1,721,026 research outputs found
Spatial-temporal model of rainfall calibrated by radar data
No full textSmall- and medium-scale catchments (1-100 km2) are generally characterized by high spatial and temporal requirements, due to their rapid reactions to rainfall inputs at fine spatial and temporal scales. However, the natural variability of rainfall fields may lead to large uncertainty and bias in rain estimation at those scales. For this reason, flood risk assessment and management (e.g. in urban areas) could greatly benefit from stochastic spatial-temporal modelling of precipitation. The calibration of the Gaussian displacements spatial-temporal rainfall model (GDSTM), i.e. a spatial analog of a point-process model, is presented. GDSTM assumes that rainfall is realized as a sequence of storms, each consisting of a number of cells with clustering in space and time. Both storms and cells are characterized by their centers, durations, and areal extent. The model is calibrated using data collected by the weather radar Polar 55C in Rome, over an area of 100×100 km2, with the radar located at the center. The parameters are estimated with the Hansen method, using data with a resolution of 2×2 km2space-time
Downscaling temporale della precipitazione: un modello a cascata stocastica discreta
No full text"I modelli di disaggregazione o di downscaling delle precipitazioni consentono di riprodurre serie temporali di pioggia ad elevata risoluzione (es. dell’ordine dei minuti) che siano coerenti in tutto o in parte con una serie nota ad una risoluzione più grossolana (es. giornaliera). Fra i modelli sviluppati in letteratura nel corso degli ultimi decenni, i modelli a cascata stocastica moltiplicativa discreta hanno ricevuto grande attenzione, grazie alla semplicità della struttura che li caratterizza. Tuttavia le serie prodotte da questi modelli mostrano un comportamento non stazionario, che non riflette le caratteristiche dei processi di precipitazione osservati in natura. Il presente lavoro propone un modello di downscaling temporale della precipitazione basato sul processo di Hurst-Kolmogorov; il modello proposto è caratterizzato da una struttura semplice come quella delle cascate moltiplicative discrete, ma in grado di riprodurre serie stazionarie. La capacità del modello di riprodurre le proprietà statistiche desiderate viene illustrata tramite simulazioni numeriche Monte Carlo.
On the use of radar reflectivity for estimation of areal reduction factor
No full textTo estimate the rainfall fields over an entire basin raingauge pointwise measurements need to be interpolated and the small-scale variability of rainfall fields can lead to biases on the rain rate estimation over an entire basin above all for small or medium size mountainous and urban catchments. For these reasons several raingauges should be installed in different places in order to determine the spatial rainfall distribution due to the evolution of the natural phenomena over the selected area. In the technical applications a lot of empirical relations are used in order to reduce heavy point rainfall measurements for vast areas. In this work we investigate on the areal reduction factor by the use of radar reflectivity maps collected with the Polar 55C, a C-band Doppler dual polarized coherent weather radar with polarization agility and with a 0.9° beamwidth. The radar rainfall estimations, over an area of 1 km2, were integrated for heavy rainfall with an upscaling process, until to have rainfall estimation over an area of 900 km2. The results obtained for several rainfall events by the use this technique are compared with the most important relations of the areal reduction factor in literature
Analisi del coefficiente di ragguaglio all’area della precipitazione a partire da dati radar
No full textScaling properties of rainfall time-series in the urban area of Rome
No full textThe rainfall fields exhibits a high space-time variability which generates a large degree of uncertainty in modelling
the process, thus causing lack of accuracy in many key hydrological problems, such as the forecasting of floods
and the management of water resources. The large amount of literature produced in the last thirty years about
this issue deals with the development of stochastic models able to represent the non-linearity and intermittence
of rainfall in order to perform the downscaling process, i.e. transferring to finer scales the information on rainfall
observed or forecasted at large scales.
Traditionally, these models are based upon point processes in both the time (e.g. Waymire and Gupta, 1981) and
the space-time domain (e.g. Rodriguez-Iturbe et al., 1986). Although this approach is cluster-based so as to model
the physical structure of rainfall, its application may involve an inconvenient mathematical complexity and a large
number of parameters, leading to several problems in parameter estimation.
Another approach to this problem is based on the empirical detection of some regularity in hydrological observations,
such as the scale-invariance properties of rainfall (e.g. Lovejoy and Schertzer, 1985). Models following
this approach are based upon the assumption of a power law dependence of all statistical moments on the scale of
aggregation. That means scaling properties can provide simple relationships to link the statistical distribution of
the rainfall process at different spatial and temporal scales, in the ranges of which the power-low assumption can
be verified (Marani, 2003).
This work focuses on the analysis of the scaling properties of rainfall time series from a high density rain gauge
network covering the Rome’s urban area. The network consists of 24 sites, and the gauge record at each site
has 10-minute time resolution and about 16-year length (1992-2007). The aim of the study is the identification
of temporal scaling regimes, their ranges of validity, and the evaluation of the corresponding scaling properties
Rainfall downscaling in time: Theoretical and empirical comparison between multifractal and Hurst-Kolmogorov discrete random cascades
No full text"Au cours des dernières décennies, une recherche intensive s'est intéressée à des techniques capables de produire des séries chronologiques de précipitations à une échelle temporelle fine, qui soient (complètement ou partiellement) cohérentes avec une série fournie à une échelle temporelle plus grossière. Dans le présent article, nous étudions théoriquement les conséquences sur les tendances des statistiques d'ensemble dans le cas d'une approche simple et largement utilisée de descente d'échelle stochastique pour séries temporelles de précipitations: la cascade aléatoire multiplicative discrète. Nous démontrons que les séries temporelles synthétiques de précipitations produites par ce modèle de cascade aléatoire multiplicative, correspondent à un processus stochastique qui n'est pas stationnaire, étant donné que son autocorrélation temporelle varie au cours du temps de façon indésirable. Nous présentons et analysons théoriquement ensuite une approche alternative de descente d'échelle fondée sur le processus de Hurst-Kolmogorov, qui est également simple, mais est stationnaire. Nous prouvons enfin le bien‐fondé de nos résultats théoriques par la méthode de simulation de Monte‐Carlo.""During recent decades, intensive research has focused on techniques capable of generating rainfall time series at a fine time scale that are (fully or partially) consistent with a given series at a coarser time scale. Here we theoretically investigate the consequences on the ensemble statistical behaviour caused by the structure of a simple and widely-used approach of stochastic downscaling for rainfall time series, the discrete Multiplicative Random Cascade. We show that synthetic rainfall time series generated by these cascade models correspond to a stochastic process which is non-stationary, because its temporal autocorrelation structure depends on the position in time in an undesirable manner. Then, we propose and theoretically analyse an alternative downscaling approach based on the Hurst-Kolmogorov process, which is equally simple but is stationary. Finally, we provide Monte Carlo experiments which validate our theoretical results.
Effect of time discretization and finite record length on continuous-time stochastic properties
No full text"Natural processes evolve in continuous time but their observation is inevitably made at discrete time. The observational time series formed are either series of instantaneous values of the natural phenomenon at a certain time step or aggregated quantities during this time step. In addition, the observation period is apparently a finite time period. Both time discretization and finite length may strongly affect the stochastic properties inferred from the data. In particular, time discretization distorts the stochastic properties at small time scales, while the finite length affects the properties at large time scales. Modelling of natural processes is typical made assuming discrete time and parameter estimation is usually done using classical statistical estimators which assume that observations are random samples. All these are inadequate practices and result in inappropriate and biased models. A different modelling strategy is proposed, in which the stochastic model is by definition a continuous-time process and the distortion due to discretization and finite-period observation is explicitly taken into account in model calibration. An additional benefit of the proposed strategy is that it avoids the too artificial, often non-parsimonious, families of discrete time stochastic models (like the ARIMA(p,d,q) models).
Investigating the scaling regimes of rainfall time series from a dense rain gauge network
No full textThe need of understanding and modeling the high space-time variability of rainfall fields produced a large amount of literature in the last thirty years. A parameter parsimonious approach to this problem is based on the empirical detection of some regularities in hydrological observations, such as the scale-invariance properties of rainfall (e.g. Lovejoy and Schertzer, 1985). Models following this approach are based upon the assumption of a power law dependence of all statistical moments on the scale of aggregation. That means scaling properties can provide simple relationships to link the statistical distribution of the rainfall process at different spatial and temporal scales, in the ranges of which the power-law assumption can be verified (Marani, 2005).
This work focuses on the analysis of the scaling properties of rainfall time series from a high density rain gauge network covering the urban area of Rome. The network consists of 24 sites, and the gauge record at each site has 10-minute time resolution and about 16-year length (1992-2007). In the hypotheses of stationary monthly rainfall series and spatial homogeneity of the rainfall fields over the study area, the scale-invariance properties in the time domain of the studied rainfall are investigated within some specific scale intervals (temporal scaling regimes) by using different methods: q-moments, PDMS, autocovariance structure. Furthermore, a multiplicative random cascade model (Rupp et al., 2009) is calibrated on each scaling regime and then the statistical properties of the simulated time series are validated with the observations
Theoretical and empirical comparison of stochastic disaggregation and downscaling approaches for rainfall time series
No full text"High-resolution rainfall time series are usually crucial for many hydrological applications, but the majority of historical. datasets available has daily resolution, which is often too coarse. Therefore, for the last three decades a large. amount of literature has been dealing with the problem of generating rainfall sequences at the timescale of interest. given the observed data at a lower resolution. In this work, we focus on existing downscaling and disaggregation. approaches with two different structures: one characterized by power-law correlations which account for long-term. persistence of rainfall (as in the fractional Gaussian noise or Hurst-Kolmogorov model) and the other characterized by a multifractal structure. The two approaches are analysed and compared in terms of their capability of reproducing the statistical behaviour of a high density (and high-resolution) raingauge network covering the urban area of Rome.
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