Observation and Analysis of Midwestern Rain Rates

Georgakakos, Konstantine P.; Cârsteanu, A. A.; Sturdevant, P. L.; Cramer, John A.

doi:10.1175/1520-0450(1994)033<1433:oaaomr>2.0.co;2

Cited by 66 publications

(53 citation statements)

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“…The results from our spectral exponent estimation for time scales larger than 1 min, for the heights of 492 and 2292 m and for both events, vary between 1.36 and 1.87 and are in general agreement with the one obtained by Georgakakos et al (1994). These exponents are close to the classical Kolmogorov f −1.66 power law relationship, which represents the spectrum predicted for the fluctuations of a passive scalar introduced into a turbulent fluid (Olsson et al, 1993).…”

Section: Fig 6 Power Spectra Of Precipitation Rates As Derived By Tsupporting

confidence: 89%

“…This indicates a near white-noise variability at smaller time scales. Similar behavior of the spectra is not apparent in Georgakakos et al (1994), but Fabry (1996) obtained similar results with the main difference being that he observed the break in the slope at smaller time scales (around 5 s). This flat regime of the spectra is translated as a lack of precipitation structure at these small time scales.…”

Section: Fig 6 Power Spectra Of Precipitation Rates As Derived By Tsupporting

confidence: 57%

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Comparative rainfall data analysis from two vertically pointing radars, an optical disdrometer, and a rain gauge

Nikolopoulos

Kruger

Krajewski

et al. 2008

Nonlin. Processes Geophys.

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Abstract. The authors present results of a comparative analysis of rainfall data from several ground-based instruments. The instruments include two vertically pointing Doppler radars, S-band and X-band, an optical disdrometer, and a tipping-bucket rain gauge. All instruments were collocated at the Iowa City Municipal Airport in Iowa City, Iowa, for a period of several months. The authors used the rainfall data derived from the four instruments to first study the temporal variability and scaling characteristics of rainfall and subsequently assess the instrumental effects on these derived properties. The results revealed obvious correspondence between the ground and remote sensors, which indicates the significance of the instrumental effect on the derived properties.

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Section: Fig 6 Power Spectra Of Precipitation Rates As Derived By Tsupporting

confidence: 89%

Section: Fig 6 Power Spectra Of Precipitation Rates As Derived By Tsupporting

confidence: 57%

Comparative rainfall data analysis from two vertically pointing radars, an optical disdrometer, and a rain gauge

Nikolopoulos

Kruger

Krajewski

et al. 2008

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

show abstract

“…The scale break was also noticed in their box dimension analysis around 20 min to one hour depending on the low rainfall threshold. Georgakakos et al (1994) analyzed the 5 s resolution rainfall time series of seven storms that occurred between May 1990 and April 1991. Based on the power spectrum analysis, they reported a scaling regime from 10 s to 20 s for all the storms except one storm for which they reported a regime from 20 s to 10 min.…”

Section: Brief Review Of Temporal Analysesmentioning

confidence: 99%

“…In the last two decades, multiscaling-based framework has been increasingly used by researchers to statistically characterize the rainfall variability over a range of temporal and spatial scales (e.g., Schertzer and Lovejoy, 1987;Tessier et al, 1993;Gupta and Waymire, 1993;Georgakakos et al, 1994;Veneziano et al, 1996;Venugopal et al, 1999;Lilley et al, 2006;Lovejoy and Schertzer, 2006;Lovejoy et al, 2008). While the temporal scales varied from few seconds to years (e.g., Georgakakos et al, 1994;Olsson et al, 1993), the spatial scales varied from few hundreds of meters to continental scales (e.g., Lovejoy and Schertzer, 2006;Lovejoy et al, 2008). Although rainfall is comprised of individual rain drops, it is usually studied as a continuous field under the assumption of large number (N) of drops.…”

Section: Introductionmentioning

confidence: 99%

Multiscaling analysis of high resolution space-time lidar-rainfall

Mandapaka

Lewandowski

Eichinger

et al. 2009

Nonlin. Processes Geophys.

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Abstract. In this study, we report results from scaling analysis of 2.5 m spatial and 1 s temporal resolution lidar-rainfall data. The high resolution spatial and temporal data from the same observing system allows us to investigate the variability of rainfall at very small scales ranging from few meters to ∼1 km in space and few seconds to ∼30 min in time. The results suggest multiscaling behaviour in the lidar-rainfall with the scaling regime extending down to the resolution of the data. The results also indicate the existence of a space-time transformation of the form t∼L z at very small scales, where t is the time lag, L is the spatial averaging scale and z is the dynamic scaling exponent.

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“…This data set (size 9679; skewness 4.83; lag one autocorrelation 0.88) corresponds to one of several storms that were measured at the University of Iowa using devices that are capable of high sampling rates (Georgakakos et al, 1994). As described in , the results of the standard algorithm do not support nor prohibit the existence of low-dimensional deterministic dynamics but those of the Graf von Hardenberg et al (1997b) algorithm excluding data values smaller than 1% of the maximum value (to recover from zero slopes that again are due to round-off errors) show a tendency that D 2 (m) = m, which indicates the absence of chaotic behaviour.…”

Section: Real-world Examplesmentioning

confidence: 99%

On the quest for chaotic attractors in hydrological processes

Koutsoyiannis

2006

Hydrological Sciences Journal

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In the last two decades, several researchers have claimed to have discovered low-dimensional determinism in hydrological processes, such as rainfall and runoff, using methods of chaotic analysis. However, such results have been criticized by others. In an attempt to offer additional insights into this discussion, it is shown here that, in some cases, merely the careful application of concepts of dynamical systems, without doing any calculation, provides strong indications that hydrological processes cannot be (low-dimensional) deterministic chaotic. Furthermore, it is shown that specific peculiarities of hydrological processes on fine time scales, such as asymmetric, J-shaped distribution functions, intermittency, and high autocorrelations, are synergistic factors that can lead to misleading conclusions regarding the presence of (low-dimensional) deterministic chaos. In addition, the recovery of a hypothetical attractor from a time series is put as a statistical estimation problem whose study allows, among others, quantification of the required sample size; this appears to be so huge that it prohibits any accurate estimation, even with the largest available hydrological records. All these arguments are demonstrated using appropriately synthesized theoretical examples. Finally, in light of the theoretical analyses and arguments, typical real-world hydrometeorological time series, such as relative humidity, rainfall, and runoff, are explored and none of them is found to indicate the presence of chaos.Keywords attractors; capacity dimension, chaos; chaotic dynamics; correlation dimension; entropy; hydrological processes; nonlinear analysis; stochastic processes; time series analysis; rainfall; runoff Sur la recherche d'attracteurs chaotiques dans des processus hydrologiquesRésumé Durant les deux dernières décennies, plusieurs chercheurs ont prétendu avoir découvert le déterminisme bas dimensionnel dans des processus hydrologiques, tels que les précipitations et l'écoulement, en utilisant des méthodes d'analyse chaotique. De tels résultats, cependant, ont été critiqués par d'autres. Afin d'essayer d'offrir des avis supplémentaires dans cette discussion, on montre ici que, dans certains cas, la simple application soigneuse des concepts des systèmes dynamiques, sans aucun calcul, fournit de fortes indications que les processus hydrologiques ne peuvent pas être chaotiques déterministes (bas dimensionnels). En outre, on montre que les particularités spécifiques des processus hydrologiques aux échelles temporelles fines, telles que l'asymétrie, les fonctions de distribution en forme de J, l'intermittence et les autocorrélations élevées, sont des facteurs synergiques qui peuvent mener à des conclusions fallacieuses concernant la présence du chaos déterministe (bas dimensionnel). En outre l'identification d'un attracteur hypothétique à partir d'une série chronologique est posée comme un problème statistique d'estimation, dont l'étude permet, entre d'autres, la quantification de la taille requise de la série; celle-...

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Observation and Analysis of Midwestern Rain Rates

Cited by 66 publications

References 0 publications

Comparative rainfall data analysis from two vertically pointing radars, an optical disdrometer, and a rain gauge

Comparative rainfall data analysis from two vertically pointing radars, an optical disdrometer, and a rain gauge

Multiscaling analysis of high resolution space-time lidar-rainfall

On the quest for chaotic attractors in hydrological processes

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