dataesr/scientific-paragraphs-categorization huggingface dataset:
This indicates that all data types help improve the state estimate from the data assimilation system. However, it should be noted that all experiments with the ECMWF system adopt a bias correction scheme, which applies corrections to temperature, salinity and, in the equatorial waveguide, to the pressure gradient. The mean seasonal cycle of the bias correction is estimated a priori from a previous DA experiment using all in situ observations, and therefore the above OSEs still implicitly include information from all observing systems on the ocean mean state. A supplemental run assimilating all data without the bias correction indicated that the bias correction has the largest impact, illustrating the role of the observations in correcting the mean. As mentioned earlier, some in situ and satellite data are used in deriving the MDT.
But the OSEs results presented here do not account for the contributions of these data to the MDT that was assimilated as part of the SSH assimilation. Balmaseda and Anderson (2009)  show that the indirect impact of the in situ data through the MDT can be as large as the direct impact measured by the data retention experiments in the previous ECMWF operational system. Although the initial conditions from ALL, noArgo, noAltim and noMoor have been used to initialize seasonal forecasts, using the current operational ECMWF seasonal forecasting system S4, it was not possible to measure a significant contribution of individual observing systems. This is contrary to the results reported by  Balmaseda and Anderson (2009) , who used a similar methodology to evaluate the impact of the observing system in the previous ECMWF operational seasonal forecast system S3.
These consist of the nominal operational forecast system and a second system where a single observation component of the observing system is withheld. By comparing the results of the run excluding a particular observation with the operational run, the impact of the withheld observation type on the forecast system is assessed. NRT OSE experiments were performed in 2011 with the UKMO's operational ocean forecasting system  (Lea et al, 2014). Following this GODAE Ocean View initiative, NRT OSEs were also conducted over successive months of year 2013 by Mercator. During March 2013, temperature and salinity profiles from the tropical moorings (including those in the Atlantic and Indian Oceans) are withheld from assimilation in a simulation. All the other in situ observations, satellite SSH data and SST maps are assimilated as usual.
In another simulation, all in situ observations are assimilated as well as satellite SSHs and SSTs. The real-time ocean analysis and forecasting system used for those simulations is based on a 1/4 • global ocean model configuration and described in  Lellouche et al, (2013). A 3D temperature/salinity bias correction computed with in situ model-observation misfits available three months prior to the analysis is applied below the thermocline. The effect of withholding a part of the in situ dataset can then be underestimated for simulations shorter than three months due to the memory of this bias correction. Different observation-based statistics and ocean state quantities are compared between the two simulations to evaluate the impact of withholding data from the tropical moorings. Statistics of the temperature difference between the simulations and in situ observations (including those made by TAO/TRITON) are computed over the month for both simulations.
Misfits for the tropical moorings are included. Figure  11(a)  shows the profiles of the mean and RMS temperature misfits in the NINO-3 region. The higher level of errors, RMS and mean differences, are found at the thermocline depth, which is shallower when going eastward. The assimilation of the TAO observation reduces the RMS difference and bias (mean difference) of temperature by about 15% at that depth. Under the thermocline, the bias correction is still active and takes into account innovations from the previous two months. can reach 0. 5 PSU at the surface (not shown). After one month, changes have already propagated away from the mooring points through ocean tropical dynamics. The SST data prevent larger differences in temperature close to the surface, but the surface salinity is much less constrained by data assimilation.
The number of salinity data is also much smaller than the temperature data from TAO moorings. These results are generally consistent with  Lea et al (2013). Further investigation and longer simulations will be required to fully assess the impact on the analysis and forecast of the 1/4 • global ocean of the assimilation of the temperature and salinity data from the tropical mooring arrays because TAO data return was already low in 2013; a year prior to 2011 should be chosen to have a higher return data rate. The results highly depend on the assimilation system, the physical model and observation error a priori specification. Those quantities are not always well known. One month does not allow a full assessment of the observation impact but does give some indication.
It is shown that the greatest data impacts for reducing forecast error in the Pacific basin of global HYCOM are for observations in the Tropics. This result is an indication that HYCOM model errors are greatest in the tropical Pacific and that continued and routine observing is needed there to adequately constrain the model. On a per observation basis, the impact of Argo and the tropical fixed moorings are shown to be equivalent at low latitudes. Thus, the two observing systems can be considered to be complementary when initializing global HYCOM. The routine assimilation of observations from both Argo and TAO/TRITON work together to consistently reduce HYCOM 48 h forecast error for both temperature and salinity. Therefore, both observing systems are needed in the future.
Evaluation for decadal predictions Impacts of TPOS on decadal predictions have not been substantially evaluated yet since it is a relatively new science field. However, Doblas-Reyes et al (2011) investigated impacts of ocean observation data on decadal predictions using a version of the ECMWF coupled forecast system. They performed three decadal hindcast experiments. The first one is not initialized. In contrast, the second and third ones are initialized using ocean observation data, although the correction of XBT bias proposed by  Wijffels et al (2008)  and  Ishii and Kimoto (2009)  is adopted only in the third one. Their Figure  5  shows improvement of prediction skill for 2-5 year lead time over the tropical Pacific by assimilating ocean observation data, which implies the importance of TPOS for decadal prediction.
Summary This article has introduced the current status of ocean DA systems used for a variety of applications and discussed their requirements for the TPOS including the TAO/TRITON array and Argo floats. It summarizes past and current studies to evaluate impacts of those observation data. It can be considered as background information which can guide the evaluation exercise of TPOS. Temperature data from the TAO/TRITON array are assimilated in most ocean DA systems, and are generally considered to be essential for constraining the ocean heat content, stratification, and circulation in the Tropics in these systems. The intercomparison of the temperature fields along the Equator among the DA systems for Seasonal-to-Interannual (S-I) forecasting in NCEP, JMA, ECMWF, ABoM (Figure  1 ) reveals that the recent decrease of TAO/TRITON data may severely affect the accuracies of the analysed fields making it more difficult to issue reliable forecast statements, although the representativeness of this case should be evaluated in a further study.
Acknowledgements We are very grateful to two reviewers for their constructive comments. We also thank  Drs D. Anderson  and  T. Suga , and other members of science organizing committee of the TPOS2020 workshop, as well as their secretary,  Dr K. Hill , for their fruitful advice and suggestions on the article. Parts of the data used were collected and made freely available by the  International Argo Program  and the national programs that contribute to it. (http://www. argo. ucsd. edu;accessed 24 May 2015, http://www. jcommops. org/argo;accessed 1 July 2015). The Argo Program is part of the Global Ocean Observing System. This work was partly supported by  JSPS KAKENHI  grant number  24740324.
Heart rate (HR) was monitored throughout. Here as well as in the second session, we also recorded electrodermal activity, but technical failures precluded our using the data. Electrodes were located on the medial side of both ankles and the distal anterior aspect of the right forearm (BIOPAC MP150, Goleta, CA). Saliva samples were sent to the Molecular Biology Laboratory of the Universidad de La Frontera, for quantitative determination of cortisol concentration. Salivary concentrations of cortisol were obtained using an enzyme immunoassay commercial kit following the manufacturer's instructions (DRG Salivary Control ELISA Kit, DRG Instruments GmbH, Germany). Concentrations were obtained by interpolating from a standard curve plotted using the software GraphPad Prism version 5. 0 (GraphPad Software, San Diego CA, USA).
This work was supported by  FONDECYT  ( 1120572 , Chile) to L. R. and J. R. S. , by  FONDECYT  ( 1130810 , Chile) to J. R. S. , by a  Fund for Innovation and Competitiveness (FIC) of the Chilean Ministry of Economy, Development and Tourism , through the  Millennium Scientific Initiative  ( IS130005 ) to J. R. S. , by a  doctoral fellowship  ( CONICYT 72090838 , Chile) to G. R. , and by a grant from the  Agence Nationale de la Recherche  ( ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL ) to G. R. and J. S.
At the end of the SPOT4 mission, a four-month experiment was conducted in 2013 to acquire high spatial (20 m) and high temporal (5 days) resolution satellite data. In addition to the SPOT4 (Take5) dataset, we used several Landsat5, 7, 8 images to document the variations in suspended particulate matter (SPM) concentration in the turbid Gironde and Loire estuaries (France). Satellite-derived SPM concentration was validated using automated in situ turbidity measurements from two monitoring networks. The combination of a multi-temporal atmospheric correction method with a near-infrared to visible OPEN ACCESS Remote Sens. 2015, 7 9508 reflectance band ratio made it possible to quantify SPM surface concentration in moderately to extremely turbid waters (38-4320 g•m -3 ), at an accuracy sufficient to detect the maximum turbidity zone (MTZ) in both estuaries.
Due to its global coverage and ability to zoom in specific areas, satellite remote sensing provides synoptic observations of water reflectance in the visible and near infrared (NIR) spectral regions, which in turn can be used to map SPM surface concentration in turbid waters  [15]  at worldwide scale. Landsat and SPOT high spatial resolution data have been widely used to detect SPM concentration in estuaries and nearshore areas. Since the early observations of the turbid discharge of the Mississippi waters in the Louisiana Bight  [16] , a variety of Landsat and SPOT images have been analysed to map SPM surface concentration in the world's main estuaries, including the Amazon  [17] , Gironde  [18] , Mekong  [19] , Rhone  [20] , and Yangtze  [21, 22]  estuaries. Due to their low acquisition frequency, Landsat and SPOT observations are however temporally limited to document SPM variability in highly dynamical coastal areas.
With a revisit time of 1-3 days, MODIS and MERIS data can be used more systematically to quantify surface SPM dynamics in large estuaries  [23] [24] [25] , but their spatial resolution (250 m -1 km) is not sufficient to detect small scale SPM variations in narrow estuaries and nearshore areas. Launched in June 2015 by the European Space Agency (ESA), Sentinel-2 is the first satellite mission to systematically observe the world's coastal zone at both high spatial (10-60 m) and temporal resolution  (5 days). With 13 spectral bands from the visible to the short wave infrared (SWIR) spectral regions, the Multispectral Instrument (MSI) on board Sentinel-2 will provide continuity and improvements to SPOT and Landsat image archives. To prepare the analysis of Sentinel-2 data, the French Space Agency (Centre National d'Etudes Spatiales, CNES, Paris, France) implemented the SPOT4 (Take5) experiment in 2013, providing a four-month time series of images simulating the revisit frequency, spatial resolution and large field of view of Sentinel-2 data.
SPOT4 (Take5) data have been used for a variety of applications, from land and habitat studies to monitoring of inland and coastal waters. We present here an analysis of the SPOT4 (Take5) images acquired over two major estuaries of the French Atlantic coast, namely the Gironde and Loire estuaries. Satellite data were analysed in conjunction with automated in situ turbidity measurements, in order to evaluate the accuracy of the SPM algorithms previously proposed for both study sites  [26]. The  Doxaran et al. (2003)  algorithm has previously been validated in the Gironde estuary at moderate resolution using MODIS data  [23] , and already applied to SPOT and Landsat images  [18] , but a thorough evaluation of the approach to high resolution satellite images has never been done, neither in the Gironde, nor in the Loire estuary.
For the Gironde study, the SPOT4 (Take5) spatial coverage was restricted to the southern part of the estuary, focusing on the Garonne river (Figure  1 ), which is a narrow (400 m wide in Bordeaux) meandering and macrotidal river. The SPOT4 (Take5) image extent does not cover the whole estuary, and is susceptible to match the records of only one automated in situ turbidity station, as described in Section 2. 3. A dataset of Landsat images covering the whole Gironde estuary was therefore used to complement SPOT4 (Take5) data. Landsat images were acquired from January 2011 to December 2013. For the Loire estuary, the SPOT4 (Take5) footprint covers the whole estuary, and a total of four automated in situ turbidity stations were available for match-up purpose (Figure  2 ). SPOT4 (Take5) and Landsat data were processed and distributed by the Theia Land Data Centre, a French national inter-agency organization designed to foster the use of images issued from the space observation of land surface and nearshore coastal areas.
7878)/0. 2577] ( 3 ) where Rrs is the remote sensing surface reflectance, XS1 and XS3 are SPOT4 spectral bands in the green (0. 5-0. 59 nm), and near infrared (XS3: 0. 79-0. 89 nm), and LG and LNIR are Landsat spectral bands corresponding to 0. 53-0. 59 nm, and 0. 85-0. 88 nm, respectively. Doxaran et al. (2003 [26] ) demonstrated that relations based on reflectance ratios between near infrared and visible spectral bands were robust to variations in sediment type and to changes in illumination conditions. The robustness of the approach was previously evaluated in the Gironde estuary using MODIS Aqua data. Based on 75 match-ups, the relative uncertainty was found to be around 20% over a range of SPM concentration from 10-2250 g•m -3  [23]. Though the algorithm has been already applied to SPOT and Landsat data in the Gironde estuary  [18] , a thorough validation of its performance with high resolution satellite data is still lacking in the Gironde and Loire estuaries.
Data were qualitycontrolled and validated following a protocol adapted to the estuarine environment  [6] , including a regular calibration of the turbidity sensors. Turbidity measurements were used to estimate SPM concentration using a regional empirical relationships derived from laboratory calibration. The most recent turbidity vs. SPM concentration relationship  [23] , which was obtained for a range of SPM concentration up to 2000 g•m -3 , was used for this study (Equation (  4 )): SPM concentration = 0. 9946 NTU; R 2 = 0. 97; n = 65 (4) A recent analysis of the 2005-2013 turbidity time series in the fluvial section showed seasonal variations from <100 NTU during droughts to >9000 NTU during floods  [39]. These changes can be related to MTZ migration from the Gironde estuary to the Garonne River  [40].
In Situ Turbidity Measurements in the Loire Estuary Along the Loire estuary, continuous turbidity measurements have been performed since 2007 in the frame of the SYVEL (Système de veille dans l'estuaire de la Loire) monitoring network operated by the GIPLE (Groupement d'Intérêt Public Loire Estuaire, Nantes, France) using MAREL stations (Figure  2 ). Sensors are housed inside an instrumented chamber fixed on a pier. As in the Gironde estuary, the Loire water was sampled at less than 1 m below surface, pumped to the sensors, and turbidity measurement was automatically performed every 30 min using a Turbimax CUS31 sensor (Endress & Hauser, Reinach, Switzerland) covering the range 0-9999 NTU range. Turbidity data were calibrated in SPM concentration using a regional relationship  [44].
Satellite Data Summary During the SPOT4 (Take5) experiment, satellite acquisitions were successfully performed every 5 days from February to early June 2013 in the Gironde and Loire estuaries. Due to poor weather conditions however, only a limited number of (partially) cloud free data was available. A total of nine SPOT4 images were selected within the four months of the Take5 experiment (Tables  2  and 3 ). Selected images were acquired over a wide range of hydrological conditions. River flow varied from 581 to 1650 m 3 •s -1 in the Gironde estuary, and from 1400-3200 m 3 •s -1 in the Loire estuary. A total of 11 Landsat images acquired in 2011 and 2013 were selected and added to the Gironde satellite dataset. The addition of Landsat imagery in 2011 made it possible to extend the lower limit of hydrological conditions experienced during satellite overpasses, with river flow smaller than 100 m 3 •s -1 on several occasions.
In the Gironde estuary, the addition of SPOT4 (Take5) and Landsat8 data in 2013 made it possible to study SPM concentration changes on a monthly basis from February to October 2013. For the selected satellite imagery, average tidal range respectively varied between 3. 30 and 5. 15 m in the Gironde estuary, and between 2. 24 and 5. 55 m in the Loire estuary. In the Loire estuary, SPOT4 images were acquired during both neap and spring tides, whereas Gironde's SPOT4 and Landsat cloud free images were mostly acquired during neap tides, thus probably leading to an underestimated picture of SPM concentration in the fluvial section of the estuary. The high river flows observed during the SPOT4 (Take5) experiment further contributed to flush SPM seaward and decrease SPM surface concentration in the Garonne section of the Gironde estuary.
SPM Match-Ups in the Gironde and Loire Estuaries A total number of 30 data points was available for SPM match-up purpose (Figure  4 ). In the Gironde estuary, the Bordeaux turbidity station provided 15 match-ups. In the Loire estuary, though only five images were selected, a total of 15 pairs of concomitant in situ and satellite measurements were extracted at four in situ turbidity stations. The match-up dataset covered a very wide range of SPM concentration, from 37. 9-4320 g•m -3. The RRMSE and MNB were 220. 0% and 89. 3%, respectively. There was a significant linear relationship between in situ and satellite-derived SPM concentration (r-squared regression coefficient is 0. 75, p-value < 10 -9 ), with a slope of 0. 66. The accuracy of satellite SPM concentration retrieval was better in the 100-1000 g m -3 interval, with a RRMSE and a MNB of respectively 49.
SPM Concentration Maps in the Gironde Estuary Around Bordeaux, in the Garonne section of the Gironde estuary, SPOT4 (Take5) data generally displayed low SPM concentrations in February 2013 (Figure  5a ). Due to strong precipitation in winter and spring, enhanced land runoff increased water height and vertically diluted SPM concentrations in the estuary's fluvial section. The SPOT4 (Take5) time series of SPM concentration did not exhibit strong spatio-temporal variations, and turbidity levels were quite similar from February to May 2013. In June, a patch of turbid water was observed to the north of Bordeaux (Figure  5b ), with SPM surface concentration 5 to 30 times higher than in February. This increase in SPM concentration can probably be explained by bed sediment resuspension resulting from spring tide currents and increase in river flow.
The spatial frame of the SPOT4 (Take5) images in the Gironde estuary did not allow us to observe SPM spatial distribution outside the Garonne section. The addition of Landsat data made it possible to observe the rest of the Gironde estuary, from the Garonne and Dordogne confluence to the Gironde's mouth (Figure  5c, d ). In the July 2013 example, the Landsat8 image confirmed the confinement of the relatively clear waters within the upstream sections of the estuary, whereas the most turbid waters stretched from the Garonne and Dordogne confluence down to the sea (Figure  5c ). In October 2013, the situation was reversed: maximum SPM concentration was found in the fluvial section, whilst surface waters were generally less turbid downstream Pauillac (Figure  5d ). Longitudinal SPM transects displayed reversed gradients on 10 July and 30 October (Figure  7 ).
e. , >40 m) from the station location. Pixels too close to the shore were removed, in order to take into account the possible contamination of marine pixels by land adjacency effect  [50]. In the present study, the size of SPOT4 (20 m) and Landsat (30 m) pixels was small enough to select the best configuration of match-up pixels, and it is expected that the use of higher resolution satellite data such as Sentinel-2 (10-60 m), SPOT5 (10 m), SPOT6 (6 m) or Pléiades (2 m) might improve pixel selection and match-up accuracy. Further studies are needed to implement a standard match-up protocol for the validation of remote-sensing data using land-based in situ turbidity station with nearshore water sampling. The  Doxaran et al. (2003)  algorithm is based on empirical relationships between SPM concentration and a NIR/VIS reflectance ratio established using a large dataset of in situ measurements  [26].
As this latter method has not been implemented for SPOT4 data, another approach was used in the present study for SPOT4 atmospheric correction. In the present study, the MACCS atmospheric correction was used for cloud detection and atmospheric correction of satellite data  [35]. This method has been developed for the correction of multi-temporal images acquired using a constant viewing angle. Over land, it assumes that the surface reflectance in the blue does not change quickly with time. Changes in TOA reflectance will be detected and identified as clouds or aerosol optical thickness variations, making it possible to characterize the atmospheric reflectance, and to remove it from the TOA signal to determine surface reflectance  [35]. A notable advantage of the MACCS method is its ability to be applied to different sensors, as long as a sufficient number of multi-temporal data is available, and that the viewing angle remains constant for each sensor  [36, 37].
Conclusions and Future Directions In this paper, we validated the use of SPOT4 (Take5) and Landsat data to detect and quantify SPM surface concentration in the Gironde and Loire estuaries. We demonstrated that the combination of the MACCS atmospheric correction  [35]  with a simple NIR/VIS reflectance band ratio algorithm  [26]  satisfactorily quantified SPM concentration in moderately to extremely turbid waters (38-4320 g•m -3 ) at an accuracy sufficient to detect the maximum turbidity zone in two optically distinct estuaries. We presented a multi-sensor approach that can be applied to SPOT and Landsat archives, as well as to other high spatial resolution Earth Observation sensors such as the Sentinel-2 mission by the European Space Agency. Using moderate resolution remote sensing, it has been previously demonstrated that decadal satellite time series can be used to study the turbidity response of large estuaries to global changes  [22, 23, 25, 56].