dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2005). One limitation of Dobson/Brewer UV instruments is the so-called stray light effect  (Kerr et al. , 1981; Van Roozendael et al. , 1998; Fioletov et al. , 2000) , which prevents the use of Dobson/Brewer instruments to retrieve TCO at large solar zenith angles (SZAs, above 80°). Since the 1990s, a zenith-sky UV-visible ozone monitoring group has been operating within the Network for the Detection of Atmospheric Composition Change (NDACC)  (Sarkissian et al. , 1995; Vaughan et al. , 1997; Van Roozendael et al. , 1998; Van Roozendael and Hendrick, 2009; Hendrick et al. , 2011). Unlike Dobson/Brewer instruments, NDACC UV-visible instruments use the zenith-sky visible spectrum (Chappuis bands) to retrieve TCO. The use of visible spectroscopy makes it possible to measure TCO at higher SZAs (up to 91°), which allows for the collection of data at high latitudes during polar sunrise and sunset.
1). However, at this time, there is no cloudscreen (detection) algorithm developed specifically for ZS-DOAS measurements at high latitudes, where the limited SZA range makes it challenging to apply any of the previously developed algorithms. For example, the algorithm developed by  Wagner et al. (2016)  needs measurements when SZA < 60, whereas these small SZA measurements only account for about 7% of UT-GBS year-round measurements at Eureka, which is located at 80 N, where the lowest SZA is about 56. The objective of this work is to develop a cloud detection algorithm for high-latitude measurements using data collected by ZS-DOAS instruments deployed at Eureka to improve TCO data quality. This paper is organized as follows. Section 2 describes the measured and modelled TCO data used in this study, with additional information about Eureka weather records.
Data and methodology Observations and atmospheric reanalysis have been used. SSTs have been taken from the HadISST dataset  (Rayner et al. 2003)  for the period 1870 to 2014. Sea level pressure (SLP) and surface wind come from the ERA-20C reanalysis  (Poli et al. 2013)  for the period 1900 to 2010. The isotherm of 168C, considered as a proxy of the thermocline depth, has been taken from the SODA reanalysis for the period 1871-2002  (Giese and Ray 2011). The precipitation comes from the CRU dataset for the period 1901-2013  (Harris et al. 2014 ). Seasonal anomalies have been calculated by subtracting the climatological seasonal cycle of the whole period. To isolate the interannual variability, a high-pass Butterworth filter  (Butterworth 1930)  has been applied to the anomalies. The cutoff frequency selected (7 yr) is adequate for interannual studies, since it removes the low-frequency signal and the global warming trend.
The independence of the EOFs has been assessed using the North criterion  (North et al. 1982). Because of the orthogonality constraint, PCA has a tendency to produce unphysical modes. This feature can be partly solved by rotating the spatial EOF patterns  (Horel and Wallace 1981; Richman 1986 ). Here we present the unrotated EOFs. However, the coherence of these spatial structures has been confirmed through the rotation of the SST patterns, using the varimax algorithm  (Kaiser 1958). Similar EOF patterns are found, independent of the number of modes retained (not shown), which gives robustness to the results. Moreover, the same modes are obtained when the PCA is applied to different SST datasets (e. g. , ERSST and SODA, not shown). Following the results of  Martín-Rey et al. (2014) , two subperiods of negative and positive AMO phases have been considered.
To create the two samples, we have chosen the longest common period for all datasets: 1900-2008. For the AMO positive period, we have selected years in which the AMO standardized index exceeds a value of 10. 5. In this way, a total of 34 years makes up the  AMO positive period: 1928 AMO positive period: -42, 1951 AMO positive period: -60, and 1999 AMO positive period: -2007. Consistently, for the AMO negative period we have chosen years in which AMO index takes values below 20. 5, making a total of  38 years: 1906-09, 1918-23, and 1968-95. To compute the AMO index we have followed the methodology described by  Mohino et al. (2011)  and  Villamayor and Mohino (2015). First of all, a global warming (GW) index has been calculated as the low-pass filtered (40-yr cutoff) time series of average SST between 458S and 608N.
Moreover, very low (close to zero) correlation scores between the HS pattern and NNTA anomalous SSTs are shown at all lags (blue line, Fig. 4c ), revealing a lack of connection between the HS and NTA modes along their development and decay. Similar results are obtained when the NTA pattern is correlated to the SNTA, WEQ, and WSTA regions (red, purple, and green lines, Fig. 4d ). We conclude that during negative AMO phases there are two different and independent modes that define the SST variability in the north tropical region: the HS and the NTA patterns. The abovementioned results evidence that during negative AMO phases the interannual TAV has increased, favoring the emergence of a new variability mode, the HS pattern. This finding has been also confirmed with several additional analyses: 1) using other SST datasets, ERSST  (Huang et al.
In addition to the dynamical processes commonly associated with the Bjerknes feedback  (Keenlyside and Latif 2007) , other mechanisms have been recently revisited: oceanic Rossby and Kelvin waves  (Foltz and McPhaden 2010; Lübbecke et al. 2010; Lübbecke and McPhaden 2012; Lübbecke et al. 2014) , meridional equatorward advection of NTA SST anomalies  (Richter et al. 2013) , or air-sea thermodynamic feedbacks  (Nnamchi et al. 2016). Regarding the new overlooked HS pattern, a detailed description of the air-sea interactions responsible for its development is required. Acknowledgments. The research leading to these results received funding from the EU FP7/2007-2013 under Grant Agreement 603521 (PREFACE project). This study was also supported by the Spanish MINNECO project CGL2012-38923-C02-01.
With this novel knowledge, climate models could be improved to accurately predict responses to future climate forcing events. The development of in situ sensors and oceanic observing systems to be deployed on autonomous underwater vehicles will provide extensive databases and unprecedented opportunity to study the ocean at much wider spatio-temporal scales than what was possible only a few years ago  (COCA Working Group, 2014). Oceanographic nutrient measurements are commonly made using traditional, discrete shipboard sampling techniques and on-board analyses  (Patey et al. , 2008; Ma et al. , 2014). Over the past decade, significant progress has been made in the development of in situ nutrient sensors and a few sensors are commercially available to measure nitrate and phosphate  (Thouron et al.
Electrochemical Cells and Designs Two electrochemical cells (i. e. , "open cell" and "small volume laboratory prototype") were used to validate the method and optimize all the parameters before transferring them on the in situ electrochemical sensor. All the electrochemical measurements using the 2 laboratory cells were carried out at room temperature, under atmospheric conditions with µ-Autolab III potentiostat (Metrohm) using the NOVA software. The "open cell" (not shown) corresponds to a 20 mL beaker separated into two compartments. In the first compartment of 2 mL, a molybdenum electrode was oxidized at 1V in order to produce molybdates (MoO 2- 4 ) and protons (H + ) according to Equation (1). To reach the acidic pH needed (pH ≈ 1. 5) to form the silicomolybdic complex (Equation  2 ), the counter electrode was isolated from the molybdenum electrode behind a 180 µm thick non-proton exchange membrane (N117 Du Pont TM Nafion R PFSA Membrane) to limit the reduction of formed H +  (Lacombe et al.
ACKNOWLEDGMENTS DC's contract and present work were supported by the  SenseOCEAN  project:  Marine  sensors for the twenty first century funded by the  European Commission Seventh Framework Programme  ( FP7)  under Grant Agreement n • 614141 (http:// www. senseocean. eu/). The authors would like to thank  Marcel Belot  from the  Groupe d'Instrumentation Scientifique of the Observatory Midi-Pyrénées, Toulouse, France  for mechanical manufacturing and assistance. The authors also thank  Cécile Guieu ,  Laure Mousseau ,  Antoine Poteau  from the  Laboratoire d'Océanographie de Villefranche-sur-Mer   UMR7093  and  Jean-Yves Carval , captain of the SAGITTA 3 vessel of the  Observatoire Océanologique de Villefranche-sur-Mer  for their help and their implication during the PROVOR float deployment, and the crew of the scientific vessel  Stella Maris II, Coquimbo, Chile.
Measurements were performed using the ImageJ software. Close-up of distal pleon and telson was further documented using reflectance transformation imaging (RTI; see  Béthoux et al. 2016  for details about the setup used). This technique is particularly well suited for documenting fossils preserved as imprints  (Hammer et al. 2002; Béthoux et al. 2016) , as it allows a modification of light orientation at will, and is easily disseminated. For that matter, we provide the file in an online Dryad Digital Repository  (Yang et al. 2017)  and SOM (Supplementary Online Material available at http://app. pan. pl/ SOM/app63-Yang_etal_SOM. pdf). Photographs reproduced in Fig. 2A  3 -A 6 were extracted from the RTI file (and are bookmarked in the online file).
Acknowledgements. -We thank  Séverine Toussaint  ( Sorbonne Universités & Université Paris Diderot, France ) and  Artémis Llamosi  ( Université Paris Diderot & INRIA Saclay, France ) for the elaboration of the RTI setup. We warmly acknowledge  Neil Clark  ( University of Glasgow, The Hunterian, Scotland ),  Matúš Hyžný  ( Comenius University, Bratislava, Slovakia ) and an anonymous reviewer, whose comments and suggestions helped to improve the manuscript. The project was supported by the  National Natural Science Foundation of China  (grant nos. 31730087 ,  41602014 , and  41688103 ),  Program for Changjiang Scholars and Innovative Research Team in University  ( IRT-17R75 ) and  Support Project of High-level Teachers in Beijing Municipal Universities  ( IDHT20180518 ).
(ii) Once the mechanism of anisotropy evolution in energy cascade is clear, another underlying question is the time evolution of anisotropy distribution, which means in a specific isotropic turbulence if the intensity of anisotropy at a certain scale will increase or decrease with time. Note that according to Ref. 15, the shape of energy spectrum has a significant influence on anisotropy distribution, hence we expect that the anisotropy distribution remains the same except in some transient cases where the shape of energy spectrum changes. This will be demonstrated with the help of Direct Numerical Simulation (DNS). We remark that these observations on the time evolution of anisotropy distribution will not only inspire but also give a theoretical support on the further study of minimizing anisotropy error and constructing corrected database in post-processing, such as the decomposition method by employing spherical harmonies.
When one of the nine parameters is being adjusted, the other parameters remain unchanged. The parameters that have been adjusted will be kept at the optimal value. Because most of the references of HSI classification, including the ENVI (Environment for Visualizing Images) software used by remote-sensing professionals and image analysts, have chosen the OA expression as  [54] , in this paper, in order to facilitate to comparison with the results in other works, the 3D-CNN parameters are optimized in turn according to the single variable principle based on the OA value defined as  [54] : OA = 1 λ R ∑ r=1 a rr ( 3 ) where λ is the total number of samples, and a rr is the number of test samples that actually belong to class S r (r = 1, 2,. R where R is the total number of classes in the HSI) and are also classified into S r.
Experiments In order to evaluate the performance of the proposed classification method, some typical classification methods, such as support vector machines (SVM)  [55, 56] , deep belief networks (DBNs)  [57]  and 2D-CNNs are compared in the classification experiment of a real-world HSI. To obtain better classification results of 2D-CNNs, the parameter optimization is also introduced to this model in this paper. Both HSIs are acquired by the Reflective Optics System Imaging Spectrometer (ROSIS) sensor during a flight campaign over the city of Pavia, northern Italy, which makes the disparity between the PaviaU HSI (target data) and the PaviaC HSI (source data) become small. For the Pavia data set, there are strong bands and weak bands; some bands have a higher signal-to-noise level, some bands have a lower signal-to-noise level.
Real-World Hyperspectral Image (HSI) Data Sets Two widely used hyperspectral data sets, i. e. , the University of Pavia (PaviaU) shown in Figure  5a  and the center of Pavia (PaviaC) city shown in Figure  5b  are used in the experiment. Both HSIs are acquired by the Reflective Optics System Imaging Spectrometer (ROSIS) sensor during a flight campaign over the city of Pavia, northern Italy, which makes the disparity between the PaviaU HSI (target data) and the PaviaC HSI (source data) become small. For the Pavia data set, there are strong bands and weak bands; some bands have a higher signal-to-noise level, some bands have a lower signal-to-noise level. In addition, some bands are degraded by random noise, some bands may suffer from residual fixed pattern phenomena, and atmospheric effects may have a different visibility and impact in many bands.
Some low-quality bands could be visibly distinguished and should simply be disregarded. Some other noisy bands can be found by denoising algorithms  [58]. Therefore, the remaining number of spectral bands is 103 for the PaviaU HSI and 102 for the PaviaC HSI. In order to further evaluate the data quality of the two HSIs in different bands, a Frobenius norm (F-norm)  [59]  is introduced: (:,:, ) ( , , ) I I F i i I i I i i i = =   =      ( 5 ) where I is a tensor consisting of I1 rows, I2 columns and I3 spectral bands with i1 = 1, …, I1, i2 = 1, …, I2 and i3 = 1, …, I3. The smaller the corresponding F-norm value of the image, the lower the corresponding energy, and in consequence, the less useful the information contained. According to Equation (  5 ), the square of the F-norm of each band in two HSIs is shown in Figure  6.
In this paper, 10% of the samples of each class from the part-PaviaU HSI are randomly chosen to train the network and the remaining 90% are for testing. The total number of samples, training samples and testing samples for each class are shown in Table  1. In this paper, 10% of the samples of each class from the part-PaviaU HSI are randomly chosen to train the network and the remaining 90% are for testing. The total number of samples, training samples and testing samples for each class are shown in Table  1. SVM are one of the supervised learning models  [60]  and have been applied in classification, regression and outlier detection etc. The effectiveness of a SVM depends mainly on the kernel function which can be well designed by the generalized power spectral density (GPSD) in  [56]. In this paper, the SVM in ENVI software  [61]  is used for the comparison.
1. The PaviaC HSI can be used as the source data to pre-train the 3D-CNN model with optimal parameters and to obtain the weights to be transferred. To ensure the stability of the network performance, 70% of samples of each class in the PaviaC HSI are randomly chosen as the training set and the remaining 30% belong to the testing set. Then, the weights of the convolution and the pooling layers in the 3D-CNN model of the target data, the part-PaviaU HSI, could be transferred from the trained 3D-CNN model of the source data, which can help to improve the feature extraction capability of the network and alleviate the problem of insufficient samples in the target data. After transfer learning, the epoch needed to achieve the peak OA value could be less for the 3D-CNN model of the part-PaviaU HSI and was set to 100 in the experiment.
The number of convolution kernels in the first layer is 16, the batch size for training is 32 and the number of units in the fully-connected layer is 128, and the dropout in the P 1 , P 2 and F layer is set to 0. 1. The PaviaC HSI can be used as the source data to pre-train the 3D-CNN model with optimal parameters and to obtain the weights to be transferred. To ensure the stability of the network performance, 70% of samples of each class in the PaviaC HSI are randomly chosen as the training set and the remaining 30% belong to the testing set. Then, the weights of the convolution and the pooling layers in the 3D-CNN model of the target data, the part-PaviaU HSI, could be transferred from the trained 3D-CNN model of the source data, which can help to improve the feature extraction capability of the network and alleviate the problem of insufficient samples in the target data.
Data Analysis Energy spectra, PSD (f), were computed from detrended, tapered data segments of 1,800 points (900 s). The runup and surf zone data were then partitioned to determine the incident band component (0. 05 Hz < f < 0. 24 Hz) and the infragravity band component (0. 004 Hz < f < 0. 05 Hz). Runup, R, and surf zone wave heights, H, were calculated as follows: R; H ¼ 4 Ã ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ∑ f 2 f 1 PSD f ð Þdf s (2) Runup and surf zone heights in the incident band (respectively R inc and H inc ) and in the infragravity band (respectively R ig and H ig ) were calculated by summing only over frequencies within the specified limits. The distance between the shoreline and the inner bar crest was estimated by measuring the mean shoreline position and the bar crest position.
2011 ) to be the linear slope within the region between ± two standard deviations around the mean runup elevation. The data discussed below consist of 120 15-min wave and runup elevation time series centered on low tide and measured along eight individual cross-shore transects. The mean water level elevations for the time series considered in this study, according to in situ pressure measurements, varied by less than 0. 4 m. Journal of Geophysical Research: Oceans 3. Results.
Conclusions Observations of wave runup data derived from video recordings were collected at low tide in the presence of complex beach morphology during stationary energetic conditions characterized by offshore wave heights of 2 m and peak periods of nearly 14 s. Over the study period, variations in runup were found to be largely dominated by the spatial, rather than temporal, dimension allowing us to exclude variations in the offshore wave forcing (significant wave height and period) and closely investigate the effects of other variables. Runup spectra estimated at different alongshore positions were always dominated by infragravity energy and showed a broad saturated region with an f À3 dependence extending in the infragravity band. Runup height varied by a factor 3, a much higher value than the one reported previously in the literature (e.
Acknowledgments We wish to thank all the organizations that have provided financial support for the field experiment, in particular the  French DGA  ( ECORS  project). Thanks also to  Rafael Almar ,  George Payne ,  Vincent Marieu , and  Antoine Réjas  for the support in the installation of the video system; and to  Jean-Paul Parisot ,  Sylvain Capo , and  Stephan Bujan  for the Real Time Kinematic Differential Global Positioning System data. The raw data used (video images and pressure sensors) can be obtained by directly contacting the corresponding author. The data generated (video extracted runup, etc. ) are provided in the supporting information. Finally, the authors are grateful for the detailed suggestions and constructive comments provided by  Peter Ruggiero , the other anonymous reviewer, and the Associate Editor that vastly improved this manuscript.
We use the forward model given in equation (  2 ) to derive the expression of the sensitivity to SIC, that is, the TB variation from 100% to 0% SIC: dTB dSIC = TB Ice -TB OW. ( 3 ) To compute sensitivity to SIC, we use directly the difference of the ocean and sea ice TBs estimated from AMSR-E/AMSR2 and SMOS data in the Round Robin Data Package developed for the ESA sea ice Climate Change Initiative project  (Pedersen & Saldo, 2016). The database totalizes 18,700 observations for areas with 0% SIC and 3,460 observations for areas with 100% SIC. The observation points are localized over Arctic and Antarctic for a period between 2010 and 2011. The mean values and the StDs of TB OW and TB Ice for the different channels are computed from this data set. Figure  8  shows the sensitivity of the TBs measured by satellite to SIC (top) along with the StDs of the TBs of ocean water and sea ice (bottom) between 1.
As a consequence, Q = (( dTB dSIC ) t × S -1 FM × dTB dSIC ) -1. (5) Figure  9  shows the SIC retrieval error StD as a function of SIC, first for each frequency, using both vertical and horizontal polarizations. The calculations use the winter part of the Round Robin data set, as is usually done for model developments. The 6. 9-GHz channels have the highest information content compared to the other channels. Note that the variability of the other geophysical parameters is already taken into account in the estimation error, as the TB Ice and TB OW variabilities in the database already include the variability of the environment (e. g. , SST, TCWV, and TCLW). For the combination of the CIMR channels, each channel is taken at its nominal spatial resolution. The SIC product will be at the spatial resolution of the channel which drives the retrieval.
Performances at the Global Scale The performance of the SST retrieval with CIMR is also evaluated at the global scale, using realistic surface and atmospheric fields. We use the SST and SIC produced daily on an operational basis at the UK Met Office with 0. 054 ∘ spatial resolution (http://podaac. jpl. nasa. gov/dataset/UKMO-L4HRfnd-GLOB-OSTIA; Stark et al. , 2007). They are complemented with the OWS, TCWV, and TCLW provided by the ECMWF ERA-Interim reanalysis at Figure  12  presents the precision on the SST retrieval for CIMR and for AMSR2 missions at global scale. The distributions of the SST theoretical retrieval error StDs are indicated in Figure  13. With CIMR, the mode value of the distribution is at 0. 2 K. For lower SST, the error can reach 0. 45 K. Compared to the AMSR2 performances, this is an important improvement.
Discussion and Conclusions A new satellite mission concept, CIMR, is currently being considered to estimate all weather and high spatial resolution ocean and sea ice surface parameters. It is a conically scanning passive microwave imager in a Sun-synchronous polar orbit, with a 7-m foldable antenna and low noise receivers. It observes at 1. 4, 6. 9, 10. 65, 18. 7, and 36. 5 GHz, for both perpendicular polarizations. An information content analysis is conducted, for the retrieval of SST, SSS, and SIC. The precision for each product is estimated and compared to the precision achieved with the current instruments. With the CIMR mission, the retrieval error StDs are between 0. 15 and 0. 45 K for the SST, 0. 2 and 0. 6 psu for the SSS, and 2% and 5% for SIC. Estimation of global retrieval errors, based on realistic ocean and sea ice fields data revealed that SST will have an error StD mode value of 0.