Space‐time scaling in high‐intensity Tropical Ocean Global Atmosphere Coupled Ocean‐Atmosphere Response Experiment (TOGA‐COARE) storms

Deidda, Roberto; Badas, Maria Grazia; Piga, Emanuela

doi:10.1029/2003wr002574

Cited by 52 publications

(66 citation statements)

References 27 publications

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“…The new values of the estimated parameters are listed in Table 3. The parameter c β shows a dependence on the mean intensity I , that, according to the literature results (Deidda et al, 2004), can be expressed by the exponential relation ( Fig. 6b):…”

Section: Multifractal Properties Of Rainfall Fields Over the Area Of mentioning

confidence: 99%

“…Following Deidda (2000) and Deidda et al (2004), the relationships between the multifractal properties, represented by the model parameters c and β, and the large-scale rain rate I [mm h −1 ] were investigated. Figure 6a presents the variability of the estimated values of c and β with I .…”

Section: Multifractal Properties Of Rainfall Fields Over the Area Of mentioning

confidence: 99%

“…Some preliminary analyses on PPI at different elevations were performed in order to find the best antenna elevation for rainfall estimation (Gorgucci et al, 1995;Russo et al, 2001). Low elevation angles are required to avoid contamination of the radar echoes by the melting layer; but lowelevation radar scans suffer from ground clutter contamination and blockage of radar beam by elevated ground targets or mountains.…”

Section: Rainfall Monitoring Over the Area Of Romementioning

confidence: 99%

“…Moreover, to reproduce the statistical propCorrespondence to: E. Volpi (evolpi@uniroma3.it) erties jointly observed in space and time, the space and time scales must be related to the forcing factors of the events (Lovejoy and Schertzer;1987;Venugopal et al, 1999;Deidda, 2000;Deidda et al, 2004). This paper deals with the analysis of the space and time scaling properties of the rainfall events observed over the metropolitan area of Rome, in order to calibrate the parameters of an efficient disaggregation model for intense rainfall events.…”

Section: Introductionmentioning

confidence: 99%

“…The analysis of observed storms shows that the estimated parameters of downscaling models change from storm to storm, depending on the climatic situation and on the large scale meteorological conditions (Over and Gupta, 1994;Olsson and Niemczynowicz, 1996;Perica and FoufoulaGeorgiou, 1996;Svensson et al, 1996;Deidda, 2000;Deidda et al, 2004). Moreover, to reproduce the statistical propCorrespondence to: E. Volpi (evolpi@uniroma3.it) erties jointly observed in space and time, the space and time scales must be related to the forcing factors of the events (Lovejoy and Schertzer;1987;Venugopal et al, 1999;Deidda, 2000;Deidda et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Multifractal analysis of radar rainfall fields over the area of Rome

Calenda¹,

Gorgucci

Napolitano

et al. 2005

Adv. Geosci.

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Abstract.A scale-invariance analysis of space and time rainfall events monitored by meteorological radar over the area of Rome (Italy) is proposed. The study of the scale-invariance properties of intense precipitation storms, particularly important in flood forecast and risk mitigation, allows to transfer rainfall information from the large scale predictive meteorological models to the small scale hydrological rainfall-runoff models.Precipitation events are monitored using data collected by the polarimetric Doppler radar Polar 55C (ISAC-CNR), located 15 km Southeast from downtown. The meteorological radar provides the estimates of rainfall intensity over an area of about 10 000 km 2 at a resolution of 2×2 km 2 in space and 5 min in time.Many precipitation events have been observed from autumn 2001 up to now. A scale-invariance analysis is performed on some of these events with the aim at exploring the multifractal properties and at understanding their dependence on the meteorological large-scale conditions.

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Section: Multifractal Properties Of Rainfall Fields Over the Area Of mentioning

confidence: 99%

Section: Multifractal Properties Of Rainfall Fields Over the Area Of mentioning

confidence: 99%

Section: Rainfall Monitoring Over the Area Of Romementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multifractal analysis of radar rainfall fields over the area of Rome

Calenda¹,

Gorgucci

Napolitano

et al. 2005

Adv. Geosci.

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Fractals and Similarity Approaches in Hydrology

Lanza

Gallant

2005

Encyclopedia of Hydrological Sciences

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The application of fractals and similarity concepts in hydrology has given rise to a better understanding of the space‐time organization of forms and processes that are relevant to the hydrologic cycle. Indeed, a variety of literature results support the conjecture that scaling holds for most hydrological variables in time and space. This chapter concentrates on fields where fractals and similarity approaches have proved helpful in fostering advances in specific hydrological studies. In particular, precipitation and drainage network morphology are addressed. A few specific applications to the study of natural forms and patterns relevant to the hydrological sciences are presented first, while the application of scaling concepts to the study of processes themselves is later discussed. Because of its highly irregular behavior, the rainfall process is one ideal candidate to be approached by means of self‐similarity and/or (multi)fractal description models. Such models involve increasing complexity and computational burden as soon as the interest moves from the one‐dimensional time series to the three‐dimensional case, where the full space‐time pattern of rainfall is considered. Examples of recent interesting results are presented with reference to the one‐, two‐, and three‐dimensional approaches to rainfall modeling based on similarity concepts.

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Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results

Langousis

Mamalakis

Deidda

et al. 2016

Water Resources Research

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To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall downscaling, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed downscaling schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here we study the relative performance of: (a) direct statistical correction of CM rainfall outputs using nonparametric distribution mapping, and (b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e., the Flumendosa catchment, using rainfall and atmospheric data from four CMs of the ENSEMBLES project. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima. To date, the assessment of the overall performance of different GCM/RCM combinations in modeling Earth's past, present and future climate has been greatly facilitated by open-access databases of climate model simulations developed through projects like PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects, http://prudence.dmi.dk/), PCMDI/CMIP3 (Program for Climate Model Diagnosis and Intercomparison/Coupled Model Intercomparison Project -Phase 3, http:// www-pcmdi.llnl.gov), ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts,

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Space‐time scaling in high‐intensity Tropical Ocean Global Atmosphere Coupled Ocean‐Atmosphere Response Experiment (TOGA‐COARE) storms

Cited by 52 publications

References 27 publications

Multifractal analysis of radar rainfall fields over the area of Rome

Multifractal analysis of radar rainfall fields over the area of Rome

Fractals and Similarity Approaches in Hydrology

Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results

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