dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, in the presence of snowfall, scattering might be compensated by emission by cloud liquid water in the snowing clouds  [29]. Estimating missing detection is more complicated because an independent source of precipitation measurement at the exact same time as of the AMSU measurement would be needed to perform a direct comparison. Funatsu et al. [24]  have shown examples of detection of precipitation from AMSU-B compared to radar and ground-based data (see their Figures  9  and 10 ). They found an overall good spatial match concerning the precipitation area, though there were some missing detection regions as well. The mismatch was explained to be a result of the different nature of these observations: while AMSU provides a near-instantaneous observation, the radar-derived and rain-gauge data provide an accumulated rainfall value.
HOAPS Because of the lack of reliable in situ measurements, comparisons between satellite and in situ observations for oceanic precipitation are still limited-the strong spatial and temporal variabilities of precipitation add to these difficulties. One of the longest records of precipitation derived from SSM/I passive microwave radiometer observation over the area is provided by the HOAPS product (http://www. hoaps. zmaw. de/index. php). We use the latest release, HOAPS-3. 2, which provides surface fluxes and related atmospheric variables (and among others, the rain rate) over the global ice-free ocean from July 1987 to December 2008 (  [32, 33] ). This version uses an optimum interpolation gap filling and diurnal cycle corrections. HOAPS-3. 2 datasets are available as monthly averages and six-hourly multi-satellite composite on a regular latitude/longitude grid with a spatial resolution of 0.
5 × 0. 5°. Andersson et al. [34]  have performed a global assessment of HOAPS-3 (previous release) dataset. Since our concern is the high temporal resolution to compute the rain frequency, we use the product HOAPS-C (six-hourly composites) from HOAPS-3. 2. HOAPS-3. 2 coverage after July 2006 is limited to only two satellites (DMSP F13 and F14; see: http://www. hoaps. zmaw. de/index. php), resulting in gaps over some parts of the studied area. We thus restrict the analysis to the period 1999-2005 to compare the results to NOAA-15. We therefore use the morning (0-6 UTC overpasses) and the evening (12-18 UTC overpasses) records from HOAPS-3. 2 dataset, which are close to the NOAA-15 satellite time passes over the Mediterranean region (see Table  1 ). The daily fields of rain frequency are then computed in each grid-box for several thresholds  (20, 30, 40, 50  and 60 mm/day) to be compared to the Mediterranean Sea climatology from NOAA-15.
ERA-Interim The ERA-Interim dataset is the latest reanalysis dataset released by ECMWF (extensively described in  [35] ); it uses four-dimensional variational analysis and an improved processing of input observations, particularly satellite data. ERA-Interim dataset provides since 1989 global precipitation rate and convective rain rate every six hours on a regular grid with a horizontal resolution of 0. 75° × 0. 75° latitude-longitude. It is a six-hour cumulative rain from the forecast system which starts at 12:00 UTC the day before. Cumulus convection is parameterized by a bulk mass-flux scheme which was originally described in Tiedtke  [36]. The scheme considers deep, shallow and mid-level convection. Clouds are represented by a single pair of entraining/detraining plumes, which describes updraught and downdraught processes.
For deep convection, the convective mass-flux is determined by assuming that Convective Available Potential Energy (CAPE) is adjusted towards zero over a specified timescale. For mid-level convection, the cloud base mass-flux is directly related to the large-scale vertical velocity. The intensity of shallow convection is estimated by assuming an equilibrium of moist static energy in the sub-cloud layer. The convection scheme provides tendencies of temperature, specific humidity and wind components (  [37] ; for more details see: www. ecmwf. int/research/ifsdocs/CY33r1/PHYSICS/ IFSPart4. pdf). For the comparison, we use eight years of precipitation data between 2000 and 2007 overlapped with the NOAA-15 dataset. To increase the temporal matching between ERA-Interim and NOAA-15 datasets, we use the morning (06 UTC) and the evening (18 UTC) records from ERA-Interim, which are close to NOAA-15 time passes in the Mediterranean region (see Table  1 ).
Mean Annual Cycle The mean annual cycle of MR and DC frequencies has been computed over the period 2002-2007 separately for land and sea (Figure  2 ) of the Mediterranean region (10°W-40E°/28°-50°N; sea area excluding the Atlantic Ocean and the Black Sea) and for the three platforms that carried AMSU-B at that time. The Mediterranean Sea annual cycle of MR shows a distinct seasonal signal (Figure  2a ) with maximum occurrence (~9‰) in autumn (SON) and minimum (~0. 5‰) in summer (JJA). These features were also found by Mariotti et al. [6]  and Dubois et al. [38]  in terms of rain rate. However, we note a shift compared to Mariotti et al. [3, 6]  who show a maximum of precipitation between October to December using several coarse resolution datasets. An interesting feature is that both descending and ascending annual cycles follow broadly the same evolution in the Mediterranean Sea, thus indicating that, globally, the diurnal cycle over the sea is not very pronounced.
It remains however weak compared to the land surfaces as shown in Figure  2  (lower panel). From May until October, over land, MR and DC occurrence is much larger in ascending than in descending time sampling. More specifically, the maximum occurrence is in the window 12-19 UTC. This corresponds well to the maximum of precipitating clouds found around 18 h (local time) over mountains of the Mediterranean region  [40]. Precipitation occurrences, especially in terms of DC, do not show a strong difference between the three satellites from October until March. The decreased performance of the detection algorithm in the presence of snow and/or cold surface may partly explain this feature. However, it is worth noting that Zampieri et al. [41]  found almost the same annual cycle of monthly mean precipitation climatology using the CRU dataset over the Alps region (see their Figure  5 ).
Spatial Variability and Precipitation Occurrence In this section, we compare our results to those derived from HOAPS-3. 2 (hereinafter HOAPS) over the period 1999-2005. It is noteworthy that we take into account NOAA-15 measurements also in 1999 (as mentioned earlier) to increase the length of the common period between both datasets, even though they are less reliable than after 2000 due to remaining scan asymmetries problems with respect to the angle of view  [27]. Figure  8a  displays the HOAPS climatology of rain rate frequency for a threshold of 30 mm/day over the period 1999-2005 in the Mediterranean Sea. We chose the threshold of 30 mm/day because it is the one that compares better with NOAA-15 observations. Since HOAPS measurements are limited to the open maritime areas, observations over some key areas (i.
The largest interannual variability of the precipitation occurrence estimated from the standard deviation (>16‰) takes place in the northern Ionian sub-basin and off Gulf of Lions, and the smallest one (<4‰) in the southern Ionian and southeastern Levantine sub-basins (Figure  8b ) again in good agreement with NOAA-15 observation (see Figure  4b ) and analyses performed by Romanou et al. [52]. Some differences between HOAPS and AMSU-B rain occurrences can be observed for example, in the center of the western Mediterranean basin (between Balearic and Sardinia Islands), and the mesoscale details of the low occurrence values in the southern Ionian sub-basin. However, the AMSU dataset provides finer scale details and better coverage, particularly in the coastal area where it allows a continuous sea-land screening of the whole Mediterranean region.
5‰) in April. The minimum MR frequency (~1‰) occurs during July and a secondary minimum (~2‰) in March. The annual cycle of the MR/rain frequencies from both datasets AMSU-B/HOAPS shows a striking similar behavior (for example: correlation coefficient = 0. 82 between MR frequency and HOAPS rain frequency for a threshold of 50 mm/day). While two maxima are found on November and April, two minima are seen on July and March. MR frequency from AMSU-B is slightly higher than those of HOAPS during summertime (JJA), but there are fewer occurrences than in HOAPS during wintertime (JFM). This could be related to: (i) continuous (less intermittent) heavy precipitation during the sampling time; (ii) the fact that the lower frequencies (HOAPS: around 89 GHz) are more sensitive to the full atmospheric column, including the rain emission in the liquid phase, even when the rain is not associated to a large frozen phase, as rain occurrences that are missed by the high frequencies are more likely to be captured by the low frequencies; and (iii) the UTH constraint thresholds used in our method during this season.
Regional Scale Precipitation Occurrence In order to go further into details in the comparison between NOAA-15 and HOAPS datasets, we investigate in both datasets the temporal variability in three target regions (see Figure  8 ). The first box is located in the western Mediterranean basin (indicated by wMed; 40. 5-42. 0°N, 5. 0-7. 0°E), the second one is located in the northern Ionian basin (indicated by cMed as central Mediterranean; 36. 5-38. 5°N: 18. 0-20. 0°E), and the third one is located in the Levantine basin (indicated by eMed as eastern Mediterranean; 33. 0-35. 0°N, 28. 0-31. 5°E). The three annual cycles are shown on Figure  9b-d  and exhibit a quite similar global behavior as the whole basin one (Figure  9a ) with maximum precipitation in autumn and minimum in summertime. In spite of this similarity, there are differences within the individual variation in each region.
The largest amplitude of the seasonal variability occurs in wMed and cMed and the smallest one in eMed, which is consistent with SD analysis above (Figures  4  and 8 ). Although the eastward propagation of rainfall signal is captured on the climatological annual cycle from NOAA-15, it is not the case (less clear) for HOAPS. For example, the rainfall occurrence from NOAA-15 (HOAPS) starts to increase in July (August) in wMed, in August (August) in cMed and in October (September) in eMed. The dry season becomes longer from wMed to eMed in both databases in good agreement with analyses of Romanou et al. [52]  in terms of rain rate. Note that the rain-frequency annual cycle exhibits the same behavior for all thresholds, with higher amplitude (more occurrences) for 30 and 40 mm/day. This suggests that, according to HOAPS, weak rain is more frequent than heavy rain during autumn-springtime.
2° latitude) relative to ERA-Interim (0. 75° latitude). The well-known latitudinal gradient pattern of the mean rain frequency in the Mediterranean region is captured by the reanalysis, as does NOAA-15. The local maxima of rain frequency (8-20‰) occur over the northwestern regions and are closely tied to orography: for instance, more than 15‰ of rainfall is recorded over the Alps and is spreading (>8‰) toward the Dinaric Alps and the Pyrenees. Other localized maxima (>10‰) are found along the Portuguese coasts and over the Taurus Mountains. The minima of rain frequency (<1. 5‰) occur in the southern regions below 35°N and in the eastern Mediterranean basin in good agreement with MR frequency distribution from NOAA-15. However, some dissimilarity between both datasets is observed: for example, the precipitation frequency of <5‰ over the Portugal coast in NOAA-15 coincides with high rain frequency >10‰ in ERA-Interim.
Also, the MR over the Atlas Mountains is very frequent in NOAA-15 (>10‰) and contrasts with the low rain frequency <7‰ in ERA-Interim reanalysis. The western Mediterranean basin exhibits higher rain frequency (<2. 5‰) than the central and eastern basins (<1‰), in contrast with HOAPS observations, though NOAA-15 results are consistent with the results of Romanou et al. [52]  in terms of rain rate. Since the ERA-Interim dataset provides also convective precipitation estimates (hereinafter DC), they are analyzed and the derived DC frequency fields are compared to the DC fields from NOAA-15. The spatial distribution of DC occurrence from ERA-Interim (Figure  10c ) exhibits a complex pattern. Although the latitudinal gradient is barely observed, the maxima over the Alps, the Balkan Mountains, northern Ionian, Adriatic Sea and the Anatolian coasts and the minima over the northern African and eastern Mediterranean regions are well collocated with NOAA-15 results, but with lower amplitude (Figures  4c  and 10c ).
Basin Scale Precipitation Occurrence Similarly to HOAPS analysis, we have computed the rain occurrence from ERA-Interim for several thresholds (30, 40 and 50 mm/day) and superimposed them on the monthly mean annual cycle of MR occurrence from NOAA-15 in the Mediterranean Sea over the period January 2000-December 2007 (Figure  11a ). In this section, we will not describe in details the annual cycle characteristics, but we just stress the similarities and differences between the datasets. While the annual cycle of the MR frequency from NOAA-15 shows a maximum in October and a minimum in July, rain frequency from ERA-Interim (for threshold of 30 mm/day) shows a similar annual cycle (correlation coefficient > 0. 9) with a shifted maximum towards November. The annual cycle of rain frequency from ERA-Interim is sensitive to the chosen threshold values: for example, it shows a stronger seasonality for low thresholds (30 mm/day) than for high thresholds (50 mm/day), in contrast with HOAPS observations.
This sensitivity indicates that heavy rain is much less frequent in ERA-Interim than low or moderate rain. It must be noted that the annual cycle of rain rate from ERA-Interim (not shown) exhibits a maximum in winter (DJF) and a minimum in summer (JJA). This suggests more frequent precipitation in October-November than in December-January, but with lower rain rates in ERA-Interim. The annual cycles of DC frequencies from both datasets in the Mediterranean Sea are depicted in Figure  11e. The DC frequency from ERA-Interim over the entire Mediterranean Sea shows a low seasonal signal with the maxima in autumn and minima in spring and summertime. The annual cycle of DC frequency from ERA-Interim shows weak seasonality and a different behavior than the annual cycle of DC frequency from NOAA-15; it also exhibits systematically lower occurrence values and amplitude than those of NOAA-15.
Regional Scale Precipitation Occurrence In this section, the comparison between NOAA-15 and ERA-Interim datasets is performed in the three previous targeted regions (wMed, cMed and eMed; see Figure  8  and Section 4. 1. 3). Figure  11b-d  shows the annual cycle of frequencies from both datasets. Both AMSU-B and ERA-Interim show distinct annual cycles with maxima in late autumn to early winter and minima during summer in all regions. Maxima values tend to decrease from wMed toward eMed. The largest difference between NOAA-15 and ERA-Interim rain frequency appears during the first half of the year, particularly the peaks in March-April in wMed when NOAA-15 shows less MR occurrence (Figure  11b ), and in eMed when the peak is shifted two months later (Figure  11d ). Except for springtime in wMed, NOAA-15 exhibits comparable and higher occurrences than ERA-Interim (for rain-rate threshold 30 mm/day) in contrast to HOAPS.
The zonal propagation of MR and DC occurrence is to be expected in the region dominated by the westerly wind system, while the meridional propagation could be related to disturbances coming from midlatitudes. The seasonal cycle of MR and DC is very distinct over sea and over land. A broad agreement between the three platforms has been found, with most of the differences related to the sampling time and the diurnal cycle, which is significant especially over land during the warm season. Regions with large occurrences of MR and DC such as the Adriatic Sea and the northern Ionian sub-basin are known for strong cyclone activity resulting from complex interactions involving atmospheric dynamics, orography, and land-sea temperature gradients  [54]. The comparison of MR from HOAPS and NOAA-15 datasets is restricted to maritime areas away from the coasts (due to its coarser spatial resolution of 0.
Acknowledgments This study was funded by the  Chaire de Développement Durable  of the Ecole Polytechnique-EDF and was partly sponsored by the  Centre National de la Recherche Scientifique (CNRS) , the  Centre National d'Etudes Spatiales (CNES) , the  Direction Générale de l'Armement (DGA)  at  ENSTA-ParisTech  within the  ReaMed  project, and by the  Groupe Mission Mercator Coriolis (GMMC)  within the  SiMED  project. AMSU data was obtained from the  French Mixed Service Unit ICARE , HOAPS-3. 2 data from www. hoaps. zmaw. de/index. php,and  ERA-Interim  from ECMWF http://www. ecmwf. intvia CLIMSERV-IPSL http://climserv. ipsl. polytechnique. fr. We thank the three anonymous reviewers for their constructive criticisms on the previous version of the manuscript.
Small RNAs localized in intergenic regions Among the P. tricornutum sRNAs localized in intergenic regions, the most abundant population of reads (>100,000 reads) is located on chromosome 2 (chr2: 29. 039-29. 123) (Additional file 2: Table  S2 ). These sRNAs align to a 85 nt-long region that displays a very clear distribution of two overlapping 24 and 25 nt-long fragments, with a plateau at the center of the region (Figure  2A ). This specific region concentrates around 8% of all the reads and 55% of the sRNA population matching intergenic regions. These abundant sRNAs have been detected in our five experimental libraries and in the independent dataset by Huang  [28]. We can therefore exclude artifactual biases generated during library preparations or sequencing. Furthermore, their expression in vivo was validated by low molecular weight Northern-blot analysis using a 24 nt-long oligonucleotide complementary probe.
Nucleotide composition of the reads appears biased at their 5′end, with U being the first nucleotide on almost 60% of the reads (Figure  5B ). Various classes of siRNAs and piRNAs have been reported to regulate the expression of TEs in eukaryotes, either post-transcriptionally or through cytosine DNA methylation and/or other chromatin modifications  [50]. To assess if the population of P. tricornutum sRNAs covering the TEs may also have a regulatory role, sRNA coverage was compared to the expression level of the autonomous TEs  [25]. Information regarding TE expression was obtained from two independent sources, a set of EST libraries  [51]  and a recently published RNA-Seq dataset  [26]  both obtained from cells grown under normal light conditions. Therefore, to facilitate further comparisons, we only considered in the following analyses our sRNAs dataset obtained from cells grown under Normal Light condition.
For both RNA-seq and EST datasets, the small RNA coverage is significantly higher on TEs with undetectable or low expression (Mann-Withney one sided test, RNA-seq: pvalue < 4e-5, EST: pvalue < 5e-3) (Figure  5C ). This suggests that sRNA abundance correlates with TEs characterized by low or no expression, possibly triggered by efficient repression or silencing. This hypothesis was tested by RT-qPCR using primers specific to 7 selected transposons with high (3), medium (1), or low (3) sRNA coverage, respectively. This revealed that autonomous TEs with medium or high sRNA coverage have an expression level at least 1,000 folds lower than the ones with low or no coverage (Figure  5D ). This inverse correlation further supports the hypothesis that sRNAs may contribute to the silencing of these TEs.
Taken together, these data are fully supportive of a model in which sRNAs direct DNA methylation at many TE loci in P. tricornutum. Methylated DNA is mainly found on transposable elements in the P. tricornutum genome  [26]. However, high DNA methylation levels could also be observed on The transposons are grouped on the x-axis with an increasing coverage in sRNAs: low (sRNA RPKM < 5), middle (5 < RPKM < 10) and high (RPKM > 10) coverage. A grey shaded box is reported when the product could not be amplified. Autonomous transposons whose expression is supported by both EST and RNA-seq evidence are annotated with the symbols "++". Labels above bins correspond to the Genbank identifier and localization of the transposons. (E) Comparison of methylation level and sRNA coverage on all Copia-type LTR transposons longer than 50 nt.
The functional and in silico characterization of these elements allowed us to reach several novel conclusions. We questioned the presence of canonical miRNAs in diatoms, previously reported in P. tricornutum  [28]. We used a computational approach to look for putative miRNAs in our small RNA dataset, but none of our attempts revealed canonical pre-miRNA-like hairpins that could be substrates for a Dicer-like enzyme. Moreover, none of the sRNAs we identified share significant sequence similarity when compared with microRNA sequences from miRBAse  [41]  and miRNAs from Ectocarpus siliculosus, a Stramenopile multicellular brown alga that represents the only example reported for this lineage in the literature with experimentally validated microRNAs  [55, 56]. These results strongly indicate that canonical miRNAs, such as those characterized in different eukaryotic organisms so far, are not major regulators of gene expression in P.
Our dataset allowed us to identify the most abundant population of sRNAs in P. tricornutum, showing a clear distribution peak of 24-25 nt in length, localized on an intergenic region of chromosome 2 (chr2: 29. 039-29. 123). Northern blot analyses experimentally demonstrated the existence of sRNAs in P. tricornutum for the first time (Figure  2B ). Their detection in cells grown under different conditions excludes a non-specific RNA degradation origin. Similarly to the miRNAs, these sRNAs may result from the processing of longer RNA precursors, which successfully be detected. However, despite numerous attempts, we did not succeed to amplify and validate the primary transcript and to identify the dsRNA structure that may generate these products. We can thus hypothesize that, differently from the miRNAs, the precursor is not transcribed by the RNA polymerase II  [61]  or is subjected to other 5′ or 3′ end modifications (e.
Small RNA library preparation and sequencing In all conditions (NL, HL, LL, and iron starvation), total RNA was extracted from diatom cells using the Trizol method (Invitrogen, USA). sRNAs were separated by size fractionation on denaturing polyacrylamide gels and then fragments in the 18-35 nt size range were purified. The RNA samples were sent for cDNA synthesis and Illumina sequencing to Vertis Biotechnologie AG company (www. vertis-biotech. com). The purified RNAs were poly(A)-tailed and ligated using a RNA adapter to the 5′-phosphate of the small RNAs. First-strand cDNA synthesis was performed using an oligo(dT)-adapter primer and M-MLV RT (Ambion), then followed by PCR amplification. The resulting cDNA populations were sequenced on an Illumina sequencer. The raw sequence data can be accessed at the NCBI Sequence Read Archive under the accession number SRX648639.