dataesr/scientific-paragraphs-categorization huggingface dataset:
(2013) , and their temporal transferability has been discussed in recent studies for past or future climates  (Dayon et al. , 2015; Caillouet et al. , 2016). The method requires two different archives. The first is a meteorological archive describing the state of the atmosphere at a synoptic scale, such as reanalysis datasets, at different hours of the day. The second is an archive of the target variable to be predicted, which is usually standard daily precipitation totals from 06:00 to 06:00 UTC the following day either at a target station or integrated over a target catchment. Obviously, the period to be used is limited to the smallest period common to the two archives. Ruosteenoja (1988)  and  Van Den Dool (1994)  analysed the influence of the length of the meteorological archive on the quality of the analogy.
, 2016; Caillouet et al. , 2016). AMs can also be combined with other methods (e. g. Chardon et al. , 2017). Another possibility is to use a global optimisation technique, such as genetic algorithms, in order to better optimise the method and to add new parameters  (Horton et al. , 2017a, b). However, the MTW technique is not in competition with other approaches and can be combined with them. The outline of this paper is as follows. Section 2 presents the context of the study as well as the data and methods, including the proposed MTW technique. The benefits of introducing an MTW are assessed first regarding the improvement of the analogue date selection (Sect. 3. 1) and then in terms of precipitation prediction skill (Sect. 3. 2). Finally, the results are discussed in Sect. 4, and the conclusions are found in Sect.
Study area and data The study area is the upper Rhône catchment in Switzerland. The precipitation time series were obtained from six automatic weather stations, Ulrichen, Zermatt, Visp, Montana, Sion, and Aigle (Fig. 1 ), that are subject to various meteorological influences  (Horton et al. , 2012). The data were available at an hourly time step for 29 years  and were also obtained at a standard daily time step of 06:00 to 06:00 UTC the following day for 50 years. Due to the low density of weather stations with high temporal resolution and long archives, no spatially aggregated rainfall was processed. Synoptic-scale variables used as predictors were extracted from two of the most recent global reanalysis datasets: the European Centre for Medium-Range Weather Forecasts 20th century reanalysis (ERA-20C)  (Poli et al.
, 2016) , with 3 h temporal resolution and a spatial resolution of 1 • , and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), from the National Aeronautics and Space Administration (NASA), with a 6 h temporal resolution and a spatial resolution of 0. 625-0. 5 •. MERRA-2 is an update for the first MERRA reanalysis  (Rienecker et al. , 2011). The study was originally performed on the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis 1 dataset  (Kalnay et al. , 1996) ; the conclusions were similar. ERA-20C is built by assimilating only surface observations and is thus available for a long period. On the contrary, MERRA-2 is built by assimilating observations at higher levels and from more sensors including satellite data, but it is more limited in time  (1980-present).
It is of interest to assess the relevance of an MTW with these two datasets because they represent different types of products. The specific advantages of ERA-20C are that it allows for testing an MTW with a 3 h time step, and it covers a long period. On the contrary, MERRA-2 has a higher spatial resolution and can be expected to be more accurate at higher levels of the atmosphere. The variables extracted from these datasets were geopotential heights at 500 hPa (Z500) and 1000 hPa (Z1000), the total precipitable water (TPW), and the relative humidity at 850 hPa (RH850).
The differences are processed separately in both directions. With smaller S 1 values, there is greater similarity in the pressure fields. The predictor variables extracted from reanalysis datasets are considered at different hours of the day. Based on Bontron and  Obled (2005) , geopotential heights are compared at 1000 hPa (Z1000) at 12:00 UTC and at 500 hPa (Z500) at 24:00 UTC. The time of the day for which the predictors are selected has a significant influence  (Bontron, 2004). Then, N 1 dates with the lowest values of S 1 are considered as analogues to the target day, where N 1 needs to be calibrated (Sect. 2. 3). Finally, the daily observed precipitation amount of the corresponding dates provide the empirical conditional distribution considered as the probabilistic prediction for the target day.
, 2013; Ben Daoud et al. , 2016). The AtmoSwing optimiser software  (Horton, 2017)  was used to perform the calibration. The analogy levels (e. g. the atmospheric circulation or moisture index) are calibrated sequentially. The procedure, as defined by  Bontron (2004) , is described below. 1. For every level of analogy, the following steps are taken: a. First, the most skilled unitary cell of the predictor data is sought over a large domain. Every point or cell of the full domain is jointly assessed on the predictors of the current level of analogy. b. From this most skilled point, the spatial window is expanded by successive iterations in the direction of greater performance gain. The spatial window increases until no improvement is reached. c. The number of analogue situations N 1 , which was initially set to an arbitrary value, is then reconsidered and optimised for the current level of analogy.
More advanced techniques, such as those using genetic algorithms  (Horton et al. , 2017a, b) , exist but are beyond the scope of the present study. The 29-year hourly precipitation dataset was divided into a calibration period (CP) and a validation period (VP) in order to assess the robustness of the proposed improvements on independent data. The selection of the VP was evenly distributed over the entire series  (Ben Daoud, 2010)  to reduce potential biases related to trends linked to climate change or to evolution in the measurement techniques. Thus, 1 out of every 5 years was selected for validation, which represents a total of 6 years for the VP and 23 years for the CP. The continuous ranked probability score  (CRPS; Brown, 1974; Matheson and Winkler, 1976; Hersbach, 2000)  is often employed to assess the performance of AMs (e.
The moving time window approach The MTW technique is used to find better analogue situations at different hours of the day rather than comparing the predictors at the same fixed hours. The target situation (the day to predict) is still the same as that in the conventional approach: a daily precipitation total between 06:00 and 06:00 UTC the following day characterised by predictors, which in this case are Z1000 and Z500 at 12:00 and 24:00 UTC, respectively. The difference is that candidate situations are considered not only at the same time (12:00 and 24:00 UTC) but also at other hours by allowing a time shift, according to the temporal resolution of the predictor (Fig. 2 ). The ERA-20C dataset was used here to test an MTW with 12, 6, or 3 h time steps, which are referred to as 12 h MTW, 6 h MTW, and 3 h MTW, respectively.
Results The considered AMs were applied to both ERA-20C and MERRA-2 datasets. Most of the results are presented for the ERA-20C dataset because it allows for testing a 3 h MTW, although similar conclusions can be found by using MERRA-2. The impact of an MTW approach was assessed on the two bases given below. -Original parameters: the AMs were initially calibrated for each station based on the conventional fixed 24 h approach. Then, the MTW was introduced into the AMs, but the parameters (spatial windows and number of analogues) were not reassessed. The analogy was then identical, except for the fact that the MTW had more candidates at disposal. -Recalibrated: the parameters of the AMs were reassessed after the introduction of the MTW at different time steps. Indeed, one can assume that the introduction of the MTW might change the optimum value of some parameters (different spatial windows and number of analogues).
Impact of the MTW on the precipitation prediction The changes in the performance score (CRPS, Eq. 3) of the precipitation prediction are provided in Figs. 9 and 10 for the 2Z and 2Z-2MI methods, respectively. The AMs were recalibrated on the CP and were assessed on the independent VP. The score was processed for each station and both reanalysis datasets. For ERA-20C, the 12 h MTW, 6 h MTW, and 3 h MTW could be assessed, whereas only the two first could be processed with the MERRA-2 dataset. The MTW did improve the precipitation prediction because the CRPS globally decreased for all stations, both for the CP and the VP. The VP showed greater variability, which is likely related to its shorter period of 6 years. The prediction skill for the CP was almost always improved further by reducing the time step of the MTW but not of the same magnitude for every time step.
For ERA-20C, the magnitude of the improvement of 2Z for the CP was relatively reduced when using a 3 h MTW instead of a 6 h MTW, and the variability for the VP increased. However, as shown in Fig. 4 , the selection of analogue dates was improved relatively equally for every reduction of the MTW time step. A possible reason is that the ERA-20C dataset was built by assimilating only surface observations, whereas the AMs rely on the geopotential height at 500 hPa. Thus, the timing of the atmospheric circulation at higher pressure levels might not be completely in phase with the actual perturbation systems which caused the observed precipitation. The MTW with the MERRA-2 dataset and the 2Z method also showed a certain slope break after the 12 h MTW that could not be explained and was less important for the 2Z-2MI method.
However, the 12 h MTW resulted in important variability for the 2Z-2MI method and the MERRA-2 dataset on the VP. The introduction of an MTW was previously found to show greater improvement of the S 1 criterion for days with higher precipitation values (Sect. 3. 1. 1). The impact of the MTW was then assessed in terms of changes in prediction performance per precipitation class for the target day. Figure 11 synthesises these differences for the Ulrichen station by using the original parameters with the MTW. After the introduction of the MTW, the performance score was generally further improved with reduced CRPS for days with higher precipitation than for non-rainy days and small precipitation values. This can be expected, as the CRPS values are higher when the precipitation amount to be predicted is higher.
Inflation of the archive Because sub-daily precipitation time series are often available over a shorter period than daily, a reduction of the overall archive length at disposal is necessary. This usually has a negative consequence on the skill of precipitation prediction. The role of the archive length was assessed on the ERA-20C dataset and is presented in Fig. 14  for both 2Z and 2Z-2MI methods using the conventional approach of fixed 24 h windows. As expected, the performance of the method globally increased with the length of the archive. The best performances were found with a 44-year archive, which corresponds to the period 1961-2010 with the 6 years of independent VP subtracted. This addition of 21 years of archive to the original 23 years resulted in an improvement of both 2Z and 2Z-2MI methods that was of the same order of magnitude as the introduction of a 6 h MTW (Figs.
Many reanalysis datasets begin in the 1980s due to availability of satellite data and are thus more accurate than reanalysis datasets based on conventional data only; however, they provide shorter archives. When using such an archive, the MTW approach shows a certain potential for enriching the pool of possible analogues. Moreover, in a transient climate, the eventual non-stationarity of the link between predictors and precipitation might discard the relevance of analogues from the distant past and can increase the relevance of using a more recent and shorter archive rather than a long one. In such cases, the archive inflation brought by the MTW is also relevant. Archive length -4 -3 -2 -1 0 Change in CRPS [%] Archive length -4 -3 -2 -1 0 Archive length -4 -3 -2 -1 0 Change in CRPS [%] Archive length -4 -3 -2 -1.
Proportional parts of the original daily time series were allocated into a new moving average of 24 h totals (e. g. a 00:00-24:00 UTC total would be made of one-fourth of the standard precipitation of day 1 and three-fourths of day 2). The second approach aimed at obtaining data that are closer to the chronology of the actual precipitation by relying on informative proxy variables during the reconstruction procedure. Data from NCEP/NCAR reanalysis 1 were used for this purpose. Precipitable water and relative humidity at 1000, 925, or 850 hPa were assessed at the four points surrounding the study area and at different times of the day. Precipitation time series from the reanalysis were not considered as potential proxy due to the presence of numerous zeros, which may not always match the prediction; this can lead to an undefined temporal repartition.
The best proxy variable, precipitable water, was identified through correlation analyses with the 6 h precipitation time series on the period 1982-2007 (on non-zero precipitation values). Once the best proxy selected, its temporal profile was used to disaggregate the daily precipitation time series. These two simple (and rather crude) methods did not result in valuable outputs (results not shown). Indeed, the performance improvement brought by the MTW was lost due to the poor quality of the reconstructed precipitation archives. The second method which relied on a proxy variable performed slightly better than the proportional distribution method but not well enough to obtain a relevant time series. Another reanalysis dataset with more accurate moisture variables could produce better proxies.
Conclusions The AMs are most often based on a daily time step due to the availability of long precipitation archives. However, it is unlikely that two analogue synoptic situations which evolve relatively quickly would match optimally at the same hour of the day. It is probable that better matches may be found at a different hour, which can change the selection of the analogue dates. As shown in previous research  (Finet et al. , 2008) , the introduction of an MTW enables better analogue situations in terms of atmospheric circulation to be obtained. Using recent reanalysis datasets, MTWs with 12, 6, and 3 h time steps were assessed in the present study. Improvement in the S 1 criterion values increased with the rank of the analogue due to the accumulation of better analogues within the predicted distributions.
A 3 h MTW provided 8 times more candidate situations, although they were not fully independent. Because the MTW provides additional situations to the pool of possible analogues, it can be considered as an inflation of the archive, which can be interesting in several applications. For example, the most accurate reanalysis datasets are limited to the satellite era starting in the 1980s and thus provide shorter archives than reanalysis datasets based on conventional data only. Or, in the context of a transient climate, where the eventual non-stationarity of the link between predictors and precipitation might discard the relevance of analogues from the distant past, using the MTW approach allows relying on a more recent and shorter archive rather than a long one. It is desirable for the AM to have the biggest pool of candidate analogues available.
Finally, some other predictands are point observations and not temporal integrations, such as the occurrence of hail or extreme wind gusts, which make them easier to use with the MTW, as no disaggregation is needed. The introduction of the MTW improved the selection of synoptic analogues. Regardless of the availability of precipitation series at a sub-daily time step, the MTW can be used to provide a better diagnostic of the weather situation of the target date. It can be used, for example, to identify analogue dates related to extreme weather conditions in historical databases. The MTW technique is simple and easily applicable. Therefore, it should be considered for several applications in different contexts, such as operational forecasting or climaterelated studies.
Acknowledgements. The authors thank the  Swiss Federal Office for Environment (FOEV) , the  Roads and Water Courses Service, Energy and Water Power Service of the Wallis Canton , and the  Water, Land , and  Sanitation Service of the Vaud Canton  which financed the  MINERVE (Modélisation des Intempéries de Nature Extrême des Rivières Valaisannes et de leurs Effets)  project which started this research. Thanks are also extended to  Dominique Bérod  for his support. The fruitful collaboration with the  Laboratoire d'Etude des Transferts en Hydrologie et Environnement of the Grenoble Institute of Technology (G-INP ) was made possible by the  Herbette Foundation. NCEP reanalysis data were obtained from the website of the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , at http://www. esrl. noaa. gov/psd/.
The MERRA-2 was obtained from the  Goddard Earth Sciences Data and Information Services Center, Greenbelt, Maryland , from their website at http://disc. sci. gsfc. nasa. gov/mdisc. The ERA-20C data was obtained from the  ECMWF  data server at http://apps. ecmwf. int/datasets/. Precipitation time series were provided by  MeteoSwiss. The authors would also like to acknowledge the work of anonymous reviewers and the editor who contributed to improving the manuscript. Edited by: Jan Seibert Reviewed by:  Thomas M. Hamill  and four anonymous referees.
Data availability. The MERRA-2 reanalysis can be freely downloaded from the Goddard Earth Sciences Data and Information Services Center website at http://disc. sci. gsfc. nasa. gov/mdisc. The ERA-20C reanalysis can be freely downloaded from the ECMWF data server at http://apps. ecmwf. int/datasets/. Precipitation time series were provided by MeteoSwiss and can be requested for research. The AtmoSwing software v1. 5. 0 is open source and can be found on Github through Zenodo: http://doi. org/10. 5281/zenodo. 821559 (Horton, 2017). Additional data (i. e. , parameters files, scripts, input data, and all results) can be found at Mendeley Data: http://dx. doi. org/10. 17632/j3hckp2654. 2 (Horton et al. , 2017c).
So-called 2-phase designs  (5)  allow an efficient choice of such subsets as was repeatedly shown from a theoretical as well as applied point of view  (6) (7) (8). Briefly, in 2-phase studies, the full dataset (the phase-1 dataset) is stratified on the case-control status and exposure estimates available at this stage. Within these strata, possibly unequal fractions of cases and controls are selected for further data collection (phase-2 dataset). In the present case, this second data collection is a detailed expert assessment. The statistical analysis makes use of these sampling fractions and thus uses not only the phase-2 data but also the phase-1 information, thereby adding additional power, see for example  (9). Moreover, this design allows oversampling of the most informative subjects, ie, those subjects with the rarest disease-exposure combinations.
Phase-1 case-control study The study, which is the basis of our paper and which constitutes phase 1 of our 2PCC, is a population-based lung cancer case-control study in Northeast France. Details of the data collection and the population have been presented elsewhere  (10). The ethics committee of the French data protection agency approved the study. Briefly, all the hospitals located in this area were contacted and agreed to declare their incident lung cancer cases. Cases were eligible if: (i) male and between 40-80 years old, (ii) resident in the study region, (iii) lung cancer was histologically confirmed, and (iv) written statements of informed consent were provided. Of 548 identified lung cancer cases, 246 were interviewed between 2006-2010. Eight hundred male controls, aged 40-80 years, stratified on 10-year age classes and broad socioeconomic class and selected by a random digit dialing procedure in the study area, agreed to be contacted.
Statistical analysis The statistical analysis was based on logistic regression adapted to 2-phase data. We chose the maximum likelihood approach as it is the more powerful. An alternative would have been the weighted likelihood approach, which is less sensitive to misclassification. Basically, these methods consist of modelling the detailed phase-2 data in a logistic regression weighted by stratum-specific ratios of numbers of subjects in phases 1 and 2. When using maximum likelihood, these weights are adjusted according to the model used  (13). We used the SAS/ IML® package  (14). We also developed attributable fraction computations adapted to 2-phase studies, extending work by others  (15, 16) , in order to be able to estimate within this design the overall public health burden due to these occupational exposures.
Mathematical details and a freely available add-on to the above-mentioned SAS software are available at http://tinyurl. com/schill-af2p. The logistic regression model included as independent variables the confounders selected in the prior analysis of the full phase-1 data, with the exception of wine drinking whose borderline protective effect was not considered worth keeping. In order to assess the associations with occupational exposures, we included all the expert-based quantitative phase-2 exposure scores (asbestos, manmade mineral fibers, PAH, crystalline silica, mild steel welding fumes, stainless steel welding fumes, iron mining, painting, strong acids, and DME) and performed a backward selection procedure, keeping all the exposure variables with a P-value for inclusion of <10%. For the quantitative exposure variables thus selected, we then fitted a model including the indicator variables of the exposure tertiles compared to the non-exposed in order to check for non-monotonous patterns.
Simulations In order to document the added power due to the 2PCC design as compared to the standard analysis of a random Wild et al subsample, we sampled 1000 times the phase-2 exposure for all subjects not belonging to phase 2: separately for cases and controls, we assigned the expert-assessed exposure of a phase-2 subject randomly chosen from the same stratum to each subject not in phase 2. Firstly, from each such completed phase-1 dataset, we randomly drew 215 cases, and 269 controls were analyzed using standard logistic regression for comparison with the results in table  3. Secondly, in order to document the power of the 2PCC design with less subjects in phase 2, we sampled 1000 times five cases and controls (or all subjects if <5 had been subjected to an expertise) from each stratum of phase 2, that is 105 controls and 100 cases, and analyzed them using 2P logistic regression.
Results Table  2  shows that the age-structure of cases and controls in our original data was similar in phases 1 and 2. On the other hand, the number of smokers and the number of exposed subjects was by design overrepresented in phase-2 controls. Among cases there were few differences between phases 1 and 2. A second striking feature is that the experts identified more subjects exposed to PAH and much fewer for silica than the algorithm. However, in the phase-2 dataset, the algorithm-based exposure indices were all higher than the corresponding expert-based estimates. Table  3  provides the results of the 2-phase analysis. When running the backwards selection procedure, only asbestos, PAH, crystalline silica, and DME remained in the model (data not shown). Statistically significant dose-response slopes were observed for asbestos, crystalline silica, and DME and borderline for PAH adjusting for quantitative smoking and time since cessation, age, and familial history of lung cancer by age of onset.