dataesr/scientific-paragraphs-categorization huggingface dataset:
Sounding of the Atmosphere using Broadband Emission Radiometry Ozone and temperature measurements from the SABER instrument were downloaded from 15 to 100 km. The vertical resolution for SABER temperature profiles is approximately 2 km, and the estimated accuracy is 1 to 2 K between 15 and 60 km which decreases to 5 K near 85 km, and to 10 K near 100 km  (Rezac et al. , 2015a, b). Precision estimates for SABER ozone profiles are reported as 1 % between 40 and 50 km altitude, decreasing to 2 % between 30 and 55 km and 10 % near 80 km  (Rong et al. , 2009). A more complete description of the instrument is given in  Mertens et al. (2001). SABER profiles of temperature, geopotential height, and ozone were extracted from the version 2. 0 SABER dataset.
DWD NDACC lidar upgrades and example data The HOPS campaign took place in two parts: the first period covered the nights of 21 and 22 October 2018, and the second period covered nights in 2019, from 21 March to 6 April. Table  2  shows which systems provided ozone and/or temperature profiles on each of the different nights of the campaign. Table  3  shows the details of the altitude range and important wavelengths for each instrument when making measurements of temperature and ozone.
Evaluation of the new HOH lidar The HOPS campaign provided a perfect opportunity to conduct an evaluation of the newly installed HOH lidar. The HOH lidar ran concurrently with the NASA-STROZ mobile validation lidar as well as the original HOHO lidar. This crucial overlap period allows us the opportunity to conduct a formal NDACC evaluation of the Hohenpeißenberg lidars and ensure that there are no unexplained biases or problems which could go on to cause discontinuities in one of the longest-running NDACC datasets. Table  4  shows an in-depth comparison of the technical specification for the HOH, HOHO, and NASA lidars. Figure  2  shows a comparison of the nightly average deadtime-corrected photon count rates for the high-gain channels of the HOH and HOHO lidars as well as the ratio of the high-and low-gain channels for both systems.
In the crucial ranges between 30 and 50 km for ozone and 30 and 70 km for temperature the HOH channels are linear with respect to their HOHO counterparts and do not appear to exhibit altitude-dependent biases or photomultiplier saturation effects. This is an important result to document with regards to the long-term stability of the NDACC lidar temhttps://doi. org/10. 5194/amt-14-3773-2021 Atmos. Meas. Tech. , 14, 3773-3794, 2021  perature and ozone dataset at the Hohenpeißenberg Meteorological Observatory.
(2016)  which presents a more realistic uncertainty profile for a similar instrument. The relative uncertainty profiles for MLS (violet) and SABER (magenta) were calculated us- ing the uncertainty information included in the downloaded data files. MLS has low relative uncertainty throughout most of the stratosphere, averaging 2 % to 3 %. The uncertainty rapidly increases at low ozone densities below 20 km and above 45 km. SABER ozone uncertainty appears unrealistic above 35 km and increases rapidly below 30 km. We have endeavoured to estimate accurate profiles of OMPS relative uncertainty for both the visible channel and the UV channel for the HOPS campaign. Using the 1σ measurement uncertainty estimates found in  Loughman et al. (2005) ;  Zawada et al. (2018) ;  Kramarova et al. (2018) , we have calculated the nightly relative uncertainty profiles for OMPS visible (mustard) and UV channels (burnt orange).
Conclusions The HOPS intercomparison campaign of the DWD lidars at the Hohenpeißenberg Meteorological Observatory with the travelling standard NDACC reference NASA-STROZ lidar has demonstrated the consistency of the HOH lidar measurements with respect to measurements made using the HOHO lidar. We have confidence in the continued high quality of the Hohenpeißenberg dataset for both temperature and ozone after the installation of a new lidar system. The intercomparison exercise has confirmed that the original DWD lidar, HOHO, continues to meet NDACC standards for ozone profiles at the 3 % level between 16. 5 and 43 km and at the 10 % level between 10 and 44 km. The HOHO lidar meets the NDACC temperature standards for accuracy at the ±1 K level between 18 and 70 km. The new DWD lidar, HOH, meets the 3 % ozone standard between 17 and 41 km, the 10 % ozone standard between 15 and 41 km, and the ±1 K temperature standard between 17 and 78 km.
Acknowledgements. This work is supported by the  Deutscher Wetterdienst (DWD) , the  Institut National des Sciences de l'Univers/Centre National de la Recherche Scientifique  ( INSU/CNRS ), and the  NASA   Upper Atmospheric Research Program. Financial support. This research has been supported by the Deutscher Wetterdienst (DWD; GVPL-FEHPC). Review statement. This paper was edited by  Karin Kreher  and reviewed by two anonymous referees.
Data availability. The data that support the findings of this study are openly available. 1. The data used in this publication were obtained from Hohenpeißenberg Meteorological Observatory as part of the Network for the Detection of Atmospheric Composition Change (NDACC) and are publicly available (ftp://ftp. cpc. ncep. noaa. gov/ndacc/station/hohenpei/, last access: 15 March 2020). 2. Local radiosoundings from Munich are available at http://weather. uwyo. edu/upperair/sounding. html (last access: 15 March 2020, Oolman, 2021). 3. MLS temperature and ozone profiles are available at https://doi. org/10. 5067/Aura/MLS/DATA2520 (Schwartz et al. , 2020). 4. SABER temperature and ozone profiles are available at ftp:// saber. gats-inc. com/ (last access: 15 March 2020, SABER Gats Inc. , 2020). 5.
Acknowledgments This study is the result of an international workshop financed by the  Valencian government in Spain (Generalitat Valenciana , reference AORG/ 2018/) and was supported by  Spanish Plan Nacional de I+D+i  (project  PGC2018-099027-B-I00 ). E. V. was supported by the  2017 program  for attracting and retaining talent of  Comunidad de Madrid  (no. 2017-T2/ AMB-5406 ).
This paper addresses forest height estimation for boreal forests at the test site of Edson in Alberta, Canada, using dual-baseline PolInSAR dataset measured by Intermap's single-pass system. This particular dataset is acquired by using both ping-pong and non-ping-pong modes, which permit forming a dual-baseline TomoSAR configuration, i. e. , an extreme configuration for tomographic processing. A tomographic approach, based on polarimetric Capon and MUSIC estimators, is proposed to estimate the elevation of tree top and of underlying ground, and hence forest height is estimated. The resulting forest DTM and DSM over the test site are validated against LiDAR-derived estimates, demonstrating the undeniable capability of the single-pass L-band PolInSAR system for forest monitoring.
Using Multi-baseline PolInSAR techniques, SAR tomography (TomoSAR) offers an efficient tool to carry out accurate 3D reconstruction of forested areas  [5] [6] [7] [8] [9] [10] [11] , including forest vertical structure and physical features. Forest height estimation has been successfully achieved via some well-known repeat-pass L-band PolInSAR systems in the literature. For example, the established single-baseline PolInSAR inversion techniques have been used to estimate tree heights over temperate forests, using L-band PolInSAR data acquired by DLR's F-SAR system  [12] , as well as over tropical forests using L-band PolInSAR data acquired by E-SAR sensor during the Indrex II campaign  [13]. TomoSAR techniques have also been applied to a six-pass dataset acquired by E-SAR sensor during BioSAR campaign over boreal forests  [14].
In 2007 and 2008, Intermap developed an experimental single-pass L-Band PolInSAR system operated at a relatively lower altitude (∼1000 m) with a physical baseline around 3. 5 m. The design philosophy was driven by the desire to demonstrate, relatively quickly and inexpensively, the capabilities and issues associated with tree height and Digital Terrain Model (DTM) estimation using PolInSAR techniques. The single-pass configuration permits assessing performance in forest monitoring in the absence of temporal decorrelation and residual motion issues. Mercer et al. [15]  reported RVOG inversion results from singlebaseline datasets, regarding DTM extraction and then forest height estimation over areas largely populated by pine stands, with heights ranging up to 30 m. Those results suggest that tree height accuracy (relative to LiDAR-derived heights) in sampled subsets was better than 10% of tree height, while the DTM showed biases that were typically 2-3 m above the true ground elevation.
The RVOG-based PolInSAR retrieval techniques also met some limitations at the near range of the acquired images, which reduced the useful swath of this system for forest monitoring. Regarding this PolInSAR system, a single transmitter and receiver chain provides fully polarimetric data through a switching network that allowed pulses to be recorded in both ping-pong and non-ping-pong modes simultaneously. Three independent antenna phase center positions can hence be obtained in a single flight, leading to a dual-baseline configuration, which provides the feasibility of applying SAR tomography to forest height estimation. This paper aims to evaluate the performance of forest height estimation using this dual-baseline TomoSAR configuration. Classical tomographic methods, such as Capon and MUSIC techniques, may meet some limitations for 3D reconstruction of forested areas, when dealing with this TomoSAR configuration whose vertical resolution is coarser than the observed tree heights.
Test Site Presentation The test site is a forested area near the town of Edson in central Alberta, Canada (Figure  1a )  [15]. This area is well suited for experiments related to forest height estimation, as it consists of a pattern of alternating forests and clear-cut patches. The forest over the test site is mainly composed of lodge pole pine trees with heights ranging from 10 to 30 m and with relatively homogeneous growth in the forested patches. Figure  1b  shows ground photos over different patches of forests as well as short vegetation areas at the test site. The scene topography was measured using a LiDAR device (courtesy Terrapoint Canada) and the corresponding datasets were processed, in order to compute DSM and DTM estimates. In particular, the tree top height is derived from the envelope of the measured data in Figure  2 , using the highest point within a 5.
Single-Pass Dual-Baseline Tomographic Configuration TomoSAR, with an additional aperture in elevation formed by multiple acquisitions, is able to reconstruct the 3D structures of semi-transparent media, and in particular forests. Tomographic techniques can provide very good estimates of tree heights and underlying ground topography using repeat-pass Fully Polarimetric (FP) datasets  [8, 16]. The acquisition system used in this study offers the possibility to acquire Multi-Baseline Polarimetric Interferometric SAR (MB-PolInSAR) data during a single pass of the airborne platform. This advanced functionality is based on the sequential use of two sets of antenna elements having each transmit and receive capabilities. This sequence of acquisitions is illustrated in Figure  3 , and it may be described as follows.
, k n s ]. Similar to the single polarization expression in (2), the received MB-PolInSAR data y P (l) is given by y P (l) = n s ∑ i=1 s i a(z i , k i ) + n(l) = A(z, K)s(l) + n(l) ∈ C 3M×1 (5) where s(l) ∈ C n s ×1 represents a realization of the complex amplitude of the ith source. The reflectivity, scattering vector and height σ i , k i , z i of each source, may be estimated from R P = E(y P (l)y † P (l)), but in practice these parameters are estimated from the L-look estimate of the data covariance matrix RP = 1 L ∑ L 1 y P (l)y † P (l) using polarimetric tomographic techniques. Compared to single polarization tomography, the use of several polarization channels results in an undeniable increase of performance improvement for recovering the physical features of the observed medium. Polarimetric diversity also provides a better estimation accuracy for polarimetric tomographic estimators than single polarization ones, as shown in  [17, 19].
The model order n s = 5 is selected for the tomographic processing of forested areas in this paper. To confirm this finding, a dual-baseline L-band polarimetric dataset is simulated over a homogenous forested area with varying forest parameters and ground conditions, using the PolSARproSIM software  [22]. The resulting Pauli-coded images and the corresponding coherences are shown in Figure  10 , with the PolSARproSIM parameters given in Figure  10a. The P-Capon technique and P-MUSIC estimator are both applied to the simulated dataset. Considering an intermediate ground roughness, i. e. , Roughness = 4, the P-Capon method and P-MUSIC estimator with n s = 2 are able to estimate the ground and volume phase centers in the vertical direction, as shown in Figure  11b ,e. Nevertheless they cannot localize the ground scattering contribution for rough surfaces (Figure  11c, d, f, g ).
From the methodological perspective, the proposed hybrid tomographic approach provides an excellent performance for this dataset, using a model order selected in an adaptive way, as described in Section 3. 3. 1. From the system perspective, unlike from other single-baseline PolInSAR systems, this specific system combines both ping-pong and non-ping-pong InSAR modes simultaneously, and it results in a dual-baseline configuration that permits tomographic processing. Moreover, this system performs the data acquisitions in a single flight, which effectively reduces the technical effort and cost for forest monitoring, and is intrinsically robust to temporal decorrelation, resulting in a better 3D reconstruction of forests.
Conclusions This paper addresses forest height estimation for boreal forests at the test site of Edson in Alberta, Canada, using Intermap's single-pass PolInSAR dataset. This particular dataset is acquired by using both ping-pong and non-ping-pong modes, which permits to form a dual-baseline TomoSAR configuration, i. e. , an extreme configuration of tomographic processing. Mono-dimensional tomographic techniques are preferred in this paper, considering computational complexity of tomographic processing over a large forested area. Classical polarimetric Capon and MUSIC tomographic estimators are discussed and applied to the test data. The P-Capon technique provides a robust estimate of the vertical distribution of the back scattered power over forested areas, but it cannot recover the underlying ground elevation due to its limited resolution.
However, most of these studies have some features in common: they all rely on linear (ARDL, 1 Granger causality, 2 VECM. 3 ), or quadratic (EKC 4 ), relationships. Therefore, a spurious causal structure can arise when there is any another type of nonlinearity in the data, which could lead to misinformed policy recommendations. The novelty of this paper lies in the use of a causal discovery method to estimate nonlinear interdependence networks from time series datasets in order to detect, quantify and predict causal interactions. Causal discovery is based on linear and nonparametric conditional independence tests developed in  Runge et al. (2019). Contrary to applications of Granger causality that have been limited mostly to bivariate analyzes that cannot account for indirect links or common drivers, this approach is based on a graphical causal model framework that scales well with time series datasets featuring linear and nonlinear, timedelayed dependencies.
The PCMCI method, in addition to the detected links between variables, highlights the strength of autocorrelation or cross-correlation that may exist between different variables and the time lag of each variable. Table  1  displays the results of Granger causality tests. We then use the PCMCI-related tests and report results in Fig. 4. Results are commented in the captions. To summarize, in the presence of nonlinear models Parcorr and Granger causality detect false links between variables or may not detect links between variables because it is not suitable for nonlinear series. We conclude that in the case of nonlinear series the most suitable independence tests to highlight the causal links are GPDC and CMIknn. 2019 ) on Causal discovery problem-Consider a large-scale time series dataset (A) from a complex system such as the Earth system of which we try to estimate the underlying causal dependencies (B), accounting for linear and nonlinear dependencies and including their time lags (link labels).
Findings We begin the analysis by exploring properties that emerge from the most aggregated indicators. This level of aggregation gives us an overall perspective of global challenges that is arguably less skewed than a more regional perspective. Indeed, for example, a regional GDP may be influenced by other indicators, such as energy consumption in another region through some import mechanisms. Working at the global level would allow us to have smooth global perspectives on the relationships that are at play in our dataset. Next, we will analyze the results from a regional perspective to provide insights into more specific challenges at the regional level. Comments are based on the ceteris paribus assumption over the period during which the statistical methods are applied.
Conclusion and policy implications By exploring the interactions between energy, CO 2 emissions, and economic activity using the causal discovery method to estimate nonlinear interdependence networks, we show that the dataset we use exhibits nonlinearity that challenges previous results obtained in a more linear framework. At the global level, the results unambiguously show that energy drives GDP at the global level and that there is a bidirectional relationship between energy and CO 2. Overall, these results suggest that too rapid a transition to net-zero emissions in the energy sector may have nonlinear negative consequences for economic growth and that policy should focus primarily on reducing the CO 2 emissions enabled by energy use. At the multi-regional level, the results show a complex network where each region is fully integrated into the system, which argues for coordinated policies across regions in the sense that regions (or countries) should not be analyzed in isolation.
Introduction Both the adverse effects of pesticides and the ubiquity of human exposure to these chemicals, including in the general population, have been documented by an increasing amount of datasets. Organophosphate and pyrethroid pesticides, which have been associated in different cohorts with poorer intellectual development after in utero exposure or with behavioral problems and cognitive developmental disabilities in exposed children  (Bouchard, 2011; Engel, 2011; Oulhote and Bouchard, 2013; Rauh, 2011; Viel, 2015) , have yet been detected in a large part of the population in different countries. Para-nitrophenol (PNP) and 3,5,6trichloro-2-pyridinol (TCPy), two organophosphate metabolites, were detected in the urine of 30 to 58% and 70% of American pregnant women respectively  (Castorina, 2010).
Statistical analysis Percentiles and median concentrations were calculated with Microsoft Excel software. Results below the limit of quantification and above the limit of detection were taken into account for all the calculations since we considered more relevant to use the measured values than replacing them by arbitrary values  (Helsel, 2006). To investigate correlation between hair and urine concentrations, only the compounds detected in at least 80% in the two matrices were considered. Only the samples positive for both matrices were considered for correlation tests  (Helsel, 2006). Pearson and Spearman correlation coefficients and their corresponding p-value were obtained with SigmaPlot software.
Acknowledgements This study was carried out in the framework of the call for Research Project 2010 of the  National Research Programme for Environmental and Occupational Health' (PNR-EST ) of the  French Agency for Food, Environmental and Occupational Health Safety (ANSES, Agence nationale de sécurité sanitaire , de l'alimentation, de l'environnement et du travail), with the financial support of the  National Office for Water and Aquatic Environments (ONEMA, Office National de l'Eau et des Milieux Aquatiques ) supporting the implementation of the Plan Ecophyto 2018, France.
Due to increasing surface melting on the Greenland ice sheet, better constraints on seasonally evolving basal water pressure and sliding speed are required by models. Here we assess the potential of using inverse methods on a dense time series of surface speeds to recover the seasonal evolution of the basal conditions in a well-documented region in southwest Greenland. Using data compiled from multiple satellite missions, we document seasonally evolving surface velocities with a temporal resolution of 2 weeks between 2015 and 2019. We then apply the inverse control method using the ice flow model Elmer/Ice to infer the basal sliding and friction corresponding to each of the 24 surface velocity data sets. Near the margin where the uncertainty in the velocity and bed topography are small, we obtain clear seasonal variations that can be mostly interpreted in terms of an effective-pressure-based hard-bed friction law.
Seasonal surface velocities We have derived a time series of horizontal surface ice velocity of the Russell sector at a spatial resolution of 150 m using satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in  Derkacheva et al. (2020)  and are summarized below. Normalized cross-correlation is used to estimate the features' (or radar speckle) displacement between primary and secondary images taken on two different dates, which is further converted to the v x (east-west direction) and v y (northsouth direction) surface flow speed components  (Mouginot et al. , 2017; Millan et al. , 2019). Only the measurements with time intervals shorter than 32 d (1 month) are used in order to capture rapid dynamic changes in ice flow.
Thus the median year is fairly representative of the behaviour of this sector during the considered period. For instance, we compare in Fig. 2f  the temporal evolution of the median speed with the annual data sets at a location on Insunnguata Sermia (point A in Fig. 1 ). The root-mean-squared deviation between the median and the annual data sets is about 10 m yr -1 , which is within 10 % of the mean winter speed. In addition to twicemonthly time series, we also calculated the mean velocity of early and late January, February, and March observations to produce a map of mean winter speed (MWS hereinafter). Besides the velocity maps, we generate a spatially distributed estimate of the velocity uncertainties for each temporal step. This uncertainty σ is computed per pixel as σ = SD/ √ n, where SD is the standard deviation of the averaged measurement in each time step and n is the number of averaged measurements.
Model set-up To investigate the year-around behaviour of the basal friction and velocity fields, we perform the inversions for each of 24 velocity maps presented in Sect. 2 (hereafter called "time steps") in a similar way as described above for MWS. As the observation errors are not uniform in time, to assess the sensitivity of the summer results to the regularization, we run a new L-curve analysis with the early July velocity data set. The results are shown in supplementary Fig. A5a. The minimal value of J 0 is now close to 2. This could be due either to an under-estimation of the uncertainties on observed ice speed for these 2 weeks or to the model not being able to exactly match the summer observations. The latter could be due to model errors not taken into account or because reducing the observations to a standard year leads to more inconsistencies in summer as there is more variability during this period compared to winter.