Introducing a rainfall compound distribution model based on weather patterns sub-sampling

This paper presents a probabilistic model for daily rainfall, using sub-sampling based on meteorological circulation. We classified eight typical but contrasted synoptic situations (weather patterns) for France and surrounding areas, using a "bottom-up" approach, i.e. from the shape of the rain field to the synoptic situations described by geopotential fields. These weather patterns (WP) provide a discriminating variable that is consistent with French climatology, and allows seasonal rainfall records to be split into more homogeneous sub-samples, in term of meteorological genesis. <br><br> First results show how the combination of seasonal and WP sub-sampling strongly influences the identification of the asymptotic behaviour of rainfall probabilistic models. Furthermore, with this level of stratification, an asymptotic exponential behaviour of each sub-sample appears as a reasonable hypothesis. This first part is illustrated with two daily rainfall records from SE of France. <br><br> The distribution of the multi-exponential weather patterns (MEWP) is then defined as the composition, for a given season, of all WP sub-sample marginal distributions, weighted by the relative frequency of occurrence of each WP. This model is finally compared to Exponential and Generalized Pareto distributions, showing good features in terms of robustness and accuracy. These final statistical results are computed from a wide dataset of 478 rainfall chronicles spread on the southern half of France. All these data cover the 1953–2005 period

A categorical invariant for cubic threefolds

Abstract We prove a categorical version of the Torelli theorem for cubic threefolds. More precisely, we show that the non-trivial part of a semi-orthogonal decomposition of the derived category of a cubic threefold characterizes its isomorphism class

Spectral analysis and modeling of space-time rainfall fields

During the past decade, the scale invariance in space-time rainfall, and the inter-dependence between spatial and temporal scales, have been demonstrated. The concept of dynamic scaling facilitates a description of the joint space-time variability, thus resulting in a more compact mathematical treatment. Accordingly, the space-time dynamics of rainfall fields is investigated and modeled using spectral techniques. A three-dimensional Fractional non-Brownian Motion (Lognormal and Pareto types) generator is used to produce synthetic space-time rainfall fields. Some observed fields, from the GATE I dataset, are analyzed and compared with simulated results

Collation of flash flood primary data

Hydrate project Work package 1 report. August 200

Collation of flash flood primary data

Hydrate project Work package 1 report. August 200

A simple model of rain in time: An alternating renewal process of wet and dry states with a fractional (non-gaussian) rain intensity

International audienceA simple model of rainfall in time is proposed coupling the theory of renewal processes with a scale invariant representation of rain intensity. In particular, a strictly alternating renewal process mimes the sequence of wet and dry synoptic weather states, while a Fractional Noise represents the rain variability within each wet state. The rain model is adapted to Osservatorio Ximeniano (Florence) and Osservatorio di Brera (Milan) datatasets. Some rain characteristics are considered to check the agreement between model and data, namely, the annual volume of rainfall, the wet fraction of the year, the extreme values through the ""classic"" Depth-Duration-Frequency curves, and the maximum annual volume of the rainfall event. (c) 2006 Elsevier B.V. All rights reserved