Extreme Probability Research Articles

Impacts of the boreal summer intraseasonal oscillation on precipitation extremes in Indonesia

AbstractThe relationship between boreal summer intraseasonal oscillation (BSISO) and precipitation extremes over Indonesia is investigated using observational datasets from 30 years (1987–2016) of rain gauge measurements and the gridded Asian Precipitation‐Highly Resolved Observational Data Integration Towards Evaluation of Water Resources from 1998 to 2015. The results indicate that the frequency of daily extreme precipitation events in Indonesia (defined as total precipitation above the 95th percentile) during extended boreal summer (May–August) is significantly modulated by BSISO, especially over the western and northern regions. Under the influences of BSISO1, the probability of the precipitation extremes over Sumatra and Borneo increases by 20–120% (relative to the seasonal probability) during phases 1–3 and approximately 50–80% over the eastern part of Borneo and Sulawesi during phase 4. Under BSISO2, the probability of the extremes increases up to 40% over Sumatra during phases 1 and 2 and up to 140% over Borneo and Sulawesi during phases 2 and 3. The increase in the probability of extreme summer precipitation is associated with enhanced large‐scale moisture flux convergence and upward moisture transport induced by the active phases of BSISO. These results provide potential information for medium‐ to extended‐range predictions of summer precipitation extremes in Indonesia.

Dependence of compound hot and dry extremes on individual ones across China during 1961–2014

Compound hot and dry extremes have drawn extensive attention given the devastating impacts on natural ecosystem and social economy. However, the substantial dependence of compound hot and dry extremes upon individual ones is barely discussed at the daily level. In this study, we present the dependence of compound hot and dry days (CHDDs) and events (CHDEs) upon individual ones using summer (June–July–August) daily temperature and precipitation across China during 1961–2014. Results show more CHDDs and CHDEs over regions from northeast to southwest China, with stronger intensity over the transitional area between Northwest China (NW) and the Tibetan Plateau. Simultaneously, the period with the occurrence of compound extremes generally extends since compound extremes start earlier and end later. On average over China, hot extremes are significantly (p < 0.01) influential on the frequency (r = 0.42–0.83) of compound extremes, which contribute 55–78% to changes in the frequency. Dry extremes are significantly (p < 0.01) influential on the intensity (r = 0.60–0.79) and contribute 63–70% to changes in the intensity. Both hot and dry extremes are influential on the start and end of compound extremes. More importantly, largely influenced by the high probability of dry extremes in North China, the joint probability of compound extremes is higher in NW followed by Inner Mongolia. These findings highlight the new insights into compound extremes defined by relative thresholds at the daily level.

Nowcasting extreme rain and extreme wind speed with machine learning techniques applied to different input datasets

Predicting extreme weather events in a short time period and their developing in localized areas is a challenge. The nowcasting of severe and extreme weather events is an issue for air traffic management and control because it affects aviation safety, and determines delays and diversions. This work is part of a larger study devoted to nowcasting rain and wind speed in the area of Malpensa airport by merging different datasets. We use as reference the weather station of Novara to develop a nowcasting machine learning model which could be reusable in other locations. In this location we have the availability of ground-based weather sensors, a Global Navigation Satellite System (GNSS) receiver, a C-band radar and lightning detectors. Our analysis shows that the Long Short-Term Memory Encoder Decoder (LSTM E/D) approach is well suited for the nowcasting of meteorological variables. The predictions are based on 4 different datasets configurations providing rain and wind speed nowcast for 1 h with a time step of 10 min. The results are very promising with the extreme wind speed probability of detection higher than 90%, the false alarms lower than 2%, and a good performance in extreme rain detection for the first 30 min. The configuration using just weather stations and GNSS data in input provides excellent performances and should be preferred to the other ones, since it refers to the pre-convective environment, and thus can be adaptable to any weather conditions.

Extremal Probability Bounds in Combinatorial Optimization

In this paper, we compute the tightest possible bounds on the probability that the optimal value of a combinatorial optimization problem in maximization form with a random objective exceeds a given number, assuming only knowledge of the marginal distributions of the objective coefficient vector. The bounds are ``extremal'' since they are valid across all joint distributions with the given marginals. We analyze the complexity of computing the bounds assuming discrete marginals and identify instances when the bounds are computable in polynomial time. For compact 0/1 V-polytopes, we show that the tightest upper bound is weakly NP-hard to compute by providing a pseudopolynomial time algorithm. On the other hand, the tightest lower bound is shown to be strongly NP-hard to compute for compact 0/1 V-polytopes by restricting attention to Bernoulli random variables. For compact 0/1 H-polytopes, for the special case of PERT networks arising in project management, we show that the tightest upper bound is weakly NP-hard to compute by providing a pseudopolynomial time algorithm. The results in the paper complement existing results in the literature for computing the probability with independent random variables.

A novel statistical method for long-term coronavirus modelling

Background: Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods: Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results: This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions: The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Extreme wind speed prediction in mountainous area with mixed wind climates

In addition to common synoptic wind system, the mountainous terrain forms a local thermally driven wind system, which makes the mountain wind system have strong terrain dependence. Therefore, in order to estimate the reliable design wind speeds for structural safety, the samples for extreme wind speeds for certain return periods at mountainous areas can only come from field measurements at construction site. However, wind speeds measuring duration is usually short in real practice. This work proposes a novel method for calculating extreme wind speeds in mountainous areas by using short-term field measurement data and long-term nearby meteorological observatory data. Extreme wind speeds in mountainous area are affected by mixed climates composed by local-scale wind and large-scale synoptic wind. The local winds can be recorded at construction site with short observatory time, while the extreme wind speeds samples from synoptic wind climate from nearby meteorological station with long observatory time is extracted for data augmentation. The bridge construction site at Hengduan Mountains in southwestern China is taken as an example in this study. A 10-month dataset of field measurement wind speeds is recorded at this location. This study firstly provides a new method to extract wind speed time series of windstorms. Based on the different windstorm features, the local and synoptic winds are separated. Next, the synoptic wind speeds from nearby meteorological stations are converted and combined with local winds to derive the extreme wind speeds probability distribution function. The calculation results shows that the extreme wind speed in the short return period is controlled by the local wind system, and the long-period extreme wind speed is determined by the synoptic wind system in the mountain area. The descending slope of the synoptic wind speed exceeding probability exceeds the local wind by about 20%. If the influence of mixed climate is not considered, and the wind speed samples are not divided by category, the decline slope will be 7% lower.

Forecasting extreme precipitation in the central Mediterranean: Changes in predictors' strength with prediction lead time

AbstractThis work compares forecasting strategies for improving the prediction of extreme precipitation events at different timescales. Our study focuses on forecasting extreme precipitation in Calabria, southern Italy. This region with complex and abruptly varying topography poses additional challenges in forecasting precipitation. Here, we use gridded observational precipitation data from the E‐OBS dataset, reanalysis data from ERA5, and forecasts from ECMWF reforecasts. We show that different forecasting horizons require different forecasting strategies. For short to medium‐range lead times, post‐processing the forecasted extreme precipitation probabilities provides the most informative outputs for end users. For extended‐range forecasts though, it is beneficial to use the large‐scale weather variability, as depicted based on nine Mediterranean patterns in combination with moisture‐related information, to infer reliable information about extreme precipitation. We present benefits and limitations of the methods based on long‐term statistical analysis using a range of indicators, such as the Brier skill score and its decomposition, and the cost–loss model.

Boreal Summer Intraseasonal Meridional Oscillations Over East Asia and Their Influences on Precipitation

AbstractThe intraseasonal meridional wave trains are the major features of East Asia summer monsoon (EASM), but the complete physical pictures of dynamic pathways from tropical and mid‐high latitudes and their climate impacts have not been fully illustrated. With NCEP‐DOE Reanalysis‐II daily data set, two boreal summer intraseasonal meridional oscillations (ISMOs) are isolated and identified by extended EOF of zonal wind anomalies along the coast of East Asia on 10‐ to 30‐day timescale, that is, ISMO1 and ISMO2. They both demonstrate obvious quasi‐biweekly oscillations with an equivalent barotropic structure in troposphere, and ISMO1 represents the southward propagating wave train with large variances in upper level, which is initiated from the West Europe and primarily forced by the soil moisture anomalies over mid‐high latitudes of Eurasian continent, whereas ISMO2 propagates northward in mid‐lower troposphere from the tropical western North Pacific to East Asia that is closely related to BSISO2. The two ISMOs are capable of explaining a large fraction of the total intraseasonal variances of EASM, wherein, ISMO1 is responsible for a meridional dipole pattern of precipitation anomalies over eastern China, while ISMO2 has major influence in southern China. Generally, ISMO‐caused changes of extreme precipitation probability are in good accordance with the spatial pattern of its related 10‐ to 30‐day intraseasonal precipitation anomalies. Since ISMO1 and ISMO2 can clearly separate the tropical and extratropical‐origin meridional oscillations and further have robust influences on precipitation over East Asia, they can be used as the new monitoring and predicting factors to the intraseasonal variations of EASM and their associated extreme precipitation.

The role of teleconnection in the occurrence probability of extreme precipitation over China based on extreme value theory

Non-stationarity of extreme climate events has been reported worldwide in recent decades, and traditional stationary analysis methods are no longer sufficient to properly reveal the occurrence probability of climate extremes. Based on the 0.25°C × 0.25°C gridded precipitation data (i.e., CN05.1), stationary and non-stationary models of generalized extreme value (GEV) and generalized Pareto (GP) distributions are adopted to estimate the occurrence probability of extreme precipitation over China during 1961–2018. Low-frequency oscillation (LFO) indices, such as El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO), are included as time-varying covariates in the non-stationary GEV and GP models. Results illustrate that the occurrence probability of extreme precipitation estimated from the stationary GEV and GP distributions shows a significant increasing trend in northwestern and southeastern China, and the opposite trend in southwestern, central, and northeastern China. In comparison with stationary model, the fitness of extreme precipitation series is improved for both the GEV and GP distributions if these LFO indices are used as time-varying covariates. Positive ENSO, IOD and PDO tend to cause negative anomalies in the occurrence probability of extreme precipitation in northeastern China and Tibet Plateau, and positive anomalies in southern China. Positive NAO and SAM phases mainly tend to cause positive anomalies in southern China. The circulation patterns of extreme precipitation anomalies associated with these LFO indices are discussed from aspects of precipitable water, vertical integrated moisture transport, 500-hPa geopotential height and 850-hPa wind field.

Two types of wave-current interactions and their effects on extreme waves in directional seas

The nonlinear wave-current interactions can influence and change extreme wave probability, spectral characteristics, and average shape of extreme waves significantly (Wang, J., Ma, Q. W., & Yan, S. (2021). On Extreme Waves in Directional Seas with Presence of Oblique Current. Applied Ocean Research, 112, pp. 102586). There are different scenarios of wave-wave interactions in reality. This study identifies two types of wave-current interactions: Type-I (waves propagating from current-negligible region to current-significant region) and Type-II (waves propagating from current-significant region to current-negligible region). The impacts of the two types of wave-current interactions on extreme wave probability and the spectral properties will be investigated by using fully nonlinear potential flow model. The numerical results reveal that the changes in extreme wave probability and spectral properties due to Type-I are more evident than those induced by Type-II. It is recommended that a proper type of wave-current interaction scenario should be selected in the design of the numerical modelling or laboratory experiments for wave-structure interactions to achieve more accurate representations of the local wave climate and flow conditions.

Arctic and Pacific Ocean Conditions Were Favorable for Cold Extremes over Eurasia and North America during Winter 2020/21

Abstract A sequence of extreme cold events occurred across Eurasia and North America during winter 2020/21. Here, we explore the causes and associated mechanisms for the extremely cold temperatures using both observations and large-ensemble simulations. Experiments were conducted with observed ocean surface boundary conditions prescribed globally, and regionally to discern the specific influence of Arctic, tropical Pacific, and North Pacific drivers. Increased likelihood of daily cold extremes in mid-December to mid-January are found in Eurasian midlatitudes in response to reduced Arctic sea ice. Tropical sea surface temperature anomalies, more specifically the La Niña pattern, increased probability of extreme cold over high-latitude Eurasia in early January to early February. Both reduced Arctic sea ice and La Niña increased the probability of daily cold extremes over western North America in late January to late February. We conclude that a combination of reduced Arctic sea ice, La Niña, and a sudden stratospheric warming in January 2021 were factors in the February 2021 extreme cold wave that caused huge societal disruptions in Texas and the southern Great Plains. Although the magnitude of the simulated cold extremes are relatively small when compared with observed anomalies, the Arctic and Pacific Ocean surface conditions in winter 2020/21 increased the probability of cold days as cold or colder than observed by approximately 17%–43%.

The Generalized Exponential Extended Exponentiated Family of Distributions: Theory, Properties, and Applications

Here, we propose a new generalized exponential extended exponentiated (NGE3) family of distributions. Some statistical properties of proposed family are gained. The most extreme probability method, maximum likelihood (ML), is utilized for parameter estimation. We explore an exceptional model called NGE3-Exponential (NGE3E). NGE3E is estimated with ML, and the performance of estimators is demonstrated by utilizing a simulation. Moreover, two applications are given to show the significance and adaptability of the proposed model in comparison to some generalized models (GMs).

Segmentation of retinal blood vessel using generalized extreme value probability distribution function(pdf)-based matched filter approach

Segmentation of retinal blood vessel using generalized extreme value probability distribution function(pdf)-based matched filter approach

Study on the time-varying extreme value characteristic of the transient loads on a 5:1 rectangular cylinder subjected to a thunderstorm-like wind

A reasonable extreme probability model to estimate the transient loads is essential for structural wind resistance design. The transient aerodynamic characteristics of a 5:1 rectangular cylinder under thunderstorm-like wind are experimentally studied based on the multiple-fan active wind tunnel technique. In this paper, the non-Gaussian characteristics of the transient pressures are analyzed with emphasis on the time-varying skewness and time-varying kurtosis. A generalized time-varying extreme value model for uniformly modulated nonstationary stochastic processes is proposed, which is adopted to study the extreme value of transient loads on a 5:1 rectangular cylinder under a thunderstorm-like wind. Compared with the stationary flow condition, the results show that the non-Gaussian features of the transient pressures is noticeable, which is determined by the time-varying turbulence intensity. The greater the time-varying turbulence intensity, the stronger the non-Gaussian characteristic of the transient loads is, which is consistent with the stationary wind case. Notably, the presented work confirms that the conventional mean extreme value model may seriously underestimate the extreme value of transient loads. Moreover, the extreme values under stationary flow are slightly higher than those under transient wind pressures. The results show that the peak factors in the suction region are significantly greater than that in the reattachment region, and the time-varying peak factors under transient wind pressures are slightly less than in the stationary flow case.

Identifying Strategies and Procedures of Seller Engagement of Daraz

E-commerce, web platforms and the whole internet system has become the most responsive, demanding and fast growing sector of the world as we are living in a total digital-era. But in Bangladesh E-commerce sites are just like the teenage department as an organizational sector. There are so many possibilities, several opportunities and uncountable chances of gaining and showcasing creativity with rising on a standard level to cope up internationally. As being one of the largest and leading online shopping platforms, Daraz has an extreme probability of conducting most amounts of transactions between sellers and customers. They conduct a pure individual action set which is named “Seller Engagement” to engage and train the sellers to be efficient users of internal software ofDaraz.com.bd which will be involved in all the process of using the platform. This paper has been written as a reason to identify the obstacles in “Seller Engagement” procedure of Daraz.com.bd. Recognizing the gap and lacking between sellers andacquisition or content with advantages and potential aspects are the ultimate goal of this paper . The number of respondents was 46 and all of them were sellers who are enlisted in Daraz network. Judgmental sampling of non-probability samplingmethod was applied in analysis of data which has been explained by descriptive research method. In the study, it has been found that there are some gaps existing in the process which is decreasing the whole outcome. Providing proper resources and training, assurance of the quality and authenticity along with some complex factors are the possible best keys to fill up the gaps and get to the ride.

Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based

AbstractPrecipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

Distributionally robust dispatching of multi-community integrated energy system considering energy sharing and profit allocation

Community energy consumption accounts for a large proportion of total social energy consumption. Therefore, community integrated energy systems have emerged to satisfy these demands. However, limited resource allocation of the integrated energy system in a single community is possible because of poor anti-interference and insufficient photovoltaic absorption. A distributionally robust optimal dispatching model was proposed for the joint operation of multiple community integrated energy systems, and the energy sharing and joint demand response among communities were considered. Kullback–Leibler (KL) divergence was used to describe the ambiguity set of photovoltaic output probability distribution. When the photovoltaic output prediction error obeyed the extreme probability distribution of ambiguity set, the minimum operation cost of multi-community was solved. Furthermore, to ensure successful alliance of multi-community cooperative alliance, a profit allocation mechanism based on the improved Shapley value was proposed to reallocate the excess return fairly. Finally, a typical multi community integrated energy system was simulated and analysed. The results revealed that energy sharing and joint demand response improve the economy of multi community operation, and the coordination of system robustness and economy of the model was realised. The proposed profit allocation mechanism can effectively ensure multi community cooperation.

Improving Estimates Accuracy of Voter Transitions. Two New Algorithms for Ecological Inference Based on Linear Programming

The estimation of RxC ecological inference contingency tables from aggregate data is one of the most salient and challenging problems in the field of quantitative social sciences, with major solutions proposed from both the ecological regression and the mathematical programming frameworks. In recent decades, there has been a drive to find solutions stemming from the former, with the latter being less active. From the mathematical programming framework, this paper suggests a new direction for tackling this problem. For the first time in the literature, a procedure based on linear programming is proposed to attain estimates of local contingency tables. Based on this and the homogeneity hypothesis, we suggest two new ecological inference algorithms. These two new algorithms represent an important step forward in the ecological inference mathematical programming literature. In addition to generating estimates for local ecological inference contingency tables and amending the tendency to produce extreme transfer probability estimates previously observed in other mathematical programming procedures, these two new algorithms prove to be quite competitive and more accurate than the current linear programming baseline algorithm. Their accuracy is assessed using a unique dataset with almost 500 elections, where the real transfer matrices are known, and their sensitivity to assumptions and limitations are gauged through an extensive simulation study. The new algorithms place the linear programming approach once again in a prominent position in the ecological inference toolkit. Interested readers can use these new algorithms easily with the aid of the R package lphom.

Combined predictive and descriptive tests for extreme rainfall probability distribution selection

ABSTRACT The popular approach to select a suitable distribution to characterize extreme rainfall events relies on the assessment of its descriptive performance. This study examines an alternative approach to this task that evaluates, in addition to the descriptive performance of the models, their performance in estimating out-of-sample events (predictive performance). With a numerical experiment and a study case in São Paulo state, Brazil, we evaluated the adequacy of seven probability distributions widely used in hydrological analysis to characterize extreme events in the region and compared the selection process of both popular and altenative frameworks. The results indicate that (1) the popular approach is not capable of selecting distributions with good predictive performance and (2) combining different predictive and descriptive tests can improve the reliability of extreme event prediction. The proposed framework allowed the assessment of model suitability from a regional perspective, identifying the Generalized Extreme Value (GEV) distribution as the most adequate to characterize extreme rainfall events in the region.

The Role of Risk Forecast and Risk Tolerance in Portfolio Management: A Case Study of the Chinese Financial Sector

Portfolio decisions are affected by the volatility of financial markets and investors’ risk tolerance levels. To better allocate portfolios; we introduce risk tolerance into the portfolio management problem by considering the risk contribution of portfolio components. In this paper, portfolio weights are allocated to two stages. In the first stage, the portfolio risks and the risk contribution of each share are forecasted. In the second stage, we put forward three weighting techniques—“aggressive”, “moderate” and “conservative”, according to three standard levels of risk tolerance. In addition, a new risk measure called “joint extreme risk probability” (JERP), with risk tolerance taken into account, is proposed. A case study of the Chinese financial industry is conducted to verify the performance of our methods. The empirical results demonstrate that weighting techniques constrained by risk tolerance lead to higher gains in a normal market and less loss when a market is risky. Compared with risk-tolerance-adjusted strategies, the relationship between the performance of the traditional conditional value at risk (CVaR) minimization method and the market risk level is less obviously demonstrated. Viewed from the results, JERP functions as an effective signal that helps investors to deal with potential market risks.