dataesr/scientific-paragraphs-categorization huggingface dataset:
These averages have been tabulated at a range of particle sizes  (Liu, 2008, their Table 2) , frequencies from 3 to 340 GHz, and temperatures from 233. 15 to 273. 15 K. The database offers linear interpolation and extrapolation to a specified frequency, temperature, and size. However, this extrapolation is not used within the hydrotable generator: frequencies below 3 GHz, Mie sphere results are substituted; above 340 GHz, use of the Liu database is forbidden. For temperatures below 233. 15 K the optical properties at 233. 15 K are substituted. Finally, the available particle sizes define the size range over which the PSD is integrated. This integration range is shown in Table  3 , with D max being the largest size available from the database and D min the smallest, but bounded at 100 µm on the assumption that the  Field et al.
A limitation of the Liu database is the relatively low diversity among the bulk optical properties achievable using the different habits. For example, the 4-, 5-, and 6-bullet rosettes give similar results to the sector snowflake. Then, there is a big gap to the next-most scattering particle, the 3bullet rosette, and another big gap to the intensely scattering hex plates and columns, which provide similar results to each other. However, these five hex particles with b = 3 provide a uniquely strong bulk extinction that cannot be obtained from the ARTS database (further discussion in Sect. 4).
ARTS database The ARTS scattering database  (Eriksson et al. , 2018)  was created to support sub-millimetre as well as microwave frequencies and to provide a broader range of non-spherical ice particles, including a variety of aggregates and densely rimed particles (e. g. hail and graupel). Table  3  summarises the options available. The current default frozen particles in RTTOV-SCATT are based on ARTS particles (see Table  1 ). The ARTS database provides optical properties at 34 frequencies from 1 to 886 GHz, at three temperatures  (190, 230, and 270 K) , and at least 34 sizes per habit. The ARTS standard habits are used here; these simplify the application of the database by ensuring a full coverage of size, temperature, and frequency. The size issue is that in the underlying database, the smallest size of aggregate habits can exceed 200 µm, so where necessary the standard habits consist of a habit mix.
In these cases, the small size range is covered by a single crystal habit with a similar shape to the constituents of the aggregate (see Table  3 ). For example, the "large plate aggregate" habit is complemented with the "thick plate" habit. To avoid discontinuities, there is a linear transition between the two habits over a certain size range. These "mixed" standard habits are named throughout after the habit covering the main size range and contain at least 42 particle sizes. Remaining standard habits are essentially a copy of the original ones. To improve temperature coverage in the standard habits, points at 210 and 250 K are added by a second-order interpolation, in order to decrease the error by subsequent linear temperature interpolation. Finally, due to limitations in DDA, there are some gaps in the database for combinations of large size and high frequencies.
06 2. 12 7 5-bullet rosette 1. 00 ×10 -4 1. 00 ×10 -2 0. 07 2. 12 8 6-bullet rosette 1. 00 ×10 -4 1. 00 ×10 -2 0. 09 2. 13 9 Sector snowflake 1. 00 ×10 -4 1. 00 ×10 -2 0. 002 1. 58 10 Dendrite snowflake 1. 00 ×10 -4 1. 00 ×10 Coefficients a and b describe the mass-size relation m = aD b g and are in SI units; see the code for full numerical precision. ARTS IDs are unique to RTTOV and do not correspond to  Eriksson et al. (2018) ; Liu IDs do correspond to  Liu (2008). *ARTS standard habits with IDs from 10 to 15 are a mixture of two habits, with the small size range covered by thick plate, long column, block column, short column, gem cloud ice and 8-column aggregate respectively. to snow and graupel, this arises mainly from the choice of PSD, which provides generally smaller particles to represent cloud ice (Sect.
1  that the frozen particles have small oscillations with frequency, particularly obvious in the radar reflectivity at lower frequencies. However, these are understood 2 and they should not be an issue near the frequencies of typical satellite channels, since the ARTS frequencies have been chosen with this in mind  (Eriksson et al. , 2018). Figure  2  illustrates the full range of frozen particle representations available from the ARTS database, along with the Mie sphere. The ARTS particles fall into two classes. The first is less dense particles with branched shapes including rosettes, snowflakes, and most of the aggregates. These are shown with solid lines and typically generate smaller SSA, extinction, asymmetry, and radar reflectivity. The second class is denser and more compact particles including pristine crystals, densely rimed particles (graupel and hail), and the 8-column aggregate.
( 6 ) This is the mass of the particle m(D g ) divided by the volume of a sphere of diameter D g. For spherical particles, the effective density and true density are equal. The true par-   2007 ) tropical PSD has been used in all cases, with the old integration (Sect. 3. 2) and a cutoff at D min = 1 ×10 -4 m. A soft ice sphere ("Mie") is also included, with settings as in Fig. 1. Bulk reflectivity and extinction have been normalised by those of the ARTS large plate aggregate; some lines are off scale to be able to focus on the most populated areas. The legends are in order of brightness temperature depression at 183 GHz, from most to least scattering (see Fig. 9 ). ticle mass is reported in the particle databases and is the mass of ice used in the DDA calculation; these can vary slightly from the fitted mass-size relation (see e.
Further, there are observational constraints, and for example the Brown and The ARTS mass-size relations are represented across the whole size range, even if non-physical densities may be generated, since that is how they are used in Eq. (  10 ) (later). The legend is ordered by the effective density at 1 ×10 -3 m (with the exception of the line corresponding to  Hogan et al. , 2012). Figure  11  of  Eriksson et al. (2018)  is similar but is based on the reported particle masses, rather than the mass-size relation. Francis (1995) mass-size relation gives a good description of midlatitude stratiform ice cloud  (Hogan et al. , 2012) ; this mass-size relation is shown in Fig. 3. However, there is currently no way of decoupling the mass-size relation from the DDA particle choice in the hydrotable generator; this could only be achieved by choosing an appropriate (and probably different) database particle for each size bin -in other words, a particle ensemble approach, which is not yet supported.
The Mie sphere does allow a free choice of mass-size relation (Sect. 2. 1. 1) but obviously brings many other drawbacks, so this is not advised. However, Fig. 3  shows that the available DDA particles span a wide range of mass-size possibilities. Further, but not shown on the figure, the dendrite particle in the Liu database (Table  3 ) would almost exactly match the  Brown and Francis (1995)  mass-size relation, for example. However, microwave radiances have their strongest sensitivity to convective snow particles, both in the tropics and midlatitudes, and the appropriate mass-size relation remains poorly known for these particles. Hence the dominant approach is to explore all potential DDA particle choices and to use the one that provides the best fit between model and observations (e. g.
Geer, 2021b, and references therein). Interestingly, the best choices in that work, reflected in the de-fault RTTOV-SCATT configuration (Table  1 ), seem to be particles with b around 2. 0-2. 4; particles with b closer to 3 seem to work poorly as a description of convective snow. The sensitivity of optical properties to the mass-size relation is further illustrated by the sector snowflake, which is present in both ARTS and  Liu (2008)  databases and has almost identical optical properties as a function of particle size. However, as shown in Table  3 , the a and b coefficients used with the Liu and ARTS databases are different, due to different but equally valid methodological choices in fitting those coefficients to the particle masses within the databases (see  Geer and Baordo, 2014 , Appendix B for further explanation).
These small differences still have a significant effect on the bulk optical properties. The ARTS sector snowflake provides less scattering than the Liu equivalent, and simulations of very thick clouds using the ARTS sector snowflake and the  Field et al. (2007)  PSD can be up to 20 K warmer around 300 GHz (shown in Fig. 9  later). Using an identical a and b it is possible to eliminate this difference. However, it was chosen to retain the values previously used with the Liu database to ensure full back-reproducibility, but for the ARTS database to use the coefficients that are supplied with that database. In this work, it is important to realise that when the bulk optical properties of a particle habit are discussed, this is the net result of both the physical characteristics of the individual particles and the effect of the corresponding mass-size relation on the PSD.
For Mie spheres, the integration range is given in Table  2. For particles from the Liu or ARTS databases, the integration size range is taken from Table  3 , with two exceptions. First is that the size range can optionally be extended down to the relevant D min from Table  2. If this option is selected, the relevant optical properties are computed using Mie theory, assuming this is valid for particles smaller than the minimum particle size (see Sect. 2. 1). If the optional extension is not selected, then a minimum size D min = 1 ×10 -4 m is applied when the F07 PSD is used with the ARTS shapes, to avoid extrapolating the PSD. Note that as described in Sect. 2. 1, for the implementation of the Liu database in the hydrotable generator, the D min = 1 ×10 -4 m constraint was imposed unilaterally in Table  3 , meaning that it affects the Liu particles no matter which PSD is chosen (this behaviour is undesirable but is preserved for back-compatibility).
Representing preferentially oriented particles The Liu and ARTS particles available in the hydrotable generator (and obviously the Mie sphere) represent only randomly oriented particles. However, ice hydrometeors are often preferentially oriented, as revealed by polarisation signatures in the high-frequency channels of microwave imagers (e. g. Defer et al. , 2014; Gong and Wu, 2017). The ARTS database has recently been extended with a more advanced representation of particle orientation, giving particles a preferred canting angle, but retaining random orientation in azimuth  (Brath et al. , 2020). This generates optical properties that are fully polarised; i. e. scattering can transfer energy from one polarisation to another. To model such fully polarised optical properties would require the whole Stokes vector to be represented in the radiative transfer, but this is not available in a scalar fast model like RTTOV-SCATT.
However, it is not yet possible to specify α as a function of frequency. The polarisation ratio approach works best for conical microwave imagers with zenith angles around 50 • (  Barlakas et al. , 2021). Cross-track sounders, which have polarisation and zenith angles that vary with scan position, are not yet supported and will require further scientific development. Further, an approach for radar backscattering needs to be developed. The "full" channel representation provides a framework for future support of single-particle optical property databases based on oriented particles (e. g. Brath et al. , 2020).
Upwelling brightness temperature below the cloud is 280 K (solid lines) or 100 K (dashed lines). lowest scattering TB depressions. Above this frequency of maximum scattering, these clouds start emitting more radiation again and brightness temperatures are higher; above 400 GHz all the frozen hydrometeor types have a warming effect. At these frequencies, SSA is high but extinction is high too (Fig. 1 ): a combination which must generate sufficient absorption to cause this warming. Figure  9  shows brightness temperatures for the standardised ice or snow cloud using the F07 tropical PSD with all options from the ARTS and Liu databases; only the configuration with a below-cloud upwelling TB of 280 K is shown. The amount of brightness temperature depression from the ARTS particles roughly follows the amount of extinction in Fig.
2  and hence the progression broadly from low-density aggregates and snowflakes to dense rimed particles. The Evans snow aggregate is again the least scattering, with TB dropping only to 150 K, and again the ICON hail is the most scattering particle, dropping to 90 K. The frequency of maximum scattering varies from around 500 GHz for the Evans snow aggregate to around 200 GHz for the ICON hail. Between the extremes of light aggregate and dense hail, the ARTS database provides a good spectrum of potential scattering properties. As mentioned before, the Liu database has bigger gaps, and some of the particle shapes are almost redundant. The least-scattering Liu particle is the dendrite snowflake, but this still generates a relatively deep TB depression (as low as 130 K at 340 GHz). By contrast the ARTS database provides the column aggregate and Evans aggregate, which produce even less scattering.
This provides an important new capability to reproduce cloud ice signatures. Hence the ARTS column aggregate was required in the study of  Geer (2021b)  and is used in the new default RTTOV-SCATT configuration (Table  1 ). At the strongly scattering end in Fig. 9b , the Liu columns and thick hex plates are more strongly scattering than the ICON hail from the ARTS database, giving TBs as low as 70 K at around 180 GHz. Hence these Liu shapes continue to provide a capability that is not available from the ARTS database. However, it is interesting that the biggest variations in brightness temperature depression are in frequencies below 200 GHz. The studies of  Geer and Baordo (2014)  and  Geer (2021b) , based on SSMIS observations, have already benefitted from a spectral region of great sensitivity to particle habit and PSD.
Conclusion This work has summarised the process of generating bulk hydrometeor optical properties based on physical assumptions about the sizes, masses, habits, and orientations of cloud and precipitation particles. It documents the hydrotable generator   21 ). This is a standard cloud with a thickness of 2 km (solid) or 0. 2 km (dotted), with the ARTS ICON hail particle, F07 T PSD, and other settings as seen in Figs. 9 and 10. for microwave and sub-millimetre scattering radiative transfer in version 13. 0 of RTTOV (Radiative Transfer for TOVS,  Saunders et al. , 2018 Saunders et al. , , 2020)) , a widely used satellite simulator, hopefully with relevance to the users of many similar tools. The work has overviewed the bulk optical properties and brightness temperatures generated by Mie spheres and two databases of non-spherical ice particles  (Liu, 2008; Eriksson et al.
In the event that a much richer microphysical representation is required, RTTOV-SCATT allows an unlimited number of hydrometeor categories that could be used to represent almost any level of microphysical complexity. Hence the tools are already available, but the underlying issue is whether it is possible to appropriately specify this complex microphysics. There are many ways to further improve the representation of bulk scattering from hydrometeors, most of them applicable generally: -RTTOV (as well as many other codes) uses a different mechanism and different physical assumptions for the optical properties of clouds in the IR and solar regions. A major future development should be to produce optical properties from the microwave to the UV using the same approaches as much as possible; this requires nonspherical scattering databases to be expanded to cover the whole range, a process that is only starting (e.
g. Yang et al. , 2013; Baran et al. , 2014; Ding et al. , 2017; Baran et al. , 2018). -More work is needed to unpick the tight coupling between the particle shape model, mass-size relation, and the particle size distribution. Better ways of mapping bulk hydrometeor mass to particle ensembles would be very welcome. -More work needs to be done to standardise and/or interface the physical assumptions, such as shape, PSD, and mass-size relation, with assumptions made in atmospheric models. This would particularly support efforts to learn better physical models of cloud and precipitation, directly from observations (e. g. Schneider et al. , 2017; Geer, 2021a). -A global effort is ongoing to standardise and package many of the different scattering databases that are becoming available (see  Kneifel et al.
However, there is some tension between this and the aim to do things consistently across all frequencies (and consistently with forecast models) where sometimes the geometric size of the particles is more important. Further investigation is necessary. -The lowest temperature bin, at 203 K, is at least 20 K higher than the lowest tropospheric temperatures. Hence, in a future version, it is necessary to extend the lower temperature range of frozen particles permitted by the table generator. Note that, although the lowest temperature point in the ARTS database is 190 K,  Eriksson et al. (2018)  showed that extrapolation would be reasonably accurate down to 150 K. -The default representation of rain has not been updated since  Bauer (2001) ; apart from the aforementioned points on shape and orientation, it is worth examining how well the rain PSD is represented, and potentially updating this.
eumetsat. int/ (last access: 30 November 2021) to users who have registered and agreed to the licence conditions. The licence is available at https:// nwp-saf. eumetsat. int/site/software/licence-agreement/ (last access: 30 November 2021) with the main condition being that onward redistribution of the code is not permitted. Author contributions. AG did the majority of the writing, analysis, and upgrades to the code. PB created the code originally and reviewed the manuscript. KL added new permittivity code and reviewed the manuscript. VB added polarisation code, did some writing, and reviewed and edited the manuscript. PE did some writing and reviewed and edited the manuscript. JM added scattering database code, did some writing, and reviewed and edited the manuscript. AD added new PSD code and reviewed the manuscript.
Acknowledgements. Niels Bormann ,  Stephen English ,  Robin Hogan , and two anonymous referees are thanked for reviews of the paper. Financial support. EUMETSAT NWP-SAF are acknowledged for funding and coordinating the overall RTTOV development. The module described in this article was developed with contributions from many different scientists and funding streams. Vasileios Barlakas is funded by a  EUMETSAT  fellowship. Review statement. This paper was edited by  Graham Mann  and reviewed by two anonymous referees.
databases. ID Name D min [m] D max [m] a b Liu 0 Long hex column 1. 50 ×10 -4 4. 80 ×10 -3 37. 09 3. 00 1 Short hex column 1. 00 ×10 -4 3. 30 ×10 -3 116. 1 3. 00 2 Block hex column 1. 00 ×10 -4 2. 50 ×10 -3 229. 7 3. 00 3 Thick hex plate 1. 00 ×10 -4.
The frequency-dependent deviations in optical properties of frozen particles in Fig. 1occur when interpolating away from the original temperature, size, and frequency tie points in the ARTS database. A trilinear interpolation is used to do this, but any such interpolation can only give linear results for variations along one dimension. In the figure the frequency is obviously changing, but the particle size is implicitly changing because at different frequencies the bulk optical properties are sensitive to different parts of the PSD. Hence, nonlinear variations are to be expected. Figure2does https://doi. org/10. 5194/gmd-14-7497-2021Geosci. Model Dev. , 14, 7497-7526, 2021.
Environmental DNA metabarcoding is a powerful tool for studying biodiversity. However, bioinformatic approaches need to adjust to the diversity of taxonomic compartments targeted as well as to each barcode gene specificities. We built and tested a pipeline based on read correction with DADA2 allowing analysing metabarcoding data from prokaryotic (16S) and eukaryotic (18S, COI) life compartments. We implemented the option to cluster Amplicon Sequence Variants (ASVs) into Operational Taxonomic Units (OTUs) with swarm, a network-based clustering algorithm, and the option to curate ASVs/OTUs using LULU. Finally, taxonomic assignment was implemented via the Ribosomal Database Project Bayesian classifier (RDP) and BLAST. We validate this pipeline with ribosomal and mitochondrial markers using metazoan mock communities and 42 deep-sea sediment samples.
The results show that ASVs and OTUs describe different levels of biotic diversity, the choice of which depends on the research questions. They underline the advantages and complementarity of clustering and LULU-curation for producing metazoan biodiversity inventories at a level approaching the one obtained using morphological criteria. While clustering removes intraspecific variation, LULU effectively removes spurious clusters, originating from errors or intragenomic variability. Swarm clustering affected alpha and beta diversity differently depending on genetic marker. Specifically, d-values > 1 appeared to be less appropriate with 18S for metazoans. Similarly, increasing LULU's minimum ratio level proved essential to avoid losing species in sample-poor datasets. Comparing BLAST and RDP underlined that accurate assignments of deep-sea species can be obtained with RDP, but highlighted the need for a concerted effort to build comprehensive, ecosystemspecific databases.
Recent bioinformatic algorithms alleviate the influence of errors and intraspecific variability in metabarcoding datasets. First, amplicon-specific error correction methods, commonly used to correct sequences produced by pyrosequencing  (Coissac et al. 2012) , have now become available for Illumina-sequenced data. Introduced in 2016, DADA2 effectively corrects Illumina sequencing errors and has quickly become a widely used tool, particularly in the microbial world, producing more accurate biodiversity inventories and resolving fine-scale genetic variation by defining Amplicon Sequence Variants (ASVs)  (Callahan et al. 2016; Nearing et al. 2018). Second, LULU is a recently developed curation algorithm designed to filter out spurious clusters, originating from PCR and sequencing errors, or intra-individual variability (pseudogenes, heteroplasmy), based on their similarity (minimum match) and co-occurrence rate (minimum relative cooccurence) with more abundant clusters, allowing the acquisition of curated datasets while avoiding arbitrary abundance filters  (Frøslev et al.
Accepted Article This article is protected by copyright. All rights reserved presenting their new algorithm on prokaryotic communities, the authors of DADA2 proposed that the reproducibility and comparability of ASVs across studies challenge the need for clustering sequences, as OTUs have the disadvantage of being study-specific and defined using arbitrary thresholds  (Callahan et al. 2017 ). Yet, clustering sequences may still be necessary in metazoan datasets, where very distinct levels of intraspecific polymorphism can exist in the same gene region among taxa, due to both evolutionary and biological specificity  (Bucklin et al. 2011; Phillips et al. 2019). ASV-based inventories will thus be biased in favour of taxa with high levels of intraspecific diversity, even though these are not necessarily the most abundant ones  (Bazin et al.