dataesr/scientific-paragraphs-categorization huggingface dataset:
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Acknowledgements Thanks to  Michael Korbakov  for porting the original Haskell implementation of the agent declaration code to Python. Funding W. W. , M. C. , G. M. , A. W. , and V. D. acknowledge the support from the  Engineering and Physical Sciences Research Council (EPSRC)  grant  EP/J02175X/1  and from  UK Research Councils Synthetic Biology  for Growth programme. W. W. and V. D. acknowledge the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration grant number  320823  (to V. D. and W. W. ). W. W. also acknowledges support from the  National Academies Keck Futures Initiative of the National Academy of Sciences  award number  NAKFI CB12.
, 2016)  or modelling  (Burrows et al. , 2014)  need to invoke two or more organic matter pools with different composition and turnover times. The contribution of sea spray aerosols to the CCN population was recently quantified by  Quinn et al. (2017). By reanalysing a large historical dataset, they estimated that primary aerosols contribute less than 30% of the CCN over most of the global ocean, with higher values found only in the windy Southern Ocean. As their estimates combine primary organic and sea-salt aerosol, they set an upper bound for the biogenic (primary organic) contribution. Biogenic effects on the properties of primary and secondary marine aerosols are expected to correlate with phytoplankton biomass because phytoplankton are the base of the oceanic food web and the production of organic matter.
The VIIRS on board the Joint Polar Satellite System has a day-night band with a dynamic sensitivity range that may enable such applications, but its suitability for detecting bacterial bioluminescence remains to be investigated. Indirect approaches to estimate bacterial activity using remote sensing data are reviewed by  Grimes et al. (2014). Other technological advances in radiometric ocean colour remote sensing, besides increased spectral resolution, include increased acquisition frequency and spatial resolution. Indeed, the first ocean colour satellite in geostationary orbit, the Korean GOCI  (Kim et al. , 2012) , was launched in 2010 and provides hourly imagery for the Yellow Sea, a significant improvement from the daily imagery typically available from polar-orbiting ocean colour sensors.
Lidar remote sensing While aircraft-based lidars have provided relevant ocean measurements for decades (see  Churnside et al. , 2013 , Hostetler et al. , 2018 , and references therein), the power of satellite lidar for ocean biology measurements has been demonstrated only recently  (Behrenfeld et al. , 2013 (Behrenfeld et al. , , 2017;; Churnside et al. , 2013; Lu et al. , 2014). These studies involved analysis of data from the CALIOP instrument on the CALIPSO platform (Table  1 ;  Winker et al. , 2009). Although CALIOP was designed for atmospheric measurements of clouds and aerosols, its 532-nm channels are also sensitive to ocean backscatter. The relatively coarse vertical resolution of CALIOP (30 m in the atmosphere and 23 m in the ocean) and poor detector transient response make vertically resolved ocean retrievals challenging.
However, significant scientific impacts have been realized using vertically integrated CALIOP subsurface ocean data. Behrenfeld et al. (2013)  used CALIOP data to retrieve particulate backscattering coefficient (b bp ) for the global oceans and, employing published relationships based on b bp , estimated particulate organic carbon (POC) and phytoplankton biomass (Figure  3 ). CALIOP data are particularly valuable in the polar oceans where the ocean colour record is incomplete from the late fall through the winter due to low sun angles. Supplying its own light source, CALIOP has provided an uninterrupted record of plankton stocks for the ice-free portions of the polar oceans. Behrenfeld et al. (2017)  used a decade of monthly-resolved CALIOP data to demonstrate fundamental processes governing the balance between phytoplankton division and loss rates, thereby advancing a new and evolving understanding of plankton blooms  (Behrenfeld, 2010; Behrenfeld and Boss, 2018 ).
An additional finding was that interannual anomalies in northern and southern polar-zone plankton stocks were of similar magnitude but driven by different processes. Specifically, ecological factors dominated interannual changes in the northern polar zone, while variations in plankton stocks of the southern polar zone predominately reflected changes in ice-free area. While the atmosphere-focused CALIOP instrument provides scientifically valuable ocean data products, it has exceeded its 3-year design lifetime by over 9 years. Significant advances in science capability are envisioned for a follow-on satellite lidar optimised for ocean as well as atmospheric measurements  (Hostetler et al. , 2018). One major advance would be to have higher vertical resolution. Lidar signals attenuate rapidly with depth, for instance, by a factor of 400 at three optical depths, beyond which the lidar signal is generally not useable due to low signal-to-noise.
At CALIOP's 532-nm wavelength, this three-optical-depth limit corresponds to about 50 m in geometric depth in the clearest waters and much less in more turbid waters, which leaves only one or two useable points in the 23-m resolution CALIOP profile. In future space-borne lidars, vertical resolutions of less than 3 m are easily achievable, and would enable true profiling of vertical structure in backscatter to three optical depths. Such profiling would represent a significant advantage over passive radiometric measurements, for which the measured signals are weighted exponentially toward the ocean surface (with 92% of the signal coming from the first optical depth). Vertically resolved lidar data of phytoplankton biomass, for instance, will reduce errors in estimates of net primary productivity that result from using surface-weighted retrievals to represent ocean properties at greater depths  (Platt and Sathyendranath, 1988; Zhai et al.
, 2012; Hill et al. , 2013; Schulien et al. , 2017). Further major advances are expected from a high-spectral-resolution-lidar (HSRL) instrument, summarized in Table  3. The standard elastic backscatter lidar technique used for CALIOP data requires assumptions on the change of attenuation with depth and to single out the backscatter from particles from the backscatter of seawater molecules. These assumptions can introduce large errors. By adding one or more channels in the receiver to resolve the optical signal spectrally, the HSRL technique enables independent and accurate retrieval of particulate backscatter and attenuation coefficients. The HSRL technique has been used for decades for aerosol measurements  (Shipley et al. , 1983; Piironen and Eloranta, 1994; Esselborn et al. , 2008; Hair et al.
, 2003). Single-view sensors (i. e. , sensors that view each pixel from a single angle) on polar-orbiting platforms, such as AVHRR, MERIS, MODIS, SeaWiFS, and VIIRS, have broad swaths and can provide AOD retrievals typically once per day in cloud-free conditions and away from sun glint. Regardless of sensor, most of these algorithms perform a multispectral inversion from multiple bands in the VIS and NIR spectral regions, to obtain mid-visible AOD and some weighting between different aerosol modes  (Tanré et al. , 1997; Ignatov et al. , 2004; Sayer et al. , 2012). Similar sensor types on geostationary platforms, such as SEVIRI and GOCI, have also been used for AOD retrieval from this type of algorithm as their more frequent repeat cycle increases data set coverage  (De Paepe et al. , 2008).
, 2013). However, these aerosols are easier to quantify at altitude than near the surface. For all these passive radiometric sensor types, aerosols are often modelled as mixtures of aerosol populations with lognormal distribution; aerosol 'types' are also reported in the product (giving rise to derived quantities such as spectral AOD, α, and single scattering albedo, SSA), although they are somewhat constrained by the required assumptions underlying aerosol optical models. Aerosol components are often derived from either AERONET inversions  (Dubovik and King, 2000)  or databases such as OPAC  (Hess et al. , 1998). Validation results vary depending on algorithm and sensor (e. g. ,  Kokhanovsky et al. , 2010; Petrenko and Ichoku, 2013). In recent years, the Maritime Aerosol Network (MAN) of ship-based observations  (Smirnov et al.
, 1996) , and the most recent lidar deployed in space was the CATS, in operation from 2015 to 2017  (Yorks et al. , 2016). Here we focus our discussion on CALIOP (launched in 2006;  Winker et al. , 2009) , because of the length of its data record and extensive use  that has been made from those data (to date, nearly 2000 publications are based on CALIOP observations). The CALIOP sensor employs the standard elastic backscatter lidar technique, which, fundamentally, measures attenuated backscatter from air molecules and cloud/aerosol particles at 532 and 1064 nm. In addition, measurement of backscatter polarised parallel and perpendicular to the transmitted laser pulse provides the ability to determine the presence of non-spherical particles. The calibration procedure used in CALIOP retrievals eliminates concerns about calibration drift over the lifetime of the instrument  (Powell et al.
, 2009). An average extinction-to-backscatter ratio is assigned to each type based on AERONET climatology and other relevant data. Consequently, retrieval errors can arise from misclassification of aerosol type and the natural uncertainty of extinction-to-backscatter ratios within a type  (Burton et al. , 2013). These errors can accumulate as the retrieval proceeds downward through the atmosphere toward the surface. The CALIOP sensor provided the first global view of the three-dimensional distribution of aerosols  (Winker et al. , 2013)  which, along with data from other sensors and models, has enabled several important breakthroughs, including quantification of the direct radiative effects of aerosols above low clouds (e. g. ,  Chand et al. , 2009; Oikawa et al. , 2013; Zhang et al. , 2016) , the first global estimates of all-sky aerosol radiative forcing (e.
g. ,  Oikawa et al. , 2013; Matus et al. , 2015) , and assessments of the vertical distribution of aerosol in transport models (e. g. ,  Yu et al. , 2010; Koffi et al. , 2016). CALIOP data have also been used to help quantify transport of desert dust and associated iron fertilization of regions remote from dust sources (e. g. ,  Johnson et al. , 2011; Tan and Shi, 2012; Yu et al. , 2015). In addition to information on aerosols, CALIOP provides cloud top height and, for non-opaque clouds, base height and optical depth. It is much more sensitive to optically thin clouds than passive remote sensors and does not suffer ambiguities arising from, for instance, viewing warm marine boundary layer clouds through a veil of thin cirrus. CALIOP also provides information on the vertical profile of backscatter and depolarisation within clouds (i.
We are now just starting an exciting new era of SAR imagery with openly distributed data (through the Copernicus Program (ESA/EUMETSAT), for example). Sentinel-1A was launched in April 2014, and Sentinel 1B in April 2016, extending the temporal/spatial coverage, which is mostly concentrated around Europe and in continental and coastal areas elsewhere. Software developed within the Copernicus Program (SNAP) is also helping to broaden the user community of both scientists and operational services. The open distribution policy of optical imagery in the 1980s and 1990s (introduced with the Landsat legacy) revolutionized various applications, and free SAR imagery will surely spur progress in other areas. Phenomena in the MBL that have important effects on turbulent fluxes can also be observed in SAR imagery, including rain cells, boundary layer roll vortices  (Zhang et al.
Future exploitations of SAR data can be safely foreseen to provide new and complementary information to better constrain in-water turbulence estimates at the global scale, provided coincident advances in turbulence parameterisation. Sea-ice properties (e. g. , extent, concentration, thickness), which are important for sea-air fluxes and interface turbulence, have traditionally been mapped and globally monitored with passive microwave sensors. Such measurements enable high temporal resolution retrievals, but with coarse spatial resolution. Active microwave sensors, particularly SAR, complement passive microwave datasets by providing higher-resolution imagery and additional properties that are relevant for sea-air flux estimates, such as sea-ice thickness, type, age, drift and melting rate  (Dierking, 2013).
Lidar Like scatterometers and radar altimeters, lidar also provides a measurement of the backscatter coefficient of the ocean surface. The lidar measurement of the ocean surface backscatter coefficient is directly related to the wave mean square slope variance. Hu et al. (2008)  demonstrated the value of the lidar technique by computing wave slope variance from CALIOP data and using that to estimate wind speed. Favourable comparisons with AMSR-E wind speeds have demonstrated the validity of this approach (Figure  10 ). An advantage of lidar is that the signal is more sensitive to smaller scale capillary waves, because of the much shorter wavelength of the electromagnetic pulse than those used by radio-frequency and microwave sensors. Another advantage derives from the small lidar footprint at the ocean surface.
The (A)ATSRs have been used to create stable long-term SST climate data records with small biases (<0. 1 K) and high stability (e. g. ,  Merchant et al. , 2012). A constellation of polar-orbiting and geostationary satellites with thermal infrared and passive microwave sensors provide global coverage several times a day, allowing resolution of not only the seasonal, but also the diurnal cycle of SST. The amplitude of seasonal and diurnal SST cycles could be used to understand heat fluxes in specific regions, which could be used further to parameterize buoyancy-driven fluxes. A major challenge for SST coverage of the first space missions was under-sampling due to cloud blocking. Passive microwave satellites (e. g. , ISSM, AMSR, TRMM, GPM) helped overcome this issue, but with a much degraded spatial resolution ( ~25 km, compared to 1 km for AVHRR and MODIS).
Hence, for large-scale processes, salinity from space is also a valuable measurement. In addition to its importance to the stratification problem, surface salinity influences gas solubility, and thereby air-sea exchange. Sea surface salinity (SSS) is estimated from passive microwave sensor data using inversion models that rely on differences in the relationship between the brightness temperature and the actual temperature due to the emissivity coefficient, which is dependent on salinity (and surface roughness). Global maps of SSS have been available since 2009 from the SMOS mission with 35-km resolution and a 3-day revisit time  (Reul et al. , 2014)  complemented by the measurements of AQUARIUS  (Lagerloef et al. , 2008)  over the 2011-2015 period. The broad spatial resolution limits the current SSS products (with 0.
, 2016). Rain rate can be inferred using infrared and microwave radiometric sensors and used to scale rain-induced surface turbulence. The challenge is to obtain accurate rainfall estimates due to the complexity of the measurement (see discussions in Section 3. 2. 1). Further efforts combining passive infrared, microwave, and active radar measurements should provide more insights into cloud microphysics and improve global rainfall, and hence air-sea flux estimates. Another benefit of passive microwave sensors is their utility to monitor seaice extent, which plays a critical role in sea-atmosphere exchange processes. The retrievals are based on changes of the brightness temperature due to emissivity differences of sea ice (including type, age) and the ocean surface. Despite the coarse spatial resolution ( ~25 km), the historical satellite dataset of sea-ice properties reaches down to 1973 with the Nimbus-5 mission (NASA), providing a long-term record for detecting changes and understanding feedback responses in the exchange processes.
However, radiometers do not suffer from the speckle noise characteristics of coherent SAR observations, thus allowing better observation of weaker features. Furthermore, radiometers provide an extensive dataset with worldwide coverage at a spatial resolution on the order of a kilometre (e. g. , SeaWiFS, MODIS) down to a few meters (e. g. , Pleiades, Spot, Landsat series, Sentinel-2). Therefore, low-to-high resolution optical images can be used independently or as a useful complement to active techniques for retrieving surface winds and sea roughness to better constrain near-surface oceanic turbulence. Physically, sun glint is defined as the peak of intensity due to the sunlight reflection on the rough sea surface occurring in the vicinity of the theoretical location of the specular reflection spot that would be obtained for a flat sea (i.
Polarimetric and multidirectional sensors Direct sunlight, originally unpolarised, becomes partially polarised after reflection off the sea surface  (Kattawar, 2013). Polarimetric measurements therefore can provide additional information to better discriminate the sunglint signal from space  (Harmel, 2016). As for intensity, the polarisation signal of sun glint is highly directional. The use of multi-angular (or multidirectional) data is most suitable for exploitation of remotely sensed sun glint in the marine environment  (Harmel and Chami, 2013). The sun-glint radiance as measured by a satellite sensor depends on the reflectivity of the air-sea interface and the atmospheric transmission. The former is highly directional and controlled by both the wave facet orientations and the refractive index of the sea surface.
e. , sun-glint) and passive and active microwave sensors. Combined with retrievals of sea surface slicks, whitecaps, rain rate, sea ice, and buoyancy fluxes (driven by temperature and salinity), this approach would enable a much more accurate estimation of turbulent kinetic energy and bubble-mediated fluxes of gases and particles. Beyond the retrieval process, we need innovative frameworks for data management, access and analysis. Space-borne sensors produce huge (and rapidly growing) amounts of data, such that handling and analysis with traditional systems and techniques will become increasingly challenging, being a clear example of a big data problem. Widespread implementation of big data approaches, like NOAA's Big Data Project (http://www. noaa. gov/big-dataproject), will improve our ability to utilise remote sensing data to study the ocean-atmosphere interface while leveraging the observational efforts.
For example, such approaches can greatly facilitate the task of sorting/controlling observations by meteorological state, aerosol type (pristine marine vs. continental influence), cloud type, and ocean ecosystem state, in order to separate ecosystem influences from other effects (e. g. ,  McCoy et al. , 2015). The on-going revolution in artificial intelligence and machine learning techniques is poised to play a key role in the development of remote sensing applications for the Earth Sciences  (Lary et al. , 2016). Efficient exploitation of multisensor datasets and extensive data mining can lead to new discoveries based on statistical relationships, which should ultimately result in better mechanistic understanding and predictive power. Going a step further, we also envision data exploration platforms that simplify the task of tracking air and water mass trajectories as they are advected, an approach called Lagrangian tracking.
g. , radiometer, lidar, radar) sounding both intensity and polarisation characteristics of the electromagnetic field; we advocate for synergies between these remote sensing observations to better tackle multidisciplinary oceanatmosphere science questions. Such synergies require (1) theoretical advances in the radiative transfer models in use for simultaneous retrieval of atmosphere-ocean properties, (2) merging multisensor and multiplatform datasets using advanced data assimilation schemes, (3) further developing user-and problem-oriented geophysical datasets, and (4) jointly exploiting space-borne sensors with in-situ data sets (acquired from ships or autonomous platforms) using newly developed artificial intelligence methods and facilities. This last point could help disentangle potential complex relationships between these two kinds of data sets, thereby furthering satellite retrieval algorithms.
They are now complemented by passive microwave retrieval of sea surface salinity, better resolving buoyancy fluxes and therefore convection-driven turbulence. Though less exploited, the sun-glint signal in the visible and near-infrared (both radiometric and polarimetric) provides a powerful means to retrieve sea surface roughness (and thus turbulence-related parameters) at multiple scales. VIII. The aforementioned advances are accompanied by growing constellations of polar-orbiting satellites with wide swaths, geostationary sensors with capacity to resolve diurnal cycles, and generalized increases in spatial resolution. Synergistic use of these datasets will soon enable the routine retrieval of a large variety of 3D geophysical fields with high resolution. Combined with powerful computational frameworks, such synergies should produce a leap forward in the understanding, modelling, and prediction of ocean-atmosphere interactions.
Acknowledgements We would like to thank all other participants to the  ESA/SOLAS  workshop held at  ESA-ESRIN in Frascati, Italy ,  13-15 June 2016 :  Paulo Artaxo ,  Vittorio Brando ,  Cristina Facchini ,  Véronique Garçon ,  Roberto Sabia ,  Eric Saltzman ,  Yeala Shaked  and  Knut Stamnes. We thank  Macarena Ortiz  and one anonymous reviewer for their insightful comments, which helped improve an earlier version on the manuscript.
MBL atmospheric characteristic Current passive satellite radiometers Future passive satellite radiometers Neukermans, Harmel, Gali et al: Harnessing remote sensing to address critical science Art. 71,page 15of46 questions on ocean-atmosphere interactions Aerosol optical properties Midvisible (primary) and spectral Increased retrieval accuracy of AOD and its ( secondary) aerosol optical thickness spectral variations, better spatial coverage and (AOD), and related quantities (e. g. , resolution, single-scattering albedo (for absorbing Angström exponent α) aerosols) Aerosol microphysical properties Best-fitting aerosol models (based on More robust identification of likely microphysical ('type') AERONET statistics and/or databases), properties, better spatial coverage and resolution, weighting between fine and coarse more aerosol models possibility to identify and modes (i.