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178 results on '"Philippe Naveau"'

1. Strong Linkage Between Observed Daily Precipitation Extremes and Anthropogenic Emissions Across the Contiguous United States

Author: J. S. Nanditha, Gabriele Villarini, Hanbeen Kim, and Philippe Naveau
Subjects: climate attribution, extreme events, ExtGPD, extreme value statistics, climate change, extreme precipitation, Geophysics. Cosmic physics, QC801-809
Abstract: Abstract The results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS.
Published: 2024
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2. A spatially adaptive multi-resolution generative algorithm: Application to simulating flood wave propagation

Author: Julie Carreau and Philippe Naveau
Subjects: Urban flood hazard, Extreme water depths and discharges, High resolution 2D meshes, Spatio-temporal lifting scheme, Spatial partitioning, Spatial pattern types, Meteorology. Climatology, QC851-999
Abstract: We propose a statistical model suitable for large spatio-temporal data sets exhibiting complex patterns such as simulated by physics-based hydraulic models over high resolution (HR) 2D meshes. Although necessary for impact studies such as urban flood hazard assessment, their long computation times limit their applicability leading to the development of statistical models that may emulate them quickly. Our model draws from the strengths of multi-resolution analysis and relies on an extension of the lifting scheme, a flexible implementation of discrete wavelet transforms, for spatio-temporal data. The extended lifting scheme exploits the idea that dominant spatial features, that may be identified with clustering, remain present through time. An easily interpretable non-parametric representation can be derived from the lifting scheme by combining a smoothed version of the data (obtained by simple averaging) with details (given by local regression residuals). A generative algorithm is built by introducing the information provided by a low resolution model, whose computation times are orders of magnitude smaller, yielding a downscaling model. This downscaling model assumes that sufficiently representative HR spatial patterns can be inferred from the training set. Our model is applied to a 2D dam break experiment using a synthetic urban configuration and to a field-scale test case simulating the propagation of a dike break flood wave into a Sacramento urban area. A comparison, carried out with spatial interpolation schemes and with a variant of our model based on principal component analysis, shows that the spatio-temporal lifting scheme based model is better at reproducing extreme events.
Published: 2023
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3. High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalized extreme value distributions

Author: Pauline Rivoire, Philomène Le Gall, Anne-Catherine Favre, Philippe Naveau, and Olivia Martius
Subjects: ERA-5, Spatial clustering, Precipitation extremes, Extended generalized Pareto distribution, Meteorology. Climatology, QC851-999
Abstract: Accurate estimation of daily rainfall return levels associated with large return periods is needed for a number of hydrological planning purposes, including protective infrastructure, dams, and retention basins. This is especially relevant at small spatial scales. The ERA-5 reanalysis product provides seasonal daily precipitation over Europe on a 0.25∘×0.25∘ grid (about 27 × 27 [km]). This translates more than 20,000 land grid points and leads to models with a large number of parameters when estimating return levels. To bypass this abundance of parameters, we build on the regional frequency analysis (RFA), a well-known strategy in statistical hydrology. This approach consists in identifying homogeneous regions, by gathering locations with similar distributions of extremes up to a normalizing factor and developing sparse regional models. In particular, we propose a step-by-step blueprint that leverages a recently developed and fast clustering algorithm to infer return level estimates over large spatial domains. This enables us to produce maps of return level estimates of ERA-5 reanalysis daily precipitation over continental Europe for various return periods and seasons. We discuss limitations and practical challenges and also provide a git hub repository. We show that a relatively parsimonious model with only a spatially varying scale parameter can compete well against statistical models of higher complexity.
Published: 2022
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4. Bias Reduced Peaks over Threshold Tail Estimation

Author: Jan Beirlant, Gaonyalelwe Maribe, Philippe Naveau, and Andréhette Verster
Subjects: Peaks over Threshold, Generalized Pareto distribution, Tail estimation, Mixture models, Statistics, HA1-4737, Probabilities. Mathematical statistics, QA273-280
Abstract: Bias reduction in tail estimation has mainly been performed in case of Pareto-type models; see for instance Drees (1996), Peng (1998), Feuerverger and Hall (1999), Beirlant et al. (1999, 2002), Gomes and Martins (2002) and Caeiro et al. (2005, 2009). In that context, Beirlant et al. (2009) and Papastathopoulos and Tawn (2013) constructed distributional models that are based on second order rates of convergence for distributions of peaks over thresholds (POT). Such approach also allows to connect the tail and the bulk of the distribution. Bias reduction for all max-domains of attractions, i.e. without restricting to the Pareto-type case, received much less attention up to now. Here we extend the second-order refined POT approach started in Beirlant et al. (2009) providing a bias reduction technique for the classical generalized Pareto (GP) approximation for POTs. We consider parametric and nonparametric modelling of the second order component.
Published: 2022
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5. Improved Regional Frequency Analysis of rainfall data

Author: Philomène Le Gall, Anne-Catherine Favre, Philippe Naveau, and Clémentine Prieur
Subjects: Spatial clustering, Precipitation extremes, Probability weighted moments, Extended generalized Pareto distribution, Meteorology. Climatology, QC851-999
Abstract: Rainfall are subject to local orography features and their intensities can be highly variable. In this context, identifying climatically coherent regions can greatly help interpreting rainfall patterns and improve the inference of return levels. In practice, partitioning a region of interest into homogeneous sub-regions is a delicate statistical task, especially in regards to modeling heavy rainfall features.In this work, our main goal is to propose and study a fast and efficient clustering algorithm. Compared to classical regional frequency analysis techniques, a key aspect is that our algorithm does not rely on the a priori choice of covariates. The proposed numerical scheme is only based on the precipitation dataset at hand, including low, moderate and heavy rainfall. In terms of inference, our approach builds on the easy-to-compute and reliable probability weighted moments commonly used in hydrology. While being in compliance with extreme value theory, we do not impose a parametric form on rainfall distributions and, neither thresholding nor block maxima steps are required in our proposed approach. By construction, our clustering method preserves the scale invariance principle of any classical regional frequency analysis.The performance of our clustering algorithm is assessed on a detailed experimental design based on the extended Generalized Pareto distribution.Sensitivity to the number of clusters is carefully analyzed.We apply our clustering algorithm on Switzerland daily precipitation measured at 191 sites. The found homogeneous regions are consistent with local orography and our approach outperforms the classical regional frequency analysis based on normalized elevation and coordinates as covariates. To complete our analysis of Swiss rainfall, we propose three models based on our clustering outputs. A comparison between our local, semi-regional and regional models indicates that a relatively simple model with two clusters and a spatially varying scale parameter can compete very well against complex models.
Published: 2022
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6. Extreme avalanche cycles: Return levels and probability distributions depending on snow and meteorological conditions

Author: Guillaume Evin, Pascal Dkengne Sielenou, Nicolas Eckert, Philippe Naveau, Pascal Hagenmuller, and Samuel Morin
Subjects: Avalanche cycles, Extreme value theory, Discrete distributions, French alps, Meteorology. Climatology, QC851-999
Abstract: Remarkable episodes of avalanche events, so-called snow avalanche cycles, are recurring threats to people and infrastructures in mountainous areas. This study focuses on the hazard assessment of snow avalanche cycles defined by daily occurrence numbers exceeding the 2-year return level. To this aim, extreme value distributions are tailored to account for discrete observations and potential covariates. A comprehensive statistical framework is provided including model fitting, model selection and evaluation, and derivation of quantities of interest such as return levels. In each of the 23 massifs of the French Alps, two discrete generalized Pareto (dGP) models are applied to extreme avalanche cycles extracted from 60 years of daily avalanche activity observations from 1958 to 2018, an unconditional version and a conditional version incorporating snow and meteorological covariates. In the conditional dGP model, the scale parameter is allowed to depend on snow and meteorological conditions from a local reanalysis, leading the corresponding distributions to outperform their unconditional counterparts in about half of the French Alps massifs. Unconditional dGP models provide valuable estimates of high return levels of avalanche numbers. In particular, it is shown that the number of avalanches per path which can be expected on average every 100 and 300 years for the French Alps is approximately equal to 0.25 (roughly one avalanche for four paths) and 0.32 (one avalanche for three paths). As exemplified with the January 2018 Eleanor winter storm, conditional dGP models refine the statistical description of the largest avalanche cycles by providing the information conditional to specific meteorological and snow conditions, with potential applications to avalanche forecasting and climate change impact studies. The same framework could be put to work in other mountain areas and for analyzing extreme counts of various other damaging phenomena.
Published: 2021
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7. A Comparison of Moderate and Extreme ERA‐5 Daily Precipitation With Two Observational Data Sets

Author: Pauline Rivoire, Olivia Martius, and Philippe Naveau
Subjects: ERA‐5, precipitation, reanalysis, extended generalized Pareto distribution, EOBS, CMORPH, Astronomy, QB1-991, Geology, QE1-996.5
Abstract: Abstract A comparison of moderate to extreme daily precipitation from the ERA‐5 reanalysis by the European Centre for Medium‐Range Weather Forecasts against two observational gridded data sets, EOBS and CMORPH, is presented. We assess the co‐occurrence of precipitation days and compare the full precipitation distributions. The co‐occurrence is quantified by the hit rate. An extended generalized Pareto distribution (EGPD) is fitted to the positive precipitation distribution at every grid point and confidence intervals of quantiles compared. The Kullback–Leibler divergence is used to quantify the distance between the entire EGPDs obtained from ERA‐5 and the observations. For days exceeding the local 90th percentile, the mean hit rate is 65% between ERA‐5 and EOBS (over Europe) and 60% between ERA‐5 and CMORPH (globally). Generally, we find a decrease of the co‐occurrence with increasing precipitation intensity. The agreement between ERA‐5 and EOBS is weaker over the southern Mediterranean region and Iceland compared to the rest of Europe. Differences between ERA‐5 and CMORPH are smallest over the oceans. Differences are largest over NW America, Central Asia, and land areas between 15°S and 15°N. The confidence intervals on quantiles are overlapping between ERA‐5 and the observational data sets for more than 80% of the grid points on average. The intensity comparisons indicate an excellent agreement between ERA‐5 and EOBS over Germany, Ireland, Sweden, and Finland, and a disagreement over areas where EOBS uses sparse input stations. ERA‐5 and CMORPH precipitation intensity agree well over the midlatitudes and disagree over the tropics.
Published: 2021
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8. Spatial clustering of summer temperature maxima from the CNRM-CM5 climate model ensembles & E-OBS over Europe

Author: Margot Bador, Philippe Naveau, Eric Gilleland, Mercè Castellà, and Tatiana Arivelo
Subjects: Spatial clustering, Climate extreme, Data ensemble, Multivariate extreme value theory, Meteorology. Climatology, QC851-999
Abstract: Reducing the dimensionality of the complex spatio-temporal variables associated with climate modeling, especially ensembles of climate models, is a challenging and important objective. For studies of detection and attribution, it is especially important to maintain information related to the extreme values of the atmospheric processes. Typical methods for data reduction involve summarizing climate model output information through means and variances, which does not preserve any information about the extremes. In order to help solve this challenge, a dependence summary measure appropriate for extreme values must be inferred. Here, we adapt one such measure from a recent study to a larger domain with a different variable and gridded data from observations and climate model ensembles, i.e. E-OBS observations and the CNRM-CM5 model. The handling of such ensembles of data is proposed, as well as a comparison of the spatial clusterings between two different ensembles, here a present-day and a future ensemble of climate simulations. This method yields valid information concerning extremes, while greatly reducing the data set.
Published: 2015
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9. Trends of atmospheric circulation during singular hot days in Europe

Author: Aglaé Jézéquel, Julien Cattiaux, Philippe Naveau, Sabine Radanovics, Aurélien Ribes, Robert Vautard, Mathieu Vrac, and Pascal Yiou
Subjects: atmospheric circulation, Europe, extreme event attribution, heatwaves, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: The influence of climate change on mid-latitudes atmospheric circulation is still very uncertain. The large internal variability makes it difficult to extract any statistically significant signal regarding the evolution of the circulation. Here we propose a methodology to calculate dynamical trends tailored to the circulation of specific days by computing the evolution of the distances between the circulation of the day of interest and the other days of the time series. We compute these dynamical trends for two case studies of the hottest days recorded in two different European regions (corresponding to the heat-waves of summer 2003 and 2010). We use the NCEP reanalysis dataset, an ensemble of CMIP5 models, and a large ensemble of a single model (CESM), in order to account for different sources of uncertainty. While we find a positive trend for most models for 2003, we cannot conclude for 2010 since the models disagree on the trend estimates.
Published: 2018
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10. Madogram and Asymptotic Independence among Maxima

Author: Armelle Guillou, Philippe Naveau, and Antoine Schorgen
Subjects: extremal coefficient, method-of-moment, maximum likelihood, Statistics, HA1-4737, Probabilities. Mathematical statistics, QA273-280
Abstract: A strong statistical research effort has been devoted in multivariate extreme value theory in order to assess the strength of dependence among extremes. This topic is particularly difficult in the case where block maxima are near independence. In this paper, we adapt and study a simple inference tool inspired from geostatistics, the madogram, to the context of asymptotic independence between pairwise block maxima. In particular, we introduce an extremal coefficient and study the theoretical properties of its estimator. Its behaviour is also illustrated on a small simulation study and a real data set.
Published: 2014
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11. A Folding Method for Extreme Quantiles Estimation

Author: Armelle Guillou, Philippe Naveau, and Alexandre You
Subjects: extreme quantile estimation, peaks-over-thresholds, generalized Pareto distribution, folding, generalized probability-weighted moments estimators, Statistics, HA1-4737, Probabilities. Mathematical statistics, QA273-280
Abstract: In order to estimate extreme quantiles from independent and identically distributed random variables, we propose and study a novel folding procedure that improves quantile estimates obtained from the classical Peaks-Over-Threshold method (POT) used in Extreme Value Theory. The idea behind the folding approach is to connect the part of a distribution above a given threshold with the one below it. A simplified version of this approach was studied by You et al. (2010). In this paper, an extension based on two thresholds is proposed to better combine the folding scheme with the POT approach. Simulations indicate that this new strategy leads to improved extreme quantiles estimates for finite samples. Asymptotic normality of the folded POT estimators is also derived.
Published: 2010
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12. Improving Probability-Weighted Moment Methods for the Generalized Extreme Value Distribution

Author: Jean Diebolt, Armelle Guillou, Philippe Naveau, and Pierre Ribereau
Subjects: empirical processes, maximum likelihood estimators, Statistics, HA1-4737, Probabilities. Mathematical statistics, QA273-280
Abstract: In 1985 Hosking et al. estimated with the so-called Probability-Weighted Moments (PWM) method the parameters of the Generalized Extreme Value (GEV) distribution, the latter being classically fitted to maxima of sequences of independent and identically distributed random variables. Their approach is still very popular in hydrology and climatology because of its conceptual simplicity, its easy implementation and its good performance for most distributions encountered in geosciences. Its main drawback resides in its limitations when applied to strong heavy-tailed densities. Whenever the GEV shape parameter is larger than 0.5, the asymptotic properties of the PWMs cannot be derived and consequently, asymptotic confidence intervals cannot be obtained. To broaden the validity domain of the PWM approach, we take advantage of a recent extension of PWM to a larger class of moments, called Generalized PWM (GPWM). This allows us to derive the asymptotic properties of our estimators for larger values of the shape parameter. The performance of our approach is illustrated by studying simulations of small, medium and large GEV samples. Comparisons with other GEV estimation techniques used in hydrology and climatology are performed.
Published: 2008
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13. On the evaluation of climate model simulated precipitation extremes

Author: Andrea Toreti and Philippe Naveau
Subjects: precipitation extremes, CMIP5, Euro-Mediterranean region, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract: The evaluation of precipitation extremes is a paramount challenging issue in climate sciences and there is a need of both assessing changes in climate projections and comparing climate model simulations with observations. To address these needs, a non-parametric approach specifically designed for extremes is here proposed. The method is tested and applied to observations and CMIP5 historical simulations and future projections (under the high emission scenario RCP8.5) over the Euro-Mediterranean region. Results support the existence of a scaling relationship among models and between models and observations in terms of conditional mean of the extremes. However, the rescaled tails of models’ precipitation show significant differences when compared with observations. Concerning future projections, models show an intensification of heavy precipitation (especially at the end of the 21st century) linked to a change in the conditional mean of extremes. More complex changes in the upper tails are not identified at the mid-century, while a lack of model agreement prevents drawing definitive conclusions for the end of the century.
Published: 2015
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14. Disentangling reservoir regulation patterns from natural streamflow in the Alps and their downstream regions

Author: Manuela Irene Brunner and Philippe Naveau
Abstract: Streamflow seasonality in mountain regions is besides climate often shaped by reservoir regulation. Such regulation is particularly important in the Alps where meltwater from glaciers and the snowpack are captured in reservoirs to generate hydropower during the winter season. While reservoirs affect streamflow seasonality, information on past seasonal reservoir operation patterns is rarely publicly available. Consequently, little is known about spatial variations in reservoir storage and release signals in dependence of climate and catchment characteristics. Here, we develop a generalized additive modelling approach to reconstruct daily and seasonal reservoir patterns from observed streamflow time series that encompass a period before and a period after a known year of reservoir construction.We apply this approach to reconstruct the seasonality of reservoir regulation, i.e. information on when water is stored in and released from a reservoir, for a dataset of 74 regulated catchments in the Central Alps. Using these reconstructed seasonal regulation patterns, we identify groups of catchments with similar reservoir operation strategies using functional clustering. We find that reservoir management varies by catchment elevation. Seasonal redistribution from summer to winter is strongest in high-elevation catchments, where reservoirs are mostly used for hydropower production, while seasonal redistribution is much weaker in the downstream regions, where reservoirs are used for a range of different purposes. The clear relationship between reservoir operation and elevation has practical implications. First, these elevational differences in reservoir regulation can and should be considered in hydrological model calibration. Furthermore, the reconstructed reservoir operation signals can be used to study the joint impact of climate change and reservoir operation on different streamflow signatures, including extreme events. Last, the potential of regulation as a climate adaptation measure may vary for the high-elevation and downstream regions.
Published: 2023

15. Analysis of precipitation records from climate models in a non-stationary context

Author: Paula Gonzalez, Philippe Naveau, Soulivanh Thao, and Julien Worms
Abstract: In the context of climate change, assessing how likely a particular change or event has been caused by human influence is important for mitigation and adaptation policies. Disturbances, such as increases in the frequency and intensity of extreme precipitation have been observed at continental to global scales. In this work we present an Extreme Event Attribution methodology for yearly maxima records that takes into consideration the temporal non-stationarity of climate variables and allow us to quantify record probability at a global scale in a transient setup. We apply our methodology to study records of yearly maxima of daily precipitation issued from the numerical climate model IPSL-CM6A-LR and the scenario rcp8.5 at a global scale. Focusing on decadal records, we detect a clear anthropogenic signal from the 2020's, even thought decadal record probability increases in most parts of the world, we observe a decrease of records probability in the subtropics.
Published: 2023

16. Mathematical Properties of Continuous Ranked Probability Score Forecasting

Author: Clément Dombry, Romain Pic, Philippe Naveau, Maxime Taillardat, Laboratoire de Mathématiques de Besançon (UMR 6623) (LMB), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Météo-France -Centre National de la Recherche Scientifique (CNRS)
Subjects: [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Kernel Method, Distributional Regression, Nearest Neighbor Method, CRPS, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Minimax Rate of Convergence, Probabilistic Forecast
Abstract: The theoretical advances on the properties of scoring rules over the past decades have broaden the use of scoring rules in probabilistic forecasting. In meteorological forecasting, statistical postprocessing techniques are essential to improve the forecasts made by deterministic physical models. Numerous state-of-the-art statistical postprocessing techniques are based on distributional regression evaluated with the Continuous Ranked Probability Score (CRPS). However, theoretical properties of such minimization of the CRPS have mostly considered the unconditional framework (i.e. without covariables) and infinite sample sizes. We circumvent these limitations and study the rate of convergence in terms of CRPS of distributional regression methods. We find the optimal minimax rate of convergence for a given class of distributions. Moreover, we show that the nearest neighbor method and the kernel method for distributional regression reach the optimal rate of convergence in dimension larger than 2 and in any dimension, respectively.Associated article: https://doi.org/10.1016/j.ijforecast.2022.11.
Published: 2023

17. U-Net based Methods for the Postprocessing of Precipitation Ensemble Forecasting

Author: Romain Pic, Clément Dombry, Maxime Taillardat, and Philippe Naveau
Abstract: Most Numerical Weather Prediction (NWP) systems use statistical postprocessing methods to correct for bias and underdispersion errors made by ensemble forecasting. This underdispersion leads to an underestimation of extreme events. Thus, many statistical postprocessing methods have been used to take into consideration the extremal behavior of meteorological phenomena such as precipitation. State-of-the-art techniques are based on Machine Learning combined with knowledge from Extreme Value Theory in order to improve forecasts. However, some of the best techniques do not consider the spatial dependency between locations. For example, Taillardat et al. (2019) trains a different Quantile Regression Forest at each location of interest and Rasp & Lerch (2018) uses neural networks with an embedding for the station's information in order to train a global model.The dataset used corresponds to 3-h precipitation amounts produced by the radar-based observations of ANTILOPE and the 17-members ensemble forecast system called PEAROME. The dataset spans over the south of France with a grid resolution of 0.025 degrees. Our method uses a U-Net-like neural network in order to take into account the spatial structure of the data and the output of our model is a parameterized law at each grid point. Among the choices available in the literature, we focused on the Extended Generalized Pareto Distribution and the truncated logistic with a point mass in 0. The model is trained by minimizing the scoring rules such as the Continuous Ranked Probability Score, the Log-Score or weighted versions of the aforementioned scoring rules. The method developed here is then compared to the raw ensemble as well as state-of-the-art techniques through scoring rules, skill scores and ROC curves.References :L. Pacchiardi, R. Adewoyin, P. Dueben, and R. Dutta. Probabilistic forecasting with generative networks via scoring rule minimization. Dec. 2021. arXiv:2112.08217 M. Taillardat, A.-L. Fougères, P. Naveau, and O. Mestre. Forest-based and semiparametric methods for the postprocessing of rainfall ensemble forecasting. Weather and Forecasting, 34(3):617–634, jun 2019. doi: 10.1175/waf-d-18-0149.1.
Published: 2023

18. Uncertainty quantification in variational data assimilation with deep learning

Author: Nicolas Lafon, Philippe Naveau, and Ronan Fablet
Abstract: The spatio-temporal reconstruction of a dynamical process from some observationaldata is at the core of a wide range of applications in geosciences. This is particularly true for weather forecasting, operational oceanography and climate studies. However, the re35 construction of a given dynamic and the prediction of future states must take into ac36 count the uncertainties that affect the system. Thus, the available observational measurements are only provided with a limited accuracy. Besides, the encoded physical equa38 tions that model the evolution of the system do not capture the full complexity of the real system. Finally, the numerical approximation generates a non-negligible error. For these reasons, it seems relevant to calculate a probability distribution of the state system rather than the most probable state. Using recent advances in machine learning techniques for inverse problems, we propose an algorithm that jointly learns a parametric distribution of the state, the dynamics governing the evolution of the parameters, and a solver. Experiments conducted on synthetic reference datasets, as well as on datasets describing environmental systems, validate our approach.
Published: 2023

19. Urban Heat Island Estimation from Crowdsensing Thermometers Embedded in Personal Cars

Author: Eva Marques, Valery Masson, Philippe Naveau, Olivier Mestre, Vincent Dubreuil, Yves Richard, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes ), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Nantes Université (Nantes Univ)-Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université (Nantes Univ), Centre de Recherches de Climatologie [UMR Biogéosciences] (CRC), Biogéosciences [UMR 6282] (BGS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Nantes Université (Nantes Univ)-Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Nantes Université (Nantes Univ)-Centre National de la Recherche Scientifique (CNRS), and Biogéosciences [UMR 6282] [Dijon] (BGS)
Subjects: Atmospheric Science, [SHS.GEO]Humanities and Social Sciences/Geography
Abstract: An ever-growing portion of population lives in urban areas. Cities are expanding quickly and consequently, the urban heat island effect has become a major health concern to maintain city dwellers’ thermal comfort. For this reason, city planners want to access urban meteorological databases in local areas where specific attention is needed. With the growth of connected devices, it is possible to collect unusual but massive temperature measurements from people’s activities. In this article, we study temperatures measured by thermometers embedded in everyday personal cars. To assess the quality of such opportunistic data, we first detect factors deteriorating the measurement. After pre-processing, the measurement error is then estimated thanks to two weather station networks providing a local-scale reference through Dijon and Rennes cities, France. The overall aggregation of private car temperature measurements allows to estimate very precisely the urban heat island at a 200m resolution. We detect the cooling effect of parks in Rennes and Paris urban areas. In Barcelona and Dijon, we observe the impact of regional environments and the orographic effect on the urban heat island. With our method, similar maps can be made accessible to every interested city in western Europe to target critical areas and support urban planning decisions.
Published: 2022

20. Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions

Author: Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau, Raphaël de Fondeville, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Probabilités, statistique, physique mathématique (PSPM), Institut Camille Jordan (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Ecole Polytechnique Fédérale de Lausanne (EPFL), ANR-20-CE40-0025,T-REX,nouveaux challenges pour la prédiction des extremes et sa validation(2020), ANR-19-CE46-0011,MeLODy,Bridging geohysics and MachinE Learning for the modeling, simulation and reconstruction of Ocean DYnamics(2019), ANR-10-LABX-0070,MILYON,Community of mathematics and fundamental computer science in Lyon(2010), ANR-11-IDEX-0007,Avenir L.S.E.,PROJET AVENIR LYON SAINT-ETIENNE(2011), European Project: 824158,H2020-EU.1.4. - EXCELLENT SCIENCE - Research Infrastructures ,EoCoE-II(2019), Météo-France, Department of Earth Sciences [Swiss Federal Institute of Technology - ETH Zürich] (D-ERDW), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), and CMCS-EPFL (CMCS-EPFL)
Subjects: FOS: Computer and information sciences, Mathematics - Statistics Theory, Machine Learning (stat.ML), Statistics Theory (math.ST), CRPS, Scoring rules, [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], Statistics - Applications, Methodology (stat.ME), Extreme events, Probabilistic forecasting, Calibration, Verification, Statistics - Machine Learning, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], FOS: Mathematics, Applications (stat.AP), Business and International Management, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Statistics - Methodology, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [STAT.AP]Statistics [stat]/Applications [stat.AP], [SDE]Environmental Sciences, ensemble forecasts, [STAT.ME]Statistics [stat]/Methodology [stat.ME]
Abstract: Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased conclusions are readily made. In this context, classical verification methods tailored for extreme events, such as thresholded and weighted scoring rules, have undesirable properties that cannot be mitigated, and the well-known continuous ranked probability score (CRPS) is no exception. In this paper, we define a formal framework for assessing the behavior of forecast evaluation procedures with respect to extreme events, which we use to demonstrate that assessment based on the expectation of a proper score is not suitable for extremes. Alternatively, we propose studying the properties of the CRPS as a random variable by using extreme value theory to address extreme event verification. An index is introduced to compare calibrated forecasts, which summarizes the ability of probabilistic forecasts for predicting extremes. The strengths and limitations of this method are discussed using both theoretical arguments and simulations., International Journal of Forecasting, 39 (3), ISSN:0169-2070, ISSN:1872-8200
Published: 2023
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21. Spatial variability in Alpine reservoir regulation: Deriving reservoir operations from streamflow using generalized additive models

Author: Manuela Irene Brunner, Philippe Naveau, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDU]Sciences of the Universe [physics], General Earth and Planetary Sciences, General Environmental Science
Abstract: Reservoir regulation affects various streamflow characteristics, from low to high flows, with important implications for downstream water users. However, information on past reservoir operations is rarely publicly available, and it is hardly known how reservoir operation signals, i.e. information on when water is stored in and released from reservoirs, vary over a certain region. Here, we propose a statistical model to reconstruct reservoir operation signals in catchments without information on reservoir operation. The model uses streamflow time series observed downstream of a reservoir that encompass a period before and a period after a known year of reservoir construction. In a first step, a generalized additive model (GAM) regresses the streamflow time series from the unregulated pre-reservoir period on four covariates including temperature, precipitation, day of the year, and glacier mass balance changes. In a second step, this GAM, which represents natural conditions, is applied to predict natural streamflow, i.e. streamflow that would be expected in the absence of the reservoir, for the regulated period. The difference between the observed regulated streamflow signal and the predicted natural baseline should correspond to the reservoir operation signal. We apply this approach to reconstruct the seasonality of reservoir regulation, i.e. information on when water is stored in and released from a reservoir, from a dataset of 74 catchments in the central Alps with a known reservoir construction date (i.e. date when the reservoir went into operation). We group these reconstructed regulation seasonalities using functional clustering to identify groups of catchments with similar reservoir operation strategies. We show how reservoir management varies by catchment elevation and that seasonal redistribution from summer to winter is strongest in high-elevation catchments. These elevational differences suggests a clear relationship between reservoir operation and climate and catchment characteristics, which has practical implications. First, these elevational differences in reservoir regulation can and should be considered in hydrological model calibration. Furthermore, the reconstructed reservoir operation signals can be used to study the joint impact of climate change and reservoir operation on different streamflow signatures, including extreme events., Hydrology and Earth System Sciences, 27 (3), ISSN:1027-5606, ISSN:1607-7938
Published: 2023
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22. Assessment of S2S ensemble extreme precipitation forecasts over Europe

Author: Pauline Rivoire, Olivia Martius, Philippe Naveau, and Alexandre Tuel
Abstract: Heavy precipitation can lead to floods and landslides, resulting in widespread damage and significant casualties. Some of its impacts can be mitigated if reliable forecasts and warnings are available. Of particular interest is the subseasonal to seasonal (S2S) prediction timescale. The S2S prediction timescale has received increasing attention in the research community because of its importance for many sectors. However, very few forecast skill assessments of precipitation extremes in S2S forecast data have been conducted. The goal of this article is to introduce a new methodology to assess the skill of rare events, here extreme precipitation, in S2S forecasts. We verify extreme precipitation events over Europe in the S2S forecast model from the European Centre for Medium-Range Weather Forecasts. The verification is conducted against ERA5 reanalysis precipitation. Extreme precipitation is defined as daily precipitation accumulations exceeding the seasonal 95th percentile. In addition to the classical Brier score, we use a binary loss index to assess skill. The binary loss index is tailored to assess the skill of rare events. We analyse daily events locally and spatially aggregated, as well as 7-day extreme event counts. Results consistently show a higher skill in winter compared to summer. The regions showing the highest skill are Norway, Portugal and the south of the Alps. Skill increases when aggregating the extremes spatially or temporally. The verification methodology can be adapted and applied to other variables, e.g. temperature extremes or river discharge.
Published: 2022

23. Sub-millennial climate variability from high-resolution water isotopes in the EPICA Dome C ice core

Author: Antoine Grisart, Mathieu Casado, Vasileios Gkinis, Bo Vinther, Philippe Naveau, Mathieu Vrac, Thomas Laepple, Bénédicte Minster, Frederic Prié, Barbara Stenni, Elise Fourré, Hans Christian Steen-Larsen, Jean Jouzel, Martin Werner, Katy Pol, Valérie Masson-Delmotte, Maria Hoerhold, Trevor Popp, Amaelle Landais, Glaces et Continents, Climats et Isotopes Stables (GLACCIOS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), DAM Île-de-France (DAM/DIF), Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Niels Bohr Institute [Copenhagen] (NBI), Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Alfred Wegener Institute [Potsdam], Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Department of Environmental Sciences, Informatics and Statistics [Venezia], University of Ca’ Foscari [Venice, Italy], University of Bergen (UiB), Alfred Wegener Institute for Polar and Marine Research (AWI), and Climate Sciences Department [Bremerhaven]
Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Global and Planetary Change, CHRONOLOGY AICC2012, Stratigraphy, DEUTERIUM DATA, CYCLES, Paleontology, HYDROGEN, GREENLAND, ANTARCTIC ICE, DIFFUSION, SIGNAL, Settore GEO/08 - Geochimica e Vulcanologia, TEMPERATURE, RECORDS
Abstract: The EPICA Dome C (EDC) ice core provides the longest continuous climatic record, covering the last 800 000 years (800 kyr). A unique opportunity to investigate decadal to millennial variability during past glacial and interglacial periods is provided by the high-resolution water isotopic record (δ18O and δD) available for the EDC ice core. We present here a continuous compilation of the EDC water isotopic record at a sample resolution of 11 cm, which consists of 27 000 δ18O measurements and 7920 δD measurements (covering, respectively, 94 % and 27 % of the whole EDC record), including published and new measurements (2900 for both δ18O and δD) for the last 800 kyr. Here, we demonstrate that repeated water isotope measurements of the same EDC samples from different depth intervals obtained using different analytical methods are comparable within analytical uncertainty. We thus combine all available EDC water isotope measurements to generate a high-resolution (11 cm) dataset for the past 800 kyr. A frequency decomposition of the most complete δ18O record and a simple assessment of the possible influence of diffusion on the measured profile shows that the variability at the multi-decadal to multi-centennial timescale is higher during glacial than during interglacial periods and higher during early interglacial isotopic maxima than during the Holocene. This analysis shows as well that during interglacial periods characterized by a temperature optimum at the beginning, the multi-centennial variability is strongest over this temperature optimum.
Published: 2022

24. Disentangling natural streamflow from reservoir regulation practices in the Alps using generalized additive models

Author: Manuela Irene Brunner and Philippe Naveau
Abstract: Reservoir regulation affects various streamflow characteristics from low to high flows with important implications for downstream water users. Still, information on past reservoir operations is rarely publicly available and it is hardly known how reservoir operation signals, i.e. information on when water is stored in and released from reservoirs, vary over a certain region. Here, we develop a statistical model to reconstruct reservoir operation signals from observed streamflow time series that encompass a period before and a period after a known year of reservoir construction. In a first step, a generalized additive model (GAM) regresses streamflow time series from the unregulated pre-reservoir period on four covariates including temperature, precipitation, day of the year, and glacier mass balance changes. In a second step, this GAM, which represents natural conditions, is applied to predict natural streamflow, i.e. streamflow that would be expected in the absence of the reservoir, for the regulated period. The difference between the observed regulated streamflow signal and the predicted natural baseline should correspond to the reservoir operation signal. We apply this approach to reconstruct the seasonality of reservoir regulation, i.e. information on when water is stored in and released from a reservoir, from a dataset of 74 catchments in the Central Alps with a known reservoir construction date. We group these reconstructed regulation seasonalities using functional clustering to identify groups of catchments with similar reservoir operation strategies. We find that reservoir management varies by catchment elevation, with seasonal redistribution from summer to winter being strongest in high-elevation catchments. These elevational differences suggests a clear relationship between reservoir operation and climate and catchment characteristics, which has practical implications. First, these elevational differences in reservoir regulation can and should be considered in hydrological model calibration. Furthermore, the reconstructed reservoir operation signals can be used to study the joint impact of climate change and reservoir operation on different streamflow signatures, including extreme events.
Published: 2022

25. Supplementary material to 'Sub-millennial climate variability from high resolution water isotopes in the EDC ice core'

Author: Antoine Grisart, Mathieu Casado, Vasileios Gkinis, Bo Vinther, Philippe Naveau, Mathieu Vrac, Thomas Laepple, Bénédicte Minster, Fréderic Prié, Barbara Stenni, Elise Fourré, Hans-Christian Steen Larsen, Jean Jouzel, Martin Werner, Katy Pol, Valérie Masson-Delmotte, Maria Hoerhold, Trevor Popp, and Amaelle Landais
Published: 2022

26. Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk

Author: Romain Pic, Clément Dombry, Philippe Naveau, Maxime Taillardat, Laboratoire de Mathématiques de Besançon (UMR 6623) (LMB), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Météo-France, ANR-20-CE40-0025,T-REX,nouveaux challenges pour la prédiction des extremes et sa validation(2020), European Project: 824158,EoCoE-II Horizon 2020, Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, G.3, Mathematics - Statistics Theory, Machine Learning (stat.ML), CRPS, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Statistics Theory (math.ST), Minimax Rate of Convergence, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Kernel Method, FOS: Mathematics, Nearest Neighbor Method, Distributional Regression, Business and International Management, 62G30, 62G20, Probabilistic Forecasting
Abstract: The theoretical advances on the properties of scoring rules over the past decades have broadened the use of scoring rules in probabilistic forecasting. In meteorological forecasting, statistical postprocessing techniques are essential to improve the forecasts made by deterministic physical models. Numerous state-of-the-art statistical postprocessing techniques are based on distributional regression evaluated with the Continuous Ranked Probability Score (CRPS). However, theoretical properties of such evaluation with the CRPS have solely considered the unconditional framework (i.e. without covariates) and infinite sample sizes. We extend these results and study the rate of convergence in terms of CRPS of distributional regression methods. We find the optimal minimax rate of convergence for a given class of distributions and show that the k-nearest neighbor method and the kernel method reach this optimal minimax rate., Comment: Preprint of the article available online in International Journal of Forecasting
Published: 2022
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27. Stable sums to infer high return levels of multivariate rainfall time series

Author: Gloria Buriticá, Philippe Naveau, Laboratoire de Probabilités, Statistiques et Modélisations (LPSM (UMR_8001)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Laboratoire de Probabilités, Statistique et Modélisation (LPSM (UMR_8001)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: Statistics and Probability, FOS: Computer and information sciences, [STAT.AP]Statistics [stat]/Applications [stat.AP], Ecological Modeling, extremal index, return levels, Mathematics - Statistics Theory, Statistics Theory (math.ST), Statistics - Applications, Methodology (stat.ME), cluster process, stationary time series, stable distribution, Environmental time series, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], stable distributions, FOS: Mathematics, Applications (stat.AP), multivariate regular variation, Statistics - Methodology
Abstract: Heavy rainfall distributional modeling is essential in any impact studies linked to the water cycle, e.g.\ flood risks. Still, statistical analyses that both take into account the temporal and multivariate nature of extreme rainfall are rare, and often, a complex de-clustering step is needed to make extreme rainfall temporally independent. A natural question is how to bypass this de-clustering in a multivariate context. To address this issue, we introduce the stable sums method. Our goal is to incorporate time and space extreme dependencies in the analysis of heavy tails. To reach our goal, we build on large deviations of regularly varying stationary time series. Numerical experiments demonstrate that our novel approach enhances return levels inference in two ways. First, it is robust concerning time dependencies. We implement it alike on independent and dependent observations. In the univariate setting, it improves the accuracy of confidence intervals compared to the main estimators requiring temporal de-clustering. Second, it thoughtfully integrates the spatial dependencies. In simulation, the multivariate stable sums method has a smaller mean squared error than its component-wise implementation. We apply our method to infer high return levels of daily fall precipitation amounts from a national network of weather stations in France.
Published: 2021

28. Improved regional frequency analysis of rainfall data

Author: Philomène Le Gall, Anne-Catherine Favre, Philippe Naveau, Clémentine Prieur, Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Mathematics and computing applied to oceanic and atmospheric flows (AIRSEA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Grenoble Alpes (UGA)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Within the CDP-Trajectories framework, this work is supported by the French National Research Agency in the framework of the 'Investissements d’avenir' program (ANR-15-IDEX-02). We gratefully acknowledge financial support for this study provided by the Swiss Federal Office for Environment (FOEN), the Swiss Federal Nuclear Safety Inspectorate (ENSI), the Federal Office for Civil Protection (FOCP), and the Federal Office of Meteorology and Climatology, MeteoSwiss, through the project EXAR ('Evaluation of extreme Flooding Events within the Aare-Rhine hydrological system in Switzerland'). Part of this work was also supported by the French national programs (FRAISE-LEFE/INSU and 80 PRIME CNRS-INSU), and the European H2020 XAIDA (Grant agreement ID: 101003469). Philippe Naveau also acknowledge the support of the French Agence Nationale de la Recherche (ANR) under reference ANR-20-CE40-0025-01 (T-REX project), and the ANR-Melody (ANR-19-CE46-0011)., ANR-20-CE40-0025,T-REX,nouveaux challenges pour la prédiction des extremes et sa validation(2020), ANR-19-CE46-0011,MeLODy,Bridging geohysics and MachinE Learning for the modeling, simulation and reconstruction of Ocean DYnamics(2019), ANR-15-IDEX-0002,UGA,IDEX UGA(2015), and Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )
Subjects: spatial clustering, [STAT]Statistics [stat], Atmospheric Science, precipitation extremes, Geography, Planning and Development, [SDE]Environmental Sciences, Management, Monitoring, Policy and Law, [MATH]Mathematics [math], probability weighted moments, Physics::Atmospheric and Oceanic Physics, extended generalized Pareto distribution
Abstract: International audience; Rainfall are subject to local orography features and their intensities can be highly variable. In this context, identifying climatically coherent regions can greatly help interpreting rainfall patterns and improve the inference of return levels. In practice, partitioning a region of interest into homogeneous sub-regions is a delicate statistical task, especially in regards to modeling heavy rainfall features. In this work, our main goal is to propose and study a fast and efficient clustering algorithm. Compared to classical regional frequency analysis techniques, a key aspect is that our algorithm does not rely on the a priori choice of covariates. The proposed numerical scheme is only based on the precipitation dataset at hand, including low, moderate and heavy rainfall. In terms of inference, our approach builds on the easy-to-compute and reliable probability weighted moments commonly used in hydrology. While being in compliance with extreme value theory, we do not impose a parametric form on rainfall distributions and, neither thresholding nor block maxima steps are required in our proposed approach. By construction, our clustering method preserves the scale invariance principle of any classical regional frequency analysis. The performance of our clustering algorithm is assessed on a detailed experimental design based on the extended Generalized Pareto distribution. Sensitivity to the number of clusters is carefully analyzed. We apply our clustering algorithm on Switzerland daily precipitation measured at 191 sites. The found homogeneous regions are consistent with local orography and our approach outperforms the classical regional frequency analysis based on normalized elevation and coordinates as covariates. To complete our analysis of Swiss rainfall, we propose three models based on our clustering outputs. A comparison between our local, semi-regional and regional models indicates that a relatively simple model with two clusters and a spatially varying scale parameter can compete very well against complex models.
Published: 2021

29. Probability distributions for analog-to-target distances

Author: Paul Platzer, Pierre Tandeo, Philippe Naveau, Jean-François Filipot, Maxime Thiébaut, Pascal Yiou, France Energies Marines [Brest], Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ANR-05-PADD-0010,GeDuQue,Innovations agro-écologiques et organisationnelles pour une Gestion Durable de la Qualité de l'Eau dans des régions de monoculture à forts niveaux d'intrants phytosanitaires(2005), European Project: 7138605(1971), IMT Atlantique (IMT Atlantique), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Equipe Observations Signal & Environnement (Lab-STICC_OSE), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)
Subjects: FOS: Computer and information sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, Dynamical systems theory, Computer science, Other artificial intelligence/machine learning, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], 0207 environmental engineering, Machine Learning (stat.ML), 02 engineering and technology, Dynamical Systems (math.DS), Statistics - Applications, 01 natural sciences, Data science, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, FOS: Mathematics, Applications (stat.AP), Mathematics - Dynamical Systems, Predictability, 020701 environmental engineering, 0105 earth and related environmental sciences, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [STAT.AP]Statistics [stat]/Applications [stat.AP], Atmosphere, Dimensionality reduction, Statistics, State (functional analysis), Variance (accounting), Probability distribution, Random variable, Algorithm, Curse of dimensionality
Abstract: Some properties of chaotic dynamical systems can be probed through features of recurrences, also called analogs. In practice, analogs are nearest neighbors of the state of a system, taken from a large database called the catalog. Analogs have been used in many atmospheric applications including forecasts, downscaling, predictability estimation, and attribution of extreme events. The distances of the analogs to the target state usually condition the performances of analog applications. These distances can be viewed as random variables, and their probability distributions can be related to the catalog size and properties of the system at stake. A few studies have focused on the first moments of return-time statistics for the closest analog, fixing an objective of maximum distance from this analog to the target state. However, for practical use and to reduce estimation variance, applications usually require not just one but many analogs. In this paper, we evaluate from a theoretical standpoint and with numerical experiments the probability distributions of the K shortest analog-to-target distances. We show that dimensionality plays a role on the size of the catalog needed to find good analogs and also on the relative means and variances of the K closest analogs. Our results are based on recently developed tools from dynamical systems theory. These findings are illustrated with numerical simulations of well-known chaotic dynamical systems and on 10-m wind reanalysis data in northwest France. Practical applications of our derivations are shown for forecasts of an idealized chaotic dynamical system and for objective-based dimension reduction using the 10-m wind reanalysis data.
Published: 2021

30. Using local dynamics to explain analog forecasting of chaotic systems

Author: Yicun Zhen, Pierre Ailliot, Jean-François Filipot, Pierre Tandeo, Pascal Yiou, Philippe Naveau, Paul Platzer, France Energies Marines [Brest], Equipe Observations Signal & Environnement (Lab-STICC_OSE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Laboratoire de Mathématiques de Bretagne Atlantique (LMBA), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), ANR-10-IEED-0006,FEM,France Energies Marines(2010), European Project: 338965,EC:FP7:ERC,ERC-2013-ADG,A2C2(2014), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), IMT Atlantique (IMT Atlantique), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO), Université de Brest (UBO)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS), and ANR-11-LABX-0020,LEBESGUE,Centre de Mathématiques Henri Lebesgue : fondements, interactions, applications et Formation(2011)
Subjects: FOS: Computer and information sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, Dynamical systems theory, Computer science, FOS: Physical sciences, Machine Learning (stat.ML), 01 natural sciences, 010305 fluids & plasmas, symbols.namesake, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Simple (abstract algebra), Statistics - Machine Learning, 0103 physical sciences, Linear regression, Dynamical systems, Applied mathematics, Flow map, Machine-learning, Physics::Atmospheric and Oceanic Physics, 0105 earth and related environmental sciences, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Atmosphere, Chaotic systems, Nonlinear Sciences - Chaotic Dynamics, Dynamics (music), Physics - Data Analysis, Statistics and Probability, Jacobian matrix and determinant, [NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD], symbols, State (computer science), Chaotic Dynamics (nlin.CD), Equations for a falling body, Data Analysis, Statistics and Probability (physics.data-an), Analogs, Forecasting
Abstract: Analogs are nearest neighbors of the state of a system. By using analogs and their successors in time, one is able to produce empirical forecasts. Several analog forecasting methods have been used in atmospheric applications and tested on well-known dynamical systems. Although efficient in practice, theoretical connections between analog methods and dynamical systems have been overlooked. Analog forecasting can be related to the real dynamical equations of the system of interest. This study investigates the properties of different analog forecasting strategies by taking local approximations of the system's dynamics. We find that analog forecasting performances are highly linked to the local Jacobian matrix of the flow map, and that analog forecasting combined with linear regression allows to capture projections of this Jacobian matrix. The proposed methodology allows to estimate analog forecasting errors, and to compare different analog methods. These results are derived analytically and tested numerically on two simple chaotic dynamical systems., Article submitted to the Journal of Atmospheric Sciences
Published: 2021

31. Combining random forests and class-balancing to discriminate between three classes of avalanche activity in the French Alps

Author: Pascal Dkengne Sielenou, Marie Dumont, Philippe Naveau, Léo Viallon-Galinier, Pascal Hagenmuller, Deborah Verfaillie, Samuel Morin, Nicolas Eckert, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre d'Etudes de la Neige (CEN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA), Université Catholique de Louvain = Catholic University of Louvain (UCL), Erosion torrentielle neige et avalanches (UR ETGR (ETNA)), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), French Ministry of the Environment, Risk Division (DGPR), French Gestion des Impacts du Changement ClimatiqueGICC and Obervatoire National du Changement climatiqueONERC programs (ADAMONT grant), and ANR-16-CE01-0006,EBONI,Dépot, devenir et impact des impuretés absorbantes dans le manteau neigeux(2016)
Subjects: business.industry, Computer science, Physics::Instrumentation and Detectors, Forest decision trees, Moderate activity, Geotechnical Engineering and Engineering Geology, Machine learning, computer.software_genre, Snow, Class (biology), Random forest, Physics::Geophysics, Avalanche forecasting, Variable (computer science), [SPI]Engineering Sciences [physics], Avalanche statistical forecasting, Snow and meteorological conditions and reanalyses, [SDE]Environmental Sciences, General Earth and Planetary Sciences, High activity, Artificial intelligence, business, Cluster analysis, French Alps, computer
Abstract: International audience; Determining avalanche activity corresponding to given snow and meteorological conditions is an old problem of high practical relevance. To address it, numerous statistical forecasting models have been developed, but intercomparisons of their efficiency on very large datasets are seldom. In this work, an approach combining random forests with class-balancing is presented and systematically compared with competing methods currently described in the avalanche literature. On more than 50 years of daily avalanche observations, in the 23 massifs of the French Alps, the competing classifiers are evaluated on their ability to distinguish three classes of avalanche activity: non-avalanche days, days with moderate activity, and days with high activity. Moreover, the variables of higher importance in the random forest classifiers are shown to be coherent with current avalanche literature and a clustering based on these variable importance separates massifs which are known to have different avalanche activities. Our approach opens perspectives to support operational avalanche forecasting.
Published: 2021

32. Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble Forecasting

Author: Anne-Laure Fougères, Maxime Taillardat, Philippe Naveau, Olivier Mestre, Météo France, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Probabilités, statistique, physique mathématique (PSPM), Institut Camille Jordan [Villeurbanne] (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut Camille Jordan (ICJ), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, extreme events, [STAT.AP]Statistics [stat]/Applications [stat.AP], Atmospheric Science, 010504 meteorology & atmospheric sciences, Ensemble forecasting, Meteorology, rainfall, Extreme events, non-parametric statistical post-processing, 01 natural sciences, 010104 statistics & probability, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], 13. Climate action, Range (statistics), Environmental science, Precipitation, ensemble forecast, 0101 mathematics, 0105 earth and related environmental sciences
Abstract: Published version is accepted in Weather and Forecasting, DOI: 10.1175/WAF-D-18-0149.1; International audience; To satisfy a wide range of end-users, rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We introduce local statistical post-processing methods based on Quantile Regression Forests and Gradient Forests with a semi-parametric extension for heavy-tailed distributions. These hybrid methods make use of the forest-based outputs to fit a parametric distribution that is suitable to model jointly low, medium, and heavy rainfall intensities. Our goal is to improve ensemble quality and value for all rainfall intensities. The proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the Météo-France ensemble prediction system called PEARP. They are verified with a cross validation strategy and compete favorably with state-of-the-art methods like Analog Ensemble or Ensemble Model Output Statistics. Our methods do not assume any parametric links between the variable to calibrate and possible covariates. They do not require any variable selection step, and can make use of more than 60 predictors available such as summary statistics on the raw ensemble, deterministic forecasts of other parameters of interest, or probabilities of convective rainfall. In addition to improvements in overall performance, hybrid forest-based procedures produced the largest skill improvements for forecasting heavy rainfall events.
Published: 2019

33. Variability patterns of the annual frequency and timing of low streamflow days across the United States and their linkage to regional and large‐scale climate

Author: Maryam Pournasiri Poshtiri, Upmanu Lall, Indrani Pal, Erin Towler, and Philippe Naveau
Subjects: River ecosystem, 010504 meteorology & atmospheric sciences, Range (biology), 0207 environmental engineering, 02 engineering and technology, STREAMS, 01 natural sciences, Stream gauge, Sea surface temperature, Streamflow, Environmental science, Physical geography, Precipitation, 020701 environmental engineering, Scale (map), 0105 earth and related environmental sciences, Water Science and Technology
Abstract: Low‐flow events can cause significant impacts to river ecosystems and water‐use sectors; as such, it is important to understand their variability and drivers. In this study, we characterise the variability and timing of annual total frequency of low‐streamflow days across a range of headwater streams within the continental United States. To quantify this, we use a metric that counts the annual number of low‐flow days below a given threshold, defined as the cumulative dry days occurrence (CDO). First, we identify three large clusters of stream gauge locations using a Partitioning Around Medoids (PAM) clustering algorithm. In terms of timing, results reveal that for most clusters, the majority of low‐streamflow days occur from the middle of summer until early fall, although several locations in Central and Western United States also experience low‐flow days in cold seasons. Further, we aim to identify the regional climate and larger scale drivers for these low‐streamflow days. Regionally, we find that precipitation deficits largely associate with low‐streamflow days in the Western United States, whereas within the Central and Eastern U.S. clusters, high temperature indicators are also linked to low‐streamflow days. In terms of larger scale, we examine sea surface temperature (SST) anomalies, finding that extreme dry years exhibit a high degree of co‐occurrence with different patterns of warmer SST anomalies across the Pacific and Northern Atlantic Oceans. The linkages identified with regional climate and SSTs offer promise towards regional prediction of changing conditions of low‐streamflow events.
Published: 2019

34. Narrowing uncertainties in climate projections using data science tools

Author: Pierre Tandeo, Pierre Ailliot, Christophe Bello, Bertrand Chapron, Trang Chau, Knutti, R., Pierre Le Bras, Philippe Naveau, Valérie Monbet, Ruiz, J., Florian Sevellec, Anne-Marie Tréguier, Equipe Observations Signal & Environnement (Lab-STICC_OSE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Brest (UBO), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Universidad de Buenos Aires [Buenos Aires] (UBA), Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), IMT Atlantique (IMT Atlantique), Laboratoire de Mathématiques de Bretagne Atlantique (LMBA), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR), SIMulation pARTiculaire de Modèles Stochastiques (SIMSMART), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut de Recherche Mathématique de Rennes (IRMAR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and ANR-11-LABX-0020,LEBESGUE,Centre de Mathématiques Henri Lebesgue : fondements, interactions, applications et Formation(2011)
Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2021

35. Editorial to the special issue: Statistical modeling of environmental extremes

Author: Philippe Naveau, Daniel Cooley, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)
Subjects: Statistics and Probability, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 010104 statistics & probability, Management science, 010102 general mathematics, Economics, Econometrics and Finance (miscellaneous), Statistical model, 0101 mathematics, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 01 natural sciences, Engineering (miscellaneous), ComputingMilieux_MISCELLANEOUS, Mathematics
Abstract: International audience
Published: 2021

36. A Comparison of Moderate and Extreme ERA‐5 Daily Precipitation With Two Observational Data Sets

Author: Olivia Martius, Philippe Naveau, Pauline Rivoire, Institute of Geography [Bern], University of Bern, and Oeschger Centre for Climate Change Research (OCCR)
Subjects: Mediterranean climate, CMORPH, Percentile, 010504 meteorology & atmospheric sciences, Astronomy, 0207 environmental engineering, reanalysis, QB1-991, 02 engineering and technology, precipitation, 01 natural sciences, Divergence, extended generalized Pareto distribution, Generalized Pareto distribution, ERA‐5, Precipitation, 910 Geography & travel, 020701 environmental engineering, 550 Earth sciences & geology, 0105 earth and related environmental sciences, [STAT.AP]Statistics [stat]/Applications [stat.AP], QE1-996.5, Geology, 500 Science, Confidence interval, 13. Climate action, Climatology, Middle latitudes, EOBS, Environmental science, Quantile
Abstract: Both mean and extreme precipitation are highly relevant and a probability distribution that models the entire precipitation distribution therefore provides important information. Gamma distributions are often used to model low and moderate precipitation amounts and extreme value theory allows to model the upper tail of the distribution. We apply the Extended Generalized Pareto Distribution (EGPD). Thanks to a transition function, this method overcomes the problem of finding a threshold between upper and lower tails. The transition cumulative distribution function of the EGPD is constrained on the upper tail and lower tail to enable a GPD behavior for both small and large extremes.EGPD is used here to characterize ERA-5 precipitation. ERA-5 is a new ECMWF climate re-analysis dataset that provides a numerical description of the recent climate by combining a numerical weather model with observations. The data set is global with a spatial resolution of 0.25° and currently covers the period from 1979 to present. ERA-5 precipitation is computed from model forecasts and therefore needs validation against observational datasets. ERA-5 daily precipitation is compared to EOBS precipitation, a gridded dataset spatially interpolated from observations over Europe, and to CMORPH precipitation, a global satellite-based dataset. Simultaneous occurrence of extreme events is assessed with a hit rate. An intensity comparison is conducted with quantiles confidence intervals and a Kullback Leibler divergence test, both derived from the EGPD.Overall, good agreements but also strong mismatches between ERA-5 and the observational datasets can be found, depending on the feature of interest in precipitation data. This work highlights both. For example, extreme event occurrences between ERA5 and the observational datasets appear to agree. The overlap between 95% confidence intervals on quantiles depends on the season and the probability of occurrence. Over Europe, the best agreement results are generally reached in regions with high station density in EOBS. The global intensity comparison between ERA5 and CMORPH shows a good agreement for moderate quantiles, except for some mountainous regions, but presents a large signal of disagreement in the tropics for large quantiles.
Published: 2021

37. Modelling rainfall from sub-hourly to daily scale with a heavy tailed meta-Gaussian model

Author: Pierre Ailliot, Aurore Chaubet, Boutigny Marie, Philippe Naveau, and Benoît Saussol
Subjects: symbols.namesake, Scale (ratio), symbols, Probability distribution, Environmental science, Precipitation, Atmospheric sciences, Gaussian network model
Abstract: The time scale of usual hydrological applications can vary from a few minutes to a few days. An accurate description of the precipitation probability distributions at the appropriate time scale is ...
Published: 2021

38. Spatial clustering of heavy clustering in ERA-5 precipitation over Europe}

Author: Pauline Rivoire, Philomène Le Gall, Olivia Romppainen-Martius, Philippe Naveau, and Anne-Catherine Favre
Subjects: Climatology, Spatial clustering, Environmental science, Precipitation, Cluster analysis
Abstract: Extreme precipitation often cause floods and lead to important societal and economical damages. Rainfall is subject to local orography features and their intensities can be highly variable. In this context, identifying climatically coherent regions for extremes is paramount to understand and analyze rainfall at the correct spatial scale. We assume that the region of interest can be partitioned into homogeneous regions. In other words, sub-regions with common marginal distribution except a scale factor. As an example, considering extremes as block maxima or excesses over a threshold, a sub-region corresponds to a constant shape parameter. We develop a non-parametric clustering algorithm based on a ratio of Probability Weighted Moments to identify these homogeneous regions and gather weather stations. By construction this ratio does not depend on the location and scale parameters for the Generalized Extreme Value and Generalized Pareto distributions. Our method has the advantage to only rely on raw precipitation data and not on station covariates.A simulation data study is performed based on the extended GPD distribution that appears to well capture low, moderate and heavy rainfall intensities. Sensitivity to the number of clusters is analyzed. Results of simulation reveal that the method detects homogeneous regions. We apply our clustering algorithm on ERA-5 precipitation over Europe. We obtain coherent homogeneous regions consistent with local orography. The marginal precipitation behaviour is analyzed through regional fitting of an extended GPD.
Published: 2021

39. Evaluating the dependence structure of compound precipitation and wind speed extremes

Author: Jakob Zscheischler, Philippe Naveau, Olivia Martius, Sebastian Engelke, Christoph C. Raible, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, 530 Physics, 13. Climate action, [SDU]Sciences of the Universe [physics], 0208 environmental biotechnology, 02 engineering and technology, 910 Geography & travel, 01 natural sciences, 550 Earth sciences & geology, 020801 environmental engineering, 0105 earth and related environmental sciences
Abstract: International audience; Estimating the likelihood of compound climate extremes such as concurrent drought and heatwaves or compound precipitation and wind speed extremes is important for assessing climate risks. Typically, simulations from climate models are used to assess future risks, but it is largely unknown how well the current generation of models represents compound extremes. Here, we introduce a new metric that measures whether the tails of bivariate distributions show a similar dependence structure across different datasets. We analyse compound precipitation and wind extremes in reanalysis data and different high-resolution simulations for central Europe. A state-of-the-art reanalysis dataset (ERA5) is compared to simulations with a weather model (Weather Research and Forecasting - WRF) either driven by observation-based boundary conditions or a global circulation model (Community Earth System Model - CESM) under present-day and future conditions with strong greenhouse gas forcing (Representative Concentration Pathway 8.5 - RCP8.5). Over the historical period, the high-resolution WRF simulations capture precipitation and wind extremes as well as their response to orographic effects more realistically than ERA5. Thus, WRF simulations driven by observation-based boundary conditions are used as a benchmark for evaluating the dependence structure of wind and precipitation extremes. Overall, boundary conditions in WRF appear to be the key factor in explaining differences in the dependence behaviour between strong wind and heavy precipitation between simulations. In comparison, external forcings (RCP8.5) are of second order. Our approach offers new methodological tools to evaluate climate model simulations with respect to compound extremes.
Published: 2021

40. Record events attribution in climate studies

Author: Julien Worms, Philippe Naveau, Laboratoire de Mathématiques de Versailles (LMV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)
Subjects: Statistics and Probability, [SDU]Sciences of the Universe [physics], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Ecological Modeling, Weibull distribution, Causality theory, Generalized Extreme Value, Climate Detection and Attribution
Abstract: Within the statistical climatology literature, inferring the contributions of potential causes with regard to climate change has become a recurrent research theme during this last decade. In particular, disentangling human induced (anthropogenic) forcings from natural causes represents a non-trivial statistical task, especially when the focal point moves away from mean behaviors and goes towards extreme events with high societal impacts. Most studies found in the field of Extreme Event Attributions (EEA) rely on Extreme Value Theory (EVT). Under this theoretical umbrella, it is often assumed that, for a given location, temporal changes in extremes can be detected in both location and scale parameters of an extreme value distribution, while its shape parameter remains unchanged over time. This assumption of constant tail shape parameters between a so-called factual world (all forcings) and a counterfactual one (without anthropogenic forcing) can be challenged due to the fact that important forcing changes could impact large scale atmospheric and oceanic circulation patterns, and consequently, the later can reshape the full distribution, including its shape parameter that drives extremal behavior.In this paper, we study how allowing different extremal tail indices between the factual and counterfactual worlds can affect the analysis of records. In particular, we extend the work of Naveau, Rides, Zwiers, Hannart, Tuel, You (Journal of Climate, 2018) in which this case was not treated. We also add properties and theoretical inferential results about records in EEA and propose a procedure for model validation. A simulation study of our approach is detailed. Our method is applied on records of yearly maxima of daily maxima of near-surface air temperature issued from the numerical climate model CNRM-CM5 of Météo-France.
Published: 2020

41. Characterization of ERA5 daily precipitation using the extended generalized Pareto distribution

Author: Olivia Martius, Philippe Naveau, and Pauline Rivoire
Subjects: Generalized Pareto distribution, Environmental science, Applied mathematics, Precipitation, Characterization (materials science)
Abstract: Both mean and extreme precipitation are highly relevant and a probability distribution that models the entire precipitation distribution therefore provides important information. Very low and extremely high precipitation amounts have traditionally been modeled separately. Gamma distributions are often used to model low and moderate precipitation amounts and extreme value theory allows to model the upper tail of the distribution. However, difficulties arise when making a link between upper and lower tail. One solution is to define a threshold that separates the distribution into extreme and non-extreme values, but the assignment of such a threshold for many locations is not trivial. Here we apply the Extended Generalized Pareto Distribution (EGPD) used by Tencaliec & al. 2019. This method overcomes the problem of finding a threshold between upper and lower tails thanks to a transition function (G) that describes the transition between the empirical distribution of precipitation and a Pareto distribution. The transition cumulative distribution function G has to be constrained by the upper tail and lower tail behavior. G can be estimated using Bernstein polynomials.EGPD is used here to characterize ERA-5 precipitation. ERA-5 is a new ECMWF climate re-analysis dataset that provides a numerical description of the recent climate by combining a numerical weather model with observations. The data set is global with a spatial resolution of 0.25° and currently covers the period from 1979 to present.ERA-5 daily precipitation is compared to EOBS, a gridded dataset spatially interpolated from observations over Europe, and to CMORPH, a satellite-based global precipitation product. Simultaneous occurrence of extreme events is assessed with a hit rate. An intensity comparison is conducted with return levels confidence intervals and a Kullback Leibler divergence test, both derived from the EGPD.Overall, extreme event occurrences between ERA5 and EOBS over Europe appear to agree. The presence of overlap between 95% confidence intervals on return levels highly depends on the season and the probability of occurrence.
Published: 2020

42. Attribution of Extreme Wave Height Records along the North Atlantic Coasts using Hindcast Data: Feasibility and Limitations

Author: Philippe Naveau, Jeremy Rohmer, Xavier Bertin, Jessie Louisor, Gonéri Le Cozannet, Soulivanh Thao, Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), LIttoral ENvironnement et Sociétés - UMRi 7266 (LIENSs), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS), We acknowledge financial support of Project EUPHEME, which is part of ERA4CS, an ERA-365 NET initiated by JPI Climate, and funded by FORMAS (SE), BMBF (DE), BMWFW (AT), 366 IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). The R script for implementing the EEA of Naveau et al. (2018) is available at: https://thaos.github.io/farr_vignette/. The wave data are, European Project: 690462,H2020,H2020-SC5-2015-one-stage,ERA4CS(2016), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), LIttoral ENvironnement et Sociétés (LIENSs), and La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Counterfactual thinking, 010504 meteorology & atmospheric sciences, fraction of attributable risk, Climate change, wave, 01 natural sciences, Latitude, Hindcast, 14. Life underwater, extreme event attribution, 0105 earth and related environmental sciences, Earth-Surface Processes, Water Science and Technology, Event (probability theory), geography, geography.geographical_feature_category, Ecology, 010505 oceanography, uncertainties, 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Climatology, Oceanic basin, Maxima, Significant wave height
Abstract: Rohmer, J.; Louisor, J.; Le Cozannet, G.; Naveau, P.; Thao, S., and Bertin, X., 2020. Attribution of extreme wave height records along the North Atlantic coasts using hindcast data: Feasibility and limitations. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1268-1272. Coconut Creek (Florida), ISSN 0749-0208.Extreme Event Attribution (EEA) aims at answering questions about how much climate change influenced the probability or intensity of a specific type of extreme meteo-oceanic event. Here, we focus on wave record breaking i.e. the occurrence of an extreme significant wave height (hs) value that exceeds all past observations. The objective is to evaluate the probability changes related to climate change by estimating the fraction of attributable risk far=1–p0/p1, where p0 and p1 are probabilities of the wave record event in two different worlds: a counterfactual world without anthropogenic forcings i.e. a “world that might have been”, and the factual world, i.e. “world that is.” To define these different worlds, we rely on a wave hindcast database, which provides very long time series (1900-2008) of hs over the whole North Atlantic Ocean Basin (NAOB). We assume that the counterfactual world corresponds to the series of annual hs maxima over the period 1900-1930, and that the factual world corresponds to the annual hs maxima over the period 1978-2008. The extreme event attribution approach dedicated to record breakings was applied over NAOB, and we show large far values (>0.5) along the northern Canadian, Scottish and southwestern Norwegian coasts (over the latitudes 50°-65°N). These results are, however, carefully discussed with respect to different uncertainty sources, namely the validity of the assumptions underlying the EEA, the statistical uncertainties, the use of hindcast data instead of global climate model's results, and the limitations in the hindcast database setup.
Published: 2020

43. Statistical Methods for Extreme Event Attribution in Climate Science

Author: Aurélien Ribes, Philippe Naveau, Alexis Hannart, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS)
Subjects: Statistics and Probability, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [STAT.AP]Statistics [stat]/Applications [stat.AP], History, 010504 meteorology & atmospheric sciences, extreme value theory, Event (relativity), [SDE.MCG]Environmental Sciences/Global Changes, detection, Climate science, attribution, 01 natural sciences, 010104 statistics & probability, 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Climatology, 0101 mathematics, Statistics, Probability and Uncertainty, Attribution, Extreme value theory, 0105 earth and related environmental sciences, causality theory
Abstract: International audience; Changes in the Earth's climate have been increasingly observed. Assessing the likelihood that each of these changes has been caused by human influence is important for decision making on mitigation and adaptation policy. Because of their large societal and economic impacts, extreme events have gar-nered much media attention-have they become more frequent and more intense, and if so, why? To answer such questions, extreme event attribution (EEA) tries to estimate extreme event likelihoods under different scenarios. Over the past decade, statistical methods and experimental designs based on numerical models have been developed, tested, and applied. In this article, we review the basic probability schemes, inference techniques, and statistical hypotheses used in EEA. To implement EEA analysis, the climate community relies on the use of large ensembles of climate model runs. We discuss, from a statistical perspective, how extreme value theory could help to deal with the different modeling uncertainties. In terms of interpretation, we stress that causal counterfactual theory offers an elegant framework that clarifies the design of event attributions. Finally, we pinpoint some remaining statistical challenges, including the choice of the appropriate spatio-temporal scales to enhance attribution power, the modeling of concomitant extreme events in a multivariate context, and the coupling of multi-ensemble and observational uncertainties. 16.1
Published: 2020

44. Wave group focusing in the ocean: estimations using crest velocities and a Gaussian linear model

Author: Jean-François Filipot, Paul Platzer, Pierre Tandeo, Pascal Yiou, Philippe Naveau, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), France Energies Marines [Brest], Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ANR-10-IEED-0006,FEM,France Energies Marines(2010), European Project: 338965,EC:FP7:ERC,ERC-2013-ADG,A2C2(2014), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)
Subjects: Physics, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 021110 strategic, defence & security studies, Atmospheric Science, 010504 meteorology & atmospheric sciences, Velocity gradient, Gravitational wave, Gaussian, Wave packet, Mathematical analysis, 0211 other engineering and technologies, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], 02 engineering and technology, 01 natural sciences, Schrödinger equation, symbols.namesake, Amplitude, Earth and Planetary Sciences (miscellaneous), symbols, Crest, 14. Life underwater, Rogue wave, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Water Science and Technology
Abstract: Wave group focusing gives rise to the formation of large gravity waves at the surface of the ocean, some of which are called rogue waves and represent a natural hazard for ships and offshore platforms. For safety purposes, it is crucial to predict when and where these large waves will appear and how large they will be. This work focuses on crest velocities, a quantity that is relatively easy to extract from sea surface elevation fields. It is shown that there is a direct link between crest velocity gradient and wave group linear dispersive focusing. Studying analytically the focusing of one-dimensional Gaussian wave packets under linear evolution makes it possible to derive estimates of quantities at focus, based only on crest velocity measurements. In this way, the focusing time, focusing size and focusing amplitude (relative to instantaneous amplitude) of an isolated Gaussian wave packet can be estimated. Our work is also applicable to second-order non-linear waves. Limitations due to higher-order non-linear effects are studied in numerical simulations of the non-linear Schrodinger equation.
Published: 2020

45. Analyses of the Northern European Summer Heatwave of 2018

Author: Julien Cattiaux, Aglaé Jézéquel, Aurélien Ribes, Philippe Naveau, Nikolay Kadygrov, Yoann Robin, Pascal Yiou, Mathieu Vrac, Geert Jan van Oldenborgh, Soulivanh Thao, Davide Faranda, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Royal Netherlands Meteorological Institute (KNMI), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL)
Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Atmospheric Science, Geography, 010504 meteorology & atmospheric sciences, 010501 environmental sciences, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, 01 natural sciences, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract: International audience
Published: 2020

46. Revising Return Periods for Record Events in a Climate Event Attribution Context

Author: Aurélien Ribes, Philippe Naveau, Francis Zwiers, Alexis Hannart, Pascal Yiou, Alexandre Tuel, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] ( LSCE ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Groupe d'étude de l'atmosphère météorologique ( CNRM-GAME ), Institut national des sciences de l'Univers ( INSU - CNRS ) -Météo France-Centre National de la Recherche Scientifique ( CNRS ), Pacific Climate Impacts Consortium, University of Victoria [Canada] ( UVIC ), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [ SDU.OCEAN ] Sciences of the Universe [physics]/Ocean, Atmosphere, Atmospheric Science, 010504 meteorology & atmospheric sciences, Computer science, 0208 environmental biotechnology, Inference, Climate change, Context (language use), 02 engineering and technology, 01 natural sciences, Field (computer science), [ SDU.ENVI ] Sciences of the Universe [physics]/Continental interfaces, environment, Econometrics, Statistical techniques, Risk assessment, 0105 earth and related environmental sciences, Event (probability theory), [STAT.AP]Statistics [stat]/Applications [stat.AP], Statistics, Statistical model, Extreme events, 020801 environmental engineering, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, 13. Climate action, Sample size determination, Climatology, Changepoint analysis, Attribution
Abstract: International audience; Both climate and statistical models play an essential role in the process of demonstrating that the distribution of some atmospheric variable has changed over time and in establishing the most likely causes for the detected change. One statistical difficulty in the research field of detection and attribution resides in defining events that can be easily compared and accurately inferred from reasonable sample sizes. As many impacts studies focus on extreme events, the inference of small probabilities and the computation of their associated uncertainties quickly become challenging. In the particular context of event attribution, the authors address the question of how to compare records between the counterfactual “world as it might have been” without anthropogenic forcings and the factual “world that is.” Records are often the most important events in terms of impact and get much media attention. The authors will show how to efficiently estimate the ratio of two small probabilities of records. The inferential gain is particularly substantial when a simple hypothesis-testing procedure is implemented. The theoretical justification of such a proposed scheme can be found in extreme value theory. To illustrate this study’s approach, classical indicators in event attribution studies, like the risk ratio or the fraction of attributable risk, are modified and tailored to handle records. The authors illustrate the advantages of their method through theoretical results, simulation studies, temperature records in Paris, and outputs from a numerical climate model.
Published: 2018

47. Quantifying the Risk of Extreme Events under Climate Change

Author: Eric Gilleland, Richard W. Katz, and Philippe Naveau
Subjects: 010504 meteorology & atmospheric sciences, Flood myth, business.industry, 0208 environmental biotechnology, Environmental resource management, Extreme events, Climate change, 02 engineering and technology, General Medicine, 01 natural sciences, 020801 environmental engineering, Water resources, Environmental science, Engineering design process, business, 0105 earth and related environmental sciences
Abstract: We are all familiar with the proverbial “100-year flood,” a concept instrumental in engineering design for water resources management. This concept, generally termed a “return level” corresponding ...
Published: 2017

48. Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation

Author: Philippe Naveau, Luc Neppel, and Julie Carreau
Subjects: 0208 environmental biotechnology, Inference, Estimator, 02 engineering and technology, 01 natural sciences, Synthetic data, Shape parameter, 020801 environmental engineering, Multivariate interpolation, 010104 statistics & probability, 13. Climate action, Generalized Pareto distribution, Statistics, Spatial ecology, Spatial variability, 0101 mathematics, Water Science and Technology, Mathematics
Abstract: The French Mediterranean is subject to intense precipitation events occurring mostly in autumn. These can potentially cause flash floods, the main natural danger in the area. The distribution of these events follows specific spatial patterns, i.e., some sites are more likely to be affected than others. The peaks‐over‐threshold approach consists in modeling extremes, such as heavy precipitation, by the generalized Pareto (GP) distribution. The shape parameter of the GP controls the probability of extreme events and can be related to the hazard level of a given site. When interpolating across a region, the shape parameter should reproduce the observed spatial patterns of the probability of heavy precipitation. However, the shape parameter estimators have high uncertainty which might hide the underlying spatial variability. As a compromise, we choose to let the shape parameter vary in a moderate fashion. More precisely, we assume that the region of interest can be partitioned into subregions with constant hazard level. We formalize the model as a conditional mixture of GP distributions. We develop a two‐step inference strategy based on probability weighted moments and put forward a cross‐validation procedure to select the number of subregions. A synthetic data study reveals that the inference strategy is consistent and not very sensitive to the selected number of subregions. An application on daily precipitation data from the French Mediterranean shows that the conditional mixture of GPs outperforms two interpolation approaches (with constant or smoothly varying shape parameter).
Published: 2017

49. Skillful prediction of multidecadal variations in volcanic forcing

Author: Alexandre Tuel, Caspar M. Ammann, Philippe Naveau, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Explosive material, Statistical model, Volcanism, Geophysics, Forcing (mathematics), Radiative forcing, 01 natural sciences, 010104 statistics & probability, Volcano, 13. Climate action, Climatology, Binary data, General Earth and Planetary Sciences, 0101 mathematics, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, Historical record, Geology, 0105 earth and related environmental sciences
Abstract: Stratospheric sulfate injections from explosive volcanic eruptions are a primary natural climate forcing. Improved statistical models can now capture and simulate dynamical relationships in temporal variations of binary data. Leveraging these new techniques, the presented analysis clearly indicates that the number of large eruptions in the most recent records of explosive volcanism cannot be considered to be fully random. Including dynamical dependence in our models improves their ability to reproduce the historical record, and thus forms a strong basis for skill in statistical prediction. Possible geophysical mechanisms behind the identified multi-decadal variations are discussed, including variations in the observed length-of-day.
Published: 2017

50. A semi-parametric stochastic generator for bivariate extreme events

Author: Giulia Marcon, Simone A. Padoan, and Philippe Naveau
Subjects: Statistics and Probability, Multivariate statistics, Mathematical optimization, 05 social sciences, Bivariate analysis, Function (mathematics), 01 natural sciences, Semiparametric model, 010104 statistics & probability, 0502 economics and business, Poisson point process, Parametric model, Generalized extreme value distribution, Applied mathematics, 0101 mathematics, Statistics, Probability and Uncertainty, Extreme value theory, 050205 econometrics, Mathematics
Abstract: The analysis of multiple extreme values aims to describe the stochastic behaviour of observations in the joint upper tail of a distribution function. For instance, being able to simulate multivariate extreme events is convenient for end users who need a large number of random replications of extremes as input of a given complex system to test its sensitivity. The simulation of multivariate extremes is often based on the assumption that the dependence structure, the so-called extremal dependence function, is described by a specific parametric model. We propose a simulation method for sampling bivariate extremes, under the assumption that the extremal dependence function is semi-parametric. This yields a flexible tool that can be broadly applied in real-data analyses. With the aim of estimating the probability of belonging to some extreme sets, our methodology is examined via simulation and illustrated by an analysis of strong wind gusts in France. Copyright © 2017 John Wiley & Sons, Ltd.
Published: 2017

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