salinity processing, on a grid sampled at about 25 km.
For this reason we apply a second step in which we first filter the pixels likely to be affected by rain using S ref_first_step. We remove pixels where (S sat -S ref_first_step ) is less than -2 SMOS and we recompute S ref as the mean salinity in 3 • × 3 • areas. The advantages of this approach with respect to using S ref derived from Argo SSS as was done in  Boutin et al. (2014)  are that (a) it does not use external information for rain determination, hence avoiding errors due to temporal shift between the two measurements, and (b) it will remove any SMOS systematic error with a spatial coherence (e. g. coming from SMOS calibration or from large-scale bias of wind speed  (Yin et al. , 2014) ). This methodology has been applied to the dataset used in Boutin et al. ΔS have a distribution better centred around 0 and with a lower noise than the one obtained using a reference salinity equal to Argo monthly interpolated fields (ISAS:  Gaillard et al.
ACKNOWLEDGEMENTS This work is supported by the  ESA STSE SMOS + Rainfall  (coordinated by  M. Arias -ARGANS  company) and by the  CNES TOSCA SMOS-OCEAN  projects. We thank  Stéphane Marchand  for his computing support,  Hugo Bellenger  for very fruitful exchanges about the formation of freshwater lenses and  Steven Delwart  and  Diego Martinez  ( ESA ) for very constructive discussions.
The state-of-the-art processing of AMT data typically employs windowed Fourier transform techniques and assumes that source fields are quasi-stationary. Most of the methodological developments have been focused on the introduction of C The Authors 2017. Published by Oxford University Press on behalf of The Royal Astronomical Society. statistical schemes  (Chave 2014)  or the analysis of stations arrays  (Egbert 2002) , to exploit and analyse the variability of source fields, but only a few studies actually investigate EM waves and their properties for the processing of magnetotelluric (MT) timeseries. Among those studies, a detailed analysis of the seasonal variation of magnetic field amplitude was conducted by  Garcia & Jones (2002). They demonstrated that the optimal season to acquire AMT data is summer in the northern hemisphere at mid-latitude.
In the framework of AMT processing,  Zhang & Paulson (1997)  proposed a methodology using a moving threshold in time-frequency domain to detect atmospheric waves before processing. We previously extended Zhang & Paulson method to consider the choice of the wavelet depending on the frequency domain to be processed  (Larnier et al. 2016). In this paper, we propose a new methodology: Automatic Detection of Electromagnetic Waves (ADEM), to automatically detect EM waves in the frequency range 10 Hz-10 kHz. ADEM is based on the time-frequency properties of sought waves using continuous wavelet transform (CWT). We first show in Section 2, AMT data acquired in November 2012 in Guadeloupe, French Overseas Territories. They exhibit interesting features of typical EM waves. The ADEM method, including details on the computation of wavelet coefficients and polarization attributes, is given in Section 3.
Sites locations and data In this paper, we consider EM time-series from three stations (Fig. 1 ) acquired during an MT campaign on La Soufriere lava dome (Guadeloupe, France) in November 2012  (Sailhac et al. 2016). All MT data were acquired using the Metronix Geophysics ADU07 data logger as a recording system. The first two stations, named Guenon and Cratère Sud respectively, were located on the lava dome. Magnetic fields were recorded using MFS07e induction coils (using chopper off). Guenon measurements employed 41 and 31 m electric dipoles for the NS direction and EW direction respectively. 50 m dipoles were employed in measurements at Cratere Sud. The third station, named Youketi, was used as a remote station for AMT processing. It is located 8 km off the lava dome and 600 m off the Soufriere Volcano Observatory.
This station was composed by 50 m electric dipole for NS and EW measurements and MFS06 induction coils (using chopper off) to measure the magnetic field. Sample time-series analysed in this paper were sampled at 16384 Hz. Guenon time-series were acquired for 5 min starting at 19 hr 45 min in Coordinated Universal Time (UTC) (14 hr 53 min 9 s solar time), 2012 November 16, in the afternoon. Cratère Sud time-series were acquired for 5 min starting at 19 hr 45 min UTC (14 hr 53 min 34 s solar time), 2012 November 14, and again for 5 min starting at 15 hr 45 min UTC (10 hr 53 min 22 s solar time), 2012 November 15. No high frequency night data were recorded during this acquisition. Many types of EM waves coexist in the frequency band of 1 Hz up to 10 kHz for the time-series analysed here. In this paper, we focus on three types of EM waves, atmospherics, slow tails and whistlers, all of which are well represented in the analysed time-series, thus providing an excellent basis for testing ADEM.
First criterion of significant amplitude We want to detect time-frequency patterns of atmospheric waves by comparing their amplitude to the background noise and, thus, determine scale-dependent thresholds that are generally useful for selecting segments of data with high signal-to-noise ratios. Background noise power spectrum is estimated by the method of Torrence & Compo (1998). They have used Monte Carlo methods and considered stationary time-series s of length N described by order one Auto-Regressive (AR-1) process or Gaussian white noise. They demonstrated that the square modulus wavelet distribution: D(a k ) = {|W ψ (a k , b i )[s]| 2 , i = 1. N } at each scale a k , is related to the χ 2 distribution. D(a k ) is distributed as P k σ 2 χ 2 2 /2 (with same variance and confidence levels), where σ is the noise standard deviation and P k the mean normalized Fourier background energy spectrum of signal s at frequency f k corresponding to the scale a k (such that a k = (ω 0 + 2 + ω 2 0 )/4π f k ).
Test on synthetic data We first apply the ADEM algorithm on synthetic data created by following  Wait (1960) , as described in Appendix C. Three types of slow tails time-series are synthesized with different noise level (Fig. 6 ). The lowest noise level is typical of most AMT data while the moderate noise level corresponds to noisy AMT data. The highest noise level is σ = 0. 02 nT and is used to test the robustness of the method but is not representative of most AMT data. Higher values for σ result in time-series where the noise level is too high and unrealistic of field AMT data. This test was carried out to assess the efficiency of the code in the detection and characterization of non-monochromatic waves. The synthetic time-series are 40 s long. For each event we attribute a random time index and random polarization attributes.
( 15 ) One major advantage of EM waves such as slow tails and atmospherics is their broad frequency content. This broadness helps to constrain Z over a wide frequency band at once. Of critical concern is the low signal-to-noise ratio in the so-called AMT dead band, from 1 to 5 kHz, where standard processing procedures often fail to properly determine MT response functions Z  (Garcia & Jones 2002). Using the ADEM method to detect atmospherics and slow tails, and thus defined data segments with higher signal/noise ratios (Sections 4. 1 and 4. 2) serves to improve AMT impedances estimated for the dead band. First let us consider the relationship between the relationship between inclusion of ADEM-derived knowledge of atmospheric activity and results from traditional 'robust' processing using Huber weighting schemes.
We have shown that for Cratère Sud station, the occurrence rate of atmospheric events is lower in the morning than in the evening  (Figs 11 and 15). Fig. 16  shows the difference between the processing of the corresponding morning and afternoon time-series using the BIRRP (Bounded Influence Remote Reference Processing) code from the work of  Chave & Thomson (2004). Even though estimations of the response functions outside of the AMT dead band are similar, data quality, as evinced by larger uncertainties and scatter in estimates of the mean value, is lower for the morning versus afternoon data. Inside the AMT dead band, processing of morning time-series does not result in acceptable estimations of the response functions. Inversion of such data might lead to a loss of accuracy in the inverted resistivity distribution.
Afternoon processing of time-series results in acceptable response functions for the whole frequency range, even in the AMT dead band. However, Figs 11 and 15 demonstrate that atmospherics and slow tails with significant frequency content inside the AMT dead band occur in the morning time-series. Let us now propose to use the ADEM algorithm to select data segments exhibiting high lightening activity, particularly with substantive frequency content within the dead band, for further processing using standard schemes. We use the algorithm to detect, slow tails presenting maxima chains from 128 to 1024 Hz providing a catalogue of significant geomagnetic events. We use a confidence level of 0. 9 for amplitude thresholding. Each segment of 'active' time-series starts 0. 1 s before the occurrence of a selected event.
CONCLUSION We develop a methodology for the automatic detection of lightningbased electromagnetic waves (ADEM). It is based on the characteristics of the EM waves in time-frequency plane. Two criteria are used: one relying on the amplitude of the expected waves; the second one lies in the behaviour of local maxima in the timefrequency domain. Using both criteria, this procedure allows for direct detection, segmentation and characterization of three types of lightning source EM waves, namely atmospherics, slow tails and whistlers. This scheme can be extended to events such as q-Bursts or tweeks. It allows either for a large detection where many events are recovered or a precise detection for events with wide frequency content. Using this technique, we have shown that large signal-tonoise ratio events have significant impact on MT data processing, specially for frequencies near the AMT dead band.
Finally, the use of a remote station is critical for a proper detection of lightningbased EM waves. This does not change the framework of AMT acquisition where a remote station is almost always set up for processing. Only one site location is shown here but this methodology has been successfully applied to other locations at different times of day (including night-time) and at different latitudes. However, we have shown that large events with significant content in AMT dead band have low occurrence frequency. This suggests that longer time-series may be necessary to acquire proper AMT data depending on the atmospheric activity or new methodologies need to be adapted to cope with the scarcity of events in AMT time-series. In our examples, 1 s of data may not be sufficient if no slow tails are present in the signal.
The increased resolution on the vertical aims to "close" the stratospheric circulation. It is also necessary to generate a realistic quasi-biennial oscillation (QBO) as discussed below. de la  Cámara et al. (2016)  provide a more extensive description of the stratospheric dynamics modelled with this vertically enhanced configuration of LMDZ. Our configuration of the LMDZ model differs from that described in  Hourdin et al. (2013)  in that it has a different radiative transfer code. In the shortwave, the code is an extension to six bands of the initial two-band code that is used in LMDZ5A  (Fouquart and Bonnel, 1980) , as implemented in a previous version of the ECMWF numerical weather prediction model. In the longwave, we use the ECMWF implementation of the RRTM radiative transfer scheme  (Mlawer et al.