scholarly journals Modeling of Future Extreme Storm Surges at the NW Mediterranean Coast (Spain)

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 472
Author(s):  
Jue Lin-Ye ◽  
Manuel García-León ◽  
Vicente Gràcia ◽  
María Ortego ◽  
Piero Lionello ◽  
...  
Keyword(s):  
Probability Density ◽  
Large Scale ◽  
Joint Probability ◽  
Storm Surges ◽  
Additive Models ◽  
Change Scenario ◽  
Event Duration ◽  
Extreme Storm ◽  
Climate Patterns ◽  
Joint Probability Density

Storm surges are one of the main drivers for extreme flooding at the coastal areas. Such events can be characterized with the maximum level in an extreme storm surge event (surge peak), as well as the duration of the event. Surge projections come from a barotropic model for the 1950–2100 period, under a severe climate change scenario (RCP 8.5) at the northeastern Spanish coast. The relationship of extreme storm surges to three large-scale climate patterns was assessed: North Atlantic Oscillation ( N A O ), East Atlantic Pattern ( E A W R ), and Scandinavian Pattern ( S C ). The statistical model was built using two different strategies. In Strategy #1, the joint probability density was characterized by a moving-average series of stationary Archimedean copula, whereas in Strategy #2, the joint probability density was characterized by a non-stationary probit copula. The parameters of the marginal distribution and the copula were defined with generalized additive models. The analysis showed that the mean values of surge peak and event duration were constant and were independent of the proposed climate patterns. However, the values of N A O and S C influenced the threshold and the storminess of extreme events. According to Strategy #1, the variance of the surge peak and event duration increased with a fast shift of negative S C and a positive N A O , respectively. Alternatively, Strategy #2 showed that the variance of the surge peak increased with a positive E A W R . Both strategies coincided in that the joint dependence of the maximum surge level and the duration of extreme surges ranged from low to medium degree. Its mean value was stationary, and its variability was linked to the geographical location. Finally, Strategy #2 helped determine that this dependence increased with negative N A O .

scholarly journals A Joint Probability Density–Based Decomposition of Turbulence in the Atmospheric Boundary Layer

2018 ◽  
Vol 146 (2) ◽  
pp. 503-523 ◽  
Author(s):  
Maria J. Chinita ◽  
Georgios Matheou ◽  
João Teixeira
Keyword(s):  
Probability Density ◽  
Vertical Velocity ◽  
Large Scale ◽  
Sampling Method ◽  
Joint Probability ◽  
Convective Cloud ◽  
Conditional Sampling ◽  
Large Eddy Simulation Model ◽  
Joint Probability Density ◽  
Cloud Core

Abstract In convective flows, vertical turbulent fluxes, covariances between vertical velocity and scalar thermodynamic variables, include contributions from local mixing and large-scale coherent motions, such as updrafts and downdrafts. The relative contribution of these motions to the covariance is important in turbulence parameterizations. However, the flux partition is challenging, especially in regions without convective cloud. A method to decompose the vertical flux based on the corresponding joint probability density function (JPD) is introduced. The JPD-based method partitions the full JPD into a joint Gaussian part and the complement, which represent the local mixing and the large-scale coherent motions, respectively. The coherent part can be further divided into updraft and downdraft parts based on the sign of vertical velocity. The flow decomposition is independent of water condensate (cloud) and can be applied in cloud-free convection, the subcloud layer, and stratiform cloud regions. The method is applied to large-eddy simulation model data of three boundary layers. The results are compared with traditional cloud and cloud-core decompositions and a decaying scalar conditional sampling method. The JPD-based method includes a single free parameter and sensitivity tests show weak dependence on the parameter values. The results of the JPD-based method are somewhat similar to the cloud-core and conditional sampling methods. However, differences in the relative magnitude of the flux decomposition terms suggest that an objective definition of the flow regions is subtle and diagnosed flow properties like updraft characteristics depend on the sampling method. Moreover, the flux decomposition depends on the thermodynamic variable and convection characteristics.

scholarly journals Relationship Between Kendall's tau Correlation and Mutual Information

2020 ◽  
Vol 43 (1) ◽  
pp. 3-20
Author(s):  
Mohammad Bolbolian Ghalibaf
Keyword(s):  
Probability Density Function ◽  
Mutual Information ◽  
Probability Density ◽  
Density Function ◽  
Joint Probability ◽  
Joint Probability Density Function ◽  
Kendall’S Tau ◽  
Kendall's Tau ◽  
Joint Histogram ◽  
Joint Probability Density

Mutual information (MI) can be viewed as a measure of multivariate association in a random vector. However, the estimation of MI is difficult since the estimation of the joint probability density function (PDF) of non Gaussian distributed data is a hard problem. Copula function is an appropriate tool for estimating MI since the joint probability density function ofrandom variables can be expressed as the product of the associated copula density function and marginal PDF’s. With a little search, we find that the proposed copulas-based mutual information is much more accurate than conventional methods such as the joint histogram and Parzen window-based MI. In this paper, by using the copulas-based method, we compute MI forsome family of bivariate distribution functions and study the relationship between Kendall’s tau correlation and MI of bivariate distributions. Finally, using a real dataset, we illustrate the efficiency of this approach.

A novel prediction model of traffic accidents based on big data

2019 ◽  
Vol 10 (04) ◽  
pp. 1950022 ◽  
Author(s):  
Minglei Song ◽  
Rongrong Li ◽  
Binghua Wu
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Probability Distribution ◽  
Prediction Model ◽  
Probability Density ◽  
Traffic Accidents ◽  
Joint Probability ◽  
Distributed Database ◽  
Joint Probability Distribution ◽  
Joint Probability Density

The occurrence of traffic accidents is regular in probability distribution. Using big data mining method to predict traffic accidents is conducive to taking measures to prevent or reduce traffic accidents in advance. In recent years, prediction methods of traffic accidents used by researchers have some problems, such as low calculation accuracy. Therefore, a prediction model of traffic accidents based on joint probability density feature extraction of big data is proposed in this paper. First, a function of big data joint probability distribution for traffic accidents is established. Second, establishing big data distributed database model of traffic accidents with the statistical analysis method in order to mine the association rules characteristic quantity reflecting the law of traffic accidents, and then extracting the joint probability density feature of big data for traffic accident probability distribution. According to the result of feature extraction, adaptive functional and directivity are predicted, and then the regularity prediction of traffic accidents is realized based on the result of association directional clustering, so as to optimize the design of the prediction model of traffic accidents based on big data. Simulation results show that in predicting traffic accidents, the model in this paper has advantages of relatively high accuracy, relatively good confidence and stable prediction result.

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