Evidence of dynamic scaling in space‐time rainfall

Venugopal, Vengatesan; Foufoula‐Georgiou, Efi; Sapozhnikov, Victor B.

doi:10.1029/1999jd900437

Cited by 96 publications

(91 citation statements)

References 20 publications

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“…The top cascade levels (0, 1 and 2) represent the low spatial frequencies (large precipitation structures), while the bottom cascade levels (5, 6, 7) represent the high spatial frequencies (small precipitation structures). Another important behavior of rainfall fields is known as dynamic scaling, which is the empirical observation that the rate of temporal development of rainfall structures is a power law function of their spatial scale (Venugopal et al, 1999;. This means that large precipitation features are more persistent and hence predictable compared with small precipitation cells, which is closely related to the concept of scaledependence of the predictability of precipitation (Germann and Zawadzki, 2002;Turner et al, 2004).…”

Section: Steps Descriptionmentioning

confidence: 99%

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Foresti

Reyniers

Seed

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic Lagrangian extrapolation of radar images. The stochastic perturbations are designed to account for the unpredictable precipitation growth and decay processes and to reproduce the dynamic scaling of precipitation fields, i.e., the observation that largescale rainfall structures are more persistent and predictable than small-scale convective cells. This paper presents the development, adaptation and verification of the STEPS system for Belgium (STEPS-BE). STEPS-BE provides in realtime 20-member ensemble precipitation nowcasts at 1 km and 5 min resolutions up to 2 h lead time using a 4 C-band radar composite as input. In the context of the PLURISK project, STEPS forecasts were generated to be used as input in sewer system hydraulic models for nowcasting urban inundations in the cities of Ghent and Leuven. Comprehensive forecast verification was performed in order to detect systematic biases over the given urban areas and to analyze the reliability of probabilistic forecasts for a set of case studies in 2013 and 2014. The forecast biases over the cities of Leuven and Ghent were found to be small, which is encouraging for future integration of STEPS nowcasts into the hydraulic models. Probabilistic forecasts of exceeding 0.5 mm h −1 are reliable up to 60-90 min lead time, while the ones of exceeding 5.0 mm h −1 are only reliable up to 30 min. The STEPS ensembles are slightly under-dispersive and represent only 75-90 % of the forecast errors.

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Section: Steps Descriptionmentioning

confidence: 99%

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Foresti

Reyniers

Seed

et al. 2016

Hydrol. Earth Syst. Sci.

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“…With respect to rainfall, Lovejoy and Schertzer [1991], Tessier et al [1993], and Marsan et al [1996] have demonstrated the presence of dynamic scaling in rainfall fields, finding a value of the scaling exponent • roughly equal to 2/3. De Michele and Rosso [1999] and Venugopal et al [1999aVenugopal et al [ , 1999b have applied this concept to study the evolution of a rainfall field.…”

Section: I(ta) Arf(t A): I(t A0)'mentioning

confidence: 99%

The derivation of areal reduction factor of storm rainfall from its scaling properties

Michele¹,

Kottegoda²,

Rosso³

2001

Water Resources Research

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Abstract. We present a method of modeling the areal reduction factor (ARF) of storm rainfall. The ARF is widely used in reducing point rainfall to obtain areal average values for the same duration, probability of exceedance, and specified area. The concepts of scaling and multiscaling, developed in recent years, provide a powerful framework for studying spatial and temporal variability of hydrological processes. It is our view that ARF must reflect the scaling properties of rainfall in space and time. We develop a simple statistical approach to the ARF of extreme storm rainfall based on the scaling properties of the underlying process in space and time. We derive the scaling relations of mean rainfall intensity over an area A and for a duration T using the concepts of dynamic scaling and statistical self-affinity. A new physically based formula for the ARF is then obtained. Applications are made to observations from the metropolitan area of Milan, Italy, and to data in the United Kingdom, as given in the Natural Environmental Research Council Flood Studies Report. These studies indicate that storm rates in space and time are scaling for extreme events, and hence this concept is shown to provide a useful practical approach to the evaluation of design storms for specified areas.

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“…In both systems blending is achieved in physical space and the weight given to the extrapolation component takes the form of a fixed exponential decay with time. Later works (Venugopal et al, 1999;Germann and Zawadzki, 2002) have shown that predictability of rainfall structures has a scale-dependence based on dynamic scaling processes. In the Short-Term Ensemble Prediction System (STEPS, see Bowler et al, 2006) the merging of the extrapolation and NWP component forecasts is performed in a scale-dependent way using several levels on cascade processes.…”

Section: Introductionmentioning

confidence: 99%

Improving QPF by blending techniques at the Meteorological Service of Catalonia

Atencia

Rigo

Sairouni

et al. 2010

Nat. Hazards Earth Syst. Sci.

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Abstract. The current operational very short-term and shortterm quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of Correspondence to: A. Atencia (aatencia@meteo.cat) episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.

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Evidence of dynamic scaling in space‐time rainfall

Cited by 96 publications

References 20 publications

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

The derivation of areal reduction factor of storm rainfall from its scaling properties

Improving QPF by blending techniques at the Meteorological Service of Catalonia

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