Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution

Akrour, Nawal; Chazottes, Aymeric; Verrier, Sébastien; Mallet, Cécile; Barthès, Laurent

doi:10.1002/2014wr016357

Cited by 12 publications

(6 citation statements)

References 51 publications

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“…Variable IET (Previous IET) : the map is not structured, reflecting the independence of the characteristics of a rain event with respect to the drought period preceding the event. This corroborates several previous works Gole, 1998, 2006;Akrour et al, 2015) relative to rain support simulation. These authors have noticed that successive rain and no rain periods are found to be uncorrelated, thus a rain time series can be considered by an alternation of rain event and no rain independently drawn periods.…”

supporting

confidence: 93%

Data driven clustering of rain events: microphysics information derived from macro scale observations

Dilmi¹,

Mallet²,

Barthès³

et al. 2016

Preprint

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Abstract. The study of rain time series records is mainly carried out using rainfall rate or rain accumulation parameters estimated on a fixed duration (typically 1 min, 1 hour or 1 day). In this paper we used the concept of rain event. Among the numerous existing variables dedicated to the characterisation of rain events, the first part of this paper aims to obtain a parsimonious characterisation of these events using a minimal set of variables. In this context an algorithm based on Genetic Algorithm (GA) and Self Organising Maps (SOM) is proposed. The use of SOM is justify by the fact that it allows to maps a high dimensional data space to a two dimensional space while preserving as much as possible the initial space topology in an unsupervised way. The obtained 2D maps allow to provide the dependencies between variables and consequently to remove redundant variables leading to a minimal subset of variables. The ability of the obtained 2D map to deduce all events characteristics from only five features (the event duration, the rain rate peak, the rain event depth, the event rain rate standard deviation and the absolute rain rate variation of order 0.5) is verified. From this minimal subset of variables hierarchical cluster analysis were conducted. We show that a clustering in two classes allows finding the classic convective and stratiform classes while a classification in five classes allows refining this convective/stratiform classification. Finally, the last objective of this paper is to study the possible relationship between these five classes and their associated rain event microphysics. Some relationship between these classes and microphysics parameters are highlighted.

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supporting

confidence: 93%

Data driven clustering of rain events: microphysics information derived from macro scale observations

Dilmi¹,

Mallet²,

Barthès³

et al. 2016

Preprint

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“…vious studies Gole, 1998, 2006;Akrour et al, 2015;de Montera et al, 2009) dealing with rain support simulations. When studying temperate midlatitudes for relatively short periods, these authors noticed that successive rain and no-rain periods are uncorrelated, such that a rain time series could be considered as an independently drawn, alternating series of rain events and periods without rain.…”

Section: Projection Of the Selected And Unlearned Variables Onto The Sommentioning

confidence: 98%

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

et al. 2017

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Abstract. Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of "rain events". The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective-stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.

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“…For the rainfall maps, we simulated the map at the initial time, 0 t , and we then used the advection model to generate the other maps. To create this individual fi rst map, we used a method developed by [16] that allows simulating realistic two-dimensional rainfall maps. In this study, we worked with the map that is shown in Figure 3.…”

Section: Methods Employed To Generate the Simulated Datamentioning

confidence: 99%

Fine-scale evaluation of rainfall from TV-sats: A new method for water monitoring and flash flood prevention

Mercier

Akrour

Barthès

et al. 2017

URSI Radio Sci. Bull.

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In this paper we present a method for rebuilding rainfall maps at high resolution (500 m × 500 m, 1 min). This method is based on the assimilation of opportunistic measurements of the attenuation that aff ects the signals coming from TV satellites in a model of spatiotemporal advection of rainfall fi elds. At the frequencies used (Ku band), the attenuation aff ecting the signals in the atmosphere is mainly due to rain. We set a sensor (fi eld analyzer) on the ground, and then measured the mean rainfall over the link. This method was applied to a simulated network of sensors. These simulated sensors were realistically set over the Paris area, on a zone assumed to be typical of an area with high socio-economic issues (fl ood prevention, water resources management). We compared the simulated rainfall maps with the maps rebuilt by our algorithm. We then showed the feasibility of our approach for measuring the rainfall in urban areas with high resolution.

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Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution

Cited by 12 publications

References 51 publications

Data driven clustering of rain events: microphysics information derived from macro scale observations

Data driven clustering of rain events: microphysics information derived from macro scale observations

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

Fine-scale evaluation of rainfall from TV-sats: A new method for water monitoring and flash flood prevention

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