dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiment 1 Only the first ten frames of each sequence of dataset A were used, with a window size of M = 10. The results are presented in Table  1  and Figure  5  -in this illustration, only the pictures at 5 meters are shown as they are more degraded by turbulence than images at 10 meters. This experiment, applied to sequences which can be considered short exposure, gives quite varied results: the values of PSNR and MSSIM are best for the SPCA method in four out of the six tests, which concern Lena and the boat. The most important difference being for Lena (10 m). Visually, the SPCA includes deformations and accentuates rebounds, as can be seen with respect to the hat of the Lena image. However the noise seems more penalizing with the Laplacian filter when trying to distinguish the letters of the poem.
Conclusion This paper has studied a well known method  [7, 8]  that proposes to use the PCA to restore image sequences degraded by turbulence. The study led us to develop a variant of the method proposed in  [7, 8]  named SPCA. The proposed variant applies to image sequences acquired with short as well as long exposure times which is an improvement over  [7, 8]. the SPCA, should be applied in limited cases of atmospheric turbulence. In addition, a dataset of real atmospheric turbulence degraded sequences has been recorded and is made available for download. • a sequence for which the heat source is placed midway between the telescope and the print, i. e. between 4. 40 m and 5. 60 m from the telescope; Videos are recorded in AVI format, then frames are extracted in PNG 8-bit format without dynamic stretching between [0, 255] using the software tool convert.
Acknowledgements Work partly founded by  Centre National d' Études Spatiales (CNES ,  MISS  Project),  European Research Council  (advanced grant Twelve Labours),  Office of Naval research (ONR  grant  N00014-14-1-0023 ),  Direction Générale de l'Armement, ANR - DGA  (project  ANR-12-ASTR-0035 ),  FUI  (project  Plein Phare ) and  Institut Universitaire de France.
Mapping Sea Ice Concentration (SIC) and understanding sea ice properties and variability is important especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (T ef f ) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snowice interface temperature (T Snow-Ice ) and the T ef f of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from Ice Mass Balance (IMB) buoys and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice.
Here we propose a simple algorithm to retrieve the snow depth and the T Snow-Ice from passive microwave observations from the Advance Microwave Scanning Radiometer 2 (AMSR2), based on a large dataset of collocated in situ and satellite observations. An extensive Round Robin Data Package (RRDP) has been developed during the European Space Agency (ESA) sea ice Climate Change Initiative (CCI) project (http://www. seaice. dk/ecv2/rrdb-v1. 1/). It contains in situ data from the Ice Mass Balance (IMB) buoys, and the Operation Ice Bridge (OIB) airborne campaigns collocated with AMSR2 brightness temperature measurements between 6 and 89 GHz. Algorithms already exist to retrieve the snow depth from microwave observations. Markus and Cavalieri (1998)  and  Comiso et al. (2003)  use the spectral gradient ratio of the 18.
, 2008; Rosenkranz and Mätzler, 2008; Harlow, 2009 Harlow, , 2011;; Tonboe, 2010; Tonboe et al. , 2011). The relationship between T ef f and the physical temperature profile is complicated, especially at microwave frequencies ≥18 GHz, but it has been shown that from 6 to 50 GHz, there is a high correlation between the T ef f and the T Snow-Ice  (Tonboe et al. , 2011). With T Snow-Ice estimated from the AMSR2 observations, we will deduce the sea ice T ef f at AMSR2 frequencies between 6 and 89 GHz, using linear regression. Section 2 describes the dataset and the methodology used in this study. The snow depth retrieval is presented in Section 3. Section 4 reports on the T Snow-Ice retrieval. Finally, microwave sea ice T ef f at 50 GHz is derived, for application to temperature atmospheric sounding (section 5).
The database of collocated satellite observations and in situ measurements The RRDP from the ESA sea ice CCI project is a dataset openly available. It contains an extensive collection of collocated satellite microwave radiometer data with in situ buoy or airborne campaign measurements and other geophysical parameters, with relevance for computing and understanding the variability of the microwave observations over sea ice. It covers areas with 0% and 100% of SIC and different sea ice types (thin ice, first-year ice, multiyear ice), for all seasons including summer melt. In our study, we will focus on Arctic sea ice during winter in regions with 100% sea ice cover. Two different dataset from the RRDP are used: AMSR2 brightness temperatures (TBs) collocated with IMB buoy measurements, and AMSR2 TBs collocated with OIB airborne campaign measurements.
It measures the physical temperature with an accuracy of 0. 1 K. The acoustic sounder measures the position of snow and ice surfaces (i. e. , the snow depth) with a precision of 5 mm. The buoys also include instruments to measure air temperature, barometric air pressure and GPS geographical position  (Perovich et al. , 2017). Several IMB buoys are deployed by the CRREL at different locations and times during the year. We only use Arctic buoy data recorded during winter (December 1 st to April 1 st ) to avoid cases where ice has started to melt. A summary of buoys information corresponding to these criteria is given in Table  1  and the IMB buoy locations are shown in Figure  1. IMB buoy measurements collocated with AMSR2 TBs used in this study totalize 2845 observations. For snow depth retrieval, we also used data from the OIB airborne campaign.
The NASA OIB project has collected ice and snow depth data in the Arctic during annual flight campaigns (March-May) since 2009. The data are especially valuable in this context since they contain snow depth information from the snow radar onboard the aircraft, not only from single points, but continuously along the flight path. In the RRDP, the snow depth data from OIB radar are averaged into 50 km sections to be collocated with AMSR2 observations. For our study we use the OIB data from the 2013 campaign. It totalizes 408 observations over 8 days in March and April. Figure  1  summarizes the location of IMB buoys and OIB campaigns over the Arctic ocean. It is important to note that there are discrepancies due to the scale, when comparing point measurements from buoys with the spatially averaged data from satellites or aircrafts  (Dybkjaer et al.
, 2012). The buoy data are neither interpolated or smoothed  in time. E. g. , in terms of surface air temperatures measured at adjacent buoys and ice stations there are about 1 K to 4 K differences per 100 km  (Lindsay et al. , 1994). 4 The Cryosphere Discuss. Snowpacks (MEMLS)  (Wiesmann and Mätzler, 1999)  described in  Mätzler (2006). The simulations were part of an earlier version of the RRDP and the simulation methodology is described in Tonboe (2010).
Methodology In this study, we propose simple algorithms, using multilinear regressions, to derive the snow depth, the T Snow-Ice , and the T ef f of sea ice from AMSR2 TBs. The measurements from the IMB buoys 2012G, 2012H, 2012J, and 2012L, collocated with AMSR2 TBs, are used as the training dataset for the different regressions to retrieve snow depth and T Snow-Ice. These buoys have been selected because they are located in different regions across the Arctic and show a large range of snow depths. The measurements from IMB buoys 2013F, 2013G, 2014F and 2014I which are all located in the Beaufort sea are used as the testing dataset. First, the IMB snow depth is expressed as a function of the AMSR2 TBs using a multilinear regression. The OIB data are used for the forward selection and the IMB buoy training dataset is used to perform the regression.
Second, the T Snow-Ice is expressed as a function of TBs and snow depth, using linear regressions. An automated method to detect the position of the snow-ice interface on the vertical temperature profile measured by the IMB buoy thermistor string is developed (Section 4. 1). Then, the IMB buoy training dataset is used to perform the regressions. For this part there are two consecutive regressions: the first one is done between the centered (the average was subtracted) T Snow-Ice and TBs ; the second one is done between the T Snow-Ice corrected for the TB dependence and the snow depth. Third, the sea ice T ef f at different microwave frequencies is expressed as a function of the T Snow-Ice. This final step is using the simulations from a thermodynamical model and MEMLS to derive linear regression equations for the T ef f at frequencies between 6 and 89 GHz.
Multilinear regression to retrieve the snow depth A forward selection method is used to choose the best AMSR2 channels to retrieve snow depth. It is a statistical method to determine the best predictor combinations (here, AMSR2 TBs) to retrieve a variable (here, snow depth). Our training dataset for this forward selection is the OIB snow depth from the 2013 campaign included in the RRDP. OIB data are chosen for forward selection because the data cover a large area with a wide range of snow depths. In addition, the scale of the averaged OIB data is closer to satellite footprint than buoy measurements, increasing the consistency with the satellite observations. that the best channel combination for snow depth retrieval is the combination of the 3 channels at 6. 9, 18. 7, and 36. 5 GHz in vertical polarization (6V, 18V, and 36V).
Then, a multilinear regression is conducted using the training dataset (IMB buoys G, H, J, L in 2012 collocated with AMSR2 TBs). The snow depth is given as a linear combination of the TBs at 6V, 18V, and 36V : SD = 1. 7701 + 0. 0175 • T B 6V -0. 0280 • T B 18V + 0. 0041 • T B 36V , (1) with SD the snow depth expressed in m and TB in K. This model was trained with snow depths between 5 and 40 cm. The forward selection has been also tested constraining the number of predictors to 2 and 4. The combinations obtained are: 18V and 36V for 2 channels, and 6V, 18V, 36V, and 89V for 4 channels. Then, the multilinear regression has been performed using these combinations of 2 or 4 channels. The results show that the 3 channel combination is the best (see results in Table  2 ), in terms of RMSE and correlation, comparing to the 2 or 4 channel combination (Comparing with IMB buoys measurements except 2013F buoy, RMSE=4.
The buoy 2013F observes a large snow depth (> 40 cm) which is outside the bounds of our snow depth model. Tests are conducted to improve the estimation, including the 2013F buoy in the training dataset, with equal numbers of observations for different range of snow depths: it does not improve the results. The Markus and Cavalieri (1998) model and our model obtained the same snow depth estimation between buoys 2013G and 2013F. It is consistent because these buoys are spatialy very close. Therefore, we suspect that the 2013F buoy is located on a ridge or hummock where the local snow depth is large but not detectable at the satellite footprint scale. Without including the buoy 2013F in the computation, the RMSE for our snow depth model is 5. 1 cm and the correlation coefficient is 0. 77. We also compare the snow depth retrievals with the measurements of the 2013 OIB campaigns (see Figure  3 ).
Correlation between the brightness temperature and the snow-ice interface temperature During winter, the vertical position of the snow-ice interface, with respect to the buoy thermistor string, is fixed. The thermistor string is frozen into the ice which means that the thermistor at the snow-ice interface will stay at that interface unless there is surface melt or snow ice formation and this rarely happens during winter. For each IMB buoy, the snow-ice interface is detected with our automated method described in section 4. 1. We use a correlation analysis to select the TBs at different frequencies describing the variability of the T Snow-Ice. Figure  5  shows the correlation coefficient between T Snow-Ice and AMSR2 TBs computed using the data from all IMB buoys (Table  1 ). The 89 GHz TBs are highly correlated with the air temperature (R>0.
The T Snow-Ice dependence on snow depth can be explained by the thermal insulation of snow (Maaß et al. , 2013). Here, we establish an empirical relationship between the T Snow-Ice corrected of the TB linear dependence at 10V or 6V, and the snow depth as follows: T Snow-Ice -a 1 • T B 10V or 6V = a 2 • f (SD) + a 3 , (2) with f (SD) a function of snow depth. Three different linear regressions have been tested to relate the T Snow-Ice using: the snow depth directly, the inverse of the snow depth, and the logarithm of snow depth. Figure  7  shows the T Snow-Ice corrected from TB dependence as a function of snow depth. The different regressions are tested using the training dataset (IMB buoys: G, H, J, and L in 2012). The regression showing the best results is the one using the logarithm of the snow depth (solid black line in Fig.
Results of the snow-ice interface temperature retrieval using the in situ snow depth with the 10V TBs (Eq. 3) is slightly better (RMSE = 1. 69 K) than the regression with the 6V TBs (Eq. 4) (RMSE = 1. 95 K). The variability due to the snow depth is better described with the regression using the 10V TBs. Figure  9  is the same as Figure  8  but using our snow depth estimation (Eq. 1). The RMSEs are 2. 78 K for the 10V regression and 2. 84 K for the 6V regression. The results are degraded because of the snow depth regression especially for the buoys with thick snow (∼50 cm) or thin snow (∼5 cm) (e. g. , buoy 2013F and buoy 2012L). Note that uncertainties can be introduced  The T ef f is only available in a simulated dataset using a thermodynamical model and MEMLS. The model set-up and the simulations are described in  Tonboe (2010).
In this dataset the TBs, the T Snow-Ice , and the T ef f s are simulated together using the input snow and ice profiles from the thermodynamical model. Even though the simulated data are comparable to observations in terms of mean and standard deviation, both the thermodynamical model and the emission model are physical models and they are not tuned to observations. This means that a bias is expected between the T Snow-Ice from MEMLS and the T Snow-Ice from our regressions based on in situ observations. The bias obtained is the mean value of the difference between the T Snow-Ice from MEMLS, and the T Snow-Ice regressed from equations 3 and 4 using the inputs of the simulated data. Biases of 3. 97 K and 4. 01 K are estimated, for the regressions with 10V and 6V respectively. The RMSEs computed between the T Snow-Ice from MEMLS and the T Snow-Ice regressed and corrected of the bias at 10V and 6V are 2.
Conclusions We derive simple algorithms to estimate sea ice parameters such as the snow depth, the T Snow-Ice , and the T ef f of sea ice at microwave frequencies, from AMSR2 channels. This is achieved using the ESA RRDP which contains AMSR2 data collocated with IMB buoy data and OIB campaign data. In addition, simulated TB output from a sea ice version of MEMLS are used for the regression of the T ef f. All the equations to retrieve these sea ice parameters are derived using several linear and multilinear regressions. Our regression to retrieve the snow depth over winter Arctic sea ice uses the TBs at 6. 9, 18. 7 and 36. 5 GHz in vertical polarization. A RMSE of 6 cm is obtained on the estimated snow depth. To retrieve the T Snow-Ice , two relations are derived using two different AMSR2 channels: 10V and 6V, and the snow depth.
, https://doi. org/10. 5194/tc-2018-223 Manuscript under review for journal The Cryosphere Discussion started: 12 November 2018 c Author(s) 2018. CC BY 4. 0 License. 2. 2 The database of simulated effective temperature and brightness temperature from sea ice properties For estimation of the T ef f , we use a microwave emission model and a thermodynamical model to relate physical snow and ice properties to TBs and T ef f s of sea ice. The model used here is a sea ice version of the Microwave Emission Model of Layered.
estimate, the RMSEs are 6. 26 cm and 7. 25 cm, respectively, and the correlation coefficients are both 0. 87. All the results are summarized in Table2. Note that the uncertainties on OIB data for the 2013 campaigns are between 2 cm and 22 cm with a mean standard deviation of 11 cm. Spatial scales are different when comparing satellite measurements or airborne campaign measurements (which have an extended footprint) with buoy6The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2018-223 Manuscript under review for journal The Cryosphere Discussion started: 12 November 2018 c Author(s) 2018. CC BY 4. 0 License.
, 2018) , the aquifer rising in a context of rainfall deficit (the socalled Sahelian paradox;  Leduc et al. , 2001)  or the modification of the partitioning between sensible heat fluxes and latent heat fluxes  (Guichard et al. , 2009) , not to mention many other results presented below. Over the years, AMMA-CATCH has grown from a rainfall observatory to a holistic observing system, documenting most of the continental water cycle at high frequency thanks to the momentum gained from the setup of the AMMA program in 2002  (Redelsperger et al. , 2006; Lebel et al. , 2011). We start here by summarizing the motivations for maintaining such a complex observing system and by describing the main eco-climatic characteristics of the sites instrumented in AMMA-CATCH. We then detail the long-term observation strategy, some specific campaigns embedded in the AMMA-CATCH framework, and data management.
, 2014; Rockström et al. , 2017). Moreover, considerable uncertainties in future simulations by climate models remain, particularly concerning the water cycle and precipitation. These uncertainties are higher in the intertropical zone, considered as one of the hotspots of climate research  (Toreti et al. , 2013; IPCC, 2014). Maintaining good quality observations across this region is thus a responsibility that falls on the shoulders of the research community, and this is the central motivation for the continued commitment of AMMA-CATCH in providing good quality data to the academic world and to the socioeconomic actors altogether. AMMA-CATCH has three main goals: (i) provide appropriate data for studying the impacts of global change on the West African critical zone; (ii) unite a large community of researchers from different countries and disciplinary backgrounds to analyze these data with the aim of better understanding the dynamics of the system across a range of scales and to detect significant changes in its key components; and (iii) disseminate data and associated results outside of the academic community.
The observatory is consequently organized into three thematic axes that drive the observation and instrumentation strategy, namely: (i) analyze the long-term evolution of the eco-hydro-systems within a regional framework; (ii) better understand the critical zone processes and their variability; and (iii) link with decision makers and end users, VZJ | Advancing Critical Zone Science p. 3 of 24 so that the knowledge gained from the AMMA-CATCH data can be used to meet the socioeconomic and development needs based on proper mastering of environmental conditions. This involves a systemic approach that AMMA-CATCH is sharing with the critical zone community, and it is thus part of the French network of critical zone observatories (Observatoires de la Zone Critique Application et Recherche or OZCAR)  (Gaillardet et al.
Site Characteristics West Africa is characterized by a latitudinal climatic gradient that induces a gradient of vegetation. In the southern part, the coast of the well-watered Gulf of Guinea is covered with dense vegetation; rainfall gradually decreases from south to north, until the limit of the Sahara, which is arid and covered by scattered vegetation. The AMMA-CATCH observatory gathers data from four densely instrumented mesoscale sites (with surface areas ranging between 14,000 and 30,000 km 2 ) located at different latitudes to sample the regional eco-climatic gradient. We use the term mesosite to refer to these mesoscale sites. From south to north we find (i) the Sudanian site (Benin) where rainfall is ?1200 mm yr -1 , (ii) the cultivated Sahelian site (Niger) with ?500 mm of annual rainfall, and (iii) the pastoral Sahel site distributed in two locations (Mali and Senegal) with an average annual rainfall of ?300 to 400 mm.
The measured variables in each of the seven categories as well as the measurement periods are shown in Table  1. In 2018, a total of 290 stations (including 850 sensors) are in operation in the four countries (Table  1 ). The stations are grouped into 42 "instruments. " An instrument aims to answer a scientific question and focuses on a specific spatial and temporal scale. It may be either a group of identical sensors organized in a network (e. g. , a rain gauge-stream gauge network) or a set of complementary sensors located in the same place (e. g. , a surface flux station composed of a flux tower with radiative budget and soil heat flux). Each instrument is under the scientific responsibility of one or two principal investigators. An instrument corresponds to a dataset in the observatory database and is identified by a doi.
Dedicated Campaigns and Experiments Besides the long-term observation system, specific field campaigns are organized to (i) document the critical zone architecture, such as the geometry but also the hydrodynamic properties of the groundwater reservoirs and soil layers, (ii) study fine processes, such as the paths of water transfers between surface and groundwater, and (iii) calibration/validation (Cal/Val) of satellite missions, remote sensing being a key additional data source for our large mesosites and for upscaling to the data-poor region. These campaigns allow, in particular, better characterization of processes and inputs to modeling approaches across the AMMA-CATCH sites.
Providing In Situ Datasets for Calibration/Validation of Satellite Missions Satellite missions require in situ measurements to calibrate and validate their products for various climates and continents. The AMMA-CATCH observatory provides a unique opportunity for the so-called Cal/Val activities in Sahelian and Sudanian climates. Indeed, the AMMA-CATCH sites are often the only Cal/Val sites in West Africa. To match the requirement of Cal/Val activities, the setup of some in situ sensors has been specially designed or reinforced  (Kergoat et al. , 2011). Several studies have used the AMMA-CATCH rain gauge networks to evaluate satellite rainfall products. The network density across these sites (especially the Niger and Benin sites with about 40 gauges within a 1 by 1° area) is unique in Africa and even in the tropics.