Definitions Of Extreme Events Research Articles

Diverging identifications of extreme precipitation events from satellite observations and reanalysis products: A global perspective based on an object-tracking method

Extreme precipitation can trigger various natural hazards, causing catastrophic losses worldwide. As global warming accelerates, it is widely acknowledged that extreme precipitation will become more frequent and intense, calling for more accurate historical and projected precipitation estimation. Identifying extreme precipitation events is understudied particularly on the global scale, given that most studies only focus on pixel-by-pixel characteristics while ignoring the space-time continuity and evolution of extreme events. This study utilizes an object-based tracking method to track the precipitation system objects in time, extracting spatiotemporal attributes of precipitation events and investigates variable definitions of extremes for a better understanding of the performance of existing precipitation datasets. Five satellite and two reanalysis precipitation products (IMERG, GSMaP, PERSIANN-CCS, TRMM 3B42, CMORPH, ERA5, and ERA-Interim) are involved to investigate (1) the difference and similarity of those datasets in depicting the global pattern of extreme precipitation, (2) the impact of various definitions of extreme events, and (3) the trend of global extreme precipitation events. Results show that the object-based method can capture the motion of precipitation events. Different extreme definitions have a large impact on the distribution, trend, and features of extreme precipitation. The inter-comparison of extreme precipitation events from different products shows low correlation, indicating that accurately capturing the evolution of extreme events is still challenging. Reanalysis product-based analysis shows variable trends (magnitudes and signs) based on different attributes of precipitation objects and notable scale effect. Satellite precipitation products exhibit distinct inter-annual variability which could affect their hydrometeorological applications. In summary, this study leverages the object-based tracking method for comprehensive extreme precipitation analysis from the multi-dataset, multi-scale, and multi-definition perspectives, which can advance the understanding of the status and trend of global extreme precipitation.

EXTREME EVENTS ANALYSIS OF NON-STATIONARY TIME SERIES BY USING HORIZONTAL VISIBILITY GRAPH

In this paper, we analyze the extreme events of non-stationary time series in the framework of horizontal visibility graph (HVG). We give a new definition of extreme events, which incorporates the temporal structure of the series and the degree of the nodes in the HVG. An advantage of the new concept is that it does not require ad hoc treatment even when the non-stationarity arises in time series. We also use the information-theoretic methods to analyze the degree of nodes in the HVG. In the numerical analysis, we study the statistical characterizations of the extreme events of synthetic time series, including the random noises, periodic time series, random walk processes, and the long-range auto-correlated time series. Then, we study 9 time series in stock markets to identify the extreme events evolving in these non-stationary systems. Interestingly, we find that the daily closing price series perform rather close to the random walk processes, while the daily trading volume series behave quite similar to the random noises.

Extreme events defined—A conceptual discussion applying a complex systems approach

‘Extreme event’, a term today most commonly understood and used in relation to extreme weather phenomena and experiencing an upsurge in its usage due to their increased frequency caused by climate change, is applied in a variety of scientific disciplines. Its multitude of users understands and defines the term differently. However, consistency in language is vital to eradicate confusion, support the transfer of knowledge from one field to another, and make results from different disciplines comparable. Therefore, this work gives an in-depth discussion of the various aspects of relevance, ultimately proposing a comprehensive, systems-based definition of the term.Novel to this definition is the complex systems approach, utilized throughout to allow the definition to be applied for both macrolevel and microlevel occurrences and across various disciplines. In contrast to most authors who separate incident and impacts and use either the former or the latter in definitions of extreme events, it is shown that a disruption to a system or systems is prerequisite. Only by applying this perspective can interdisciplinary research be successfully conducted on extreme events. This and other central aspects come particularly to light in a case study of the 2006 European Blackout, on which the meta-definition is tried.

The Changing Context of Hazard Extremes: Events, Impacts, and Consequences

Recognizing that it does not take an extreme event to produce extreme consequences, White argued for an equal emphasis on technologically-oriented solutions to natural hazards problems, along with emphases on the social, economic, and political factors that lead to the adoption or non-adoption of alternatives for managing disaster risk. The paradox of modern society is that we are not achieving this balance — we have more knowledge and abilities to manipulate nature, yet simultaneously have increased our exposure and susceptibility to natural hazards through our own actions, myopia, and development patterns. The changing context of hazards necessitates more progressive approaches to disaster risk management. This means a reframing of current programs and policies away from response to a more proactive and longer term emphases on enhancing resilience at local to global scales. It also means expanding our definitions of extreme events and extreme consequences. Linking hazards science and practice to larger societal changes and development objectives is the path toward a more resilient future, a goal that personifies the scholarship of White and his student, Mitchell.

Analysis of the Christmas Storm Over Northern Italy

Between the 24th and the 26th of December 2013 Northern Italy was interested by heavy and diffuse precipitations events. The same stormy event, called "Christmas storm", interested all European area producing numerous damages and diseases especially in France and UK. The aim of the present research paper is to describe such meteorological event for Veneto and Trentino regions and to investigate if it has been exceptional or not compared with typical climate of the area. First of all, a definition of extreme events is proposed, followed by a meteorological description of the event. After that, the reported precipitation values are compared with long time series (about 30 years) of historical observed precipitation and temperature data. Finally, a detailed description of the soil impacts is reported.

Trends and climate evolution: Statistical approach for very high temperatures in France

The existence of an increasing trend in average temperatures during the last 50 years is widely acknowledged. Furthermore, there is compelling evidence of the variability of extremes, and rapid strides are made in studies of these events. Indeed, by extending the results of the “extreme value theory” (EVT) to the non-stationary case, analyses can examine the presence of trends in extreme values of stochastic processes. Definition of extreme events, their statistical significance as well as their interpretations have to be handled with great care when used for environmental concerns and public safety. Thus, we will discuss the validity of the hypothesis allowing the use of mathematical theories for these problems. To answer safety requirements, respect installation norms and reduce public risk, return levels are a major operational goal, obtained with the EVT. In this paper, we give quantitative results for observations of high temperatures over the 1950–2003 period in 47 stations in France. We examined the validity of the non-stationary EVT and introduced the notion of return levels (RL) in a time-varying context. Our analysis puts particular accent on the difference between methods used to describe extremes, to perform advanced fits and tests (climatic science), and those estimating the probability of rare future events (security problems in an evolving climate). After enouncing the method used for trend identification of extremes in term of easily interpretable parameters of distribution laws, we apply the procedure to long series of temperature measurements and check the influence of data length on trend estimation. We also address the problem of choosing the part of observations allowing appropriate extrapolation. In our analysis, we determined the influence of the 2003 heat wave on trend and return-level estimation comparing it to the RL in a stationary context. The application of the procedure to 47 stations spread over France is a first step for a refined spatial analysis. Working on the behavior of distribution parameters while assessing trend identification is a primary tool in order to classify climatic change with respect to the location of the station and open a systematic work using the same methodology for other variables and multivariate studies.

Prediction of missing values and detection of ‘exceptional events’ in a chronological planktonic series: a single algorithm

The detection of extreme events is of primary importance because they often change the initial conditions of a dynamic system. However, the definition of what constitutes an extreme or exceptional event is unclear; what threshold or which rate of occurrence delineates an anomaly? An alternate and precisely specified type of definition might be an event which cannot be predicted by a particular model at a chosen probability. Missing values are unfortunately characteristic of biological oceanographic time series. This characteristic precludes a great deal of numerical treatments. Consequently, several interpolation techniques have been proposed to predict missing values. Most of them are not adequate for planktonic data which are characterized by high heterogeneity. An iterative approach based on the principles of the eigenvectors filtering (EVF) method is examined. The limits of the technique are determined through simulation. The same method is then applied for the detection and definition of extreme events. We first apply a crude method to select some maximal or minimal values in a data series (the extreme events), then the selected values are coded as missing values, and finally we evaluate how well the EVF is able to reproduce the original ‘extreme’ values. These simulations provide insight into why large peaks (or holes) can be identified as extremes events or not, based on the degree of their prediction.