dataesr/scientific-paragraphs-categorization huggingface dataset:
The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment.
Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0°, 30, and 60° assuming clear skies, standard atmosphere, and wind speed of 5 m s -1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.
Introduction The investigation of the propagation of natural light in the ocean can be addressed experimentally through in situ measurements and theoretically through numerical simulations of radiative transfer. The understanding of the relationships between the radiometric quantities (i. e. , radiance and irradiances) that characterize the light fields within and leaving the ocean and the inherent optical properties (IOPs) of the water column, boundary conditions at the sea surface (i. e. , surface illumination conditions and sea state) and at the ocean bottom (i. e. , bottom depth and albedo) requires comprehensive datasets of multiple variables acquired over a broad range of environmental conditions. For example, of particular interest are the relationships between the spectral remote-sensing reflectance of the ocean, Rrs(λ), which is an apparent optical property (AOP) derivable from radiometric quantities, and the seawater IOPs that are directly linked to various seawater constituents because these relationships form the cornerstone of various applications of optical (ocean color) remote sensing.
Recent technological developments and broader accessibility of optical in situ instrumentation have led to significant increase in optical datasets collected across diverse oceanic environments and efforts have been undertaken to merge data from various sources within publicly available databases (e. g. ,  Werdell and Bailey, 2005; Valente et al. , 2019; Casey et al. , 2020). Although the importance of field data collection across diverse environments cannot be overstated, the existing database compilations are subject to certain limitations. In addition to typical measurement errors, it is difficult to ensure consistent data quality and characterization of uncertainties across all merged data because individual datasets are often obtained with different instruments as well as measurement and data processing methods.
Also, even the large databases such as NASA's SeaBASS cannot ensure the balanced representativeness of collected field data in terms of a broad range of optical conditions across diverse ocean environments. In this context, radiative transfer (RT) simulations provide a useful tool to generate comprehensive synthetic databases and complement the existing datasets of field measurements in support of studies in ocean optics and optical remote sensing. Over the past decades various radiative transfer models that employ different numerical solution techniques have been developed and used to address a wide range of problems related to optics of natural water bodies (e. g. ,  Mobley et al. , 1993; Mobley, 1994; Stamnes et al. , 2017). Since the 1990s the HydroLight code based on invariant imbedding technique  (Mobley, 1989; Mobley et al.
, 1993; Mobley, 1994)  has been among the most commonly used radiative transfer models in oceanographic optics. The HydroLight code solves the scalar (i. e. , polarization of light is not included) time-independent radiative transfer equation for a horizontally homogeneous water body, in which the inherent optical properties can vary with depth, under given boundary conditions at the surface and bottom of the water body. The inelastic radiative processes within the water column that include Raman scattering by water molecules, fluorescence of chlorophyll-a pigment, and fluorescence of colored dissolved organic matter (CDOM) can be included in HydroLight simulations. The radiative transfer simulations with HydroLight code have proven useful for generating synthetic databases of light field characteristics (i.
e. , radiance and irradiances) within and leaving the ocean and the AOPs derived from the simulated radiometric quantities for various scenarios of seawater IOPs that provide input to the simulations. In particular, as a result of efforts dedicated to inverse bio-optical algorithms and coordinated under the auspices of the International Ocean Colour Coordinating Group  (IOCCG Report, 2006) , the publicly available synthetic database was generated within the spectral range 400 -800 nm with a 10 nm resolution for clear sky conditions with three different sun zenith angles (0°, 30°, and 60°), a sea surface state corresponding to a wind https://doi. org/10. 5194/essd-2023-80 Preprint. Discussion started: 9 March 2023 c Author(s) 2023. CC BY 4. 0 License. speed of 5 m s -1 , and 500 different IOP combinations driven by chlorophyll-a concentration, Chla, within the surface ocean layer.
The input IOP data included the spectral absorption coefficients of phytoplankton, aph(λ), nonalgal particles (also referred to as depigmented or detrital particles which can include various types of particles such as organic detritus, mineral particles, heterotrophic bacteria, and depigmented phytoplankton cells), ad(λ), colored dissolved organic matter (CDOM), ag(λ), and the spectral backscattering coefficients of phytoplankton, bb-ph(λ), and non-algal particles, bb-d(λ) (λ represents the wavelength of light in vacuum in units of nm and the IOP coefficients are typically expressed in units of m -1 ). The output parameters provided by those simulations which are available in the public database included the following AOPs: the spectral remote-sensing reflectance, Rrs(λ), the remote-sensing reflectance just below the sea surface, rrs(λ), the irradiance reflectance just below the sea surface, R(z=0 -, λ), and the diffuse attenuation coefficient for downwelling plane irradiance, Kd(λ, z), at the depths z = 0 -, 5, and 10 m (where 0 -indicates the depth just beneath the sea surface).
Another synthetic database that is publicly available was developed as part of the CoastColour Round Robin project  (Nechad et al. , 2015). This project was focused on coastal waters and IOPs were driven by 5000 combinations of Chla, ag(λ), and mass concentration of mineral particles. The HydroLight simulations were run from 350 nm to 900 nm at 5 nm intervals for cloudless sky, three sun zenith angles (0, 40, and 60°), and a wind speed of 5 m s -1. The output parameters included in the publicly available database are the water leaving reflectance, RLw(λ) = π Rrs(λ), Kd(λ), the photosynthetically available radiation, PAR, and the euphotic depth, zeu. Most recently, a synthetic database was also developed by the first NASA PACE (Plankton, Aerosols, Cloud, and Ocean Ecosystem) Science Team where the ocean contribution to the top of the atmosphere radiances were simulated by HydroLight  (Craig et al.
, 2020). These simulations were performed from 350 to 800 nm with a 5 nm step for a cloudless sky, three sun zenith angles (10°, 30°, and 60°), wind speed of 5 m s -1 , and a set of 720 IOP combinations driven by aph(λ). The publicly available output of these HydroLight simulations is Rrs(λ). While these existing synthetic databases have offered valuable information to the ocean color radiometry (OCR) community, especially for the purpose of algorithm development where the ocean AOPs are linked to IOPs, there are several reasons that have motivated the present study aiming at generating a new optical synthetic database. First, the inelastic Raman scattering and fluorescence processes were ignored in the previous RT simulations. These inelastic radiative processes are known to be important for simulating realistic characteristics of light fields within and leaving the ocean, including Rrs(λ) that is a primary optical quantity used in ocean color remote sensing.
For example, Raman scattering by water molecules may have an important influence on light within and leaving the ocean and AOPs, especially in the green and red parts of the spectrum (e. g. ,  Marshall and Smith, 1990; Stavn, 1993; Sugihara et al. , 1984; Westberry et al. , 2013). Second, the three synthetic databases described above are based on the use of the spectral pure seawater absorption, aw(λ), and scattering bw(λ), coefficients values as defined by  Pope and Fry (1997)  and  Morel (1974)  in the visible part of the spectrum, respectively. However, more recent measurements and theoretical considerations provide new recommendations for spectral values of aw(λ) and bw(λ)  (IOCCG Protocol Series, 2018; Zhang and Hu, 2009; Zhang et al. , 2009). Third, the suites of IOP data combinations and probability distributions of different IOPs that were used as input to previous RT simulations do not appear to match well with the IOP distributions observed in extensive field datasets or satellite-derived datasets representing the global ocean.
This issue may have a biasing effect when the synthetic databases are used to develop the optical algorithms based on the AOP vs. IOP relationships, especially when the underlying goal is to represent a broad range of IOPs encountered within the global ocean, even if the https://doi. org/10. 5194/essd-2023-80 Preprint. Discussion started: 9 March 2023 c Author(s) 2023. CC BY 4. 0 License. primary interest is in open-ocean pelagic environments. Finally, the previous synthetic databases were developed specifically for OCR-oriented studies and the publicly accessible data generally include only the surface reflectances, Rrs(λ), R(λ), rrs(λ), and Kd(λ) at selected depths. These databases do not include many of the various output variables obtained from RT simulations, such as the various underwater AOPs, which can be useful in supporting a broader range of studies in ocean optics beyond ocean color remote sensing.
In this article, we present a new synthetic optical database generated using RT simulations which addresses some of the limitations of similar databases developed in the past. First, we describe the development of the synthetic IOP dataset that is required to run RT simulations. The key roles in this development are played by the measured data of phytoplankton absorption coefficient and desired consistency between the probability distributions of synthetic IOPs and the global distributions based on satellite observations. Following this, we describe different configurations of RT simulations which were performed with the HydroLight code. The next section is dedicated to consistency between the new optical synthetic database and in situ data, including some previously reported empirical relationships.
Development of synthetic dataset of seawater inherent optical properties The scope of the synthetic database generated with RT simulations and the degree of its representativeness of diverse marine optical environments within the global ocean depend most critically on a dataset of seawater IOPs that are used as input to RT simulations. In the present study, our approach to generate the IOP dataset was driven largely by an underlying goal to obtain the probability distributions of IOPs which are generally consistent with the distributions observed in the global ocean dominated by open-ocean pelagic environments. The key IOPs involved in the creation of our IOP dataset include the spectral absorption and backscattering coefficients associated with the main categories of seawater constituents representing suspended particulate matter and CDOM.
Specifically, the constituent-specific absorption IOPs are the spectral absorption coefficients of phytoplankton, aph(λ), non-algal particles, ad(λ), and CDOM, ag(λ). Note that the sum aph(λ) + ad(λ) = ap(λ) represents the particulate absorption coefficient with combined contributions of phytoplankton and non-algal particles, and the sum ad(λ) + ag(λ) = adg(λ) represents the non-phytoplankton absorption coefficient with combined contributions of non-algal particles and CDOM. The constituent-specific backscattering IOPs are the spectral backscattering coefficients of phytoplankton, bb-ph(λ), and non-algal particles, bb-d(λ), such that the sum bb-ph(λ) + bb-d(λ) = bbp(λ) is the particulate backscattering coefficient. Among these constituent-specific IOPs, the phytoplankton absorption coefficient, aph(λ), plays the most fundamental role in the creation of the synthetic dataset of IOPs in this study.
The aph(λ) spectra in this dataset were derived from actual measurements of phytoplankton absorption made on near-surface samples collected across diverse oceanic environments. Thus, the aph(λ) data are not "synthetic" in a sense that these data were not obtained from a modeling approach although some spectral interpolation or extrapolation was applied to measured data as described in more detail below. In contrast, the remaining four constituent-specific IOPs in the  IOP dataset, i. e. , ad(λ), ag(λ), bb-ph(λ), and bb-d(λ) , are "synthetic" in a sense that they are entirely based on calculations using a modeling approach with some assumptions about the magnitude and spectral behavior of the modeled IOPs. Importantly, the measured values of aph(λ) were used in the calculations of these IOPs.
These calculations are also described in detail below. Thus, each combination of the five constituent-specific IOPs in the synthetic IOP dataset consists of the measured aph(λ) and the calculated ad(λ), ag  (λ), bb-ph(λ), and bb-d(λ)  where the results of these calculations depend on the measured aph(λ). As a result of this approach, it would seem justifiable to refer to the created IOP dataset as a quasi-synthetic dataset. For simplicity, however, we refer to it as the synthetic IOP dataset while bearing in mind that aph(λ) spectra were derived from measurements. aph(λ) and ag(λ) (Fig. 1b ), aph(λ) and adg(λ) (Fig. 1c ), and aph(λ) and bbp(λ) (Fig. 1d ). We recall that while the in situ data of ag(λ), adg(λ), and bbp(λ) were not used in the development of synthetic IOP dataset, they were assembled for the purpose of comparison with corresponding coefficients that were calculated and included in the synthetic IOP dataset.
Many in situ data of IOP coefficients used in the present study were utilized in previous studies (e. g. ,  Babin et al. , 2003; Huot et al. , 2008; Bricaud et al. , 2010; Antoine et al. , 2011; Loisel et al. , 2018; Aurin et al. , 2018; Reynolds and Stramski, 2019; Stramski et al. , 2019; Jorge et al. , 2021; Bonelli et al. , 2021). Some data are https://doi. org/10. 5194/essd-2023-80 Preprint. Discussion started: 9 March 2023 c Author(s) 2023. CC BY 4. 0 License. described in publications devoted to compilation of various datasets  (Valente et al. , 2019; Casey et al. , 2020)  and are included in several databases (e. g. , SeaBASS, CoastlOOC, and GOCAD). As the IOP coefficients in the in situ dataset were measured over a broad range of trophic and environmental conditions, their spectral values span more than 3 or 4 orders of magnitude.
This large dynamic range is illustrated in terms of probability distributions at selected light wavelengths, i. e. , at 440 nm for the constituent absorption coefficients and 550 nm for the particulate backscattering coefficient (Fig. 2 ). The first task necessary for development of the synthetic IOP dataset was to assemble data of hyperspectral absorption coefficient of phytoplankton, aph(λ), from field measurements collected across diverse open ocean and coastal environments (Fig. 1a , Fig. 2a ). These aph(λ) data were obtained with the filter-pad spectrophotometric method as a difference between the measurements of ap(λ) and ad(λ)  (Kishino et al. , 1985; IOCCG Protocol Series, 2018). Historically, most of these measurements were acquired with the transmittance configuration of the filterpad method and such measurements are included in our dataset.
However, some data in our dataset were obtained with the inside integrating-sphere configuration of the filter-pad method, which is superior to the transmittance configuration of measurement  (Stramski et al. , 2015; IOCCG Protocol Series, 2018). A significant portion (23. 7%) of the initial dataset of aph(λ) consisting of 4382 measurements covers a spectral range from 400 to 750 nm with high spectral resolution of data reported at 1 nm interval. In some cases, the original measurements extended to near-UV spectral region and/or longer wavelengths in the near-infrared spectral region (800 or 850 nm). The data beyond 750 nm are not used in this study because our RT simulations target the spectral range from 350 to 750 nm. It is notable that the absorption measurements of marine particles and phytoplankton are generally unavailable or are not reported in the UV because of increased methodological challenges and uncertainties in this spectral region  (Stramski et al.
, 2015; IOCCG Protocol Series, 2018; Kostakis et al. , 2021). As a result, only a relatively small fraction of aph(λ) measurements in the initial dataset were reported in the near- UV region. In addition, the initial dataset included a relatively large fraction of aph(λ) measurements that were reported at wavelength intervals larger than 1 nm. These lower resolution data (hereafter referred to as multispectral) ranged from a small wavelength interval of 2 nm to data reported at more limited number of wavelengths (as small as <10) within the visible spectral range. It is likely that the multispectral data available from some data sources that we used in this study were originally measured at higher spectral resolution but eventually were reported only for some selected wavelengths, such as those corresponding to spectral bands available on satellite ocean color sensors.
The first objective of the analysis of aph(λ) was to consider the initial aph(λ) dataset within the 400-750 nm range and convert the measurements that were reported at lower spectral resolution to uniformly hyperspectral data at 1 nm interval. In this analysis, all measurements originally available at 1 nm interval were considered to provide reference spectral shape functions of aph(λ). The originally multispectral data of aph(λ) were converted to hyperspectral data using a couple of different approaches depending on the spectral features of lower resolution data. One approach utilized the reference spectral shape functions of aph(λ) and was applied to multispectral aph(λ) data if they were reported at fewer wavelengths than 100. In this case, a given multispectral spectrum of aph(λ) was converted to hyperspectral spectrum using a specific hyperspectral measurement that exhibited the highest correlation with the multispectral measurement under consideration.
For this purpose we used a separate subset of reference hyperspectral measurements of aph(λ) that includes the near-UV spectral region. This reference subset of data consisted of 233 measurements collected across bio-optically diverse marine environments in the Pacific and Atlantic Oceans and western Arctic seas. The majority of these 233 spectra (170) were collected with the inside integrating-sphere configuration of filter-pad method, while the remaining 63 measurements were done using either the transmittance or transmittance-reflectance filter-pad configuration  (Zheng et al. , 2014). A correlational analysis was applied to pairs of spectra, each consisting of a spectrum covering the 400-750 nm range and a reference spectrum covering the 350-750 nm range. The correlation coefficient was calculated using data at common wavelengths from the 400-750 nm range.
The reference spectrum that yielded the highest correlation with the investigated 400-750 nm spectrum was selected as a basis for extrapolation of the investigated spectrum into the 350-400 nm range. This extrapolation ensured that a given investigated spectrum maintained its magnitude at 400 nm and its extrapolated near-UV portion had the spectral shape of the selected reference spectrum. The final aspect of extrapolation in the UV is related to the spectral range 300-350 nm. The rationale for IOP data extending to 300 nm is to ensure that the results of RT simulations that start at 350 nm account for possible effects of Raman scattering by water molecules in the UV spectral region. Therefore, for the 300-350 nm range we simply assumed that aph(λ) in this range is equal to aph(350). The limitation associated with this assumption is not considered to be serious given the limited role of the 300-350 nm range in the RT simulations and weak Raman scattering effects in UV spectral region.
Example spectra of aph(λ) in the 350-750 nm range from contrasting marine environments are presented in Fig. 3. These examples show significant variation in both the magnitude and spectral shape of aph(λ). In the next step of analysis, the subset of 2204 aph(λ) spectra that was created from the initial aph(λ) dataset as described above was subject to additional modifications to ensure that the final aph(λ) dataset is characterized by the probability distribution that resembles the distribution representative of the global ocean. This process and background information on the motivation for such adjustments in the probability distribution are described below. When the end goal is to achieve a high degree of representativeness of global ocean like in this study, the process of assembling in situ datasets of IOPs is unavoidably subject to limitations, even if relatively large amount of data from many field experiments and cruises are considered.
This is mainly because the global ocean is dominated by vast areas of open-ocean pelagic environments and the amount of IOP data collected in these environments is disproportionally limited compared to amount of data collected in coastal regions that represent a relatively small portion of the global ocean. Thus, the probability distributions based on in situ datasets, such as those presented in Fig. 2  ocean. In particular, the maxima of probability distributions and the measures of central tendency, such as the median and mean values, obtained from compilations of relatively large amount of in situ IOP data (such as in Fig. 2 ) are expected to be shifted to larger values compared to actual global distributions because the IOPs exhibit a general tendency of higher values in coastal regions compared to open ocean environments.