dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we provide an overview of the physics of the derivation of SST and the history of the development of satellite instruments over half a century. As demonstrated accuracies increased, they stimulated scientific research into the oceans, the coupled ocean-atmosphere system and the climate. We provide brief overviews of the development of some applications, including the feasibility of generating Climate Data Records. We summarize the important role of the Group for High Resolution SST (GHRSST) in providing a forum for scientists and operational practitioners to discuss problems and results, and to help coordinate activities world-wide, including alignment of data formatting and protocols and research. The challenges of burgeoning data volumes, data distribution and analysis have benefited from simultaneous progress in computing power, high capacity storage, and communications over the Internet, so we summarize the development and current capabilities of data archives.
The relatively new field of Operational Oceanography, in which satellite-derived SSTs have immediate applications, is introduced, followed by the contribution of accurate SSTs from satellites to a longer term application, that of generating Climate Data Records. Synergistic analyses of satellite-derived SSTs with other remotely sensed data are then discussed. The role of the Group for High-Resolution SST (GHRSST) in coordinating research and operational applications of SST is presented before a discussion of the facilities at various data centers from where users can access satellite data. The review concludes with an outlook into anticipated developments in the foreseeable future. For more information than is possible in this review, the interested reader is referred to, for example, Chapters 7 and 8 of  Robinson (2004) , Chapters 7 to 9 of  Martin (2014), and Robinson (2010).
These definitions achieve the closest possible agreement between what is defined and what can be measured, taking into account our current understanding of near surface thermal variability. The definitions are in agreement with the Climate and Forecast (CF) metadata convention and are now used across GHRSST-format SST products. The definitions are shown schematically in Fig. 2  and are briefly summarized below. Further details on their derivation are discussed by  Minnett and Kaiser-Weiss (2012). Five different SST definitions are specified: (1) The interface temperature (SST int ) -the temperature at the air-sea interface on molecular scales; (2) the skin sea surface temperature (SST skin ) -the temperature measured by an IR radiometer typically operating at wavelengths 3. 7-12 μm (chosen for consistency with the majority of IR satellite measurements), which represents the temperature within the conductive diffusion-dominated sub-layer at a depth of ~10-20 μm; (3) the subskin sea surface temperature (SST subskin ) -the temperature at the base of the conductive laminar sub-layer of the ocean surface, which for practical purposes can be well approximated to the measurement of surface temperature by a microwave radiometer operating in the 6-11 GHz frequency range; (4) the surface temperature at depth (SST z or SST depth ) -all measurements of water temperature beneath SST subskin ; and (5) the foundation temperature (SST fnd ) -the temperature free of diurnal temperature variability, i.
The threshold values and the weighting given to each test were determined by a Machine Learning algorithm  (Hall et al. , 2009)  applied to a subset of matchup data in four conditions: night-time, daytime, moderate sun-glitter and strong sunglitter. When applied to MODIS and VIIRS (Visible and Infrared Imaging Radiometer Suite; see Section 5. 1. 6) data, the ADT algorithm improves on the decision-tree approach for all metrics considered  (Kilpatrick et al. , 2019).
, 2009b) , and on the water vapor distribution in the vertical  (Minnett, 1986). Using the AVHRR (Section 5. 1. 2) Pathfinder dataset  (Kilpatrick et al. , 2001) ,  Merchant et al. (2009b)  demonstrated that in nearly all cases the sensitivity of retrieved SST to actual SST variation is < 1. 0, with the global average being 0. 93. The minimum sensitivity was found to occur where atmospheric water vapor column content is high, specifically in the equatorial Atlantic and Pacific Oceans, especially the Tropical Warm Pool where the sensitivity may be < 0. 5. Similarly, estimates of the amplitude of diurnal heating deduced from satellite-derived SST may be smaller than the real amplitude if the temporal sensitivity of the satellite data is < 1. 0  (Merchant et al. , 2009b; Merchant et al. , 2013).
Early radiometers Starting in 1964, only seven years after the launch of the first artificial satellite, Sputnik, NASA began to launch research and development satellites called Nimbus. The Nimbus satellites were sun-synchronous and stabilized so that instruments on board could be directed to the Earth throughout the orbits. The instruments were mostly prototypes, but once demonstrated to be successful successive versions were flown, often with incremental improvements. Included in the instrument suite of Nimbus-1 was the High Resolution Infrared Radiometer (HRIR) which had a single IR channel, responsive to the 3. 4-4. 2 μm wavelength range, and having a spatial resolution of 8. 6 km at the sub-satellite point with a swath extending from horizon to horizon. Because of solar contamination, these data were restricted to use only at night.
, 2010 ; https:// www. star. nesdis. noaa. gov/sod/sst/squam/) and the Monitoring & Evaluation of Thematic Information from Space (METIS; http://metis. eumetsat. int/) enable interactive comparisons between various SST products (in situ, satellite and analysis). New open source software developments such as Felyx  (Taberner et al. , 2013)  offer community tools for diagnostics and match-up generation.
Indeed, this information is vital for producing higher level products as it offers a way to quantitatively merge data from multiple sources. As SST datasets become more diverse and satellite data volumes grow (with the new generation of geostationary satellites for example), the role of GHRSST is arguably more important now than when the group was initiated. To address these new challenges, the Group is currently evolving the RGT/S framework to ensure seamless data provision to users well into the future. Information on the GHRSST project may be accessed through the main home page at http://www. ghrsst. org.
Data handling, data sources and archives In the half century of satellite remote sensing of SST, the methods available to distribute data to operational centers and researchers has undergone a revolution. Initially, data were mailed from receiving stations to researchers on computer-readable 9-track magnetic tapes and the processing of the data was a challenge, not least how to display imagery in a cost-effective but scientifically useful fashion, often as photographic prints. The growth in data volume that began with the ERS series in the early 1990s and more so with the NASA Earth Observing System in the early 2000s led to the formation of large data processing and archive centers, which coincided with the development of the Internet and the ability to deliver large datasets on a computerto-computer basis using unified protocols.
The POET tool was a major step forward in data distribution in several ways: 1) allowing data subsetting; 2) implementing data interoperability standards through a common format (HDF); 3) flexibility in allowing users different output formats for implementation in their own tools and software programs. The model used for the NOAA/NASA Pathfinder project was instrumental in moving forward with the GHRSST (Section 13). Following on the success of the NOAA/NASA Pathfinder project the GHRSST project was one of the first to implement the common data format Network Common Data Form (NetCDF). The goal of satellitederived SST data sets to be implemented in a common data format with compliant standards allowed for interoperability among all the satellitederived SST data sets. This also allowed for a unique cooperation between NASA and NOAA to allow users access to both near real time and historical data.
Cloud-based technologies will allow for users to implement their own algorithms for gridding and optimal interpolation. A major hurdle to the user community will be implementing software on the cloud that allows for functionality beyond the traditional downloading of data and processing on a laptop. Cloud based solutions are currently being developed by both NOAA and NASA to prepare for these future challenges. Additional challenges include the visualization of multiple data sets and accessing such data sets in near real time. The example in Fig. 27  shows an image of the SST anomaly derived from the MUR SST data set  (Chin et al. , 2017). The image was generated using NASA's State of the Ocean Tool (SOTO) (http://podaac. jpl. nasa. gov/soto). The SST anomaly is overlaid on a true color image from MODIS on Aqua.
Near real-time and historical OSI SAF data can be accessed by several means including EUMETCast or FTP servers. Some products are also archived and available at the EUMETSAT Data Center (EDC) and at the PO. DAAC. Details on how to access OSI SAF products can be found on the "Access to data" page at https://www. osi-saf. org. OSI SAF is also performing reprocessing activities. For instance, it has recently released an MSG  SST data record (2004 SST data record ( -2012) )  which was created using state-of-the-art methodologies. Future OSI SAF activities are strongly tied to the future EUMETSAT meteorological satellite programs and as such will aim at providing the best possible SST products for the new generation sensors expected in the next few years: Metop-Second Generation and Meteosat Third Generation.
Users require both global and regional products designed for specific European regions. These data are produced by CMEMS-SST-TAC (Thematic Assembling Centre) both for dissemination to external users and for use internally within CMEMS. CMEMS SST products are available in near real time and operationally assimilated by CMEMS global and regional modelling forecasting systems to produce ocean reanalysis and forecasts  (Bell et al. , 2000; Lellouche et al. , 2018; Oddo et al. , 2009). The CMEMS SST products are global and regional L3 multi-sensor SST and L4 analysis products, and are produced ingesting all L2 data provided by Space Agencies, using an ad hoc processing chain developed by the CMEMS SST-TAC. The processing chain includes additional quality checks of upstream data, bias adjustment among sensors, and interpolation techniques (e.
The SST TAC is responsible for ensuring the quality of their products by planning and implementing validation of their products, instigating quality control checks of incoming data streams and then monitoring the quality of the data produced. The SST TAC makes a continuous effort to include any new sources of data that are proven to improve the quality of the CMEMS output products, such as newly available satellite sensors. These new data sets, for example the SLSTR data from both Sentinel 3A and Sentinel 3B missions, are included in all regional and global products. Interaction with users is another important component of the CMEMS, for example via provision of quality information documents and user manuals and by responding to queries received through the CMEMS service desk.
Validating sensors The drifting buoy array used in the validation of satellite-derived SSTs has evolved during the last three decades. At first, the common drifter type was based on the ARGOS system for geo-location and data transmission, SST resolution was 0. 1 K and position and SST measurements were asynchronous. The migration to a new drifter type, called HRSST-1, began around 2010 and is almost completed. HRSST-1 buoys transmit their data through Iridium satellites, are geo-located by GPS, SST resolution is 0. 01 K and position and SST measurements are synchronous. Simultaneously, another drifter type, called HRSST-2, characterized by a better calibrated, digital SST probe, has been developed and tested. HRSST-2 buoys are not all the same, some of them have additional improvements such as two SST probes, at 0.
17 m and 0. 48 m, or a careful insulation of the SST sensor from anything other than sea water  (Poli et al. , 2018). HRSST-2 buoys provide a more accurate SST and information on its representativeness, which is very useful when comparing satellite and buoy SST. With > 60 buoys deployed so far and 100 buoys expected in the next three years, HRSST-2 buoys are not sufficient by themselves for satellite SST operational validation, but they will significantly contribute to the validation of future instruments. It is hoped that the current suite of ship-radiometers (Section 6) will continue to be deployed to provide SI-traceable assessments of the satellite-derived SST skin accuracy and thereby contribute to the SST CDR. The fact that reliable, well-calibrated ship-board radiometers capable of providing SST skin values are few in number, but slowly increasing, means that in the foreseeable future the data for assessing the accuracy of satellite-derived SST skin will be comparatively sparse.
The high correlations of 0. 96 and greater are consistent with the L4 fields reproducing a significant part of the coastal variability in this area. In the summer of 2019, a fleet of six Saildrones was deployed in the Alaskan sector of the Arctic Ocean in a collaboration between NOAAs Pacific Marine Environmental Laboratory (PMEL) and a NASA-sponsored component of a US National Oceanographic Partnership Program (NOPP) project called the Multi-sensor Improved Sea-Surface Temperature (MISST). As with the 2018 Saildrone deployment, the two MISST Saildrones carry additional sub-surface thermometers, and also a pair of infrared radiometers, one viewing the sea-surface ahead of the Saildrone, and the other viewing the sky to provide data for the correction of reflected sky radiation to permit the retrieval of SST skin from the Saildrone.
Gridded SST analyses For the gridded (L3 and L4) SST analyses in the near future, wider availability of 1-km and higher spatial resolution L2 input data sets such as VIIRS, SGLI, SLSTR, and METimage, and new-generation geostationary imagery could lead to new practices in data interpolation. The analysis interval would need to be shortened to a sub-daily level to avoid aliasing of the sub-10 km scale SST features which tend to evolve faster than their mesoscale counterparts. The current NOAA 5 km Geostationary-Polar SST Analyses are generated on a daily basis for day and night, nighttime and diurnally corrected L4 products  (Maturi et al. , 2017). The availability of new, rapidly-sampled SST retrievals from geostationary satellites complemented by high latitude sampling from the new polar-orbiting sensors will support the development of a unified methodology to investigate the global characteristics of diurnal variability.
Data distribution In terms of data archive and distribution, the primary challenges for the future include allowing access and manipulation to larger amounts of data over longer periods of time, developing cloud technologies that allow users to implement their own processing algorithms (for example cloud masking/optimal interpolation), and visualization and extraction tools for near real time applications. Data quality standards should be measured by two issues: 1) Adherence to metadata standards that allow for interoperability, while maximizing implementation across a wide range of tools and services and 2) access to validation (matchup databases) that allow users to assess the quality of the data in their area of interest. Cloud technologies must be adopted cautiously, as it will require major paradigm shifts in both accessing data and information.
Acknowledgements PJM acknowledges support from the  NASA Physical Oceanography Program. The contributions by  TMC  and JV to this paper were carried out at the  Jet Propulsion Laboratory (JPL), California Institute of Technology , under a contract with the  National Aeronautics and Space Administration (NASA). MAS acknowledges support from  ONR   N00014-17-1-2545 ,  NSF   OPP-1751363 , and  NASA 80NSSC18K0837. IK is supported by the  Copernicus & Mercator-Ocean  through the  Service Evolution Call 2 project DIVOST-COM. The authors thank  Dr. C. J. Merchant  for providing a preprint of the paper (Merchant et al. , 2019) to support the description (in Section  5. 1. 2 ) of the generation of the AVHRR time series of consistent SSTs.
Medium Spatial Resolution MODIS MCD43A3 white-sky albedo (WSA) products, combining observations from Aqua and Terra, available at 500 m were used to characterize the temporal stability and spatial homogeneity at 20 / 100 km resolutions over 400×400 km² candidate regions. WSA products are chosen over blacksky albedo or normalized reflectance products as WSA minimizes the impact of the surface anisotropy. MCD43A3 products are freely available from NASA's Land Processes Distributed Active Archive Center (LP DAAC) at http://e4ftl01. cr. usgs. gov/. Daily WSA values are derived from the inversion of a kernel-driven semi-empirical BRDF model based on the RossThick-LiSparse kernel functions  [27] [28] [29]  over a 16-day running period. In order to reduce the volume of data to download and process, we limited our analysis to the data in the red and near infrared (670 and 865 nm) provided every 8 days over the 2011-2015 period.
In order to discriminate the types of aerosol (among dust, sea salt, organic matter, black carbon, and sulfate), the MACC (Monitoring Atmospheric Composition and Climate) re-analysis  [33]  over the 2003-2012 period is used (http://apps. ecmwf. int/datasets/data/macc-reanalysis/levtype=sfc/). It is derived from the assimilation of satellite data (including MODIS AOD) into a global chemistry and transport model. This re-analysis provides the total AOD, as well as the AOD of each of the five aerosol types, therefore enabling the calculation of their relative contribution. The 3-hourly data provided at 1° × 1° resolution are selected, but only the noon data to reduce the amount of the data to process.
Global Scale Iteration A four-step selection procedure was applied to establish a first list of relevant areas at the global scale. It relied mostly on space-borne PARASOL data but also considered existing calibration sites (including the 20 Cosnefroy PICS). Monthly PARASOL data over year 2008 were used to establish a first list of relevant coarse resolution desert pixels (~7 km) from a global search. We sought for potential calibration pixels distributed all over the globe verifying several selection criteria: (1) the pixels should be thematically homogeneous desert areas, (2) the surrounding areas should be as flat as possible to minimize directional effects associated with topography variations as well as environmental effects, (3) their optical properties should be stable in time, and (4) their spectro-directional signatures should be reproducible using semi-empirical BRDF models.
The MODIS products were re-projected into the World Geodetic System 84 to facilitate the geo-referencing across sites, using an increment in latitude and longitude that was the closest possible to the original resolution of the products (0. 00462° which is about equivalent to 500 m). The re-projection was performed using the Gdal library in Python. • Step2-R: For each area, the temporal stability (Equation 2 and Equation  3 ) was then quantified and also for the spatial homogeneity at 20 km and 100 km (Equation  1 ) of each pixel. In order to reduce the computational time, we further limited the processing to 150 × 150 km² areas (~300× 300 pixels at 500 m resolution) surrounding the locations exhibiting the highest spatial uniformity at 20 km (estimated from a visual appraisal of the maps generated, see Section 4.
BRDF Model Inversion The BRDF models were fitted against the monthly PARASOL observations described earlier. We applied a different optimization procedure depending on the model linearity. For linear models, the weighting parameters K of each kernel were optimized so as to minimize the RMSD between measurements and model outputs using a simple matrix inversion 𝐊 = (𝐅 𝐅) 𝐅 𝐘, ( 7 ) where Y is a 1xN matrix representing the column vectors of the N observations and F an mxN matrix representing corresponding N simulations for each of the m kernel. For non-linear models, the optimization was performed using the curve_fit non-linear least square function of the Scipy library for Python. In order to minimize the impact of the set of initial parameter guesses on the solution, 10 optimizations were run with different initial guesses.
Fitting Performance of the BRDF Models Before quantifying the magnitude of the directional effects of the ten sites considered, the fitting performances of the BRDF models were evaluated against monthly PARASOL data in each band. Observations around the hot spot direction (phase angle lower than 10°) were removed for the model optimization. The residual RMSD between the calibrated model and the observations averaged over all sites are presented as boxplots for each waveband in Figure  7. The Snyder and RPV models generally provide the best fitting performances in all wavebands for all sites. The fitting performances of Ross-Li-HS, followed by Ross-Li/Roujean/Roujean-HS are slightly lower than those of Snyder and RPV: the median RMSD values obtained with Ross-Li-HS are between 5% to 12% higher than Snyder depending of the waveband, between 6%-12% for RossLi, and between 5 and 16% for the two versions of the Roujean model.
Atmosphere Characterization The monthly climatologies (monthly medians) of aerosol optical depth (AOD) and cloud fraction (CF) derived from MODIS MYD04_L2 and MACC products are shown in Figure  9. For MYD04_L2 (~0. 1° resolution), we considered all observations within a 0. 2° lat/lon distance from the considered PICS; For the MACC data (1° resolution), we considered the grid cells containing the PICS. The "typical" monthly values are determined as the median of all data within each month. The AOD climatologies derived from the two datasets agree generally well in terms of median AOD and seasonality, the maximum AOD values being reached in spring or summer depending on the site. Discrepancies are, however, noticeable for Libya4 and for the four sites identified in this study: AOD estimates inferred from MYD04_L2 are higher than the MACC estimates at Libya4 and Algeria_PICSAND1 while the opposite is observed at the three other sites.
MYD04_L2-derived climatology also produces weaker amplitudes at Libya4 and Namibia_PICSAND1 than MACC. For all sites considered, the median AOD along a typical year is below 0. 35. The Mauritanian sites have the highest AOD (from the two datasets). The other sites have median AOD values typically about 0. 25-0. 3, with the noticeable exception of Namibia_PICSAND1 which exhibit the lowest AOD (below 0. 2, depending on the dataset). High AOD values (above 0. 6) can be reached in spring or summer. Values above 1 in the two products possibly indicate occurrence of sand storms. The AOD composition derived from the MACC products indicates that dust is the dominant aerosol type for all sites. The dust fractions are: 49% for Namibia_PICSAND1, 52% for Libya4, 55% for Algeria_PICSAND1, 59% for Algeria5, 62% for Algeria3, 63% for Arabia_PICSAND1, 70% for Sudan_PICSAND1 and Libya1, 74% for Mauritania2 and 76% for Mauritania1.