dataesr/scientific-paragraphs-categorization huggingface dataset:
In the Lepsämänjoki DRN, the removal of the stations data from the training dataset does not impact the prediction of drying. However, in the Albarine and Genal DRNs some of the stations are located on intermittent reaches and their removal decreases the POD of drying events to 61% for the Albarine, and 77% for Genal. Crowdsourced data, which represents at most 1% of all observations collected in the DRNs, have a visible impact on the prediction of dryness, especially during the summer. For the Albarine, the mean annual maximum of dry river network decreases from 27 to 23% without the crowdsourced data in early September. In the Genal DRN, the uncertainty increases without the crowdsourced data, for example, in late July -early August, the fraction of dry river network ranges between 78 and 80% when the RF model is trained with all of the observed data, and ranges between 78 and 85% when the crowdsourced data is removed from the training dataset.
Observed flow state data for the modelling of flow intermittence The results of the RF model are highly dependent of the training dataset. This study highlights the challenges of obtaining observed flow state data to train or validate the models. To accurately represent flow intermittence along river networks, the observed data ideally need to be uniformly distributed both spatially and temporally, which can be difficult to achieve. Most studies focusing on the catchment scale collect observations from field campaigns (e. g. ,  Llanos-Paez et al. , 2023; Jaeger et al. , 2023; Van Meerveld et al. , 2019; Sefton et al. , 2019) , but such surveys generally do not allow rivers to be monitored over many years and are usually limited to portions of the river network as they can be very time-consuming.
Acknowledgements. This study was supported by the  H2020 European Research and Innovation  action Grant Agreement no. 869226  (DRYvER). We thank  Thibault Datry  and  Bertrand Launay  ( INRAE RiverLy ) for their expertise on the Albarine DRN,  Heikki Mykrä  and  Henna Snåre  ( SYKE ) for their expertise on the Lepsämänjoki DRN, and  Nuria Bonada ,  Maria Soria  ( University of Barcelona ),  Amaia Angula Rodeles  ( Universidad de Cantabria ) and  Nuria Cid  ( INRAE ) for their expertise on the Genal DRN. We also thank all the other members of  DRN Teams  of the DRYvER project for sharing local data and collecting flow intermittence observations in the DRNs.
2. 3. 1 Sensitivity to the size of the training sample First, the sensitivity of the RF model to the size of the training sample is tested by randomly selecting 75% of the flow state observations to train the RF model for each of the 20 runs. The RF model is then evaluated on the remaining 25%. For the 20 runs, the selection of the 75% of training data is based on a different random draw. This first test aims at evaluating the impact of using a reduced training dataset on the prediction of flow intermittence. It also aims at evaluating the error of the RF model on a validation sample. 2. 3. 2 Sensitivity to the type of flow state observed data As presented in Section 2. 1. 4, the collected flow state observation datasets used to train the RF model are heterogeneous in terms of spatial and temporal distributions of the observations, and representativity of different types of flow regimes.
The sensitivity of the RF model to each type of observed data (stations, field campaign, crowdsourced data, Google Earth, expertise) is evaluated by removing in turn each type of data from the training dataset, and then comparing the RF performances and the predicted flow intermittence patterns. The RF performances are evaluated on the whole dataset of flow state observations in order to compare the performance on the same validation dataset. The objective of this analysis is to assess the amount of useful information brought by each type of data. https://doi. org/10. 5194/egusphere-2023-1322 Preprint. Discussion started: 28 July 2023 c Author(s) 2023. CC BY 4. 0 License.
3. 5 Figure 11. 511 Figure11shows the impact of the size of the training sample on simulated seasonal pattern of drying in the DRNs. The RF model is either trained with 100% of the available observed data (configuration 0), or 75% of the observed data (configuration 1). For the Lepsämänjoki DRN there is no visible impact of reducing the training dataset on the predicted flow intermittence. In the Albarine and Genal DRNs, the results show that the uncertainty increases particularly during the winter season, when there are fewer observations. These results show that the RF model is more sensitive to the representativity of drying in the observed data recorded than in the amount of data itself. The Lepsämänjoki DRN has fewer observations and a poorer spatial and temporal coverage of the observed data than the Genal DRN, but the model is more robust in Lepsämänjoki than in Genal.
classification model, developed in this study allows to predict the daily state of flow (dry or flowing) at the reach scale along river networks. The results show that the models allow to successfully predict the main spatio-temporal patterns of drying in 3 contrasted European river networks. This study also discusses the difficulty of collecting flow intermittency data to train and validate random forest models. The results show that the combination of various sources of observed flow state data is essential to form a training dataset that is https://doi. org/10. 5194/egusphere-2023-1322 Preprint. Discussion started: 28 July 2023 c Author(s) 2023. CC BY 4. 0 License. representative of the actual spatio-temporal drying patterns in the drying river networks and to reduce the uncertainty of the prediction of flow intermittence.
A real-time alert issued by a high frequency (HF) Wellen radar (WERA) at Tofino on January 5, 2020 was identified as a possible meteotsunami because of the anomalous surface currents. Reanalysis of the HF radar data and consideration of associated meteorological data from Global Forecast System North West Pacific forecasts, the GOES-17 weather satellite and the NDBC buoy at La Pérouse, indicate that the anomaly in ocean currents was a combination of a wind setup at the coast over several preceding hours and an abrupt change in wind direction driven by the passage of a severe cold front. It is unlikely that the Proudman resonance condition between the wind speed and gravity wave phase velocity existed, except perhaps very close to the coast. The inclusion of the wind direction measurements by the HF radar will improve the real-time alerts at the coast by discriminating between tsunamis and wind-driven storm surge events.
The wind direction at the 10 km range starts to move from near northwards about 20 min before the start of the radar alert period and reaches eastwards during that period. Fig. 10(b ) demonstrates the varying precision of the direction estimate, contrasting with the consistency in precision of the log 10 (R) parameter in Fig. 10(a). Wind direction data are available from the WERA HF radar at individual grid points every 33 s with a time resolution of 133 s.
Experimental results are conducted on a labeled crop data-set (PASTIS) as well as a dense land cover data-set (MultiSenGE). Two ways of exploiting U-BARN pre-training are considered: either U-BARN weights are frozen or fine-tuned. The obtained results demonstrate that representations of SITS given by the frozen U-BARN are more efficient for land cover and crop classification than those of a supervisedtrained linear layer. Then, we observe that fine-tuning boosts U-BARN performances on MultiSenGE dataset. Additionally, we observe on PASTIS, in scenarios with scarce reference data, that the fine-tuning brings a significative performance gain compared to fully-supervised approaches. We also investigate the influence of the percentage of elements masked during pre-training on the quality of the SITS representation.
dumeur@univ-tlse3. fr, silvia. valero@cesbio. cnes. fr, jordi. inglada@cesbio. cnes. fr). This work is supported by the DeepChange project under the grant agreement ANR-DeepChange CE23 multi-variate, and irregularly sampled. Data gaps induced by cloud contamination and data quality issues lead to a significant lack of information between optical valid acquisitions. In addition, undetected clouds can produce misleading results in land surface analysis. Besides the challenges associated with complex satellite data, DL methodologies in large-scale remote sensing applications face a major bottleneck. The limited availability and quality of the labeled data restrain the training of deep complex models. Over the past few years, selfsupervised learning (SSL) has emerged as a potential solution to mitigate or even eliminate the need for costly collection of labeled data-sets  [6].
First, the masking strategy is different, and the effect of the masking rate is studied. Then, in opposition to  [18] , our pre-training dataset contains cloudy images. Eventually, our unlabeled and labeled datasets have a great geographical variability which demonstrate the scalability of our approach. The general framework presented in this work is summarized in Fig. 1. On the left, we show the main blocks describing the backbone network of U-BARN, which is described in Section III-A. On the right, the use of pre-trained U-BARN in the semantic segmentation downstream task is illustrated. In a nutshell, the main contributions of this paper are: • The construction of a novel spatio-temporal architecture for SITS, named U-BARN • The self-supervised training of U-BARN with a generative pretext task • The assessment of the self-supervised training strategy on two different downstream tasks To evaluate the performance of the proposed U-BARN architecture and the self-supervised training strategy, we con- duct several experiments using the semantic segmentation downstream tasks defined by the labeled PASTIS data-set  [19]  and MultiSengGE  [20].
Ben-Ali et al. [20]  inferred the behavior of turbulence modeling in the opensource platform OpenFOAM using experimental pressure measurements on the surface of the building. They developed an adjoint method to perform such optimization. Their results showed that the accuracy of the RANS model could be improved. However, this improvement was associated with the development of advanced numerical techniques, including modification of the dynamic equations for the turbulence physical quantities (turbulent kinetic energy , and energy dissipation rate 𝜀 or specific energy dissipation rate 𝜔). Optimizing global model constants, which are accessible to a standard user of the code, would not produce significant improvements in this case. On the other hand, Zhao et al. [21]  used an EnKF code to infer the behavior of such model constants, always using the software OpenFOAM.
In order to perform the optimization, they employed time-averaged velocity measurements available on 230 sensors sparsely distributed in the flow volume around the building. In this case, they obtained an efficient calibration of the model constants. However, such a global distribution of data is difficult to obtain in realistic conditions, where sensors are mostly clustered in local regions where measurements can be efficiently performed. Another important aspect to consider is the influence of sensor placement on the performance of the data assimilation algorithm. In the context of variational data assimilation applied to the unsteady flows past a rotationally oscillating cylinder, Mons et al. [23]  addressed this challenge known as sensor placement problem. They proposed a first-order adjoint-based procedure to maximize the sensitivity of the observations with respect to changes in initial and boundary conditions.
Numerical code: OpenFOAM Numerical simulations in this work are performed using a C++ open-source library known as OpenFOAM  [26]. This library includes a number of solvers based on Finite Volumes (FV) discretization  [27] , as well as a number of utilities for preprocessing, postprocessing, and data manipulation. Owing to the free license and the very large number of modules available, allowing for extended multi-physics analyses, this code has been extensively used in the literature for research work in fluid mechanics  [20, 28, 29]. For this work, the FV numerical discretization is performed for the RANS Navier-Stokes equations for stationary, incompressible flows and Newtonian fluids  [1] : 𝑢 𝑗 𝜕𝑢 𝑖 𝜕𝑥 𝑗 = - 𝜕𝑝 𝜕𝑥 𝑖 + 𝜕𝜏 𝑖𝑗 𝜕𝑥 𝑗 - 𝜕𝜏 𝑇 𝑖𝑗 𝜕𝑥 𝑗 𝑖 = 1, 2, 3 (1) ∇ 2 𝑝 = - 𝜕𝑢 𝑗 𝜕𝑥 𝑖 𝜕𝑢 𝑗 𝜕𝑥 𝑖 - 𝜕 𝜕𝑥 𝑖 ( 𝜕𝜏 𝑇 𝑖𝑗 𝜕𝑥 𝑗 ) (2) where Eq.
Observation: experimental data obtained in wind tunnels The sampled data is heterogeneous as different sensors are used to capture features of the velocity field on the roof and pressure measurements on the surface of the building. The velocities above the roof are mainly measured at two different heights (𝑧∕𝐻 = 1. 1, 1. 15) at locations marked with black circles in Fig. 2(d ). In addition, above two locations at the roof, including the center of the roof, Fig. 2(d) , nine heights are considered with the spacing of (𝑧 -𝐻)∕𝐻 = 0. 025. The measurements are performed using a hot-wire anemometer, which consists of two cross wires allowing for measurement of both streamwise and vertical velocity components. All velocity data are sampled with the frequency of 2000 Hz. In addition to the velocity measurements, the surface pressure is also sampled at different locations, as shown in Fig.
2 (b) and (c) marked with light gray circles. Surface pressures are acquired with a sampling frequency of 1000 Hz using a multi-channel simultaneous scanning measurement system. The tubing effects are numerically compensated  [37]. More details about the wind tunnel experimentation and the analysis of the flow around high-rise buildings, with a special focus on above the roof, are presented in  [19]. In this analysis, experimental data is used to improve the predictive capabilities of two stationary RANS models (standard  -𝜀 and  -𝜔 SST). Therefore, the time series available for the velocity components and the pressure have been averaged in time.
CONES: Coupling OpenFOAM with numerical environments CONES (Coupling OpenFOAM with Numerical EnvironmentS) is a C++ library designed to couple the CFD software OpenFOAM with any other kind of open-source code. It is currently employed to carry out sequential DA techniques and, more specifically, advanced datadriven methods based on the EnKF. The communications between the EnKF-based code and OpenFOAM are performed by CWIPI (Coupling With Interpolation Parallel Interface)  [51] , which is an open-source code coupler for massively parallel multi-physics/multi-components applications and dynamic algorithms. The main favorable features of CONES in performing DA with OpenFOAM are the following: • It is not needed to modify the installation of OpenFOAM but only compile user-made functions. • The coupling between the CFD code, the observation, and the DA algorithm is performed preserving the original structure of the existing CFD solvers.
Every CONES-related function is contained in a Pstream (Part of OpenFOAM) modified library, hence, data exchange is done at the end of the solver loop by calling specific functions, and the calculation loop remains unmodified. • Simulations and DA are run simultaneously online. The coupling is intrusive from the solver's point of view. It is done in such a way that the analysis phases can be performed on the fly by pausing the CFD calculation. Therefore, there is no need to stop and restart CFD calculations, which can require an important amount of computational resources. • It is very efficient to exchange information about the physical state and the mesh (arrays of millions of elements can be sent and received simultaneously and rapidly). • Direct HPC communications are established between multiple processors, which handle partitions of the numerical simulations and the DA processes.
The coupler CWIPI developed by ONERA and CERFACS has been chosen due to its powerful management of fully parallel data exchanges based on distributed mesh definition and its ability to interpolate between non-coincident meshes (very useful for some advanced tools based on the EnKF, like the MGEnKF  [44] ). Most of its uses are related to gas turbine designs  [52, 53] , but it has also recently been employed in the field of aeroacoustics with OpenFOAM  [54]. CWIPI communication protocol is based on the MPI library. Thus, MPI and CWIPI environments must be initialized within the codes. This will allow the use of CWIPI primitives to exchange information between two codes. In Fig. 4  direct communications between two codes through CWIPI and some of the main primitives are illustrated. In this work, CONES couples the solver SimpleFOAM, which is designed to simulate flows using RANS, with a sequential DA library developed by the team.
The structure of a single run is exemplified in Fig. 5. The MPI communications and the coupler CWIPI are initialized in both codes (in OpenFOAM and in the DA library). Despite the fact that applications of EnKF-based tools are tied with the time advancement of the solution, the application to stationary flows is straightforward. An analysis virtual time window is fixed in terms of the number of iterative steps of the code. Once that number of time steps is performed simultaneously by the 𝑁 𝑒 ensemble members (CFD runs), they send their information to the EnKF code and wait online for the updated flow field/parametric description. Currently, the information exchanged is the velocity field 𝐮 𝑓 ∕𝑎 and the parameters of the model 𝜃 𝑓 ∕𝑎. Hence, the state matrix, composed of as many state vectors as members (CFD simulations) in the ensemble, is the one expressed in Eq.
Thus, the DA code receives information from the model and the sensors, producing an updated set of states and parameters (𝐮 𝑎 , 𝜃 𝑎 ), which are sent back to the OpenFOAM simulations. The pressure 𝑝 is updated for each ensemble member via a Poisson equation, and this complete set of data is used to start a new set of iterative steps. Once the convergence of the model parameters complies with a threshold set by the user, the coupling is deleted, and both MPI and CWIPI environments are finalized.
[11]. This ensures a sufficiently large initial distribution of the parameters so that the EnKF can successfully target an optimized configuration. Details about the choice for 𝜎 𝑁 for each model constant are provided in Appendix. The number of ensemble members 𝑁 𝑒 = 40 is chosen considering other works in the literature relying on CFD for the model part of the EnKF  [44, 56, 57]. The observation is obtained from time-averaged data from a total of 118 sensors. Among these, 90 sensors are pressure taps, and 28 sensors are hot wires measuring two components of the velocity field, the streamwise velocity 𝑢 𝑥 and vertical velocity 𝑢 𝑧. This adds up to 146 time-averaged observation values. The data is loaded at the beginning of the first analysis phase in the following format: change throughout the calculation, it is stored in RAM and directly used at each analysis phase.
Conclusions The newly developed platform CONES has been used to perform a data-driven investigation of the flow around a high-rise building. More precisely, heterogeneous experimental samples, in the form of data from pressure taps and hot wires, have been integrated with RANS CFD runs, performed using the open-source code OpenFOAM. The coupling has been performed using techniques based on the Ensemble Kalman The results have shown that a global improvement has been observed for the physical quantities of investigation, and the results obtained with the different DA strategies are equivalent. For this last point, physical and covariance localization, which have been compared for the  -𝜀 model, appear to be effective for the study of complex flows. The reduction of degrees of freedom of the DA problem has not affected the quality of the results, while globally reducing the time needed for the data-driven procedures.
Acknowledgments Our research activities are supported by the funding of the  French Agence Nationale de la Recherche (ANR)  through projects  PRC 2020   ALEKCIA  and  JCJC 2021 IWP-IBM-DA. This work was granted access to the HPC resources of GENCI in the framework of allocation A12 for project  A0122A01741  on the IRENE supercomputer (TGCC). Florent Duchaine  and  Miguel Ángel Moratilla-Vega  are warmly acknowledged for the help provided during the early stages of the development of CONES. The co-author A. S. G. would like to acknowledge the support of the '' Fonds National de la Recherche, Luxembourg'' (FNR)  for funding the  CORE Junior  project  DATA4WIND -''Data-Driven Approach for Urban Wind Energy Harvesting' ',  C19/SR/13639741.
WWA webpage [https://www. worldweatherattribution. org], online, accessed 2023-10-15. Here we show that this method can also be used to compare the rarity of different events, for example, two heatwaves of different spatio-temporal scales occurring in different years and at different locations. It can therefore serve both meteorological services to create inventories of historical events, and the event attribution community to address the issue of selection bias. Ultimately, we provide objective selections of historical heatwaves and cold waves at a national level (France) and at the global level, which constitute new information on observed extremes and can serve as databases for the validation of climate models or statistical methods.
Scanning worldwide To extend the scanning procedure to the global scale, we use daily-mean 2-meter temperatures provided by the ERA5 reanalysis over the 1959-2022 period  (Hersbach et al. 2020 ). The advantage of ERA5 is that it is a physically consistent dataset globally (model simulation) with a sufficiently fine resolution for our purpose and without missing values, which is particularly convenient for the calculation of annual maxima or minima. The main disadvantage is that it is not temporally homogenized (assimilated observations evolve in time), which can cause problems for both our detrending and 1 calculations. Nevertheless, we have verified that the main results of this section, i. e. the selections of extremes, do not suffer from obvious heterogeneity problems. To our knowledge, there exists no observational dataset of global daily temperature with time homogeneity, long history, and no missing values.
Lastly, exploring extreme events in a generalized way over so many years and regions requires a high quality data set of temperature observations with temporal homogenization. Here we use ERA5 which performs well over many regions where the data assimilation and the numerical model are efficient. For France we have verified that ERA5 and the Météo-France thermal index are very close. However there are some regions where one may question the data quality (Appendix B and Figure  S4 ) as previously noted by  Thompson et al. (2022)  in their ranking of heatwaves. Repeating the scanning exercise with other datasets could allow to quantify how observational uncertainties reverberate in our results. Overall, we show that 1 is an appropriate metric to identify the most extreme weather events in recent history.
Acknowledgments. The authors are grateful to  M. Wehner  ( LBNL ) and two anonymous reviewers for their careful proofreading. They thank  Y. Robin  and  M. Schneider  ( Météo-France ) for providing the France thermal index and the associated selections of events and  D. Stone  ( National Institute of Water & Atmospheric Research ) for the WRAF regions files. They acknowledge the ERA5 dataset provided by the ECMWF and climate modeling groups involved in CMIP6 for producing and making available their simulations. J. C. particularly thanks  S. Qasmi  and  R. Samacoïts  ( Météo-France ) for their great support on spatial and interactive visualization with R and the shiny package (https://cran. r-project. org) and  M. Goar  ( Le Monde ).