dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010a). In case of large misalignments, the DW dataset will not provide reliable measurement and thus a distortion correction procedure has to be applied first to avoid bias during DW image analysis  (Mangin et al. , 2002; Rohde et al. , 2003) , independently of using CLASR. Registration-based approaches usually perform a registration of the corrupted DW image to the b0 image, since it is considered to be less subject to distortions. Evaluation of such methods showed a distortion correction accuracy of approximately 2 mm  (Netsch and van Muiswinkel, 2004). More experiments should be completed for larger distortions. However, this type of analysis is challenging since in case of important misalignments a correction step has to be applied first. We showed that such correction introduces image blurring on the gold standard making the validation results ambiguous.
Acknowledgments We thank the reviewers for their useful comments that helped improve the paper. We also want to thank the  Pr Louis Collins  for proofreading this paper and his fruitful comments. Finally, we want to thank  Martine Bordessoules  for her help during image acquisition of DWI used to build the phantom. This work has been supported by the  French grant  " HR-DTI "  ANR-10-LABX-57  funded by the TRAIL from the  French.
Therefore the first objective of this paper is to report the calibration and the profile qualification procedure on a unique 2-yr fluorescence dataset collected by southern elephant seals (SES hereafter) within the Indian sector of the Southern Ocean. From this data we will assess how in situ measurements compare with surface chl a concentration measured by ocean colour satellites. This new approach allows sustained acquisition of chl-a fluorescence profiles (proxy for chl-a concentration) in areas where data scarcity is the rule and how they complement satellite ocean colour data.
5 μg l -1 a range matching well the chl a concentration generally encountered within the SO. The Cyclops 7 was integrated in a new CTD-Fluo Satellite Relay Data Loggers (Tags hereafter). They were built by the Sea Mammal Research Unit (SMRU) (University of St. Andrews, Scotland). Fluorescence was sampled continuously between the surface and 180 m. As Argos messages are restricted in length we had to reduce the resolution of fluorescence data. Therefore values were averaged for eighteen 10 m vertical sections. For each section the mean fluorescence value was allocated to the mid depth point of the corresponding section. Fluorometer calibrations, relying essentially on chl a solutions or on phytoplankton cultures, are generally provided by manufacturers. Most of the time these calibrations are established for large range of chl a concentrations not always representative of in situ ones.
Deployment on elephant seals Instruments were deployed on SES either at the end of their moult in late summer to cover their pre-breeding, winter foraging trips or in October on post-breeding females. Animals were anesthetized with intravenous injection of tiletamine and zolazepam 1 : 1, and then instruments were attached to the fur on their head by using a two component industrial epoxy. Seals dove repeatedly with CTD-Fluo data being collected every 2 s during the ascent phase of dive and processed onboard before being transmitted via the Argos satellite system when animals were at the surface. On average, 1. 8 ± 0. 5 vertical temperature (T ), conductivity (C) and fluorescence profiles were transmitted daily. Because of the narrow bandwidth of Argos transmitters, each profile was transmitted in a compressed form consisting of 18 fluorescence and 24 Temperature (T ) and Conductivity (C) data points.
Estimation of the tag specific chl a correction coefficient: a Bayesian approach Using a Bayesian adjustment framework, and by combining for each at sea test the information available for each profiles and the HPLC values available, a chl a calibration coefficient was calculated for each CTD-Fluoro SRDL tags. A Bayesian framework is especially suited as it guarantees that between-casts variability is taken into account in estimating these inter-fluorometer coefficients. An attractive feature of this framework is the relative ease with which different data sets are combined in a single Figures.
Back Close Full   , 2005). Weakly informative and robust priors were favoured. We used uniform priors  (Gelman, 2006)  for standard deviation parameters. Because all slopes were expected to be positive, we used Student-t priors with mean 0, scale 10 and 7 df on a log scale  (Gelman et al. , 2008). Batches of tag-specific coefficient were assumed to follow a bivariate Gaussian distribution with covariance matrix. We used the prior described in Tokuda et al. (  2011 ) for the covariance matrix. We built a model to predict HPLC [Chl a] from either in situ fluorescence (Fluo) at measured by instrumented SES, or from MODIS chl. Specifically we first used longterm data at the Boussole site to estimate the relationship between HPLC [Chl a] and measurement from the reference fluorometer: HPLC [Chl a] = δ • Fluo[reference] + ε1 (2) where ε1 is a Gaussian residual error term.
Secondly, we used data from the intercalibration experiment to estimate the relationship between a specific tag j and the reference fluorometer: Fluo[reference] = αj • (Fluo j -Offset j ) + ε2 (3) where ε2 is a Gaussian residual error term. When measurements from the reference fluorometer were unavailable but measurements from the different tags at a specific location were available, we rewrote Eq. (  3 ) as: Fluo[reference] -αj • (Fluo j -Offset j ) + ε2 = 0 (4) and put a weakly informative prior (Half-Student-t with mean 0, scale 5 and 4 df, Gelman et al. , 2008) on the value of Fluo[reference]. We then used the WinBUGS "zerotrick"  (Spiegelhalter et al. , 2003)  to incorporate these data into the model. Note that the error term ε2 is the same for Eqs. Finally, we evaluated the relationship between in situ fluorescence as measured from a specific tag j and MODIS: (Fluo j -Offset j ) = βj • MODIS + ε3 (5) where ε3 is a Student residual error term.
9 ). In-situ chl a concentration provided by the fluorometer was weekly correlated with the MODIS. Data points were highly dispersed, but this is not surprising due to the low spatial and temporal resolution of the MODIS data used to investigate this relationship while surface values collected by the tags were associated with a unique location within that 9 × 9 km sector and therefore small scale variation which can be measured by the fluorometer are likely to be overlooked by the 9 × 9 km MODIS and weekly data. This finding is consistent with several studies showing that standard satellite ocean colour algorithms tend to underestimate chl a concentrations in the SO compared to in situ HPLC measurement or from calibrated fluorometer  (Mitchell and Holm-Hansen, 1991; Dierssen and Smith, 2000; Holm-Hansen et al.
However the real issue is the quenching effect observed during daylight hours and requiring to be properly dealt with  (Xing et al. , 2012). Models can only provide useful answers if there are sufficient data to constrain the underlying processes and validate the model output. New approaches to assimilate biological and chemical data into these models are advancing rapidly  (Brasseur et   2009). Notably, the progressive integration of biogeochemical variables in the next generation of operational oceanography systems is one of the long-term objectives of the GODAE OceanView international program. Nevertheless, and in view of refining these models for improving their representativeness and predictive capabilities, the datasets currently available remain too scarce. There is an obvious and imperative need to reinforce biological and biogeochemical data acquisition and to organize databases and SES equipped with CTD-Fluo SRDL tags are contributing efficiently to this need.
Both the RF signal generator and the BAW filter are molded with a resist on a module which is then soldered on a larger PCB. The output of this subset circuit is 100 Ω differential  [26]. The baseband input signals are generated by an external Arbitrary Waveform Generator (Tektronix AWG 420), in which a Matlab sequence has been programmed to generate a WCDMA modulated signal on I and Q channels. The external clock reference comes from a synthesizer delivering a clock signal at 2. 6GHz with an output level of -5dBm. This clock is the nominal frequency clock of the modulators and of the digital image-reject mixer and fully determines the center frequency of the RF signal. Using a 2. 6GHz, the ΔΣ generator outputs an RF modulated signal at 1. 95GHz using the first image-band. The measurement results (Figure  34 ) consist of a spectrum analysis of output signals to evaluate the benefits of using BAW filtering in a  modulation approach.
7 Known as the Awareness Surveillance Advocacy Prevention project, it calls for a comprehensive strategy based on the pillars of awareness-raising, surveillance, advocacy and prevention to be applied in all African countries. In 2012, the (hospital-based) Rheumatic Heart Disease Global Registry (REMEDY) is the first attempt at comprehensive characterisation of RHD in countries where it is most prevalent. 9 The aims are to collect detailed data on clinical and echocardiographic features at presentation and follow-up and meticulously document clinical and echocardiographic progression of disease and the occurrence of disease-related adverse events during the course of the study. 9.
Moreover, the non-spherical fraction retrieved by AERONET significantly increased. The real part of the complex refractive index was up to 1. 55 at 440 nm during the eruption, compared to background data of about 1. 46 before the eruption. Collocated lidar data revealed that several aerosol layers were present between 2 and 5 km, all originating from the Iceland region as confirmed by backward trajectories. The volcanic ash AOD was derived from lidar extinction profiles and sun-photometer AOD, and its maximum was estimated around 0. 37 at 532 nm on 18 April 2010. This value was observed at an altitude of 1700 m and corresponds to an ash mass concentration (AMC) slightly higher than 1000 µg m -3 (±50 %). An effective lidar ratio of ash particles of 48 sr was retrieved at 532 nm for 17 April during the early stages of the eruption, a value which agrees with several other studies carried out on this topic.
Remote sensing measurements were performed at many atmospheric stations in Europe during this event. Among them was a sun-photometer/micro-lidar system that conducted continuous observations in Lille during that period. The micro-lidar used was the Cloud and Aerosol Micro Lidar (CAML) developed by CIMEL  (Pelon et al. , 2008; Leon et al. , 2019). The data is archived at LOA and processed routinely to derive aerosol extinction profiles and effective lidar Ratio, thus providing a real-time overview of aerosol layers over Lille (http://www-loa. univ-lille1. fr/Instruments/LIDAR/). The paper presents the results of monitoring and characterization of volcanic ash characteristics as observed from Lille Atmospheric Observatory during April-May 2010. Our work shows that such an important parameter as AMC can be estimated with reasonable accuracy through combination of a single-wavelength backscattering micro-lidar with AERONET.
The paper is structured as follows: Sect. 2 is dedicated to the description and analysis of aerosol columnar properties retrieved from AERONET and of height-resolved data available from lidar, separately. In Sect. 3 the paper presents the results of joint sun-photometer/lidar inversion performed for a selection of relevant atmospheric situations between 16 April and 20 May 2010. In the last section, methodology for estimating AMC is presented, along with associated uncertainties, results and discussions.
, 2002b). The contrast between 15 and 17 April is quite significant and larger than the expected uncertainty on these parameters of 0. 03. In all these cases, AOD values at 440 nm are always larger than 0. 40; therefore, the accuracy of AERONET products is good  (Dubovik et al. , 2002b). AERONET retrievals (APF and SSA) also allow the estimation of vertically averaged or effective LR. At 532 nm an average LR of 54 sr has been determined for the afternoon of 17 April. Given the fact that AERONET provides total-, fine-and coarse-mode phase functions, the coarse-mode LR, which can be mostly attributed to volcanic ash particles, was found to be 47 ± 10 sr at 532 nm. This value is in relatively good agreement with values derived from AERONET data for natural non-spherical coarse particles, such as desert dust, in climatologic studies 40-50 sr,  (Catrall et al.
, 2005) , 55 sr  (Schuster et al. , 2012)  and volcanic particles in case study, 42 sr,  (Derimian et al. , 2012). LR was also calculated from AERONET data for 15 April, and a value of 71 sr was found. At the end of May, when the volcanic activity ceased and the atmosphere was clear of ash particles, LR values returned to their initial value of around 70 sr, again consistent with values reported for Lille by  Catrall et al. (2005). Recent studies that are focusing on the Eyjafjallajökull eruption  (Gross et al. , 2010; Ansmann et al. , 2011; Schumann et al. , 2011; Wiegner et al. , 2011; Mona et al. , 2012; Derimian et al. , 2012)  provided values of 50 ± 10 sr for ash, which are in agreement with the AERONET estimates presented in this study. Thus, RI, SSA, and LR values are indicating a modification of the chemical composition in the column that can either be explained by aerosol physico-chemical transformations and/or by a possible intrusion of new aerosol particle types somewhere in the atmospheric column.
Methodology This section focuses on relating optical extinction derived from remote sensing retrievals based on lidar and sun photometer to the AMC. The specific extinction coefficient is a parameter of particular interest in this case since AMC estimates can be derived from the extinction coefficient by applying an agreed value of the volcanic ash specific extinction. Existing estimates of the specific extinction for volcanic ash are ranging from 0. 5 m 2 g -1 to 1. 1 m 2 g -1  (Ansmann et al. , 2010; Johnson et al. , 2012a). In the present work an alternative approach is considered, given the fact that aerosol microphysical properties are available from AERONET data (version 2, Level 2), specifically the VSD that is linked to number size distribution, n(r), RI, and NSF. The uncertainty on AMC is therefore mainly driven by the uncertainty of the lidar-derived extinction profiles, aerosol density, size distribution and RI.
An estimate of AMC during nighttime has therefore been performed and is detailed below. In Sect. 3 we described how the sunphotometer AOD is used to retrieve the extinction profiles and LR during the daytime processing. However, since no sun-photometer data are available during nighttime, a prescribed LR for ash (LR AL = 48 sr) and LR for boundary layer (LR PBL ) have been used to derive extinction profiles during nighttime. In order to minimize the impact of this a priori assumption, the last LR PBL retrieved using the AERONET AODs from the evening and the first LR PBL from the morning were used. These assumptions remain reasonably valid when there is no significant change in the aerosol properties between the two inversion times (daytime and nighttime, as considered here). Usually, the AERONET AOD measurements are available a short time before (after) sunset (sunrise).
Acknowledgements. This study was supported by  CNRS-INSU ,  CNES ,  Lille 1 University  and  Région Nord-Pas de Calais  and  FP6/Mobility program  under the  Marie-Curie  project  TOTECAT. Authors are very grateful to AERONET (http://aeronet. gsfc. nasa. gov/) team and  NOAA HYSPLIT Air Resource Laboratory model  (http://ready. arl. noaa. gov/HYSPLIT. php). The authors want to thank  CNRS-DRI  for supporting cooperation with Belarus Academy of Science. Edited by:  M. King  The publication of this article is financed by  CNRS-INSU.
Leaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for the validation and evaluation of ESMs from regional to global scales. Several decades' worth of satellite data products are now available at global scale which represents a unique opportunity to contrast observations against model results. The objective of this study is to assess whether ESMs correctly reproduce the spatial variability of LAI when compared with satellite data and to compare the length of the growing season in the different models with the satellite data. To achieve this goal we analyse outputs from 11 coupled carbon-climate models that are based on the set of new global model simulations planned in support of the IPCC Fifth Assessment Report.
The existence of vegetation models that use prescribed climate represents a unique opportunity to compare and contrast the effect of inner climatic variation on ESMs against the effect of differences in the vegetation modules. In other words, comparing different LSMs allows the detection of flaws in the vegetation dynamics, while comparing ESMs allows the identification of climate effect on vegetation processes, and the comparison of the two leads to the weighing of both effects. In this context, we check the ability of different ESMs to reproduce the spatial and temporal variability of the satellite observed LAI. Specifically, the objective of this study is to assess whether ESMs correctly reproduce the spatial variability of LAI when compared with satellite data, and asses how long the growing season is in the different models compared with the satellite data over the Northern Hemisphere.
Satellite Data The LAI data set used in this study (LAI3g) was generated using an Artificial Neural Network (ANN) from the latest version (third generation) of GIMMS AVHRR NDVI data for the period July 1981 to December 2010 at 15-day frequency  [86]. The ANN was trained with best-quality Collection 5 MODIS LAI product and corresponding GIMMS NDVI data for an overlapping period of 5 years (2000 to 2004) and then tested for its predictive capability over another five year period (2005 to 2009). The accuracy of the MODIS LAI product is estimated to be 0. 66 LAI units  [86]  and the data is for 1-sided LAI. Further details on the LAI3g and the comparison with other satellite products are provided in  [86, 87].
Leaf Phenology Analysis Growing season onset, dormancy and length were all calculated based on the LAI seasonal amplitude. In fact, LAI has been shown to have a normal distribution over the year in northern latitudes  [71] , so we consider the start of the growing season to be 20% of the maximum amplitude. The values of 20% was defined after different tests were conducted using different thresholds ranging between 5% and 30% of the maximum amplitude; we found that this value provided the best results. Overall, this method has being proven to be more stable for monthly data, compared to an approach based on sudden LAI changes  [8]. It also should be noted that due to the lack of daily data for the LAI we were unable to use other methods used in previous studies based on the daily LAI variability  [8].
LAI represent the minimum and maximum LAI over one year for the grid cell (x,y). This procedure was repeated on each grid cell and for each year for any given CMIP5 model. The length of the growing season was then calculated as the number of months with a value above this threshold; the onset is the first month above that value and the dormancy is the last. Since naturally part of the growing season might occur after the end of the year  [88] , we included the first three months of the next year in the calculations of the dormancy. Hence the growing season offset can occur on of the following year, having a DOY (day of year) higher than 365. Finally, mean length, onset and dormancy were calculated as the average over the whole time period. It should be noted that, even when calculated monthly, all results are presented as days; we retrieved the daily values from the monthly data by multiplying all monthly results by the number of days within the given month.
It should also be noted that, to be consistent with LAI, we show the precipitation and temperature only over the land points of the Northern Hemisphere. Finally, we selected CRU as reference data for the validation of climatic variables since it has been used as input data in the offline simulations  [90]. The evaluation of the simulated precipitation and temperature is needed to assess whether any bias in the simulated LAI can be related to poor performance of the ESMs at reproducing physical variables, or is mainly due to the poor representation of some biogeochemical processes in the land surface models of ESMs. Looking at the LAI (Figure  1 ), in general, except CanESM2 and INMCM4, all the models overestimate the mean annual LAI over the Northern Hemisphere. The poorest performance has been found in GFDL-EMS2G, which shows a mean value of 2.
The large bias found in BNU-ESM could be related in some way to the strong wet bias that this model has in reproducing the observed precipitation. Specifically, whilst the mean annual precipitation as reported by CRU data is 520 mm/yr, BNU-ESM shows a mean value of 802 mm/yr. However, it should also be noted that, except CanESM2, all the other models also have a wet bias. The wet bias found in all the CMIP5 ESM could explain the LAI overestimation: in fact the best agreement between observed and simulated LAI is found for CanEMS2, this being the only model without a wet bias. Although in the boreal and Arctic region the temperature is the main limiting factor for the carbon assimilation, at mid-latitudes the precipitation plays a pivotal role through its control on the soil moisture  [91, 92].
The seasonal amplitude patterns show large disagreement between the models and the satellite data (Figure  3 ). Some models (e. g. , BNU-ESM and MIROC-ESM) clearly overestimate the mean amplitude, which is particularly evident over the whole North America and Eurasia. Other models (e. g. , CESM1-BGC, HadGEM2-CC, MPI-ESM-MR, and NorESM1-ME) show a smaller seasonality than satellite data, while INMCM4, CanESM2 and BCC-CSM1 perform better than the rest of the models in reproducing the satellite-derived observations. The RMSE, indicating the mean error of the models in reproducing a given variable, suggests that CanESM2 has the lowest error: in fact this model, albeit it slightly underestimates the seasonal amplitude over the Russia, has the correct magnitude for the observed seasonal amplitude.
It is evident that all models are keeping inactive leaves for longer than they should, which does not have any impact on the carbon cycle but could potentially modify surface radiation budget and turbulent fluxes, affecting therefore the PBL dynamics, which in turn could lead to potential bias in lower atmospheric dynamics simulated by ESMs. In addition to those ESMs having an interactive tropospheric chemistry component, the presence of inactive leaves could modify the deposition fluxes that strongly depend on the area of the canopy  [11]. Conversely, the longer offset simulated by offline models does not affect simulation results since the climate is provided as input data and the feedbacks between the land surface and the atmosphere are not taken into account. Looking at the LAI trends, all the coupled models show a clear greening over the whole Northern America and Eurasia, consistent with satellite data, while not all the offline models show the same pattern over the high latitudes of the Northern Hemisphere.