dataesr/scientific-paragraphs-categorization huggingface dataset:
(2002)  and  Ha et al. (2002). Additional effort to evaluate GNSS slant delays using WVR and NWM data was done at GFZ Potsdam over the last few years. Bender et al. (2008)  showed an existing high correla-tion within the three sources (GPS, WVR, NWM) of slant wet delays (SWDs) and tried to quantify the effect of removing multipath from GPS post-fit residuals using a stacking method what was also done by  Kačmařík et al. (2012). Deng et al. (2011)  validated tropospheric slant path delays derived from single-and dual-frequency GPS receivers with NWM and WVR data. Shangguan et al. (2015)  compared GPS versus WVR slant IWV values (SIWVs) using a 184day dataset. They also analysed the influence of the elevation angle setting and the meteorological parameters (used for the conversion to IWV) on the comparison results.
More recently, a validation of multi-GNSS slant total delays retrieved in real time from GPS, GLONASS, Galileo and BeiDou was presented by  Li et al. (2015a)  using WVR and NWM as independent techniques for the assessment. Using multiple GNSS constellations brought a visible advantage, in terms of not only the number of available slants but also their higher accuracy and robustness. Nevertheless, most of the studies presented thus far were limited to only a single strategy for obtaining GNSS STDs and usually restricted to a limited set of stations and/or a relatively short time period. The main purpose of this study is an extensive comparison of various solutions from GNSS processing, NWM ray tracing and WVR measurements using one common dataset as well as a comparison of results from collocated stations.
The GNSS solutions evaluated in this work used 5 different software programs and 11 strategies and exploited the GNSS4SWEC benchmark dataset  (Douša et al. , 2016). Then, the paper studies the impact of various approaches on STD estimates and aims to find the most suitable strategy for estimating the GNSS-based STDs. Section 2 briefly introduces the validation study dataset, and Sect. 3 describes the process of retrieving GNSS STDs including an overview of the different GNSS solutions. Section 4 provides a description of STDs generated from NWMs, and Sect. 5 summarizes WVR principals and WVRbased STD solutions. Section 6 introduces the methodology used in the validation of STDs, and Sects. 7 and 8 study the results achieved at single GNSS reference stations and at closely collocated stations, respectively.
Experiment description The presented work has been carried out in the context of the EU COST Action ES1206 "Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate" (GNSS4SWEC; http: //www. cost. eu/COST_Actions/essem/ES1206, 2013-2017). Three mutually cooperating working groups (WG) have been established to cover the proposed topics: (1) WG1 for Advanced GNSS processing techniques, (2) WG2 for GNSS for severe weather monitoring, and (3) WG2 for GNSS for climate monitoring. Validation of STDs belongs mainly under WG1, which is oriented toward the development of new advanced tropospheric products, among other topics. The idea of preparing a common benchmark dataset, which could serve efficiently for most planned activities, was designed in the beginning of the project; the data were collected, cleaned and documented, and reference products were generated and assessed  (Douša et al.
, 2016). The selected geographical area is situated in central Europe (Austria, Germany, the Czech Republic, Poland) where severe weather events, including extensive floods on Danube, Moldau and Elbe rivers, occurred between May and June 2013. The benchmark dataset gathers observations from 430 GNSS reference stations, 610 meteorological synoptic stations, 21 radiosonde launching sites, 2 WVR, 2 meteorological radars and output fields from the ALADIN-CZ NWM over a period of 56 days. ZTDs and horizontal tropospheric gradients from the reference GNSS and NWM-derived tropospheric products were already evaluated, and all resulted in very good agreement  (Douša et al. , 2016). All STDs used in this paper were computed by exploiting the benchmark dataset. From the complete benchmark dataset, we selected a subset of 10 GNSS reference stations situated at six different locations (Table  1 ).
Then, the differences have been mapped back at 90 •. For day of year (DOY) 174 (i. e. 23 June 2013), we can see a contribution of hydrometeors up to 10 mm. Looking at the whole period of the benchmark campaign, the variation ranges of STD, SHD and SWD (mapped at 90 • ) are 275, 80 and 230 mm, respectively. Figure  3  illustrates the use of GNSS delay observations in meteorology (detection of variation of water vapour represented by the wet delay, as well as detection of heterogeneities from hydrostatic delays and occasionally from hydrometeors in specific severe weather cases). Note that there are no data available for POTS station between DOY 121 and 125 and DOY 160 and 163. For this reason, we have not simulated the slant delays for this period, as shown by the gaps in Fig. 3. The simplified strategy used to simulate curve slant paths gives some inaccurate simulations of slant delays for elevations between 3 and 5 • , shown by isolated values in Fig.
Colours in Fig. 4  indicate the processing software used in individual solutions. Medians of all solutions (dotted lines in each bin) are displayed for each station in order to highlight differences among the stations. These were observed mainly as biases ranging from -3. 6 to 0. 6 mm. The better agreement between GOP and GFZ solutions could be attributed to a similar strategy of both solutions compared to others. It is particularly visible for LDB0 and POTM stations where median values over all solutions differ by -2. 3 and -3. 6 mm, respectively. The reason for the divergent behaviour at the two stations has not been identified although site metadata were cross-checked carefully. A significant difference can also be noticed for TUW_3 and TUW_7 at the station KIBG where these solutions used individual antenna calibration files while all others solution used type mean calibration  (Schmid et al.
Significantly higher values of biases and standard deviations were observed at particular days for NWM solutions. A detail evaluation of daily statistics with respect to the extreme weather conditions is one of the topics that we will study in future. slant observations which entered the comparison was 32 794 at station POTM and 36 070 at station POTS. Two remarks can be made on the evaluation of biases. Firstly, an overall bias of about 4 mm between the stations POTM and POTS, visible for all GNSS solutions already in Fig. 8 , indicates a common issue with the GNSS data processing at the station POTM. It was particularly increased for GOP_F, GOP_S and GFZ PPP solutions. Secondly, a bias of about 5. 5 mm in the zenith direction can be found between WVR and GNSS solutions even at station POTS.
Uncertainties of the simplified STDs at low elevations surpass additional uncertainties due to applying clean residuals (green curves vs. red curves). According to the magnitude of clean residuals at these elevations (Fig. 12 ), the small uncertainties from calculated differences indicate the presence of tropospheric signals in the residuals at low elevations, roughly below 30 •. It seems to be almost independent from the weather conditions and is supposed to represent mainly unmodelled horizontal asymmetry in the troposphere. However, further study on detail impact of residuals on GNSS STDs modelling during severe weather conditions requires longer datasets, which will be subject of our upcoming study. Figure  14  displays results for comparisons of individual dual stations in slant directions calculated from all days of the benchmark period.
Conclusions We presented results of validating tropospheric slant total delays obtained from GNSS data processing with those obtained from NWM ray tracing, WVR measurements and collocated GNSS stations, in search of the optimal method for estimating GNSS STDs. Ten GNSS reference stations were selected, exploiting data from the 56-day COST ES1206 benchmark campaign. Eleven GNSS solutions, four NWMbased solutions and one WVR-based dataset entered this validation study. Eight out of 11 GNSS solutions delivered STDs in three variants: (1) without post-fit residuals, (2) with raw post-fit residuals and (3) with cleaned post-fit residuals. The comparisons were carried out into two scenarios, firstly for STDs at their true elevation angles and secondly for STD differences mapped into the zenith direction using a simple mapping function.
The majority of evaluated GNSS solutions used deterministic models with rather long validity of estimated tropospheric parameters for which the residuals are important to overcome modelling deficiencies of low-resolution parameter estimates in time. Our future study will focus on the evaluation of GNSS STDs estimated using a stochastic process easily applicable in real time and on a long-term evaluation of azimuthal dependency of post-fit residuals under severe weather conditions. Data availability. GNSS data from the EUREF Permanent Network (EPN) stations are freely available through the anonymous FTP, e. g. from the EPN historical data centre at ftp://epncb. oma. be/ pub/obs/ maintained by the Royal Observatory of Belgium. Other GNSS and WVR data were primarily collected for the purpose of the COST Action ES1206 (GNSS4SWEC project; see  Dousa et al.
Acknowledgements. This study has been organized within the  EU COST  action  ES1206  ( GNSS4SWEC ). The authors thank all the institutions that provided data for the benchmark campaign on which the validation was based on. Namely we want to thank  S. Heise  ( GFZ ) for providing the WVR data. The GFS data were provided by the  National Centers for Environmental Prediction  (www. ncep. noaa. gov). The ERA-Interim data were provided by the  European Centre for Medium-Range Weather Forecasts  (http://www. ecmwf. int/en/forecasts/datasets). M. Kačmařík ,  J. Douša  and  P. Václavovic  acknowledge the support from the  Czech Ministry of Education, Youth and Sports  (project nos. LD14102  and  LM2015079 ). E. Pottiaux (ROB) and  H. Brenot  ( BIRA ) acknowledge the support from the  Solar-Terrestrial Centre of Excellence (STCE).
Emissions Our simulations use daily, 1 km horizontal resolution fire emissions from the Fire INventory from NCAR (FINN-v1)  (Wiedinmyer et al. , 2011) , based on fire count information from the MODIS (Moderate Resolution Imaging Spectrometer) instrument. Appropriate fire properties are obtained from a synergy between remote sensing observations, land use and carbon fuel datasets to determine in which columns the fires are located and the plume rise is simulated explicitly, accounting for thermal buoyancy associated with fires and local atmospheric stability  (Freitas et al. , 2007; Grell et al. , 2011; Sessions et al. , 2011). Anthropogenic emissions are taken from the HTAPv2 (Hemispheric Transport of Air Pollution version 2) 0. 1 • × 0. 1 • inventory (http://edgar. jrc. ec. europa. eu/htap_v2/).
Precipitation The lofting of BC can occur isentropically at low RH or can be caused by rapid ascent and heavy precipitation  (Matsui et al. , 2011). Studying precipitation during transport is therefore crucial and it needs to be modeled correctly to understand the role of wet removal of aerosols. Daily GPCP (Global Precipitation Climatology Project) precipitation data are used to assess the reproducibility of precipitation in the BASE simulation. Here we use GPCP data (version 1. 2) at 1 • resolution, retrieved from a combination of satellite observations and rain gauge measurements  (Huffman et al. , 2001). Model estimates of rainfall are interpolated every day to the GPCP 1 • grid and then averaged between 4 and 21 July to give an assessment of the mean precipitation during the AC-CESS campaign.
Acknowledgements. The research leading to these results received funding from the  European Union  under grant agreement 265863-ACCESS (http://www. access-eu. org)within the Ocean of Tomorrow call of the  European Commission Seventh Framework Programme. The French co-authors acknowledge support from the national  Chantier Arctique  project,  PARCS (Pollution in the Arctic System ), funded by  CNRS-INSU. We thank the pilots, engineers, and scientists from the  DLR  flight department for their excellent support during the field campaign. Computer modeling performed by  Jean-Christophe Raut  benefited from access to IDRIS HPC resources (GENCI allocations  2015-017141  and  2016-017141 ) and the IPSL mesoscale computing center ( CICLAD: Calcul Intensif pour le Climat, l'Atmosphère et la Dynamique ).
Code availability. We used WRF-Chem version 3. 5. 1 modified to include the KFCuP scheme. After version 3. 8, the KFCuP scheme is included in the released code, available at http://www2. mmm. ucar. edu/wrf/users/download/get_source. html. Data availability. The ACCESS BC and CO data are available by request from the DLR. The PI for the DLR Falcon SP2 data for ACCESS can be reached at bernadett. weinzierl@univie. ac. at. The PI for the CO measurements during ACCESS can be reached at hans. schlager@dlr. de. DARDAR data are publicly available at http: //www. icare. univ-lille1. fr.
Reads mapping, SNP calling and filtering Reads were mapped against the high-quality reference genome of the Microbotryum lychnidis-dioicae p1A1 Lamole (CLDV0100001-22), with finished, ungapped chromosomes or chromosomal arms, sequenced using the Pacific Biosciences SMRT technology  (Badouin et al. 2015). We used the glint software (T. Faraut & E. Courcelle, unpublished; http://lipm-bioinfo. toulouse. inra. fr/ download/glint/) with parameters set as follows: minimum length of the high-scoring pair hsp ≥90, with ≤5 mismatches, no gap allowed, only best-scoring hits taken into account. The percentage of paired-end reads mapped in a proper pair, that is in the expected orientation and distance, was 40% for the outgroup and ranged otherwise from 59. 1% to 94. 9% (Table  S2 , Supporting information).
Pairs that were not properly mapped were removed. Because the mating-type chromosomes in Microbotryum exhibit suppressed recombination on 90% of their lengths  (Hood 2002; Hood et al. 2013; Badouin et al. 2015) , tests for selective sweeps cannot be applied. Therefore, only autosomal data were included in the present study. Variants were called with VARSCAN V2. 3  (Koboldt et al. 2012)  separately in each strain with the following criteria: coverage ≥20, variant reads ≥10, average quality ≥30, minor allele frequency ≥0. 3. This later threshold was chosen not too high to take into account a possible drift among haploid genotypes during the growth on Petri dishes of the haploid sporidia resulting from meioses of a diploid individual. Low complexity regions were masked, SNPs close to indels were removed with the VarScan filter function, and SNPs showing a significant strand bias were excluded (Fisher's exact test, P-value 0.
05). For each strain and each chromosome, we masked positions whose coverage was higher than five times the peak of the Gaussian distribution, with per-base coverage computed with BAMTOOLS  (Barnett et al. 2011 ) and coverage histograms generated with BEDTOOLS  (Quinlan & Hall 2010). Only biallelic SNPs were retained in analyses, with a maximum rate of missing data of 0. 2 per cluster. This threshold of a maximum rate of 0. 2 of missing data was applied to all sites when normalizing statistics variation. To assess the overall quality of SNP calling, we mapped 3009 of Illumina paired reads resequencing data of the Lamole reference strain on the corresponding reference genome obtained using the Pacific Biosciences SMRT technology. We analysed the proportion of heterozygous SNPs in the haploid strains for assessing the reliability of heterozygous sites, focusing on autosomes (Table  S3 , Supporting information).
Genetic subdivision To analyse genetic subdivision, we selected all autosomal SNPs. We performed a discriminant analysis of principal components (DAPC) with the R package ADE-GENET  (Jombart 2008). FASTSTRUCTURE  (Raj et al. 2014)  was run for K = 1 to K = 10 (ten runs per K value). Although Microbotryum fungi are highly selfing, previous worked showed that outcrossing and effective recombination occur frequently enough for Bayesian clustering algorithms to be able to accurately identify genetic clusters  (Giraud et al. 2005; Vercken et al. 2010; Gladieux et al. 2011). We also built a dendrogram with the neighbor-joining method, using the nj function of the R package APE, that used the neighbor-joining algorithm  (Saitou & Nei 1987)  with default parameters  (Paradis et al. 2004). To find potentially admixed strains that were not detected by these methods, we analysed the percentage of heterozygous SNPs of each strain in regard to its geographical location.
Inference of demographic history and recombination rates We used the python package DADI  (Gutenkunst et al. 2009)  to infer the demographic history of MvSl and MvSd. The method implemented in DADI infers demographic parameters based on a diffusion approximation to the site frequency spectrum (SFS). Unlike coalescent approaches to demographic inference, which are based on simulations, the diffusion method enables the use of efficient optimization methods to fit a demographic model to the observed SFS. We analysed the MvSl and MvSd data sets separately, as the method cannot handle more than three population samples. The compared models included scenarios of population size changes, strict isolation, continuous postdivergence gene flow, ancient migration and secondary contact, assuming or not heterogeneous migration rates across the genome.
We therefore simulated genomes as combinations of multiple 1-Mb fragments, which was the maximum length that could be simulated and was much larger than the extent of LD. Data sets were simulated using the coalescent simulator msms  (Ewing & Hermisson 2010)  based on the best-fitting model and estimated parameters. Command lines are provided in Supporting information. Recombination rates were estimated using the INTERVAL program in LDHAT version 2. 2  (Auton & McVean 2007)  (Table  S7 , Supporting information). Data sets for INTERVAL were prepared based on VCF files using custom scripts. Singletons and sites with missing data were excluded and the reversible-jump Markov chain Monte Carlo scheme implemented in INTERVAL was run for 5e6 iterations, with a block penalty of 10, samples taken every 5000 iterations and a burn-in phase of 5e5.
For each cluster, LDHAT's program COMPLETE was used to generate likelihood look-up tables for INTERVAL with the population-scaled mutation rate estimated as total nucleotide diversity (p per pb estimated on 100-kb nonoverlapping windows, see Section 'Summary statistics of genomic variation'), and the population-scaled recombination rate ranging from 0 to 100 with a increment of 0. 5. Results of INTERVAL were summarized using STAT, in LDHAT, discarding the first 100 samples as burn-in.
Diversity and fixed divergence were normalized by the number of genotyped sites, defined as the number of sites that passed filters for at least 80% of the individuals in a given cluster or pair of clusters (see detail of filters in Section 'Reads mapping, SNP calling and filtering' section above). To produce input files for EGGLIB, VCF files were converted with custom python scripts (available upon request) to pseudo-alignments in fasta format, where the reference sequence was substituted with the variant nucleotides for each strain. Linkage disequilibrium was computed as r 2 , the coefficient of correlation between a pair of SNPs, with RSQ  (Thornton 2003) , excluding singletons SNPs. To compute a mean LD for each window, 10 SNPs were selected randomly from each window of 100 kb and 5 SNPs for each window of 50 kb, and the mean r 2 was computed.
Detection of genes under selection and analysis of gene categories Functional annotation and detection of specific gene categories. Genes were assigned to functional categories using INTERPROSCAN V5  (Zdobnov & Apweiler 2001). Pfam annotations were used to detect genes of the major facilitator superfamily (PF07690) and sugar transporters (PF00083). Both categories correspond to transmembrane proteins highly represented in Microbotryum, and that may play a role in pathogenesis  (Perlin et al. 2015). Putative secreted proteins were identified as proteins carrying a signal peptide using PHOBIUS  (K€ all et al. 2004)  or SIGNALP V4. 1  (Petersen et al. 2011 ) and without any detected transmembrane structures (TMHMM,  Krogh et al. 2001). Expression data. A previous study  (Perlin et al. 2015)  generated RNA-seq data in three different conditions: in vitro cultures, in low and rich nutrient conditions, respectively, and in vivo late infection, corresponding to infection of flower buds by the fungi.
Analysis of differential expression  (Perlin et al. 2015 ) yielded a list of 1432 genes that were differentially expressed between at least two conditions, including 307 genes upregulated during infection compared to both in vitro conditions, 208 upregulated in low nutrient in vitro and 59 upregulated in rich nutrients [false discovery rate (FDR) < 0. 001]. We built a correspondence table between the transcripts assembled by the Broad Institute used by  Perlin et al. (2015) , and the gene models of high-quality reference genome sequenced using the Pacific Biosciences SMRT technology  (Badouin et al. 2015). For this goal, transcripts were mapped using GMAP  (Wu & Watanabe 2005)  on the high-quality reference genome with default parameters, and the corresponding gene models were retrieved using the IntersectBed program of the bedtools suite  (Quinlan & Hall 2010).
P N , P S , D N and D S and the average number of synonymous and nonsynonymous sites  (Nei & Gojobori 1986)  were computed with egglib and libsequence, and the pN/pS and dN/dS ratios were computed with a custom python script. The neutrality index, defined as NI = (D N /D S )/(P N / P S ), was computed by adding one pseudo-count to each class of mutation in the contingency table to assure that NI was defined for all genes. We performed an enrichment analysis for categories of interest (genes upregulated in planta, secreted proteins, major facilitator superfamily) on the tails of the NI distribution (0. 05 and 0. 95 quantiles), with one-tailed Fisher's exact tests. FDR corrections were applied to correct for multiple testing.
Genome scans for selection We used genome scans to detect selective sweeps in the two largest clusters, MvSd and the northwestern cluster of MvSl, by looking for genomic regions where the allelic frequency spectrum deviated from neutrality, taking into account the demographic history and the genomewide allelic frequency spectrum. The results of the demographic simulations used to assess the significance of the deviation of the SFS from neutrality, and thus to detect outliers to the expected distribution under neutrality, are given in Appendix S1, Fig. S4  and Table  S10  (Supporting information). Derived SFS for each species and cluster, for simulated data sets and models fitted to simulated data sets, and joint-SFS and per-gene distributions of h, p and Tajima's D are presented in Figs S5-S7 (Supporting information).
Supporting information Additional supporting information may be found in the online version of this article. Table  S1  Samples used in the study, with ID, fungal species name, host species name, location of collection, sequence accession number and names of collectors. Table  S2  Statistics of mapping: total number of reads, number of reads mapped as proper pairs and proportion of reads mapped as proper pairs. Table  S3  Number of single nucleotide polymorphisms (SNPs) per strain, percentage of heterozygous SNPs, ploidy, mean and median coverage of heterozygous and homozygous SNPs. Table  S4  Number of shared, exclusive or fixed single nucleotide polymorphisms (SNPs) between clusters of Microbotryum lychnidis-dioicae (MvSl) and M. silenes-dioicae (MvSd). Table  S5  Orientation of SNPs. Table  S6  Comparison of demographic models for Microbotryum lychnidis-dioicae (MvSl) and M.