Temporal dependence structure in weights in a multiplicative cascade model for precipitation

2012 ◽  
Vol 48 (1) ◽  
Author(s):  
Athanasios Paschalis ◽  
Peter Molnar ◽  
Paolo Burlando
Keyword(s):  
Cascade Model ◽  
Dependence Structure ◽  
Temporal Dependence ◽  
Multiplicative Cascade

scholarly journals Towards Subdaily Rainfall Disaggregation via Clausius–Clapeyron

2014 ◽  
Vol 15 (3) ◽  
pp. 1303-1311 ◽  
Author(s):  
G. Bürger ◽  
M. Heistermann ◽  
A. Bronstert
Keyword(s):  
Temperature Dependence ◽  
Cascade Model ◽  
Proof Of Concept ◽  
Short Term ◽  
Temporal Disaggregation ◽  
Multiplicative Cascade

Abstract Two lines of research are combined in this study: first, the development of tools for the temporal disaggregation of precipitation, and second, some newer results on the exponential scaling of heavy short-term precipitation with temperature, roughly following the Clausius–Clapeyron (CC) relation. Having no extra temperature dependence, the traditional disaggregation schemes are shown to lack the crucial CC-type temperature dependence. The authors introduce a proof-of-concept adjustment of an existing disaggregation tool, the multiplicative cascade model of Olsson, and show that, in principal, it is possible to include temperature dependence in the disaggregation step, resulting in a fairly realistic temperature dependence of the CC type. They conclude by outlining the main calibration steps necessary to develop a full-fledged CC disaggregation scheme and discuss possible applications.

scholarly journals Discrete-Time Risk Models Based on Time Series for Count Random Variables

2010 ◽  
Vol 40 (1) ◽  
pp. 123-150 ◽  
Author(s):  
Hélène Cossette ◽  
Etienne Marceau ◽  
Véronique Maume-Deschamps
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Regime Switching ◽  
Risk Model ◽  
Dependence Structure ◽  
Serial Dependence ◽  
Bernoulli Process ◽  
Temporal Dependence ◽  
Risk Models ◽  
Numerical Examples

AbstractIn this paper, we consider various specifications of the general discrete-time risk model in which a serial dependence structure is introduced between the claim numbers for each period. We consider risk models based on compound distributions assuming several examples of discrete variate time series as specific temporal dependence structures: Poisson MA(1) process, Poisson AR(1) process, Markov Bernoulli process and Markov regime-switching process. In these models, we derive expressions for a function that allow us to find the Lundberg coefficient. Specific cases for which an explicit expression can be found for the Lundberg coefficient are also presented. Numerical examples are provided to illustrate different topics discussed in the paper.

scholarly journals Which catchment characteristics control the temporal dependence structure of daily river flows?

2014 ◽  
Vol 29 (6) ◽  
pp. 1353-1369 ◽  
Author(s):  
Andrew Chiverton ◽  
Jamie Hannaford ◽  
Ian Holman ◽  
Ron Corstanje ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Dependence Structure ◽  
Temporal Dependence ◽  
River Flows ◽  
Catchment Characteristics

scholarly journals Generalized binomial multiplicative cascade processes and asymmetrical multifractal distributions

2014 ◽  
Vol 21 (2) ◽  
pp. 477-487 ◽  
Author(s):  
Q. Cheng
Keyword(s):  
Fractal Dimension ◽  
Cascade Model ◽  
Nonlinear Properties ◽  
Cascade Processes ◽  
Insight Into ◽  
Multiplicative Cascade ◽  
Multifractal Modeling

Abstract. The concepts and models of multifractals have been employed in various fields in the geosciences to characterize singular fields caused by nonlinear geoprocesses. Several indices involved in multifractal models, i.e., asymmetry, multifractality, and range of singularity, are commonly used to characterize nonlinear properties of multifractal fields. An understanding of how these indices are related to the processes involved in the generation of multifractal fields is essential for multifractal modeling. In this paper, a five-parameter binomial multiplicative cascade model is proposed based on the anisotropic partition processes. Each partition divides the unit set (1-D length or 2-D area) into h equal subsets (segments or subareas) and m1 of them receive d1 (> 0) and m2 receive d2 (> 0) proportion of the mass in the previous subset, respectively, where m1+m2 ≤ h. The model is demonstrated via several examples published in the literature with asymmetrical fractal dimension spectra. This model demonstrates the various properties of asymmetrical multifractal distributions and multifractal indices with explicit functions, thus providing insight into and an understanding of the properties of asymmetrical binomial multifractal distributions.

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