Rainfall nowcasting by at site stochastic model P.R.A.I.S.E.

Sirangelo, B.; Versace, Pasquale; Luca, Davide Luciano De

doi:10.5194/hessd-4-151-2007

Cited by 9 publications

(14 citation statements)

References 22 publications

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“…In this period, correlation structure, mean and variance of the sample appear significantly homogeneous (Sirangelo et al, 2007). So the d/p ratio is equal to about 2050 and about 150 considering only rainy intervals.…”

Section: Rain Fields Generation Algorithmsmentioning

confidence: 93%

“…the parameter ν, has been determined for every raingauge starting from sample partial autocorrelogram, and using the technique cited at point 2.1 (Sirangelo et al, 2007). The value of χ r,cr has been fixed equal to 0.025, and the estimate of ν has beenν=8, as depicted in Fig.…”

Section: Rain Fields Generation Algorithmsmentioning

confidence: 99%

“…the extension of the temporal "memory" ν of the rain field, for every cell, can be assumed equal to the minimum value of ν for which the sample maximum absolute scattering χ r (ν) (Sirangelo et al, 2007) results less than a fixed critical value χ r,cr .…”

Section: Identification Of Random Variablesmentioning

confidence: 99%

“…This work introduces a space-temporal models to forecast rainfall fields named PRAISEST (Prediction of Rainfall Amount Inside Storm Events: Space and Time). It is a multidimensional space-time model, that can be considered like the generalization of the at-site model PRAISE proposed by the authors (Sirangelo et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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A space-time generator for rainfall nowcasting: the PRAISEST model

Versace

Sirangelo

Luca

2009

Hydrol. Earth Syst. Sci.

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Abstract. The paper introduces a stochastic technique for forecasting rainfall in space-time domain: the PRAISEST Model (Prediction of Rainfall Amount Inside Storm Events: Space and Time). The model is based on the assumption that the rainfall height H accumulated on an interval t between the instants i t and (i+1) t and on a spatial cell of size x y is correlated either with a variable Z, representing antecedent precipitation at the same point, either with a variable W , representing simultaneous rainfall at neighbour cells. The mathematical background is given by a joined probability density f H,W,Z (h, w, z) in which the variables have a mixed nature, that is a finite probability for null value and infinitesimal probabilities for the positive values. As study area, the Calabria region, in Southern Italy, has been selected. The region has been discretised by 10 km×10 km cell grid, according to the raingauge network density in this area. Storm events belonging to 1990-2004 period were analyzed to test performances of the PRAISEST model.

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Section: Rain Fields Generation Algorithmsmentioning

confidence: 93%

Section: Rain Fields Generation Algorithmsmentioning

confidence: 99%

Section: Identification Of Random Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A space-time generator for rainfall nowcasting: the PRAISEST model

Versace

Sirangelo

Luca

2009

Hydrol. Earth Syst. Sci.

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“…Marsigli et al 2005Marsigli et al , 2008; those from stochastic models of rainfall evolution (e.g. Sirangelo et al 2007); and those derived from historical analogues (e.g. Duband 1970, Obled et al 2002.…”

Section: Introductionmentioning

confidence: 99%

Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts

Smith

Panziera

Beven

2014

Hydrological Sciences Journal

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Data-based mechanistic (DBM) models can offer a parsimonious representation of catchment dynamics. They have been shown to provide reliable accurate flood forecasts in many hydrological situations. In this work, the DBM methodology is applied to forecast flash floods in a small Alpine catchment. Compared to previous DBM modelling studies, the catchment response is rapid. The use of novel radar-derived ensemble quantitative precipitation forecasts based on analogues to drive the DBM model allows the forecast horizon to be increased to a level useful for emergency response. The characterization of the predictive uncertainty in the resulting hydrological forecasts is discussed and a framework for its representation illustrated.Key words DBM; NORA; flash flood forecasting Prévision des crues éclair à l'aide de modèles mécanistes fondés sur les données et prévisions pluviométriques du système radar NORA Résumé Les modèles mécanistes fondés sur les données (en anglais, data-based mechanistic-DBM) peuvent offrir une représentation parcimonieuse de la dynamique du bassin versant. Il a été montré qu'ils fournissent des prévisions de crue fiables et précises dans de nombreuses situations hydrologiques. Dans ce travail, la méthodolo-gie DBM est appliquée pour prévoir des crues éclair dans un petit bassin versant alpin. Par rapport aux études antérieures de modélisation DBM, la réponse du bassin versant est rapide. L'utilisation de nouvelles prévisions quantitatives d'ensemble de précipitations, dérivées du radar et basées sur des analogues pour forcer le modèle DBM, permet d'accroître l'horizon de prévision à un niveau utile pour les interventions d'urgence. La caractéri-sation de l'incertitude prédictive dans les prévisions hydrologiques résultantes est discutée et un cadre pour sa représentation est illustré.

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Copula-based mixed models for bivariate rainfall data: an empirical study in regression perspective

Serinaldi

2008

Stoch Environ Res Risk Assess

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A comprehensive parametric approach to study the probability distribution of rainfall data at scales of hydrologic interest (e.g. from few minutes up to daily) requires the use of mixed distributions with a discrete part accounting for the occurrence of rain and a continuous one for the rainfall amount. In particular, when a bivariate vector (X, Y) is considered (e.g. simultaneous observations from two rainfall stations or from two instruments such as radar and rain gauge), it is necessary to resort to a bivariate mixed model. A quite flexible mixed distribution can be defined by using a 2-copula and four marginals, obtaining a bivariate copula-based mixed model. Such a distribution is able to correctly describe the intermittent nature of rainfall and the dependence structure of the variables. Furthermore, without loss of generality and with gain of parsimony this model can be simplified by some transformations of the marginals. The main goals of this work are: (1) to empirically explore the behaviour of the parameters of marginal transformations as a function of time scale and inter-gauge distance, by analysing data from a network of rain gauges;(2) to compare the properties of the regression curves associated to the copula-based mixed model with those derived from the model simplified by transformations of the marginals. The results from the investigation of transformations' parameters are in agreement with the expected theoretical dependence on inter-gauge distance, and show dependence on time scale. The analysis on the regression curves points out that: (1) a copula-based mixed model involves regression curves quite close to some non-parametric models; (2) the performance of the parametric regression decreases in the same cases in which nonparametric regression shows some instability; (3) the copula-based mixed model and its simplified version show similar behaviour in term of regression for mid-low values of rainfall.

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Rainfall nowcasting by at site stochastic model P.R.A.I.S.E.

Cited by 9 publications

References 22 publications

A space-time generator for rainfall nowcasting: the PRAISEST model

A space-time generator for rainfall nowcasting: the PRAISEST model

Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts

Copula-based mixed models for bivariate rainfall data: an empirical study in regression perspective

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