dataesr/scientific-paragraphs-categorization huggingface dataset:
Figures  11  and 12  show the time series for the selected sites for the period 2010-2016 and for ascending and descending overpasses. The Niger and Benin sites present a very pronounced seasonal signal typical of the Sahelian sites. Over these sites the L3SM shows consistently lower soil moisture than L2SM for high soil moisture values. The L3SM is closer in this case to the site data. The time     series for LW show that the SMOS data closely follow the behaviour of the soil moisture dynamics over this site. One of the reasons for this is that the rainfall events are well separated, enabling the remote sensing data to capture the dynamics of physical processes (e. g. infiltration and evaporation) on a coarse scale. Thus, the exponential behaviour typical of a drying soil is well depicted.
Figures  13  and 14  show the CDF of the in situ, L2SM and L3SM data for ascending and descending orbits. From these figures it can be concluded that the SMOS soil moisture is drier than the 5 cm in situ data across the different values of soil moisture. This can be explained by the SMOS penetration depth with respect to the depth of the installation of the in situ sensors. Nevertheless, the shape of the distribution function, describing the extreme and seasonal cycles, is well captured in most cases. The Niger site's Sahelian climate is well captured, with a high probability of low soil moisture values and a small number of extreme values. The differences between the L2SM and L3SM data are mainly observed for the Benin and LW sites. When comparing Figs. 13 and 14, small differences can be noted between ascending and descending orbits.
Data availability The main datasets can be accessed as follows: -MIR_CLF31A / D: SMOS-CATDS Level 3 1-day soil moisture maps for ascending (06:00 UTC) and descending (18:00 UTC) orbits version 300, link: ftp://ext-catds-cpdc@ftp. ifremer. fr/Land_products/ GRIDDED/L3SM/RE04/MIR_CLF31A/; -MIR_CDF3TA / D: SMOS-CATDS Level 3 1-day fixed-angle bin full-polarisation brightness temperatures maps for ascending (06:00 UTC) and descending (18H00) orbits version 310, link: ftp://ext-catds-cpdc@ftp. ifremer. fr/Land_products/ GRIDDED/L3SM/RE04/MIR_CLF31A/.
Conclusions The level 3 daily maps of soil moisture and brightness temperatures are presented in this paper. A multi-orbit soil moisture retrieval algorithm for SMOS data is used to obtain the soil moisture product. The main feature of the algorithm is the use of MO and of temporal autocorrelation of optical vegetation depth in the cost function. The algorithm is implemented operationally at CATDS. The processing chain delivers gridded products over the EASE 2. 0 grid at 25 km in netCDF format. The L3 angle-binned TB product is compared to SMAP brightness temperature maps at 40 •. The results show small differences in mean TB between the products for H/V polarisation and ascending and descending orbits. The SMAP product presents a wider coverage due to the on-board RFI filtering. The L3SM MO product is compared to the L2SM SO product.
The best improvements in algorithm performances are in terms of the number of successful retrievals observed over forested and RFI-prone areas. Also, the L3SM MO product shows, on average, wetter soil moisture retrievals than the L2SM SO. The comparison with local sites showed that the quality of the retrievals is comparable between L2SM SO and L3SM MO. This shows that the increase in the number of successful retrievals does not degrade quality, but rather comes at the expense of an increased time lag in product availability (6 h for L2SM SO versus 3. 5 to 7 days for L3SM MO). The SO and MO products show a slight dry bias except for the AMMA Benin site, which is smaller than the in situ data uncertainty (< 0. 04 m 3 m -3 ). More accurate auxiliary files like soil maps from SoilGrids (https://www.
Acknowledgements. The SMOS L3SM products were obtained from the  Centre Aval de Traitement des Données SMOS (CATDS) , operated for the " Centre National d'Etudes Spatiales" (CNES, France)  by  IFREMER (Brest, France). This study was supported by the  CNES "Terre, Océan ,  Surfaces Continentales , Atmosphère" program. The authors would like to thank the  USDA ARS Hydrology and Remote Sensing Laboratory ,  AMMA-CATCH  project for the in situ datasets. Edited by:  D. Carlson Reviewed  by:  M. Schwank  and one anonymous referee.
The ANTARES Neutrino Telescope can determine the arrival direction of a muonic High Energy Neutrino (HEN) with a precision well below 1 • above 1 TeV. The detection of such a HEN in coincidence with a Gravitational Wave (GW) event would then improve the localization of the GW source, facilitating the search for electromagnetic counterparts. The results of such targeted HEN searches for the 3 GW events (GW150914, GW151226, both confirmed signals, and LVT151012, an event candidate) detected during the Observation Run O1 of ADVANCED LIGO in 2015-2016 are presented. The principles of a sub-treshold analysis, which looks for time and space correlations between HEN detected by ANTARES or ICECUBE and GW candidates of low signalto-noise ratio detected by LIGO during O1 are presented. The specific procedure optimized to select HEN candidates in ANTARES data is emphasized.
HEN analysis for GW151226 and LVT151012 We searched for coincident neutrinos within ANTARES data by selecting up-going events. The search was performed with the most recent official offline data set, incorporating dedicated calibrations, in terms of positioning, timing and efficiency. This sample is dominated by background events from mis-reconstructed down-going atmospheric muons. It was optimized for each GW event individually so that one event that passes the search criteria and is located within the 90% GW probability contour would lead to a detection with a significance level of 3σ. For GW151226, a total of 1. 4 × 10 -2 atmospheric neutrino candidates are expected in the field of view within ±500 s,.
A characterisation of the different model uses (ranging from direct to indirect), which is shown to inform their utility (and vice versa) (Section 5). Our analysis draws primarily on systematic document analysis (e. g. , activity reports, scientific literature produced by both programs), formal semi-structured interviews with researchers and practitioners from both countries, and observations during science-practice engagement activities. This provided a rich data set for comparing PIREN-Seine in France and CRC for Water Sensitive Cities in Australia.
Document Analysis Document analysis focused on the work produced by the PIREN-Seine and the CRC for Water Sensitive Cities throughout the duration of each program, which included hundreds of peer-reviewed journal articles as well as grey literature such as periodic activity reports (over 700 reports from the PIREN-Seine and over 150 from the CRC for Water Sensitive Cities), synthesis documents and other communications. As a lot of the modelling in the Australian urban water sector also emerged from a long legacy of research and industry collaboration dating back to the 1990s, we were also cognisant of older documents prior to the commencement of the CRC for Water Sensitive Cities research program including those from its predecessors, the CRC for Catchment Hydrology, the CRC for Freshwater Ecology and the eWater CRC.
Pertinent documents were identified by searching different combinations of the following keywords: 'decision making'; 'exploratory modelling'; 'management'; 'model'; 'modelling'; 'planning'; 'policy'; 'strategic planning'; 'water sensitive cities' and 'water sensitive urban design' on each program's website in addition to major search engines (Scopus, Web of Science, Google Scholar). Keywords were selected and narrowed down from initial searches based on relevance to the respective research program and included the names of specific 'operational' or industry partners (practitioners) and known modelling tools in order to obtain information about their use and application in practice. Though this process was systematic, the permeable nature of the 'boundaries' between science and policy limited our ability to adequately define the models represented in this study.
Questions were adapted to individual participants according to their role and involvement in modelling activities, the program, or their position. Interviews were open-ended and lasted anywhere from 1 to 4 h with an average duration of 1. 5 h. Interviews were transcribed and coded according to the four themes listed above. Anecdotal observations were used as secondary data, which was collected throughout 2015-2017, during numerous meetings, seminars and conferences organised by the PIREN-Seine in France. This included two general assembly and annual planning meetings organised to reflect on the year's work and co-define upcoming program objectives. In Australia, anecdotal observation was limited to seminars and conferences organized by the CRC for Water Sensitive Cities from May to August 2017, which included one major national conference in Perth and two workshops.
The PIREN-Seine, France Established in 1989, the PIREN-Seine (PIREN) is an interdisciplinary research program in France comprised of 22 research teams and 140 researchers from a range of academic backgrounds. The majority are rooted in the fields of hydrology, biology, chemistry, or engineering, while a growing number of geographers, agronomists, political economists, political scientists and sociologists have become involved. The main types of actors in relation to modelling activities are represented in Table  1. Notable industry partners include the Syndicat Interdépartemental pour l'Assainissement de l'Agglomération Parisienne (SIAAP), a public institution responsible for wastewater treatment and sanitation in the Paris region and the Agence de l'Eau Seine-Normandie (AESN), a public institution responsible for the management of water resources in the Seine-Normandy watershed, both of whom, are heavily involved in modelling activities within the PIREN-Seine.
Since its creation, Seneque has undergone several revisions and has been applied to different situations in combination with other models  [52, 62, 65, 66]. Also considered to be an 'operational' model, Seneque has been appropriated directly by the AESN as a medium-to long-term planning tool, used for example, to evaluate the ecological state of the basin by amalgamating different datasets to construct 'snapshots' at different spatial and temporal scales. The model has since reverted back to a 'research' tool mostly due to a loss in internal expertise at the AESN (AESN representative, 8 June 2016). The most recent incarnation of the model, Pynuts, has allowed researchers more flexibility in terms of model development, to explore new research questions using updated technology without having to invest time and resources on interfacing.
STICS The model STICS has been developed by the Institut National de Recherche Agronimique (INRA) since 1996  [57]  in collaboration with large research and professional institutes  [58]. It was not developed in the context of the PIREN-Seine but is considered here as a PIREN support tool since it is often used to conduct research within the context of the program. STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) is an agronomic model that simulates crop growth, soil water and nitrogen balances driven by daily climatic data  [57, 58, 72]. Intended to simulate the evolution of water, carbon and nitrogen in the soil-plant system over one or more years successively  [57, 73] , STICS was designed and developed with the dual objective of calculating agronomic variables (e. g. , plant biomass, harvested yield, protein content of the grain, nitrogen balances of the crop) and environmental variables (e.
Other modules are still under development including a future climate module, which will draw from a database of future rainfall projections for major Australian cities and can be used independently or as input data for future climate scenarios  [97, 98]. An economic valuation module is also planned, to consider the likely willingness-to-pay of community members based on various improvements made to liveability and sustainability of the catchment. The WSC Toolkit is currently in closed 'beta-testing' mode, with its adoption slowly taking place in select municipalities across Australia. Much of its momentum is currently driven by the need for quick and easy microclimate assessment tools that enable local municipalities to formulate a business case for funding more WSUD and green infrastructure projects.
Objective and Expertise Empirical data suggests that the objective(s) of the program and the expertise of the individuals involved have a large influence on the scientific knowledge that is produced and subsequently, how that knowledge is used (if at all). On one hand, PIREN has a territorial focus with expertise on the Seine River basin, although some of the knowledge and tools have been applied to other basins (PIREN researcher, 2 May 2016). On the other, CRCWSC has an urban focus, which began in Australia in the early days of its Melbourne-based predecessor (the 'Cities as Water Supply Catchments' Project) and has now expanded abroad through the involvement of international partners. Although PIREN engages researchers from different disciplines, most have a background in natural sciences or engineering with a focus on water quality.
While most models serve an enlightenment function, the SIAAP also uses ProSe to support decisions (e. g. , when sizing infrastructure and implementing new projects) as well as negotiation support, since they are required to justify proposals to the regulating authority using ProSe (SIAAP representative, 27 June 2016). Despite having in-house capacity to run the model independently and contributing to model development and data collection, practitioners are not able to change the code and must turn to researchers for specific requests (PIREN-Seine researcher, 28 April 2016). In Australia, MUSIC is a similar example of a Direct+ use by industry partners. As it has become the industry standard, using MUSIC to support decisions and justify proposals, though not always required, is beneficial (CRCWSC researcher, 9 June 2017).
(2015)  considered ground-based PM 10 and CO measurements (matched to corresponding model data) within the fire region and about 1000 km downwind. However, such age-resolved measurement datasets have so far been available only from a relatively small number of case studies and field campaigns (e. g. ,  Yokelson et al. , 2009; Akagi et al. , 2012; Vakkari et al. , 2014)  and, as noted above, these data provide rather inconsistent evidence about the effects of aging on BB aerosol properties. The goal of this study is to investigate the feasibility of deriving the information on BB aerosol aging from satellite measurements of AOD and CO columns. To this end, we analyze the data retrieved from MODIS  (Remer et al. , 2005)  and IASI  (Clerbaux et al. , 2009)  measurements of a major biomass burning event that occurred in Siberia in summer 2012  (Konovalov et al.
The results of our analysis are presented in Sect. 3, which begins with a preliminary comparison of model and simulated data and continues with a presentation of our estimates and tests concerning the dynamics of BB aerosol aging. The results are summarized and discussed in Sect. 4. Concluding remarks are provided in Sect. 5. Konovalov et al. :  Probing into the aging dynamics of biomass burning aerosol 2 Data and method description 2. 1 Input datasets derived from satellite measurements I. B.
AOD measurements Our analysis is based on retrievals of AOD at 550 nm from MODIS measurements onboard the AQUA and TERRA satellites, which are positioned on sun-synchronous circular orbits. The ground path of AQUA crosses the Equator at 13:30 LST (on the ascending node); the Equator crossing time of TERRA is about 10:30 LST (on the descending node). The AOD retrievals provided nominally at 10 km × 10 km horizontal resolution were obtained as the Level-2 orbital granule data from the Level 1 and Atmosphere Archive and Distribution System (LAADS, 2016) ftp site (ftp://ladsweb. nascom. nasa. gov/allData/51/). The retrieval algorithm is described elsewhere  (Remer et al. , 2005; Levy et al. , 2007). Validation studies (e. g. ,  Levy et al. , 2010)  indicate that the multiplicative and additive errors of MODIS-retrieved AOD typically do not exceed ±15 % and ±0.
Previous studies (e. g. ,  Turquety et al. , 2009; Krol et al. , 2013; Konovalov et al. , 2014)  indicated that the IASI CO retrievals provide useful information on atmospheric CO originating from fires. The data were processed in the same way as in  Konovalov et al. (2014). In particular, the selection criterion based on the DOFS (the degree of freedom of the signal) values indicating sensitivity of the IASI measurements in the boundary layer  (George et al. , 2009)  was applied: only those data were used that featured a DOFS value exceeding 1. 7. The CO retrievals were projected at hourly resolution to the same grid as the AOD data.
We also did not require overlapping of pixels of AOD and CO measurements available for a given day, but only demanded that they fell into the same grid cell (otherwise, that grid cell was excluded from a combined dataset for a given day). Inconsistencies between the AOD and CO measurements are not expected to affect significantly the results and conclusions of our study, not only because we consider large-scale BB plumes covering multiple grid cells of our model, but mostly because CO measurements and simulations play only an auxiliary role in our analysis (as explained in Sect. 2. 4). Biases in the simulated values of CO columns and AOD are assumed to be due to inaccuracies in their background fractions (as the BB fractions are fitted to observations; see Sect. 2. 1. 3) and are evaluated in the same way as in  Konovalov et al.
In contrast, although the decreasing part of the dependence of γ a is quite pronounced, the uncertainties in our estimates of γ a preclude us from claiming that the decrease in γ a is real rather than, e. g. , being an artifact of errors in the assumed background fractions of τ o and ν o. Therefore, in the analysis and discussion that follow we will focus on the increasing part of the dependence of γ a on t a. Considerable differences between the ER estimates obtained with different aging tracers are an expected consequence of the fact that any grid cell always contains a mixture of aerosol fractions of different origins and ages, and so the same AOD (and CO) data can be attributed to different photochemical age bins, depending on the reactivity of the aging tracer. Indeed, the different fractions get a different weight in the photochemical age estimates obtained with different tracers.
6a , where p % was equal to 83 %). By definition, larger values of p % correspond to denser BB plumes: this is evident in the dependence (also shown in Fig. 7 ) of the mean near-surface concentration of PM 2. 5 in grid cells and days covered by a photochemical age bin in which the ER reaches a maximum value. The obtained dependence of the maximum ER values on p % demonstrates a clear upward trend that extends up to a value of p % of 86 %. The fact that the trend is "submerged" in the confidence intervals of individual estimates of the ER does not necessarily mean that the trend is insignificant, particularly because the uncertainties of the individual estimates are not independent (since the datasets selected for the different estimates were overlapping). Nonetheless, we cannot claim with confidence that the trend is "real" either (i.
e. , is not an artifact of our analysis), since our estimates of the ER can be biased to a different extent with different values of p %. Specifically, apart from the "vertical" uncertainty reflecting discrepancies between model and measurement data covered by a given photochemical bin, there can also be a "horizontal" uncertainty in our estimates due to mismatches between the photochemical ages of the actual BB aerosol and its simulated counterpart. The mismatches (or, in other words, uncertainties in our estimates of the photochemical age) could occur due to model transport errors, as well as due to uncertainties in modeled spatial-temporal distributions of BB aerosol and CO sources. It seems reasonable to expect that   6a ) as a function of the threshold percentile, p % , of the distributions of the BB fraction of the CO columns related to their background fraction (see Eq.
The discrepancies may be, e. g. , due to model errors in the injection height of BB emissions, but also due to probable underestimation of CO columns in the IASI retrievals close to emissions  (Turquety et al. , 2009). However, the test estimation performed with the same parameters as in Fig. 6a  but without using the CO data yields almost the same maximum increase (by a factor of 2. 0) of the ER as in the base case (see Fig. 6a ), although, as expected, the uncertainty of the ER estimates increases (by ∼ 40 % on the average). Moreover, when CO columns were processed instead of AOD values (again using Eq. 8), the corresponding estimates of γ a (see a purple line in Fig. 8a ) were not found to manifest any statistically significant deviations from unity (except for the ER estimate for the last bin, with t a larger than 26.
(  8 ); also shown are the mean IASI CO column amounts for corresponding bins; (b) the ER estimates obtained in the Monte Carlo experiments in which the measured AOD values were replaced with the synthetic data generated by means of random replacements of the simulated AOD and CO values in the horizontal domain (see experimental details in Sect. 3. 3); (c) the ER estimates derived from the two data subsets obtained by splitting the original dataset such that the first and second subsets included only odd and even days of the study period, respectively; note that the "odd days" estimates are depicted by using the upper abscissa axis, which is slightly shifted to improve readability; (d) estimates derived from the original MODIS and IASI data but using the data of the CHIMERE run in which the injection heights of BB emissions were doubled.
evaluated the "hit rate", h, defined as follows: h = n c n t , (12) where n c is the number of correct predictions (that is, the number of grid cells/days where both the model and measurements indicate that the contribution of fires to a CO column was noticeable), and n t is the total number of grid cell/days affected by fires according to the measurements. The hit rate was evaluated separately for each photochemical age bin using the original set of CO columns retrieved from the IASI satellite measurements and those simulated by our model. The values of h (see black crosses in Fig. 8b ) were then used to set up Monte Carlo experiments in which the values of AOD and CO columns from the measurements were replaced with synthetic data obtained by mixing the corresponding modeled values in the horizontal plane.
That is, the aerosol and CO data for a given grid cell (i, j ) were replaced with the data from another grid cell (k, l) (and vice versa). The differences between k and i or l and j were assigned as random values sampled from a Gaussian distribution N (0, σ s ), with the σ s value of 8. 0. The value of σ s was defined such that the hit rates calculated for each photochemical age bin with the mixed synthetic data (see black crosses in Fig. 8b ) were smaller than the corresponding hit rates calculated with the original data. The experiments were performed both without and with using CO columns for normalization of AOD values, that is, using the cost function defined by both Eqs. (  8 ) and (  9 ). The AOD values were calculated using the simulated data for the tracer T 0. The results of the corresponding Monte Carlo experiments are presented by solid and dashed lines in Fig.