dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The resolving kernel analysis discussed in the present study allows one to quantitatively assess the way in which gravity data and density muon radiographies may be joined to improve the spatial resolution of density models of geological structures. Thanks to the compact support of muon acquisition kernels, high resolution is achievable in parts of the density model sampled by muon radiography. The resolution actually obtained depends on the number and geometrical arrangement of the radiographies available for the model construction (compare Fig. 6. 2 and 6. 3 for the La Soufrière example). In parts of the model sampled by muon radiography, the fusion of gravity data and muon radiographies does not lead to significant improvement of the density model (compare Fig. 6. 3 and 6. 4 for the La Soufrière example).
A main result concerns the improvement of the resolution obtained in the deeper parts of the density model when joining muon and gravity data, and despite the fact that these parts are not directly sampled by muon tomography. Actually, a fraction of information brought by the muon data is transferred to the deep regions of the model through the longrange coupling of the gravity acquisition kernels used to construct the joined resolving kernels (compare Fig. 7. 1 and 7. 2 for the La Soufrière example). The muon tomography acquisition kernel has a conic shape, and noise considerations lead us to put the cone apex just in front of the studied structure (see Fig. 4. 2 for the La Soufrière example). It results in a large decrease in the sensitivity between the front and rear of the volcano. This problem adds to the heterogeneous tomography sampling and forbids us from using the standard Radon transform usually adopted in X-ray tomography medical experiments to inverse the density.
Acknowledgements. This study is part of diaphane project anr-14-ce04-0001. We acknowledge the financial support from the  UnivEarthS Labex program of Sorbonne Paris Cité  (anr-10-labx-0023 and anr-11-idex- 0005-02 ). This study also received funding from  Swisstopo  through the  Mont-Terri MD  project. This is IPGP contribution 3623. Edited by:  L. Eppelbaum.
They fit the body EPJ Web of Conferences  of measured level densities with different formulae: a = αA (A) a = αA/ exp β(N -Z) 2 (B) a = αA/ exp γ(Z -Z 0 ) 2 (C) Form (A) has no isospin dependence, (B) presents a moderate dependence on the neutron excess while form (C) rapidly decreases for isotopes out of the valley of stability and even cancels for very exotic nuclei, as appears in fig. 5. The best value of α was 0. 1 in the three cases. In a recent experiment Moro et al. studied the de-excitation of the proton-rich nucleus 139 Eu formed at an excitation energy of 90 MeV; they measured the cross-sections of evaporation residues and the multiplicities of evaporated protons and α-particles. They compared the data with the results of the statistical code LILITA-N97, implemented with the three forms of level densities proposed above  [31].
We have created a daytime ozone profile data set from the measurements of the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument on board the Envisat satellite. This so-called GOMOS bright limb (GBL) data set contains ∼ 358 000 stratospheric daytime ozone profiles measured by GOMOS in 2002-2012. The GBL data set complements the widely used GOMOS nighttime data based on stellar occultation measurements. The GBL data set is based on the GOMOS daytime occultations but instead of the transmitted star light we use limb-scattered solar light. The ozone profiles retrieved from these radiance spectra cover the 18-60 km altitude range and have approximately 2-3 km vertical resolution. We show that these profiles are generally in better than 10 % agreement with the NDACC (Network for the Detection of Atmospheric Composition Change) ozonesonde profiles and with the GOMOS nighttime, MLS (Microwave Limb Sounder), and OSIRIS (Optical Spectrograph and InfraRed Imager System) satellite measurements.
However, there is a 10-13 % negative bias at 40 km altitude and a 10-50 % positive bias at 50 km for solar zenith angles > 75 •. These biases are most likely caused by stray light which is difficult to characterize and to remove entirely from the measured spectra. Nevertheless, the GBL data set approximately doubles the amount of useful GO-MOS ozone profiles and improves coverage of the summer pole.
S. Tukiainen et al. : GOMOS bright limb data set retrieving ozone profiles from the GOMOS limb scattered radiances, or GOMOS bright limb (GBL) measurements as they are referred to from now on. This paper is a continuation of that study. We have processed all GOMOS daytime measurements and in this study we estimate the quality of this novel data set. We also tested the sensitivity of the retrieval method to the selected spectral band and decided to use the lower band radiance to process the GBL data set. Another important decision is the choice of the stray light removal method (GOMOS daytime radiances are badly contaminated by stray light). In this work, we adapted a simple method that estimates the average stray light from the high tangent altitude GOMOS spectra. The structure of this paper is as follows.
GOMOS bright limb data The GOMOS bright limb data set consists of ∼ 358 000 limb-scattered radiance spectra measured around 10:00 LT between March 2002 and April 2012, from the launch of Envisat to the communication failure that stopped the mission. From these measurements we have retrieved vertical ozone profiles in the 18-60 km altitude range. The retrieved profiles have approximately 2-3 km vertical resolution. The data are processed using the ESA IPF (Instrument Processing Facility) Level 1 version 6. 01 and the current GBL Level 2 version 1. 2. The Level 2 retrieval scheme is based on  Tukiainen et al. (2011)  with a few modifications. We describe the retrieval method briefly below. One GOMOS limb "scan" includes typically 120-140 individual radiance measurements at different tangent heights.
Since GOMOS records two separate radiance spectra at each tangent height, above and below the central band (which collects the combined star and limb signal), there are actually twice as many spectra. The GBL data set was processed using the lower band radiances but the upper band, or possibly a combination of both bands, could be used as well. The upper and lower band radiances are separated by around 1. 5 km in tangent height. One particular advantage of GOMOS is that the tangent height registration is very accurate: on the order of 30 m  (Bertaux et al. , 2010). Stars are point sources and their positions are well known. Because the GOMOS central band always follows the occulting star, the uncertainty in the tangent height, which is often a significant problem in limb scatter satellite observations, is a negligible issue in the GO-MOS retrievals  (Tamminen et al.
To avoid using the most corrupted wavelength regions, we use three different sets of retrieval wavelengths depending on the tangent height (Table  1 ). This reduces bias due to inconsistencies in the GOMOS spectra but also introduces a discontinuity at 40 km where we start using only visible wavelengths. We reduce the discontinuity by scaling the amount of stray light (at layers below 40 km) by an iteratively found constant factor, requiring that the ozone profile remains smooth in the 40 km transition. We tested the sensitivity of the GBL ozone to the two important assumptions in the retrieval: the choice of the charge-coupled device (CCD) band of the spectrometer (upper or lower) and the stray light correction method. The test data included 10 orbits (142 scans) from 1 April 2004. The two available CCD bands yield ozone profiles within 1 % (Fig.
Densities and error estimates are provided for the three simultaneously retrieved species: ozone, aerosols, and neutral air. In this paper we show only the results related to the ozone profiles. Figure  3  shows the number of GBL profiles and GOMOS nighttime occultation profiles during the whole Envisat mission. The GBL data set roughly doubles the amount of useful GOMOS ozone profiles. Figure  4  shows a typical 1-day coverage of GOMOS day and night measurements, and Fig. 5  shows the number of GBL profiles during 1-year (2004) as a function of latitude. The GBL data complements the night occultations in the tropics and mid latitudes and furthermore expands the global coverage towards the summer pole. Figure  6  shows an example of the zonally averaged vertical distribution of ozone from the GOMOS nighttime occultations, GOMOS daytime occultations, and GBL.
The measurements are from January 2005 and from the latitude 35 • N. The nighttime data are from the star number 2 and the daytime data from the star number 175. As practically always, the shapes of the day occultation profiles are significantly different than the shapes of the GBL and night occultation profiles. The huge fluctuations and large negative values seen in the day occultation profiles are not realistic. At least in the stratosphere, the quality of the day occultation profiles is clearly inferior to the GBL and GOMOS nighttime data.
Correlative data sets First, we compared the retrieved GBL ozone profiles against NDACC (Network for the Detection of Atmospheric Composition Change) balloon-borne ozonesonde measurements. The NDACC data can be downloaded from http://www. ndacc. org and the stations that were used in the comparison are listed in Table  2. While ozonesondes typically reach only about 30-35 km altitude, they measure tropospheric and lower stratospheric ozone with very good vertical resolution. Also, in general, the accuracy and precision of ozonesondes is at least as good as satellite measurements. To validate the GBL profiles also for altitudes above 35 km, we compared the GBL data against satellite measurements from the GOMOS nighttime occultations, MLS (Microwave Limb Sounder) on EOS (Earth Observing System) Aura  (Waters et al.
, 2006) , and OSIRIS on Odin  (Llewellyn et al. , 2004; McLinden et al. , 2012). For GOMOS night occultations, we used the latest Level 2 version 6 data. Because the occultation retrieval from cool and weak stars is also difficult due to low signal to noise ratio and often results in mediocre nighttime occultation profiles (see the data disclaimer ESA, 2012), we screened out these "bad star" profiles from the comparison. MLS on board the Aura satellite, launched in July 2004, uses thermal infrared emission to measure the atmosphere between ∼ 0 and 90 km. MLS measures globally during the day and night, and the accuracy of the MLS ozone profiles is estimated to be better than 5 % in the stratosphere  (Froidevaux et al. , 2008). In this comparison we used the MLS Level 2 version 3. 3 data  (Livesey et al.
, 2011). MLS ozone profiles are mixing ratios as a function of pressure while the GBL profiles are number densities as a function of altitude. Thus, we converted the GBL densities to mixing ratios and pressures using the ECMWF model analysis data (below OSIRIS is a UV/visible spectrograph, including a nearinfrared imager, on board the Odin satellite. OSIRIS measures the atmosphere using the limb scatter technique, measuring scattered sunlight and scanning Earth's limb between ∼ 10 and 100 km. In this work, we have used two different OSIRIS ozone profile products. The University of Saskatchewan's OSIRIS ozone product is retrieved from OSIRIS data using the SaskMART (Saskatchewan multiplicative algebraic reconstruction technique;  Degenstein et al. , 2009). The product has been the target in several validation studies (e.
, 2010). The vertical resolutions of the OSIRIS and MLS ozone products are very close to the GO-MOS resolutions and the marginal resolution differences do not seem to cause any notable issues in the comparisons. The NDACC ozonesonde profiles, which have significantly better vertical resolution, were smoothed to the approximately same resolution with the satellite products using a Gaussian filter. Some of the GBL profiles include outliers especially at the lowermost retrieved altitudes. This measurement is often corrupted because GOMOS has lost the tracking of the star. Before comparisons, we screened the GBL data with the following criteria: O 3 (density) > 0. 1, where O 3 (error) is the error estimate of the retrieved density. On average, the χ 2 screening removes ∼ 0. 3 points per profile and the error screening removes ∼ 1.
Results Figure  7  shows the result against NDACC ozonesondes for the six latitude zones. Shown are interquartile ranges and medians of the differences. The numbers represent the numbers of co-located pairs at each altitude. In these comparisons the median difference is always less than 10 % except latitudes 60-90 • S where the negative bias is more than -20 % at 19 km. Figure  8  shows the comparison against GOMOS night occultations for different solar zenith angles. There is a clear positive bias at around 50 km when the solar zenith angle is 75 • or larger. With smaller solar zenith angles the differences are rather similar. The large solar zenith angle observations account for 16 % of the GBL data and the GOMOS measurement geometry is such that these observations appear mostly at mid and high latitudes.
We suspect that the 50 km bias is linked to stray light and its removal. GOMOS daytime data suffer from serious stray light contamination and in particular the upper altitudes are sensitive to the accuracy of the removal method. We also note that the solar zenith angle seems to be the only parameter that clearly correlates with the 50 km bias. Some good individual profiles can be found even for high solar zenith angles but there is no clear pattern, or too few profiles to draw conclusions. Because the majority of the GBL measurements with a large solar zenith angle are distinctly biased at 50 km, in the remaining comparisons we only used the GBL data with the solar zenith angle smaller than 75 •. Figure  9  shows the difference in 10 • latitude bins against GOMOS night, MLS, and OSIRIS measurements.
Discussion and summary We have processed and released a GOMOS bright limb ozone data set. This data set roughly doubles the number of useful GOMOS ozone profiles. In general, the bias in the GBL ozone is less than 10 %, and often less than 5 %, but there is a clear 10-13 % negative bias at 40 km. The reason for this bias is uncertain but the most likely cause is the stray light contamination in the 300-320 nm band already noted by  Tukiainen et al. (2011). Above the 45 km tangent height, we use UV wavelengths to retrieve ozone and below 40 km we use visible wavelengths. The retrieval method is sensitive to the transition from UV to visible and it is easily disturbed by stray light. As explained above, we scale the amount of stray light to get a smooth ozone profile in the 40 km transition.
This ad hoc approach works reasonably well in practice but better ways to switch between the different wavelength domains should be investigated in future. The diurnal variation explains about 1-3 % of the observed 40 km bias as the GOMOS day measurements are made during the minimum of the diurnal cycle. At the 50 km altitude we have up to 50 % positive bias depending on the solar zenith angle. This bias cannot be explained by natural variation; it indicates some problem in the retrieval, most likely related to the stray light and its correction. Until this issue is solved, it is recommended to only use GBL measurements with a solar zenith angle of less than 75 • when using data from the altitudes above 45 km. In this work, we showed the accuracy of the GBL data as a function of latitude, altitude, and solar zenith angle.
Acknowledgements. This study was funded by the  European Space Agency  through the  SPIN  project (http://esa-spin. org) and by the  Academy of Finland  through the  MIDAT  project. The authors wish to thank the three referees for reviewing the manuscript and for suggesting relevant corrections. The authors also appreciate the MLS and OSIRIS teams for sharing their data. Special thanks go to  Viktoria Sofieva  for valuable suggestions. The (ozone sounding) data used in this publication were obtained as part of the Network for the Detection of Atmospheric Composition Change (NDACC) and are publicly available (see http://www. ndacc. org). Edited by:  C. von Savigny.
We visually inspected the direct P arrivals from all events of magnitude m b 5. 5 in the epicentral distance range 308 < D < 908 recorded between 1995 and 2012. Before analysis, the data were band pass filtered between 0. 1 and 1. 5 Hz. We picked the dominant peak or trough of the P-phase across all stations and used the multichannel cross-correlation method of VanDecar and  Crosson [1990]  to estimate relative arrival times (using a window of 1 s before and 2 s after our pick). Those arrival times with correlation coefficients >0. 80 and a minimum number of picks per event 4 are retained for inversion. Furthermore, outliers Figure 3. Two profiles of dV P /dT derivatives that we use to set up our synthetic plume resolution tests and perform our conversion of imaged velocity to temperature anomalies.
The blue profile is the full (metamorphic) derivative that includes the effects of phase transitions. It was computed along a 13008C adiabat for a pyrolite composition using mineral parameters from database stx08  [Xu et al. , 2008] , with composite attenuation model Qg (above 400 km)  [Van Wijk et al. , 2008]  and Q6 (below)  [Goes et al. , 2004]. The red profile corresponds to the smoothed isomorphic dV P /dT derivative without the effects of phase boundary topography. For our scaling, we use the isomorphic derivatives because the differential travel-time tomography cannot resolve localized phase boundary anomalies (see section 4 and supporting information Figure  S7 ). Above 70 km depth, the isomorphic scaling is set to a constant value of 22% per 100 K, more representative for a lithospheric geotherm.
[2000] ). Our temperature estimates are more consistent with the lower temperature estimates of  Thompson et al. [2015]  and  Nyblade et al. [2000]. According to the thermodynamic database we use  [Xu et al. , 2008] , the 660 discontinuity should be governed by the ringwoodite to bridgmanite1magnesiow€ ustite transition at such warm temperatures, and hence be elevated. Given the consistency between our temperature estimates and those from other studies, there is no reason to invoke other mechanisms to explain the imaged P-velocity anomalies. However, within the uncertainties of our inversion, we can also not rule out that other factors have some contribution. In the transition zone,  Thompson et al. [2015]  propose the presence of fluids and hydrous melt to explain a low-velocity layer they see above the   10b  and 10d  show the mean and standard deviation of the temperature anomalies estimated in each region.
Acknowledgments We would like to thank the many people involved in the collection of data used in this study. Data specifically provided for this study included those recorded instruments in Yemen, Ethiopia, and  Eritrea  provided by  SEIS-UK. The facilities of SEIS-UK are supported by the  Natural Environment Research Council  under Agreement  R8/ H10/64. These data will be available at the IRIS-DMC in 2015/2016 (http://ds. iris. edu/ds/nodes/dmc/). The instruments deployed in Djibouti belong to the French national pool of portable seismic instruments Sismob-RESIF (http:// sismob. obs. ujf-grenoble. fr) and are funded by  Actions-Marges. The network deployment in Yemen was funded by  ANR   YOCMAL 07BLAN0135. The instruments deployed in Uganda-Congo were provided by the  Geophysical Instrument Pool Potsdam (GIPP)  and data were archived at the  GEOFON data center  (http://geofon.
gfz-potsdam. de). The permanent instruments in Ethiopia are run by the  Institute of Space Science, Geophysics and Astronomy, Addis Ababa University  (Contact for data: atalay. ayele@aau. edu. et), and financial support for most of the Ethiopian permanent seismic stations comes from the  International Science Program (ISP) of Uppsala University (Sweden). Other data were downloaded from the  IRIS  and GEOFON data centers. We thank  Ivan Koulakov ,  Cindy Ebinger , and three anonymous reviewers for their constructive and critical comments that helped us improve our paper. Funding for data collection was provided by  NERC  grants  NE/E007414/1 , and  NE/J012297/1  and  BHP-Billiton. We thank  Ian Bastow  and  John Armitage  for discussions. C. C. was supported by a  Janet Watson Fellowship  from the  Department of Earth Science and Engineering at Imperial College , J.
The Global Sea Level Observing System (GLOSS) Group of Experts (GE) data archaeology group is collating tools and producing guidelines for historic sea level data. They aim to aid the discovery, scanning, digitising and quality control of analogue tide gauge charts and sea level ledgers. Their goal is to improve the quality, quantity and availability of long-term sea level data series. This paper examines different tools for the automatic digitisation of tide gauge charts, the methods available for transcribing handwritten tide gauge ledgers and possible future developments that might speed up and partially automate these processes.
Introduction At the 2013 Global Sea Level Observing System (GLOSS) Group of Experts (GE) meeting in Liverpool, UK, a data archaeology session was held. A number of actions were agreed upon  [1]  relating to uncovering data to digitise and sharing automatic digitisation software. GLOSS wants to provide its user community with guidance on finding, digitising, quality controlling and distributing their records. Why put all this effort into recovering historical data? Recovering unique data and making them freely available is important, as they make a vital contribution to climate studies (sea level rise), oceanography (ocean currents, tides, surges), geodesy (national datum), geophysics and geology (coastal land movements) and a number of other disciplines. Long-term sea level data series are rare and the measurements unrepeatable.
These data are used by the Intergovernmental Panel on Climate Change (IPCC), who published their Fifth Assessment Report in 2013  [2]. Global sea level reconstruction (e. g. Church and White  [3] ) combines satellite altimetry with tide gauge data. However, with only two decades of altimeter data, this reconstruction and others  [4, 5]  rely on tide gauge information going back in time prior to 1993. In the PSMSL database the mean length of a revised local reference record is 37 years but at least 60 years of data are recommended for the analysis of long-term trends  [6]. In addition, most of the time series are concentrated in the northern hemisphere (Europe, USA and Japan)  [7]  so introducing records from other, more isolated, locations would improve reconstructions. High-frequency data (typically defined as a sampling interval of one hour or less) can used to study high-frequency variability, storm surges  [8]  and the propagation of tsunamis.
Unlike satellite altimetry data, actual tide gauge observations are used for coastal engineering and protection as well as to study coastal processes and changes in extreme sea levels and their return periods. Data archaeology activities will help fill in the gaps in the global dataset, with records in the southern hemisphere being particularly valuable. Hunter et al. [9]  used historical observations from a tide gauge at Port Arthur, Tasmania, to indicate an average rate of sea level rise from 1841 to 2002 and Testut et al. [10]  used measurements taken from a tide staff on Saint Paul Island in the southern Indian Ocean to estimate a rate of relative sea level change from 1874 to 2009. GLOSS is an international programme conducted under the auspices of the WMO-IOC Joint Technical Commission for Oceanography and Marine Meteorology.