dataesr/scientific-paragraphs-categorization huggingface dataset:
8 ). Furthermore, day-to-day variations are reproduced, e. g. the max-ima being higher in the second week of July than in the first week. Figure  8  shows the diurnal cycle from CR, NR and observations averaged over all selected locations for August 2003. For ozone, all three datasets show similar diurnal variation with the lowest values in the early morning and an afternoon peak. The models do show an underestimation of the observed ozone values. This bias is regionally dependent (not shown), with larger negative values for southern Germany and central France, small biases over the Netherlands, and even positive biases in the MOCAGE model over some periods in northern Germany. For NO 2 , the NR from the MOCAGE model is missing the early morning peak due to the morning rush hour when the boundary layer is still shallow and photolysis is still limited.
During the summer period over the zoom domain (Iberian Peninsula), the RMSE in surface NO 2 decreases by about 30 % during daytime, while the temporal correlation increases by the same amount. The impact of both instruments on NO 2 columns is even larger. In the winter pe-riod, the additional assimilation of the satellite NO 2 observations counteracts the positive impact of surface ozone observations in some regions. This results from opposing effects from the bias in satellite NO 2 columns and the bias in surface NO 2 concentrations, due to different vertical NO 2 profiles in the MOCAGE NR and LOTOS-EUROS. It is thus crucial to analyse the model performance for simulating NO 2 profiles. Accurate vertical distributions in the model are a prerequisite for consistent positive results of the column data assimilation at the surface, provided there is no additional information about the vertical profile from the satellite observations.
, 2019). Code and data availability. The LOTOS-EUROS chemistry transport model is available in an open-source version for public use via https://lotos-euros. tno. nl/ (last access: 16 September 2019). The LOTOS-EUROS data assimilation code used in this study is property of TNO and not allowed to be shared publicly. The MOCAGE model is property of Météo-France and not allowed to be shared publicly. The volume of the model and synthetic observation datasets discussed in this paper is large, but for scientific purposes subsets can be made available upon request.
Acknowledgements. We would especially like to thank  William Lahoz , who passed away in April this year, for his valuable contributions to this paper and to the atmospheric sciences in general. He was a highly valued colleague with great expertise on, amongst others, data assimilation, who will be missed greatly. Financial support. This work was partly supported by the  ESA  funded project " Impact of Spaceborne Observations on Tropospheric Composition Analyses and Forecast " (ISOTROP- ESA  contract no. 4000105743/11/NL/AF ). Review statement. This paper was edited by  Federico Fierli  and reviewed by two anonymous referees.
Abstract. This article investigates the potential of W-band radar reflectivity to improve the quality of analyses and forecasts of heavy precipitation events in the Mediterranean area. The "1D+3DVar" assimilation method, operationally employed to assimilate ground-based precipitation radar data in the Météo-France kilometre-scale numerical weather prediction (NWP) model AROME, has been adapted to assimilate the W-band reflectivity measured by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere) during a 2-month period over the Mediterranean area. After applying a bias correction, vertical profiles of relative humidity are first derived via a 1-D Bayesian retrieval, and then used as relative humidity pseudo-observations in the 3DVar assimilation system of AROME. The efficiency of the 1-D Bayesian method in retrieving humidity fields is assessed using independent in-flight humidity measurements.
To complement this study, the benefit brought by consistent thermodynamic and dynamic cloud conditions has been investigated by separately and jointly assimilating the W-band reflectivity and horizontal wind measurements collected by RASTA in the 3 h 3DVar assimilation system of AROME. The data assimilation experiments are conducted for a single heavy precipitation event and then also for 32 cases. Results indicate that the W-band reflectivity has a larger impact on the humidity, temperature and pressure fields in the analyses compared to the assimilation of RASTA wind data alone. Besides, the analyses get closer to independent humidity observations if the W-band reflectivity is assimilated alone or jointly with RASTA wind data. Nonetheless, the impact of the W-band reflectivity decreases more rapidly as the forecast range increases when compared to the assimilation of RASTA wind data alone.
According to Eq. (  1 ), for each observed vertical profile Z o , the vertical column of RH pseudo-observation is a linear combination of the neighbouring RH profiles taken from the model background x RH i. The x RH i neighbouring profiles are located in a 160 km wide square centred on the aircraft location. For the AROME-WMed model, this size is sufficient to reduce the effects of spatial mismatches between model and observations  (Borderies et al. , 2018)  and to gather a database of x RH i which are consistent with the meteorological situation. In addition, the x RH i profiles would become less representative with a larger size since meteorological environments can change over ≈ 100 km. In Eq. (1), the x RH i profiles are weighted by a function (J PO ) of the difference between the observed Z o and simulated H z (x i ) column of reflectivities (cf.
Therefore, there is a lack of wind and humidity observations over sea, which is partly compensated by the assimilation of RASTA data. This is particularly evidenced for the humidity contribution term N v of the exergy distance (a and b panels). However, after 2 h forecast term, the impact of the V SOP1 and ZV SOP1 experiments on N s is of the same order of magnitude over land and over sea. Except at the analysis time on the humidity contribution term N v , the impact of the assimilation of RH pseudoobservations (Z SOP1 experiment) is always smaller than the impact of RASTA wind data (V SOP1 experiment). This can be attributed to the fact that the forecast system seems to have a short memory of RH pseudo-observations, which is consistent with the findings of  Storto and Tveter (2009). The impact of the assimilation of RASTA wind data has a larger impact on the humidity forecasts, probably by adjusting large structures, and by modifying in return the frontal and/or convective features.
In addition, the impact is always larger when RH pseudo-observations are assimilated jointly with RASTA wind data (ZV SOP1 experiment, black curve). This result was expected because the number of assimilated observations has been increased in the ZV SOP1 experiment. Besides, the ZV SOP1 experiment seems to take the benefits (or disadvantages) of both the Z SOP1 and the V SOP1 experiments. The small impact of the Z SOP1 experiment seems to indicate that it is pointless to assimilate RH pseudo-observations without modifying the wind field in a consistent way. Finally, the ZV SOP1 experiment leads to a larger impact on the kinetic contribution term N s than on the humidity contribution term N v. This is can be explained by the fact that the V SOP1 experiment has more impact on N s than the Z SOP1 experiment has on N v.
To conclude, the relative impact of the assimilation of RASTA data on the analysis and forecasts fields has been evidenced using the exergy distance. This impact study high-lighted that RH pseudo-observations have a modest impact on the analyses on the humidity field, which vanishes soon as the forecast term increases compared to the experiment in which RASTA wind data are assimilated alone. The impact on the subsequent forecasts is more important if both data are assimilated jointly. The benefit brought by this impact will be evaluated in the next sections.
Analyses evaluation: comparisons against in situ measurements The aim of this section is to assess the added value of the assimilation of RASTA data on the analyses. The evaluation is not shown against other conventional assimilated observations, because, as expected, the fit to observations is always better in CTRL SOP1 than in the RASTA experimental analyses. However, in-flight humidity measurements at flight level are not assimilated in any of the experiments, and are used as independent observations to assess the impact of the assimilation of RASTA data on the humidity analyses. As explained in Sect. 3. 3, poor quality measurements are removed for the comparisons. Hence, after the manual quality control, only 24 analysis cases remain. Figure  11  shows the standard deviation between humidity mixing ratio measurements and the analysed ones for the different experiments (Z SOP1 , V SOP1 and ZV SOP1 ) during the 24 analysis cases.
The standard deviation between the measurements and the water vapour mixing ratios from the background state is also represented by the black data points, which is a constant value because the same background states are used in all the different experiments. First, it should be noted that the analysed water vapour mixing ratios are always in better agreement with the observations compared to the background field, which is quite reassuring. Next, the standard deviation is slightly larger for the V SOP1 than for the CTRL SOP1 experiment. Hence, the assimilation of RASTA wind data alone (V SOP1 ) does not improve the analysis in terms of humidity, which was expected because RASTA wind data are only slightly related to hu- midity, so it is likely that the humidity analysis field moves away from humidity observations.
The experiment that reduces the standard deviation the most is the Z SOP1 experiment, which indicates that the assimilation of RH pseudoobservations alone positively impacts the analysis in terms of humidity. Even though slightly less pronounced, the joint assimilation of RH pseudo-observations with RASTA wind data (ZV SOP1 experiment) also leads to an improvement of the analysed humidity field. The respective impacts of the Z SOP1 and V SOP1 experiments are both present in the ZV SOP1 experiment. Therefore, since the standard deviation is slightly larger for the V SOP1 experiment, it seems logical that the standard deviation in Fig. 11  is larger for the ZV SOP1 experiment than for Z SOP1. In addition, it has been demonstrated in Fig. 9  that the humidity field in the analysis is more impacted by the assimilation of RH pseudo-observations (Z SOP1 ) than RASTA wind data (V SOP1 ).
Rainfall forecast evaluation Forecast scores are now validated using the rain-gauges, whose locations are indicated by the blue markers in Fig. 1. For the comparisons, model outputs are interpolated to the rain-gauge station locations using a linear interpolation. Heidke skill score (HSS) is calculated for the 6 h accumulated rainfall forecasts for the CTRL SOP1 and the three RASTA experiments (Z SOP1 , V SOP1 and ZV SOP1 ). HSS is calculated for the 32 assimilation cases in which RASTA data have been assimilated. Figure  12  represents the mean HSS differences in the 6 h accumulated rainfall forecasts between the RASTA and the CTRL SOP1 experiment as a function of the rainfall accumulation threshold (millimetres). The bootstrap confidence intervals are also shown for each threshold. They are quite large because HSS has only been calculated over 32 cases.
The impact of the assimilation of RASTA wind data is positive if the differences are above 0. Figure  12  indicates that the benefit of the RH pseudoobservations (Z SOP1 ) is neutral to slightly positive above the threshold of approximately 25 mm. Besides, the impact of the assimilation of wind vertical profiles (V SOP1 ) is larger than that of RH pseudo-observations (Z SOP1 ), especially for the larger rainfall accumulation thresholds. This is consistent with the fact that the impact of the RH pseudo-observations is less pronounced than the impact of RASTA wind data as the forecast term increases (see Sect. 6). Similar results were also obtained in prior studies  (Pu et al. , 2009; Zhao and Jin, 2008; Zhang et al. , 2012). Finally, the best results are obtained for the ZV SOP1 experiment, which suggests that the accumulated rainfall forecasts benefit more from the assimilation of the W-band reflectivity jointly with RASTA wind data.
The data assimilation experiments were first conducted for one of the most significant heavy precipitation events of the HyMeX-SOP1 (IOP7a). Then, to cover a larger number of meteorological situations, the different experiments have been run for the 32 cases in which RASTA data were available during the HyMeX SOP1. The 1D+3DVar assimilation method, operationally employed to assimilate ground-based precipitation radar data in AROME, has been adapted to assimilate the W-band reflectivity. Vertical profiles of relative humidity are first derived via a 1-D Bayesian retrieval, and then used as pseudoobservations in the 3DVar assimilation system of AROME. In order to fully take advantage of the W-band reflectivity in cloudy areas, a bias correction scheme was applied. The error standard deviation σ o was estimated by minimizing the standard deviation between the retrieved humidity fields and independent in situ humidity measurements.
Results indicate that the best estimate of the error standard deviation is close to 2 dB. The comparison with in situ humidity measurements highlighted the ability of the 1-D Bayesian method to retrieve the humidity field, which are in better agreement with completely independent humidity measurements. After validating the first step of the 1D+3DVar assimilation method, the exergy distance was calculated for each experiment to measure the relative impact of the assimilation of RASTA data on the analyses and the subsequent forecasts. This method allows one to independently assess the impact of the new observation type on the temperature, surface pressure, wind and humidity fields, independently. In particular, this impact study demonstrated that RH pseudoobservations have a larger impact on the humidity, temperature and pressure variables on the analyses, compared to the assimilation of RASTA wind data alone.
However, af-ter 1 h forecast, this rank order is reversed, probably because the forecast system has a short memory of the changes made by the RH pseudo-observations on the humidity in the analysis. This result is consistent with the findings of  Storto and Tveter (2009) , who employed a similar method to assimilate CPR data on-board the CloudSat satellite in the ALADIN NWP model. The impact on the analyses and forecasts is always larger if the W-band reflectivity is jointly assimilated with RASTA wind data, probably because the two observations complement each other and lead to a more consistent thermodynamic and dynamic cloud or frontal conditions in the initial state. In addition, it has been demonstrated that the impact of the assimilation of RH pseudo-observations and/or RASTA wind data is more important over sea than over land, probably because these areas are poorly covered by the conventional network.
To evaluate the benefits brought by these impacts on the analyses, all assimilation experiments have been compared by calculating the standard deviation between the humidity analysis fields and in situ humidity measurements. The comparisons demonstrated that the experiment in which RH pseudo-observations are assimilated alone improves the analyses in terms of humidity the most, slightly followed by the experiment in which RASTA wind data are also assimilated jointly. Generally, results of this study indicate that the W-band reflectivity leads to a slight positive improvement in the rainfall precipitation forecasts. Nonetheless, the impact is even more positive if RASTA wind data are assimilated alone. Finally, the best scores are reached if the W-band reflectivity is jointly assimilated with RASTA wind data.
Data availability. RASTA data and aircraft in situ data are all available from the HyMeX database (https://doi. org/10. 17616/R3M34X). Rain-gauge data are also available from the HyMeX database (https: //doi. org/10. 6096/MISTRALS-HyMeX. 904,  (Nuret, 2013) ). The simulation data that support the findings of this study are available from the corresponding author on reasonable request. Author contributions. This work was carried out by MB as part of her PhD thesis under the supervision of VD and OC. JD processed and provided RASTA data. NF provided the files and setup that are necessary to run the different assimilation experiments with AROME-WMED. PM provided the formulation of the exergy distance and helped use it in the impact study. All co-authors collaborated, interpreted the results, wrote the paper and replied to the comments from the reviewers.
Acknowledgements. This work is a contribution to the  HyMeX  programme supported by  MISTRALS, ANR IODA-MED  grant  ANR-11-BS56-0005  and  ANR MUSIC  grant  ANR-14-CE01-0014. This work was supported by the  French national programme LEFE/INSU. The authors acknowledge the  DGA (Direction Générale de l'Armement) , a part of the  French Ministry of Defense , for its contribution to  Mary Borderies 's PhD. The authors thank SAFIRE for operating the  French Falcon 20 research aircraft during HyMeX-SOP1. Pierre Brousseau ,  Thibaut Montmerle  and  Philippe Chambon  are also acknowledged for their technical support and scientific advise. The authors would also like to thank the two anonymous reviewers for their comments, which significantly improved the paper. Review statement. This paper was edited by  Eric Martin  and reviewed by two anonymous referees.
Availability of data and materials Data from the F-net network are publicly available at the NIED F-net server: http://www. fnet. bosai. go. jp. MAJO station belongs to the GSN network (https ://doi. org/10. 7914/SN/IU), MDJ station to the NCDSN network (https ://doi. org/10. 7914/SN/IC), and NE93 to the NECESS/UT network (https ://doi. org/10. 7914/SN/YP_2009). Data from the GSN, NCDSN and NECESS/UT networks are publicly available at the IRIS data management center (http://ds. iris. edu/ds/nodes /dmc/).
The objective herein is to study the wettability change of reservoir rock samples under alkaline conditions via spontaneous imbibition experiments. Indeed, when wettability is modified, the spontaneous imbibition capillary pressure curve is a sensitive indicator. To do so, reservoir samples are first aged with reservoir oil at irreducible saturation, and the imbibition of a reference injection brine or of an alkaline brine is measured. It is expected that the difference between these two experiments will give the answer. Since imbibition experiments are well known to be time consuming when using plugs of standard diameter (40mm), it was decided to perform these experiments with smaller rock samples (15 mm dia) using low field NMR to monitor the oil saturation. We present an original method to measure oil saturation during imbibition, in which we acquire data at regular time intervals varying from 1 hour up to 1 day.
Results All the measured desaturation curves are shown in Figure  5. The initial and final water saturation varies respectively from 8% to 17% and 46% to 52%, the variation of saturation being however similar for all samples (44%, Table  2 ). Data points were also collected after 150 h (up to 350 h, not shown) allowing to observe a very stable saturation. At the end of the tests, samples were removed from the glass tubes, oil droplets sticking on the surface carefully wiped off, and oil saturation measured again. Hence we could verify that the cut-off approach mentioned above gives a reasonable estimate of the true saturation inside the sample; indeed the oil saturation inside (with droplets and signal correction) and outside (without droplets) the tube could differ by at most 4 saturation units (4%), while the contribution of the components above 100ms can be much larger.
Results: In both species the olfactory secretome is composed of isoforms of OBP-like proteins, generated by posttranslational modifications, as phosphorylation, N-glycosylation and O-GlcNAcylation. Important changes were observed in the olfactory secretome between the sexual rest and the sexual activity periods, characterised in ewe by the specific expression of SAL-like proteins and the emergence of OBPs O-GlcNAcylation. In goat, the differences between SA and SR did not come from new proteins expression, but from different post-translational modifications, the main difference between the SA and SR secretome being the number of isoforms of each protein. Proteomics data are available via ProteomeXchange with identifier PXD014833.
Results In silico-predicted OBP sequences of O. aries and C. hircus The blast searches for putative ovine and caprine OBPs allowed identification of 16 and 17 sequences, respectively. These sequences were analysed with Signal-P software (DTU Bioinformatics) to remove the signal peptide, the hallmark of secreted proteins, and aligned with Multalin software  [22]  (Additional file 1: Figure  S1 ). Among these sequences, 18 displayed OBP(stricto sensu)-like features (O. aries: W5PH68, W5PGV5, W5PZN0, W5PHA2, W5PGN0, W5PHS2, WPPHN1, W5PHM2 and W5PGW3; C. hircus: XP_017899539. 1, and XP_017899201. 1). In addition to the typical lipocalin GxW pattern at N-terminal position (14-16 in OBP, 19-21 in SAL, 15-17 in VEG) and the YxxxYxG motif (at position 79-85 in OBP), some common patterns could be observed in some OBP, SAL or VEG-like sequences, but in not all (Additional file 1: Figure  S1 ).
Identification of ewe and goat olfactory secretome We compared the olfactory secretome of 3 females of each species, from samples collected in the same female in SA and SR. All spots obtained after 2D-E of proteins of the nasal mucus were analysed by bottom-up mass spectrometry. Nano-LC-MS/MS or MALDI-TOF MS allowed identifying specific peptides of OBPs among in silico predicted sequences above. It is worth to notice that there is no common peptide between all the OBP sequences in each species and that at least 2 unique peptides were retrieved for each predicted protein identified in analyses below. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [24]  partner repository with the dataset identifier PXD014833 and https://doi. org/10.
Indeed, the predicted sequences available from databases are unverified, often automatically deduced from high-throughput sequencing data (e. g. contigs assembly) and should be amplified from tissues to ascertain the sequence. The full-length sequences (Gen-Bank accession numbers: MK908982, MK908983, MK9089 84 and MK908985), when translated, showed high identity with the predicted Oari-OBP2 and Oari-OBP4 (Fig. 2 ), Chir-OBP2 and Chir-OBP4 (Additional file 1: Figure  S4 ). Oari-OBP2 was 98% identical to the predicted sequence, the main differences were located in the middle of the sequence with two frameshifts modifying the sequence (deleting C51 and C56 in the predicted), and at the 3′ end modifying the C-terminal sequence (GDCSLA -predicted and GCQAQ -cloned). For the three other sequences the identity between predicted and cloned sequences were higher (Oari-OBP4: 99%, Chir-OBP2: 99.