dataesr/scientific-paragraphs-categorization huggingface dataset:
10. 1029/2022JD037460 3 of 20 in 1 hr, around 2, 17, 32, and 47 min of each hour. The accuracy of the CTT values depends on several parameters, such as the type of clouds, the time during the day, and the geographic location on the Earth. In the study by  Taylor et al. (2017) , that compared SEVIRI CTTs from the new CLoud property dAtAset using SEVIRI, Edition 2 (CLAAS-2) data set against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, the uncertainty in night conditions was fluctuant between ∼0°C and 5°C in South Africa  (Taylor et al. , 2017, Figure   6 ). Thus, we consider in the present study the impact of an uncertainty of 2. 5°C on the height determination, which is the mean value from  Taylor et al. (2017)  for this area. In order to analyze the meteorological conditions in which the storms developed on 3 February 2019 in the study area, we use the hourly ERA5 reanalysis performed by the European Copernicus/European Centre for Medium range Weather Forecast (ECMWF) data center for most of the atmospheric parameters  (Hersbach et al.
Additionally, satellite imager channels are regularly used to monitor the development of more intense convection and rain bands. Although rainfall information can always be viewed as an individual dataset, several nations produce a multi-sensor analysis of rainfall at regular intervals during tropical cyclones, and this incorporates the strengths of each observational dataset. For example, India produces charts based on satellite, Doppler radar, and ground observations every 3 h, and on an hourly basis when TCs approach the coasts, as a standard operating procedure  (IMD, 2021). India Meteorological Department (IMD) and National Center for Medium Range Weather Forecasting (NCMRWF) Center prepare satellite and rain gauge merged plots. Typical IMD-NCMRWF satellite rain gauge merged plots during life period of TC Burevi are presented in Fig.
3 ). The most recent version (Fig. 3 ) has shown significant improvements, and is superior to ECMWF, GRAPES, GFS and SMS-WARMS (Shanghai regional model) in forecasting accumulated rainfall of ≥250 mm and ≥100 mm. Several of the cited studies noted forecast improvements after including TC intensity data, and combining the dynamical forecast with initial condition analogs from historical observations. Kim et al. (2020)  established a new statistical prediction model based on the principle of track similarity where historical TC track and data were observed to optimize the typhooninduced accumulated rainfall (TAR) over China. The new approach used Fuzzy C-Means (FCM) clustering to select typhoons with the most similar tracks, and then correct and average their rainfall (Fig. 4 ). In the Philippines, Bagtasa established an analog forecast technique from a historical dataset in 2021.
Ensemble techniques are particularly valued, to help quantify uncertainty. Multiple nations, including, but not limited to, India, Australia, and the U. S. , reported using some version of a multi-model ensemble, incorporating ensemble data from a wide variety of modeling centers. India and RSMC New Delhi, for example, creates a Grand Global Ensemble, which is used extensively in forecasting rainfall. The guidance is generated on a GIS platform and used as an objective aid for forecasters. In Australia, the Bureau of Meteorology uses the Operational Consensus Forecast (OCF) as the basis for any forecast rainfall. An intervention strategy has been created which allows meteorologists to make modifications to the gridded forecast rainfall based on local knowledge and experience (such as enhancements of rainfall due to topography).
Approaches for hydrologic modeling vary from country to country, and are mostly dependent on individual national meteorological and hydrological services. In general, uncertainty related to rainfall magnitude and placement is the dominant factor for determining flooding impacts from TC rainfall, including future river flows. However, other uncertainties are still important, including antecedent wetness of the soils, land use type, drainage basin characteristics, among many others. This introduces considerable complexity to the challenge of forecasting how rivers and streams will respond to an approaching TC, and the resulting inundation. For instance, India uses two approaches simultaneously including (a) historical database with damages and (b) an impact model using vulnerability and exposure datasets with relevant meteorological and hydrological information.
Acknowledgements The authors of this report would like to thank  Xuwei Bao ,  Shanghai Typhoon Institute/CMA Peiyan   Chen ,  Shanghai Typhoon Institute/CMA ,  Fumin Ren ,  Chinese Academy of Meteorological Sciences/CMA ,  David Roth ,  U. S. National Weather Service, Weather Prediction Center  and  Kun Zhao ,  Nanjing University, China  for their contributions through valuable discussions and input.
Medusozoan composition in the Tara Oceans data set In this study, we explored the large-scale and global plankton metabarcoding data sets from the Tara Oceans expedition (2009-2013). The sampling strategy of planktonic organisms was based on size fractionation to recover a wider variety of organisms. In the 210 sampling stations, samples were collected with different plankton net mesh sizes and at different depths (surface: 5 meters (m) average depth; deep chlorophyll maximum: 17-188 m), covering a large variety of environments. All samples were systematically processed for amplicon sequencing and analyses, which allowed the generation of high-quality and standardized data sets  18, 20, 21. To compare the diversity and abundance of medusozoan taxa across the Tara Oceans samples, we first extracted medusozoan Operational Taxonomic Units (OTUs) from the 18S V9 rRNA metabarcoding data set.
We mapped them onto our 18S rRNA medusozoan phylogeny to infer the taxa and the life cycle for each medusozoan OTU (i. e. , meroplanktonic or holoplanktonic) (Fig. S1A ). After filtering on the basis of the relative abundance and the number of samples where OTUs were detected (see Methods), 300 medusozoan OTUs were finally retained. These filters allowed to control for false positives and did not impact the sampled taxa diversity (Fig. S1B ). We then performed a series of analyses to show that the assembled data set covers a comprehensive fraction of medusozoan life cycles and diversity. All selected OTUs were at least assigned at the order level; 196 could be assigned at the family level, and 32 at the species level. Among the medusozoan OTUs, 182 were inferred as meroplanktonic medusozoan (MP), and 118 as holoplanktonic medusozoan (HP) (Table  S3 ).
Of the 13 taxa previously defined, 11 of these taxa were represented by at least one OTU in our filtered Tara Oceans data set (Fig. S1B ). 92% of the OTUs belonged to Hydrozoan (277 OTUs) and 8% to Scyphozoa (23 OTUs), and none to Cubozoa or Periphylla (a Periphylla periphylla OTU was excluded, as indistinguishable from the meroplanktonic Paraphyllina ransoni OTU). The two most speciesrich medusozoan taxa, the Leptothecata and Anthoathecata 22 , were also the bestrepresented in our data set, totaling 101 OTUs and 62 OTUs respectively (Fig. S1C-F ). Among the 814 plankton samples (station/depth/fraction) of the Tara Oceans data set, at least one medusozoan OTU was detected in 781 of them. Medusozoan relative abundances were higher in the largest size fraction (180-2000 µm) in comparison with the other size fractions (Fig.
S2 ) with still 13% of the medusozoan reads found in the smallest size fraction sampled (0. 8-5µm), which could correspond to gametes or small pieces detached from mature specimens (Table  S4 ). Rarefaction analyses of MP and HP OTUs were performed by measuring both their richness (number of OTUs) and diversity (Shannon index) as a function of the number of samples. Both metrics showed saturation at the global scale and higher values for MP compared to HP OTUs (Fig. S3 ). Thus, the assembled data set covers a comprehensive fraction of medusozoan life cycles and diversity and allows addressing questions about medusozoan distribution, diversity, and potential biotic interactions.
Discussion The Tara Oceans expedition provides standardized data about the planktonic environment on a global scale, which allows to uniquely compare oceanic regions  18, 20. Using this comprehensive data set, we showed that medusozoans (both HPs and MPs) display a latitudinal diversity gradient (Fig. 3C ), which is a widely observed pattern across both terrestrial and marine organisms  21, 26. We further detected a coastal to open ocean gradient in medusozoan distribution, with MPs preferentially located in coastal waters, while HPs being present both in the coastal and open ocean waters. Bathymetric depth emerged as the most discriminative variable between the global distribution of HPs and MPs (Fig. 5 ), which is consistent with local studies  [27] [28] [29]  and a literature review  16.
Multiple local studies agree with the prevalence of HP species across different oceanic regions  27, 30, 31  , as well as with the predominance of MPs in the Arctic  32. Taken together, the patterns we observed based on metabarcoding data are highly consistent with the literature and support the distribution and diversity conclusions at a larger and global scale. Despite the global coverage of the Tara Oceans data set, a few caveats remain and need to be acknowledged, as each site was sampled only once, and oceanic stations were more numerous than coastal stations. Further, only the most abundant species could be sampled in each sample, with representatives of some major medusozoan taxa missing (e. g. Cubozoa) or heavily undersampled (e. g. Limnomedusae and Rhizostomae). Additionally, as Tara Oceans samples were skewed toward lower sampling depths, we also restricted our analyses to surface and near-surface samples (up to 200 meters).
Medusozoan reference phylogenetic tree All non-redundant ribosomal DNA sequences taxonomically assigned to cnidarians available in the NCBI nucleotide database were retrieved on 22-02-2022. Sequences with a length < 100 nucleotides, unidentified nucleotides rate ≥ 0. 01 or described as 16S, 12S, 5S, 60S or whole genome were discarded. A local BLAST database was created from the 27 760 remaining sequences and Aurelia aurita complete 18S sequence (AY039208) was aligned by BLAST against this database in order to extract only 18S sequences. Sequences with a blast p-value = 0, an alignment length ≥ 1300 bp, and a complete V9 were retained. We made an exception and conserved Geryonia proboscidalis 18S, even if the V9 was not complete, in order to have a holoplanktonic representative of the Geryoniidae.
Only the species present in our alignment were extracted from the consensus tree, and converted to a constraint alignment as described in the FastTree documentation. The 18S phylogenetic tree was then constructed with the topology constrained by the constraint alignment using FastTree with the GTR+CAT model  53 (v2. 1. 11 ). This 18S tree was time-calibrated with calibration points from the literature (Table  S2 ) using the semiparametric method based on penalized likelihood described by Sanderson (2002)   54. The implementation available in the ape R package  55  (chronopl function) was used with a null smoothing parameter, allowing rates to vary as much as possible among branches. The evolution of the life cycle was then simulated 1000 times on the calibrated tree using a stochastic mapping method implemented in the make.
simmap function of the R package phytools  56  (v1. 2. 0). By taking the posterior density of the stochastic life cycle type mapped at each position of the tree, a total of 6 losses of the benthic stage were found in 81,3% of the simulations. Thus, only the simulations with 6 transitions were selected to date the age of the losses. The average, first and third quartile ages of each loss among the retained simulations were computed. From the reference tree, we defined 13 medusozoan taxa based on the different transitions from a meroplanktonic life cycle to a holoplanktonic life cycle. Seven meroplanktonic taxa were defined: Anthoathecata (without Porpitidae), Coronatae (without Periphylla periphylla), Cubozoa, Leptothecata, Limnomedusae (without Geryoniidae), Rhizostomae, and Semaeostomae (without Pelagia spp.
Medusozoan metabarcoding data set processing The metabarcoding data sets from the Tara Oceans expedition (2009-2013, PRJEB6610) and Tara Polar Circle (2013, PRJEB9737) were exploited (open access: http://zenodo. org/record/3768510). 781 samples (station/depth/fraction) from 141 stations collected at the surface (SRF) and at the depth of the deep chlorophyll maximum (DCM) were explored. The following size fractions were included in the analysis: "0. 8 to 5 µm and 0. 8 to 2000 µm," "3 to 20 µm and 5 to 20 µm," "20 to 180 µm," and "180 to 2000 µm". In order to have a homogeneous data set, we considered the fraction 3-20 µm, mainly used during the TARA Polar Circle expedition, equivalent to the fraction 5-20 µm used during theTara Oceans expedition, as well as fractions 0. 8-5 µm equivalent to 0.
8-2000 µm, as samples showed similar diversity and community composition  23. In the case where both size fractions were available for the same sample, the more ubiquitous 0. 8-5 µm size fraction was selected. For the sole station TARA_124_SRF for which the two sizes 3-20 µm and 5-20 µm were sampled, only the latter one was retained. For each sample, the V9-18S ribosomal DNA from Ibarbalz et al. (  2019 )  21  was analyzed. Data processing has been previously described in de Vargas et al. (  2015 )  57  and consisted of a quality check, a filtering of the metabarcodes present in at least 2 samples with more than 3 reads followed by clustering into operational taxonomic units (OTUs) using the Swarm software  58. Each OTU was then taxonomically and functionally annotated by an optimal global aligner (VSEARCH v2.
3. 1  59  ) against an updated version of the PR2_V9, an SSU V9 rDNA reference database available at http://zenodo. org/record/3768951. To determine the type of life cycle of each medusozoan OTU (e. g. meroplanktonic or holoplanktonic), an assignment at the genus, family, or order level was needed. To achieve this, the 2 796 OTUs assigned to cnidarian were extracted from the Tara Oceans data set. The representative sequence of each OTU was aligned by BLASTn against the PR2_V9 database enriched in a non-redundant manner with Opisthokonta 18S sequences with a length between 100 and 10 000 pb extracted from the nucleotide database of the NCBI. This step allowed the elimination of OTUs falsely assigned to Cnidaria. OTUs whose first hit was assigned to Cubozoa, Scyphozoa, or Hydrozoa classes with an identity percentage above 80% were retained, which represented 1 933 OTUs.
Environmental data selection The publicly available environmental data sets containing the water column features at the sampling location (https://doi. pangaea. de/   18. A meaningful environmental data set was extracted from the variable available and homogenized by replacing the missing values with the median value of the oceanic region defined by Longhurst (2010)  63  in order to perform redundancy analysis (RDA) which cannot handle missing values. The table contained environmental variables for 781 of our 785 samples. The 4 samples of the station number 011, lacking environmental information, were not included in the statistical analyses (Table  S5 ).
Statistical analysis Data analysis and statistical tests were performed using R (v4. 2. 1)  64  with the packages vegan 2. 6. 2  65  , tidyverse 1. 3. 2  66  , and custom scripts. Rarefaction curves were performed using 100 Monte Carlo resampling using the package MonteCarlo 1. 0. 6  67. Analysis of the rarefaction curves revealed that HP OTUs are more diverse than MP OTUs when the analysis is performed on a small number of samples (Fig. S3 ). This may be due to the fact that there are more different MP OTUs than HP OTUs but the sampling and sequencing depth required to detect all MP OTUs present in the environment is higher than the one required to detect all HP OTUs. To avoid underestimating MP diversity, we average the relative abundance of OTUs in the samples from the same station (depth/fraction).
All the other analyses were carried out by samples (station/depth/fraction). For the RDA, the environmental data set was used as an explanatory matrix and the OTU count matrix as a response matrix. The relative abundances of OTUs from each medusozoan were summed for each sample and normalized using a Hellinger transformation  68. Environmental data were centered prior to the analysis and then selected by stepwise model selection using permutation with the ordistep function from the vegan package (options: 1000 steps). The model selected 10 environmental response variables (bathymetric depth, acCDOM, density, nitrate, salinity, PAR, residence time, depth nominal, bbp470 and oxygen).
Phylogenetic regression An OTU phylogenetic tree was created by adding the OTUs in the calibrated 18S reference tree (see the "Medusozoan reference phylogenetic tree" section) at the node corresponding to their taxonomic assignment, and then removing the reference sequences to only conserve the OTUs. The evolution of the colonized bathymetric depth and the acCDOM was estimated by mapping their median values onto the OTUs phylogenetic tree using the anc. ML function from the phytools  56  (v. 1. 2. 0) R package. Finally, the correlations between the presence of a benthic stage in the OTU life cycles and the median bathymetric depth or acCDOM were tested by a phylogenetic logistic regression as described in Ives and Garland (2010)  71  using the R package phylolm (V. 2. 6. 2)  72.
Acknowledgments We thank  E. Houliston ,  F. Lombard ,  M. Mańko ,  C. Munro , and  C. Sinigaglia , for their feedback on the manuscript and  E. Pelletier  for help with the omics analyses. This work was funded by the  CNRS   80|Prime program  ( PENELOPE ) and supported by the  Agence Nationale de la Recherche  ( ANR-13-PDOC-0016 ). We wish to thank the  CNRS MITI  through the  interdisciplinary program Modélisation du Vivant  ( GOBITMAP grant  to SC), and the  H2020 European Commission  project  AtlantECO  (award number  862923 ). Tara Oceans (which includes both the Tara Oceans and Tara  Oceans Polar Circle expeditions ) would not exist without the continuous support of the  Tara Ocean Foundation  and 23 institutes of research (http://oceans. taraexpeditions. org). We further thank the commitment of the following sponsors:  CNRS  (in particular the FR2022-GOSEE),  European Molecular   Biology Laboratory (EMBL) ,  Genoscope/CEA , The  French Ministry of Research , and the  French Government  ' Investissements d'Avenir' programmes   OCEANOMICS  ( ANR-11-BTBR-0008 ).
2  and 4 ). Neglecting the interlayered rhyolites, the larger felsic eruptives form about 30% by volume of the total volcanic pile (Fig. 4 ). This yields their value in labeling and identifying particular formations (Figs. 3  and 6 ). The presence at certain horizons and localities of prominent silicic ignimbrites and lavas capping the basalt pile leads to various petrogenetic models of silicic magma generation. The dataset presented  suggest that these younger lavas are related to extension  (Rooney 2017). This allows for the shallow ponding of ma c magma, and subsequent silicic magma generation possibly by fractional crystallization and ultimately periodic silicic eruptions. This is supported by the presence of extensive rhyolites in the lava sequence that illustrates the time-progressive evolution of mamga.
2012; Bittig et al. 2019). Alongside collecting environmental data, BGC-Argo floats have optical sensors measuring chlorophyll a (Chl a) pigment concentration and particulate backscattering (b bp ), both of which have been used as measures of phytoplanktonic biomass  (Martiny et al. 2022). Brewin et al. (2022)  used a conceptual model to partition vertical profiles of Chl a and b bp , collected using a BGC-Argo float in the northern Red Sea, into the contributions of two phytoplanktonic communities (C 1 in the surface mixed layer, and C 2 at the deep chlorophyll maximum), to examine their seasonal dynamics. Here, the two-community model of  Brewin et al. (2022)  is extended by adding a third community that thrives in lowoxygen, low-light waters at the base of the euphotic zone. By applying the model to data collected by a BGC-Argo float in the eastern tropical North Pacific (ETNP) over a 5-year period (2016-2021), we explore the environmental niche and dynamics of this unique third phytoplanktonic community.
Pressure, temperature, PAR, and salinity data were delayed-mode quality-controlled. For each profile, density (kg m À3 ) and the Brunt-Väisälä buoyancy frequency (N 2 in units of s À2 , where N is computed as radians per second), were computed using the Python seawater package (version 3. 3). The mixed layer depth Z m ð ) was computed using  Holte and Talley (2009)  method for each profile, using the temperature algorithm (Python package https:// github. com/garrettdreyfus/python-holteandtalley). For Chl a (Adjusted mode), DO and b bp profiles (both real-time mode) additional processing steps (as described by  Schmechtig et al. 2018 Schmechtig et al. , 2019) )  were employed to ensure maximum data quality. The adjusted mode Chl a data were processed as follows: the fluorescence measurements were first corrected by subtracting a dark fluorescence value; the dark-corrected fluorescence values were then multiplied by the manufacturers scaling factor to convert to a Chl a concentration and divided by two following recommendations by  Roesler et al.
First, parameters P 1 and τ 1 , which are physical and optical in nature, were estimated empirically from Z m and K d following  Brewin et al. (2022) , where τ 1 = 0. 62 Z m K d + 2. 29 and P 1 ¼ 10 0:08τ1þ0:06. Once τ 1 and P 1 are known (and fixed), the other parameters B Ã m,2 , τ 2 , B Ã m,3 , τ 3 , and σ 3 are estimated by fitting the chlorophyll model to the data. The optimization function used was Levenberg-Marquardt iterative minimization (Python function minimize). Initial guesses of parameters were as follows: σ 2 and σ 3 were set to one, while B Ã m,2 and B Ã m,3 were initialised by extracting the maximum B Ã above and below 5 μmol kg À1 DO depth, and τ 2 and τ 3 were initialised at the depths of these respective maximums. Once model parameters were obtained, Chl a profiles were reconstructed by scaling the normalized profiles by B s and K d.
Extraction of environmental data for the three communities Environmental conditions where the three communities were present (temperature, salinity, density, Brunt-Väisälä, DO, and PAR) were extracted for each BGC-Argo profile. For community 1 (C 1 ), median values in the mixed layer were computed. Meanwhile, for community 2 (C 2 ) and 3 (C 3 ), environmental data at τ 2 and τ 3 were used, respectively. Data from all profiles were combined and used to evaluate the habitats that the communities reside in.
Future work This work has been limited to the analysis of a single BGC-Argo float in the ETNP. Quantifying the wider distribution of this low-oxygen-adapted phytoplankton community may help our understanding of biogeochemical cycles in OMZ regions. Fitting the three-community model to multiple floats and datasets from the region (ETNP), combined with linking the parameters to larger scale environmental fields amenable to satellite remote sensing and model simulations, could provide insight into the distribution of C 3 at large scales. We have demonstrated the potential of this model to successfully capture the presence and properties of the C 3 populations; however, the limitations outlined in Section 3. 1 should be considered when conducting future work and widescale application on BGC-Argo data.