dataesr/scientific-paragraphs-categorization huggingface dataset:
The contribution from the IR to the combined retrieval diminishes above about 30 km, meaning that the upper stratosphere is derived mostly from the UV part of the retrieval. The measurement response functions, shown in the right panel of Fig. 4 , confirm the previous findings. In the optimal case, the measurement response should approach unity, which is nearly reached for the combined retrieval between 10-50 km. Below 15 km, the UV-only retrieval shows a lower response than IR and UV+IR retrievals, and above 20 km the IR-only retrieval progresses towards zero. The right panels show statistical information (mean absolute differences and the standard deviations). TOCs are calculated by integrating ozone profiles from the lowermost retrieval level altitude up to the tropopause. The height of the tropopause is obtained from the ERA5 reanalysis data using the 2 PVU definition.
Tropospheric lidar For the validation in the troposphere, tropospheric lidar measurements are particularly suitable. There are only three locations where lidar measurements are carried out regularly with a high temporal frequency (up to two times a day) and with which collocations were found in the TROPOMI test data set period. Since lidars have a high vertical resolution (below 100 m), similar to the ozonesondes, the lidar altitude grid is adjusted in accordance with Eq. (  2 ) before comparisons are made. Figure  5  shows the comparison of the TOPAS-retrieved ozone profiles and tropospheric lidar measurements at three different sites. While the measurements in Huntsville take place during daytime, the ozone profiles in OHP are measured after sunset. Only Table Mountain provides night-and daytime measurements, where the latter match in time with TROPOMI/CrIS overpasses.
The reason might be the higher viewing angle of most of the collocated CrIS measurements (compared to the other stations). The absolute difference in the TOC for each site is shown in Fig. 6. To obtain these results, the ozone profiles are integrated from the lowermost retrieval level altitude up to the tropopause. The tropopause height is obtained from the ERA5 reanalysis data set using the 2 PVU (e. g. Zbinden et al. , 2006)  definition for a dynamical tropopause  (Hoskins et al. , 1985). For cloudy pixels, the lidar profile is cut at the effective scene height. Overall good agreement with the lidar TOC is found for the UV + IR retrieval and an improvement in comparison to the UV-only retrieval is observed. The boxand-whisker plots on the left give a good overview of the results and their distribution.
5 ). The vertical resolution shows a strong dependence on latitude. This dependence is due to the different solar zenith angles and the typically low ozone content in the tropical upper troposphere-lower stratosphere (UTLS) region. To investigate the larger differences observed from the combined retrieval, the validation results for northern latitudes are separated into seasons. Figure  9  presents the results in summer (JJA) and autumn (SON) (both seasons with most collocations), while plots for other seasons are provided in Fig. S3. The mean collocated ozone profiles from NASA's operational CrIS level 2 product for the same ozonesonde measurements are also shown. For the comparison with CrIS, it must be taken into account that the NASA operational retrieval provides only about 2 DOF. High vertical sampling of the CrIS data and its good accuracy in the stratosphere and troposphere results to some extent from the use of the MERRA2 ozone profile data as a priori information  (Wargan et al.
, 2017). In the comparison with the ozonesondes (solid black line), a positive bias of up to 40 % is found for the combined retrieval in the altitude region between 10 and 15 km in both seasons, as mentioned above. However, there are two different situations to be considered. In summer, the a priori profile does not agree well with the ozonesonde data and none of the retrievals can substantially improve it. The results from the three retrievals are very similar. The UV-only retrieval already has a quite good vertical resolution under the given conditions and no further improvement can be achieved by adding the IR measurements. CrIS/MERRA2 ozone profiles (blue) show the same differences with the ozonesondes as the TOPAS retrievals, which means they agree very well. It should be noted that  Wargan et al.
g. Verstraeten et al. , 2013; Boynard et al. , 2016). Its possible reasons are still a matter of debate. -Analysing an example day of collocated MLS and TROPOMI/CrIS measurements, it was shown that the inclusion of the IR spectral range affects the retrieved profiles up to 30 km altitude. In the stratosphere, improvements in comparison with the UV-only retrieval were seen especially in the subtropical region. There are still some open questions to be answered in the future. The improvement of the combined retrieval over the UV-only one was mostly small. One possible reason is a rather low spectral resolution of the CrIS data. It would be therefore interesting to combine TROPOMI data with higher-resolved IR instruments, e. g. IASI; however, collocation of TROPOMI with IASI is not as favourable as in the case for the co-flying CrIS instrument.
Figure6. Absolute differences in the TOC with respect to the tropospheric lidar data. The differences are shown as box-and-whisker plots in the left panels and as time series in the middle panels. The right panels show statistical information (mean absolute differences and the standard deviations). TOCs are calculated by integrating ozone profiles from the lowermost retrieval level altitude up to the tropopause. The height of the tropopause is obtained from the ERA5 reanalysis data using the 2 PVU definition.
Acknowledgements. This study was supported and funded by the  BMWi/DLR  project " S5P Datennutzung " (Förderkennzeichen  50EE1811A ), the  University of Bremen , and the federal state of Bremen. All calculations reported here were performed on HPC facilities of the  IUP, University of Bremen , funded under  DFG/FUGG  grant nos. INST 144/379-1  and  INST   144/493-1. The S5P L1b version 2 data are provided by  ESA/KNMI  via the S5P validation team ( S5P-VT ) activities. We acknowledge all ozonesonde providers and their funding agencies for performing regular sonde measurements and thank the WOUDC and SHADOZ networks archiving these data. The same applies to the teams from all lidar stations we used. We thank the  NDACC network , especially  Thierry Leblanc ,  Mike Newchurch  and  Shi Kuang , and  Gerard Ancellete  from the  Observatoire de Haute-Provence  for providing the tropospheric lidar measurements and ozone profiles.
Ozone profiles of the MLS limb measurements and all level 2 products from CrIS are provided by  NASA. Special thanks are given to  Nadia Smith (Science and Technology Corporation, Columbia, MD)  who supported us with productive discussions on CrIS. Retrievals described in this paper use the GALAHAD Fortran library. We thank the developers for providing the source code and support. Financial support. This research has been supported by the  Deutsches Zentrum für Luft-und Raumfahrt  (grant no. 50EE1811A ), the  Universität Bremen  (grant nos. INST 144/379-1  and  INST   144/493-1 ), and the  Freie Hansestadt Bremen. The article processing charges for this open-access publication were covered by the  University of Bremen. Review statement. This paper was edited by  Birgit Hassler  and reviewed by two anonymous referees.
Data availability. All resulting data are available upon request from Nora Mettig (mettig@iup. physik. uni-bremen. de) or Mark Weber (weber@uni-bremen. de). The L1B version of the S5P test data is available upon request to the S5P Validation Team. All CrIS L2 products and MLS ozone profiles can be downloaded from the NASA Goddard Space Flight Center Earth Sciences Data and Information Services Center (GES DISC; Barnet,  2019a,  https://doi. org/10. 5067/9HR0XHCH3IGS; Barnet, 2019b, https://doi. org/10. 5067/ATJX1J10VOMU; Schwartz et al. , 2020, https://doi. org/10. 5067/Aura/MLS/DATA2516). S5P WFDOAS total ozone and albedo data are available from Mark Weber. Ozonesonde data from the WOUDC can be downloaded from https://doi. org/10. 14287/10000008 WOUDC Ozonesonde Monitoring Community et al.
(2022). Ozonesonde data from SHADOZ (Witte et al. , 2017, https://doi. org/10. 1002/2016JD026403; Thompson et al. , 2017, https://doi. org/10. 1002/2017JD027406; Witte et al. , 2018, https://doi. org/10. 1002/2017JD027791; Sterling et al. , 2018, https://doi. org/10. 5194/amt-11-3661-2018). The tropospheric lidar data used in this publication were obtained from Thierry Leblanc, Mike Newchurch, and Shi Kuang as part of the Network for the Detection of Atmospheric Composition Change (NDACC) (De Maziére et al. , 2018) and are available through the NDACC website http://www. ndaccdemo. org/ (last access: 1 March 2022, Network for Detection of Atmospheric Composition Change, 2021). Further tropospheric lidar data from the Observatoire de Haute-Provence station are provided by Gerard Ancellete through personal contact.
(2017)  con-firmed the geochemical data  (Inguaggiato et al. , 2018; Sciarra et al. , 2018; Mazzini et al. , 2012; Mazzini, 2018)  indicating that Lusi is fuelled by magmatic and hydrothermal fluids departing from the nearby volcanic arc and migrating into the Kendeng basin (Fig. 1 ). A wealth of data spanning from geochemical  (Sciarra et al. , 2018; Mazzini, 2018; Inguaggiato et al. , 2018) , geological  (Moscariello et al. , 2018; Malvoisin et al. , 2018; Samankassou et al. , 2018) , and geophysical  (Fallahi et al. , 2017; Mauri et al. , 2018)  observations indicate that Lusi is a natural occurrence that evolved over geological times. The large Lusi eruption provides the unprecedented opportunity to observe the evolution of a new-born hybrid system combining geochemical monitoring with regional geophysical data.
e. recorded by at least 6 stations and with location error smaller than 10 km and RMS within 1. 5 s) were used to perform the tomographic inversion. This restrained the whole catalogue to 128 seismic events shown by the larger circles falling inside our network. Our catalogue counted 1892 P-and 1468 S-wave arrival times. (2020). However, by adding further stations of the national network located to the South (Figure  S1 ), it was possible to widen the target area including some seismogenic volumes located East and West of Lusi and surrounding zones. These additional data allowed us to sample the crustal volume below the volcanic region South of Lusi. Finally, to perform the travel time tomography, we selected only the events falling inside the network (identified by larger circles in Fig. 3 ) and recorded by at least 6 stations and with location error smaller than 10 km and RMS within 1.
5 s. These selection criteria are satisfied by 128 events providing a total of 1892 P-and 1468 S-wave arrival times. These have been used as input for our travel-time tomography. In Figure  S4  of the supplemental material, we show histograms reporting the frequency distribution for number of phases, location errors, RMS and azimuthal gap of the selected events. The dense crossing of seismic rays inside the target volume is a crucial aspect to obtain stable and robust tomographic models. We acknowledge that the number of available events (128) does not represent a populated dataset if compared to recent tomographic models of other volcanic regions (e. g. Koulakov et al. (2007) ). However, the use of accurate P-and S-waves arrival times ensures that recovered anomalies are a reliable expression of the first order crustal features of the region.
The kink of the L-shaped trend corresponds to the upper threshold of SF that ensures a well-resolved node and negligible smearing effects  (Toomey and Foulger, 1989). Based on the observed SF versus DWS distribution (see Figure  S3  of supplemental material), we set the SF threshold to 3. 0. We further check the reliability of the observed tomographic features performing a restore resolution test  (Zhao et al. , 1992). The final V p and V p /V s models are used as synthetic model where the forward problem is used to compute synthetic travel-times using the same source-receiver distribution of the real datasets. To simulate the real dataset, Gaussian white-noise with standard deviation deduced from the real inversion (0. 49 s and 0. 55 s for P-and Swave data, respectively), has been added to synthetic travel-times.
The new travel times have been re-weighted according to the new noise level. We then inverted the final dataset using the same starting model  (Obermann et al. , 2018)  and the same inversion parameters used in the real case. Results of the test are shown in the supplemental material (Figure  S8 ) for the layers at 0 and 5 km depths. We show the final models computing the percent deviation (%) from the starting velocity model. Synthetic tests show that, inside the well resolved part of the models (SF<3), the deviation of the final models from the input model is within 5% for both V p and V p /V s. Thus, results of synthetic tests and the analysis of the resolution matrix indicate that V p and V p /V s models are reliable within the volume with SF ≤ 3. 0. To visualize the spatial definition of averaging vectors, we plotted the 70% smearing contour for nodes with a spread function lower than or equal to 3.
g. in the Alps  Rosenberg and Berger (2009) ). The low-angle thrust faults have been proposed to characterise the Kendeng basin and the back-arc of East Java  (Nachrowi et al. , 2003; Clements et al. , 2009; Simo et al. , 2012). This compressional tectonics generates well-developed broad anticlines and synclines visible in the active seismic data that prospected the Kendeng Basin  (Mazzini et al. , 2007 (Mazzini et al. , , 2009;; Moscariello et al. , 2018; Novianto et al. , 2020). The compressional and dilatational state of the upper crust is pointed out by  (Gunawan and Widiyantoro, 2019)  who calculate the dilatation rate of the region. Their modelled geodetic data show a well-developed compression for the northern part of East Java. However, while the back-arc is in compression (including Lusi), the region of the volcanic arc seems to be less compressed, almost in extension.
This may be in line with the eruptive activity taking place at Lusi. The low Bouguer gravity anomaly of the Kendeng Basin could also be explained by a thick sedimentary cover that would also cause low V p velocities and high V p /V s ratios. However, the geochemical data show a marked mantle component in the fluids erupted at Lusi that may only be explained by the occurrence of magma at depth  (Mazzini et al. , 2012; Inguaggiato et al. , 2018; Zaputlyaeva et al. , 2019). Such geochemical signatures could not be explained by the hydrocarbon maturation process and a thick sedimentary cover alone. Because of this, we propose a new scenario that would help understanding the birth of Lusi and that would reconcile the multidisciplinary observations carried on the region over the last decade. Fig.
Conclusions Using the seismic catalogue recorded in the framework of Lusi-Lab, and complementing it with data retrieved from additional stations from the Indonesian permanent network, we performed a local earthquake tomography of the East Java region. We obtained V p and V p /V s anomaly distributions with respect to the initial velocity model. Such anomalies show sharp contrasts and regions marked by negative V p and positive V p /V s perturbations. This allowed us to identify a region where large magmatic reservoirs are suggested to occur. The Kendeng basin, hosting the Lusi sediment hosted geothermal system features the largest negative V p and positive V p /V s perturbations. The tomographic inversion shows that the Kendeng Basin is connected to the volcanic arc via a region characterised by negative V p anomalies below about 10 km depth.
Acknowledgements The work was funded by the  European Research Council  under the European Union's  Seventh Framework Program  grant agreement  308126  ( LusiLab  project, PI  A. Mazzini ). We acknowledge the support from the  Research Council of Norway through its Centers of Excellence funding scheme  (project  223272 ) and the  HOTMUD  project (number  288299 ). Riccardo Minetto  is funded by the  URBASIS-EU  project ( H2020-MSCA-ITN-2018 ), grant  813137. We thank the  Geophysical Instrument Pool Potsdam (GIPP)  for providing the instruments for the SEED experiment in the framework of the  LUSI LAB  project. Matteo Lupi  acknowledges the  Swiss National Science Foundation  for the funding scheme  Ambizione  (project  PZ00P2 154815 ). The authors thank  BPLS  for their support during field operations and  Karyono Karyono  is thanked for the initial catalogue of the LusiLab temporary network.
Processing of aerial and satellite data Photogrammetry is a powerful tool to assess the glacier-wide mass balance of several glaciers at the scale of a mountain range. In the present study, we took advantage of the availability of high-resolution aerial and satellite stereoscopic data from different sources acquired at different dates. The stereoscopic data enabled us to map every glacierised basin on the Antisana ice cap at high spatial resolution (Table  1 ), and to quantify changes in the mass balance of the glaciers at a decadal time step since 1956. 1956, 1965, 1979, 1997 and 2009. The aerial surveys were conducted by the Instituto Geografico Militar (IGM) of Ecuador. The bundle block adjustment was conducted based on a geodetic network of 21 ground control points (GCPs) collected using the DGPS method in a static mode with an uncertainty of less than ~0.
Finally, stereo- matching algorithms enabled the generation of a 3D point cloud by means of pixel-to-pixel correspondences between the images. The 3D mapping procedure for the stereoscopic pair was performed using the Ames Stereo Pipeline (ASP), a free open-source software developed by the National Administration Space Agency  (Beyer et al. , 2018). The Pleiades images were acquired in the framework of the ISIS programme of the French Space Agency (CNES) in the framework of the Pleiades Glacier Observatory initiative  (Berthier et al. , 2014).
Then, the ∆ adjustment factors are computed as follows: ∆ 𝒙 = 𝑎 • sin(𝑏) (2), ∆ 𝒚 = 𝑎 • cos(𝑏) (3), ∆ 𝒛 = 𝑐 tan (𝛼 ̅) (4), As the geometric characteristics of the 2009 geodetic survey were optimal, the digital elevation model (DEM) generated from these data was used as a reference to evaluate the bias in the geodetic data of the other acquisitions. Because high spatial resolution data were used in this study (Table  1 ), an elevation difference layer of 10x10 m cell size was computed for each geodetic comparison using the universal kriging technique based on an exponential semi-variogram model. After coregistration, analysis revealed that the geodetic datasets were slightly unaligned with respect to the reference, with a mean elevation difference ranging from -1. 8 to 3. 2 m on stable terrain.
Consequently, we decided to perform 3D geometric adjustment to minimise the difference in the geodetic data. The results are presented in Table  2  and in supplementary materials. In all cases, there was a reduction of around 8% in the standard deviation and the mean difference in elevation was improved, and a normal distribution of the ℎ on the glacier forelands is better shaped with an average standard error of 0. 03 m after 3D geometric adjustment (Fig. S1  in supplementary materials). Period  The co-registration results showed a similar displacement vector of around 8 m in all five datasets. For the aerial-based geodetic data, this was attributed to the number and distribution of the GCPs used to solve the stereo orientation. For the Pleiades-based data, this is probably explained by the fact that the triangulation and geometric adjustment of Pleiades stereo-images was based on the RPC model without including GCPs.
, 2013) ; and (ii) for precipitation calibration, we applied a Quantile Mapping approach which uses a statistical transformation function to adjust the distribution of ERA5 data to match the distribution of observations  (Thrasher et al. , 2012). To define the function, we applied a non-parametric transformation termed QUANT in the R package QMAP  (Gudmundsson et al. , 2012). Once biases were reduced, a simple statistical metric enabled us to confirm that the mean difference between ERA5 data and in-situ observations was improved by 90% and the RMSE reduced by 60%, showing that the calibration methods performed quite well except for the RMSE of M188 station, which showed no improvement (Table  3 ).
(ECMWF), which is a 0. 25° (~30 km) grid resolution. ERA5 reanalysis replaces the former version of ERA-Interim reanalysis. Reanalysis data are prone to systematic biases due to the limited spatial resolution and simplified physics used to represent climate, particularly in mountain areas where orographic factors drive climate conditions. In practice, temperature (precipitation) measurements made near glaciers are somewhat higher (lower) than reanalysis data. To adjust the ERA5 dataset to observations, we compared the reanalysis data close to the grid cell to the station location and the in-situ meteorological data at a monthly time step for the nine weather stations with the longest series that recorded climate variability from the https://doi. org/10. 5194/tc-2022-70 Preprint. Discussion started: 21 April 2022 c Author(s) 2022.
Acknowledgments This study was funded by  Universidad Regional Amazónica Ikiam  and the  Laboratoire Mixte International GREAT-ICE  (http://www. great-ice. ird. fr/)lead by the  French Institute of Research for Development (IRD). We acknowledge the  Service National d'Observation GLACIOCLIM  (http://glacioclim. osug. fr/)and the contribution of the  Labex OSUG@2020 (Investissements d'Avenir - ANR10 LABX56 ). Special thanks to the institutions that provided access to the aerial photographs and meteorological observations: the  "Ministerio del Ambiente del Ecuador" (MAE) , the  "Empresa Metropolitana de Alcantarillado y Agua Potable de Quito" (EPMAPS)  and the  "Instituto Nacional de Meteorología e Hidrología" (INAMHI). We acknowledge  Dr. Etienne Berthier  ( CNRS, LEGOS ) for the  Pleiades Glacier Observatory initiative via the French Space Agency's (CNES) ISIS  programme which facilitated access to Pleiades data.
Forecasts (ECMWF), which is a 0. 25° (~30 km) grid resolution. ERA5 reanalysis replaces the former version of ERA-Interim reanalysis. Reanalysis data are prone to systematic biases due to the limited spatial resolution and simplified physics used to represent climate, particularly in mountain areas where orographic factors drive climate conditions. In practice, temperature (precipitation) measurements made near glaciers are somewhat higher (lower) than reanalysis data. To adjust the ERA5 dataset to observations, we compared the reanalysis data close to the grid cell to the station location and the in-situ meteorological data at a monthly time step for the nine weather stations with the longest series that recorded climate variability from the https://doi. org/10. 5194/tc-2022-70 Preprint. Discussion started: 21 April 2022 c Author(s) 2022.
In more detail, the strategy aims to demonstrate its effectiveness in comparison with the already established techniques for supervised classification with SITS. We develop an algorithm that uses the combination of two methodologies, one for dimensionality reduction and the other for classification with a copula-based approach. The choice of carrying out a preliminary stage for the reduction of dimensionality was guided by the fact that in the literature, this method is widely used and allows the main information about the dataset to be preserved even if these are projected in a space with a reduced size. Among the various techniques for this task, the most useful are Principal Component Analysis (PCA)  [31] [32] [33] , Singular Value Decomposition (SVD)  [34] [35] [36]  and Independent Component Analysis (ICA)  [37, 38].