dataesr/scientific-paragraphs-categorization huggingface dataset:
The recommendations in the GIPSY-OASIS II documentation are mainly based on the results of  Bar-Sever et al. (1998)  and updated by  Selle and Desai (2016). They suggest that the parameter of the ZWD random walk should be set to at least 8. 4 mm h -1/2 when using σ 2 = a 2 /sin 2 (e) as the weighting function for the GPS observations (as opposed to 20 mm h -1/2 in the GAMIT software). However,  Jarlemark et al. (1998)  noticed that using a random walk parameter tighter than the real atmospheric variability introduces errors in the ZWD estimates. As this study is focused on MCSs which correspond to severe weather events, we can expect a very high variability in the tropospheric parameters; hence, it seems more reasonable to set the random walk parameter to the looser recommended value in both software packages rather than to the tighter.
ZWD and gradients The time series of ZWDs retrieved by the GAMIT software at  Timbuktu, Niamey, and Djougou, between 2006 and 2008 , display a well-defined annual cycle (Fig. 2 ). The dry seasons are characterized by ZWDs of less than 100 mm (ca. between December and March), whereas the wet seasons, also referred to as the monsoon seasons, between June and September (hereafter referred to as JJAS), are characterized by frequent rain events and ZWD values higher than 200 mm  (Bock et al. , 2008). The duration of the monsoon season shrinks as one goes from the wetter climate in the south to the more arid climate in the north. Table  2  presents the statistics of the differences in the ZWD estimates ( ZWD) obtained with both software packages during the monsoon season. The average differences of the ZWD values range from -0.
This can be explained by random ZWD biases induced by mis-modeling. Indeed, the tropospheric modeling used in the GAMIT software does not take the sub-daily variations of the gradients into account nor does it account for the sub-hourly variations of ZWD. Therefore, the effect is more pronounced during the wet season because of higher tropospheric variability in these parameters as shown in Figs. 3c  and 4  (and further analyzed in a case study in Sect. 3). Tropospheric gradients show a strong seasonality in phase with the WAM (Fig. 4a, b ). The daily averages of gradients are in good agreement between the two software packages (not shown) with a mean difference smaller than a tenth of a millimeter and a standard deviation smaller than 0. 22 mm due to biases induced by the un-modeled sub-daily variability.
Neglecting the sub-hourly variations of the gradient (of the order of 0. 2 mm) leads to an uncertainty of about 11 mm in the S-Trop-D. Thus, the global uncertainty in the S-Trop-D induced by these defects of tropospheric modeling in the setting of the GPS data processing with the GAMIT software is evaluated at 37 mm (i. e. , 4. 7 mm on ZTD if projected back to zenith using the GMF) during the wet season. During DJFM periods, the global uncertainty in the S-Trop-D is evaluated at 16 mm (i. e. , 2 mm on ZTD) with 0. 9, 0. 25, and 0. 09 mm as uncertainties in the ZWD, hourly, and subhourly variations of the gradient, respectively. Tropospheric mis-modeling maps onto the GPS phase residuals and its ef-fects are modulated by the season, which leads to a seasonal variation of the weighted root mean square (WRMS) of the post-fit phase residuals.
WRMS of GPS phase residuals Post-fit phase residuals of the ionosphere-free observations are traditionally inspected to check the quality of GPS observations and models  (Kouba, 2003). Usually, the quality of the data processing of each session (24 or 30 h) is summarized in the WRMS of all of the residuals of the session that is computed by the software. With GAMIT, the daily WRMS of phase residuals fluctuates around a median of 12. 1 mm with a median absolute deviation (MAD) of 2. 4 mm, over the entire period and all stations. Over the same period, with the GIPSY-OASIS software, the median is 10. 4 mm with a MAD of 1. 1 mm. The statistics are very similar, indicating that the solutions from both software are equivalent on average, although the results suggests that the data processing implemented in GIPSY-OASIS is slightly more accurate.
We know from the previous sections that the mis-modeling of tropospheric variability with GAMIT is exacerbated during the wet season. Figure  5  shows the time series of daily WRMS of post-fit phase residuals at three stations for both software packages. The contrast in WRMS between the wet and dry season is very marked in GAMIT at all three sites (e. g. , 12 mm vs. 8 mm on average at Timbuktu). The use of only one tropospheric gradient per 24 h session during the wet season is less suitable for tropical stations; thus, the unmodeled tropospheric anisotropy is mainly reflected by the behavior of the GPS phase residuals. With GIPSY-OASIS, there is also a small seasonal variation, indicating that during the wet seasons some modeling defects may persist (e. g. , higher-order tropospheric anisotropy not modeled by tropospheric gradient parameters and signal scattering from the ground, the so called multipath, which depends on soil moisture; e.
GPS estimates The GPS tropospheric estimates retrieved from the GIPSY-OASIS (black lines) software allows for the MCS to be documented with a 5 min sampling rate (Fig. 8 ). It includes the estimates from the GAMIT software (red dashed lines). In Fig. 8a , the ZWD is retrieved from the ZTD, and the hydrostatic part is computed using 1 min ARM-MF surface pressure data and by applying the formula from  Saastamoinen (1972). Figure  8a  also shows the IWV converted from the ZWD using 2 m temperature data and the formula from  Bevis et al. (1992). East and north components of the tropospheric gradients are given in Fig. 8c  and d , respectively. Comparing the ZWD estimates retrieved from GAMIT and GIPSY-OASIS (Fig. 8a ), good agreement can be seen, such as the ZWD peak coinciding with the rainfall event.
Tropospheric gradients retrieved from the GIPSY-OASIS software show the east-west motion dynamic of the MCS between 01:00 and 07:00 UTC. From 01:30 UTC onwards, the east component increases from 0. 26 mm and reaches a maximum of 2. 25 mm at 03:20 UTC, whereas the north component remains almost null over this same period. The tropospheric gradient vector actually points to the direction of the incoming convective cells and its modulus shows a first local maximum at the CPCT (Fig. 8c, d ). This is the time when the anisotropy of the troposphere around the GPS station is the most pronounced (Fig. 6c ). Following this, the east component decreases from its maximum to about zero at around 03:48 UTC, when the GPS station is surrounded by convective cells; it decreases to a minimum of -2. 79 mm at 04:35 UTC.
The modulus of the gradient vector actually shows a second maximum at the end of the motion of the convective zone through the GPS station, when, once again, the anisotropy of the troposphere around the GPS station is the most pronounced (Fig. 6d ). At around 05:40 UTC, anisotropy of the stratiform part of the MCS is detected in the northeast direction by gradients, which is confirmed by reflectivity maps (Fig. 6e ). Thus, the tropospheric gradients retrieved from the GIPSY-OASIS software appear to be relevant to contribute to the description of the dynamics of intense weather events such as MCSs. Tropospheric gradients retrieved once per 24 h session from the GAMIT software provide no such information, but the examination of the postfit phase residuals is more informative in this case.
GPS phase residuals The daily WRMS computed by the software for the sessions covering the 11 August 2006 are 15 mm with GAMIT and 10 mm with GIPSY-OASIS (see Sect. 2. 3. 2). In this subsection, we analyze the behavior of the phase residuals during the case study in detail. Therefore, Fig. 9  shows the root mean square (RMS) of the GPS phase residuals computed at a 15 min sampling interval (panel a), the number of GPS observations per epoch (panel b), and the GPS phase residuals (panel c) retrieved from the GIPSY-OASIS (black) and the GAMIT (red) software packages during the MCS event. With GIPSY-OASIS, the 15 min RMS of the GPS phase residuals exceeds 15 mm between 03:10 and 04:35 UTC during the crossover of the convective cells. This indicates that the tropospheric anisotropy is more complex than can be inferred from gradients, which is confirmed by the GPS phase residuals on the reflectivity maps (Fig.
9c ), but the 5 min sampling interval of the GPS data does not make easy to detect their temporal correlations. The number of GPS observations per epoch is usually over nine and decreases slightly during the entire event but remains above seven (Fig. 9b ). The small number of observations that are rejected indicates that the chosen settings of the GPS data processing with the GIPSY-OASIS software appear to be adequate to study this kind of extreme weather event. With the GAMIT software, the RMS of the GPS phase residuals computed at a 15 min sampling interval oscillates between 4 and 8 mm before and after the MCS event (Fig. 9a ). It skyrockets as soon as a sufficient number of GPS observations cross the MCS from 8 to 20 mm at 02:00 UTC, then it oscillates between 11 and 22 mm as long as the MCS affects the GPS data.
, 2007). The TRMM rain rates were compared point to point to the ARM-MF data at a 3 h sampling interval (2972 points). Rainfall was detected at 243 points by either TRMM or ARM-MF data but only at 74 points in both data sources. At the 169 remaining points, rainfall was observed either only by TRMM or only by ARM-MF. At 145 points from the 169 (86 %), the rain rate was less than 0. 5 mm h -1. The 16 remaining points (14 %) corresponded to more intense rainfall, but it was observed at different times. This result led us to define a rainfall event as an uninterrupted period of precipitation during which the maximum rain rate is higher than 0. 5 mm h -1. We detect 27 events using the 3 h ARM-MF rainfall data during JJAS 2006 and 47 events using the TRMM rainfall data. Among the 27 ARM events, 25 coincided with at least one TRMM rainfall event.
The two ARM events not detected by TRMM are of very low intensity and most probably correspond to small local showers. Each event is associated with a start time, a time of maximum precipitation, and an end time. If several rainfall events occur within a 13 h window they are merged together as we are mainly interested in MCSs events in which several convective cells can be detected. We keep the rainfall events with average precipitation above 0. 5 mm h -1 and with a peak rain rate above 1 mm h -1. This method detects 27 events from the TRMM data and 20 events from the ARM-MF rainfall data. Compared with the first intercomparison, the agreement between the two datasets is improved when considering longer and more intense events. The seven TRMM events that do not match with any ARM-MF rainfall event may be due to the difference in the spatial representativeness of rainfall between the two datasets: very localized for ARM data and more extended for TRMM data (0.
11 ) and GPS tropospheric estimates (Fig. 12 ) retrieved with both software approaches (GIPSY-OASIS as black lines and GAMIT as red dashed lines). The method detects 10 MCS events based on the rain rates from TRMM data (Fig. 11c, d ) and temperature from the PTU200 (Fig. 11b ). In general, all of the detected MCS cases were confirmed from the reflectivity maps of the MIT C-band Doppler radar. One event (14 August 2006) is not detected in the ARM-MF rain rate data. This event is actually a propagating cold pool from a freshly dissipated MCS  (Provod et al. , 2016). All periods detected include a rapid increase in the wind speed associated with a sudden change of direction (Fig. 11a ), which are the characteristics of the arrival of a cold pool. They also include a pronounced and sudden drop in temperature (Fig.
11b ), and an increase in relative humidity (Fig. 11c ) and ground pressure (Fig. 11d ). This is all consistent with previous studies (e. g. ,  Redelsperger et al. , 2002). There is also a clear peak in the ZWD (Fig. 12a ), a signal in the tropospheric gradients retrieved from GIPSY-OASIS (Fig. 12b, c ), and an increase in the 15 min RMS of GPS phase residuals retrieved from GAMIT (Fig. 12d ). Careful inspection of the wind, temperature, tropospheric gradients, and phase residuals reveals two potential events with coldpool-like signatures on 30 and 31 August that were not detected because there was no signal in rainfall. Furthermore, there is no peak in the ZWD data associated with these potential events (Fig. 12a ). Inspection of the MIT radar data actually shows decaying MCSs for these two cases.
Composites of meteorological variables and GPS estimates during MCS events in JJAS from 2006 to 2008 Using the detection method described in Sect. 4. 1, we flagged a total of 414 MCSs passing over the six AMMA GPS stations in JJAS from 2006 to 2008. The detailed characteristics for each event (start, peak, and rainfall end times, CPCT, temperature drop, and precipitation peak and cumulative rainfall depth) are given in the Supplement. The list only includes events for which both PTU200 and GPS data were available within the 10 h windows centered on the CPCTs (i. e. , a few events may not be documented because of gaps in our data). Table  3  reports the number of events and the statistics of the surface air variables, precipitation, and ZWD recorded by the six stations. The meridian climatic gradient between the Sahara and the Guinean coast is reflected in both the number of MCS events and the atmospheric variables.
Conclusions The characteristics of GPS tropospheric estimates (ZWD and gradients) and post-fit phase residuals during the wet season (June to September) of the West African monsoon were evaluated using two different GPS data processing approaches. The first uses the double-difference observations from a regional network of GPS stations centered on the study area (West Africa) with the GAMIT software. The second uses the undifferenced observations in PPP mode with the GIPSY-OASIS software. Hourly ZWD estimates retrieved using both strategies are highly consistent on average (RMS difference of ∼ 4 mm). Both processing solutions exhibit a strong seasonal modulation of the ZWD and gradient variability reflecting the presence of active moisture transport during the monsoon period. In this respect, the 5 min sampling interval of the ZWD and gradient estimates with GIPSY-OASIS appears to be more suitable for studying rapid weather events compared with the 1-hourly ZWD and daily gradient sampling of GAMIT.
Sub-daily gradient variability is shown to be quite large (1σ of 0. 6 mm on average with peaks up to 2 mm) and to represent a strong source of uncertainty in the GAMIT estimates. Mis-modeling of the tropospheric delay variability in the GAMIT solution is reflected in the post-fit residuals during the monsoon season (the weighted RMS of residuals passes from 10 mm during the dry season to 15 mm on average during the wet season). Mesoscale convective systems (MCSs) are a major contributor to the rapid tropospheric delay anisotropy. The case study of the squall line over Niamey on 11 August 2006 confirmed the good sensitivity of both GPS software packages to the ZWD variation associated with the passage of a MCS. In this case, a rapid increase of 40 mm was observed in the ZWD over the 2 h preceding the rainfall peak followed by a decrease leading to a slightly lower final ZWD value (20 mm below the initial value).
Data availability. GPS and PTU200 data of AMMA stations can be accessed at ftp://igs. ign. fr//pub/igs/data/campaign/amma (last access: 27 November 2018). ARM-MF data can be accessed at http: //www. archive. arm. gov (last access: 27 November 2018). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-9541-2019-supplement.
A possible explanation was that the evaluated fission cross section in that neutron energy region was overestimated by several percent. Although the 235 U(n,f) cross section in that range is not a standard such a large difference was unexpected, in particular since most evaluations available at that time quoted an uncertainty on this cross section below or around 1% (see for example the compilation of standard cross sections in Refs. [7]  and  [8] ). The evaluated 235 U fission cross section (in ENDF/B-VII. 1  [9]  and more recently in ENDF/B-VIII. 0 [10]  and JEFF3. 3 [11] ) in this neutron energy region relies on the available EXFOR data which mostly date back to the 70's and 80's  [12] ÷  [18]. Those data are shown in the region of interest in Figure  1 , in comparison with the two more recent evaluations which apparently tend to overestimate most datasets.
Its use allowed us to accommodate into a single range the low energy alphas and tritons as well as the highly energetic fission fragments and, especially, to minimize the effect of the so-called g-flash, i. e. the prompt signal produced in the detector by relativistic particles and g-rays from the spallation process. Such a signal was particularly large in this measurement, considering that the detectors were directly exposed to the beam. The data for each neutron bunch were collected by sampling the amplifiers' output signals by means of flash ADCs and recording the waveforms for 100 ms. In the following the six silicon detectors will be named as Li_f, Li_b, B_f, B_b, U_f, U_b, standing respectively for 6 Li, 10 B, 235 U, forward and backward with respect to the beam direction.
Small differences in the neutron beam impinging on each sample, related to the geometrical shape of the sample and to the neutron beam absorption along the setup (i. e. in the various samples and detectors) are taken into account by the correction factor fX. This was estimated by Monte Carlo simulations of the neutron interaction with all the layers of different materials encountered along its trajectory. To this purpose the full geometry of the apparatus was implemented in detail in the GEANT4 Monte Carlo code  [29]. 1. 25•10 7 incident neutrons per decade were randomly generated in the 10 meV to 1 MeV energy range, each decade divided into 1000 bins, with a uniform probability inside each bin. The geometrical distribution of these neutrons in the transverse plane was gaussian with a standard deviation of 7 mm, reflecting the known spatial profile of the neutron beam.
3% for the 6 Li data and ≤1% for 10 B data. No appreciable effect was observed for the 235 U data. The experimental apparatus was initially aligned to the nominal beam position. However, the real position of the neutron beam was then checked throughout the measurement by means of a photographic emulsion (Gafchromic foil) placed in front of the vacuum chamber hosting the setup. The effective beam center position was found to be displaced by 10. 0 (0. 7) mm and 5. 0 (0. 7) mm, respectively in the horizontal and vertical direction, with respect to the detector center. This was adopted as the reference beam center for the efficiency simulations and data analysis. To estimate the uncertainty related to the detector alignment, the data analysis and Monte Carlo efficiency calculations were performed again by shifting the reference beam center up to ±2 mm (about 3 standard deviations) in X and Y.
8÷11. 0 eV was preferred, as recommended by IAEA  [8]. It should be noticed, however, that the two normalization methods are consistent with each other, within the statistical uncertainty. This can be clearly seen in Figure  13 , where the ratio between the thermal cross section and the integrated one in the 7. 8÷11. 0 eV range is reported for several evaluations and for the data of this work. The present results agree with the recommended IAEA, ENDF/B-VIII and JEFF3. 3 values, and are in all cases within 1%. The following remarks summarize the calculations described above and the corresponding results: • The independent normalization of the count rate of the reference reactions in the 1-10 eV region, eliminates completely the systematic uncertainty related to the areal density of the used reference samples, or their possible inhomogeneity.
Furthermore, normalizing in this energy range, rather than at thermal energy, further minimizes the overall uncertainty, considering the larger uncertainty in the thermal neutron energy region previously seen (Figure  11 ). • An additional systematic uncertainty is related to the angular distributions of the products in the 6 Li(n,t) and 10 B(n,a) reactions as a function of neutron energy. Indeed, even though reasonable evaluations are available  [10] , the true angular distributions are not exactly known. This has an impact on the efficiency calculations. Due to possible differences between ENDF-B/VIII and the real angular distributions, slight residual differences between results from the forward and backward detectors could still be present. These possible differences were however further smeared out by using the weighted average between forward and backward data, thus obtaining two distinct reference data sets (Lithium and Boron).
Conclusion A high accuracy, high resolution measurement of the 235 U(n,f) cross section, relative to the 6 Li(n,t) and the 10 B(n,a) reference reactions was performed at n_TOF in the Experimental Area 1, by means of stacks of samples and silicon detectors placed directly in the neutron beam. Data have been collected for all these three reactions in both forward and backward direction, to minimize the uncertainty related to angular anisotropy in the charged particle emission from the reference reactions. The measured count rate distributions as a function of neutron energy have been normalized to each other slightly above thermal neutron energy, in an interval recommended by IAEA. The new experimental setup and the employed analysis technique have resulted in an unprecedented low uncertainty of 1-2% on the data for all measured reactions.
Acknowledgments arXiv:1902. 10631v1[physics. nucl-ex]27 Feb 2019 This research was partially funded by the  European Atomic Energy Community (Euratom) Seventh Framework  Programme No. FP7/2007-2011 under Project  CHANDA  (Grant No. 605203 ). We also acknowledge the support of: • the  Narodowe Centrum Nauki (NCN) , under Grant No. UMO-2016/22/M/ST2/00183 ; • the  Spanish Ministerio de Ciencia e Innovaciòn  under grants  FPA2014-52823-C2-1-P, FPA2017-83946-C2-1-P  and the program  Severo Ochoa  ( SEV-2014-0398 ); • the  Croatian Science Foundation  under the project  IP-2018-01-8570.
However, both the placement of the root as well as the identity of early diverging lineages has to be confirmed using this updated dataset of microbial diversity in combination with more complex models of evolution, which are better suited to prevent phylogenetic artifacts such as long branch attraction  (Bergsten 2005). These artifacts are expected to particularly affect microbial lineages comprised predominantly of symbiotic members such as the CPR bacteria and DPANN archaea, as genomes of known symbionts are often compositionally biased and characterized by faster evolutionary rates  (Dombrowski et al. 2019). Various attempts in this direction are currently being made but this is clearly a challenging undertaking due to large computational demands of using complex evolutionary models on big datasets.