Linkage of scaling and thermodynamic parameters of rainfall: Results from midlatitude mesoscale convective systems

Perica, Sanja; Foufoula‐Georgiou, Efi

doi:10.1029/95jd02372

Cited by 87 publications

(91 citation statements)

References 22 publications

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“…As mentioned earlier, Perica and Foufoula-Georgiou (1996) found a linear dependency of statistical scaling parameters on another atmospheric stability measure, the Convective Available Potential Energy (CAPE), for a small number of rainfall fields of selected convective storms over relatively flat topography. This result was tested in our simulated fields which have a wider range of CAPE values, and a more complex nonlinear relation was obtained (Fig.…”

Section: Scaling In Cloud Simulated Fields -3-d Analysismentioning

confidence: 95%

Multifractal properties of embedded convective structures in orographic precipitation: toward subgrid-scale predictability

Nogueira

Barros

Miranda

2013

Nonlin. Processes Geophys.

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Abstract. Rain and cloud fields produced by fully nonlinear idealized cloud resolving numerical simulations of orographic convective precipitation display statistical multiscaling behavior, implying that multifractal diagnostics should provide a physically robust basis for the downscaling and sub-grid scale parameterizations of moist processes. Our results show that the horizontal scaling exponent function (and respective multiscaling parameters) of the simulated rainfall and cloud fields varies with atmospheric and terrain properties, particularly small-scale terrain spectra, atmospheric stability, and advective timescale. This implies that multifractal diagnostics of moist processes for these simulations are fundamentally transient, exhibiting complex nonlinear behavior depending on atmospheric conditions and terrain forcing at each location. A particularly robust behavior found here is the transition of the multifractal parameters between stable and unstable cases, which has a clear physical correspondence to the transition from stratiform to organized (banded and cellular) convective regime. This result is reinforced by a similar behavior in the horizontal spectral exponent. Finally, our results indicate that although nonlinearly coupled fields (such as rain and clouds) have different scaling exponent functions, there are robust relationships with physical underpinnings between the scaling parameters that can be explored for hybrid dynamical-statistical downscaling.

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Section: Scaling In Cloud Simulated Fields -3-d Analysismentioning

confidence: 95%

Multifractal properties of embedded convective structures in orographic precipitation: toward subgrid-scale predictability

Nogueira

Barros

Miranda

2013

Nonlin. Processes Geophys.

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“…There are also statistical approaches to space-time downscaling of precipitation based on scale invariance also referred in literature as dynamic scaling (Perica and Foufoula-Georgiou, 1996;Venugopal et al, 1999a, b;Nykanen et al, 2001). These studies have considered precipitation from several storm events for downscaling to smaller time and space scales.…”

Section: Methods Of Downscalingmentioning

confidence: 99%

Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine

et al. 2008

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This paper presents a methodology to downscale monthly precipitation to river basin scale in Indian context for special report of emission scenarios (SRES) using Support Vector Machine (SVM). In the methodology presented, probable predictor variables are extracted from (1) the National Center for Environmental Prediction (NCEP) reanalysis data set for the period 1971-2000 and (2) the simulations from the third generation Canadian general circulation model (CGCM3) for SRES emission scenarios A1B, A2, B1 and COMMIT for the period 1971-2100. These variables include both the thermodynamic and dynamic parameters and those which have a physically meaningful relationship with the precipitation. The NCEP variables which are realistically simulated by CGCM3 are chosen as potential predictors for seasonal stratification. The seasonal stratification involves identification of (1) the past wet and dry seasons through classification of the NCEP data on potential predictors into two clusters by the use of K-means clustering algorithm and (2) the future wet and dry seasons through classification of the CGCM3 data on potential predictors into two clusters by the use of nearest neighbour rule. Subsequently, a separate downscaling model is developed for each season to capture the relationship between the predictor variables and the predictand. For downscaling precipitation, the predictand is chosen as monthly Thiessen weighted precipitation for the river basin, whereas potential predictors are chosen as the NCEP variables which are correlated to the precipitation and are also realistically simulated by CGCM3. Implementation of the methodology presented is demonstrated by application to Malaprabha reservoir catchment in India which is considered to be a climatically sensitive region. The CGCM3 simulations are run through the calibrated and validated SVM downscaling model to obtain future projections of predictand for each of the four emission scenarios considered. The results show that the precipitation is projected to increase in future for almost all the scenarios considered. The projected increase in precipitation is high for A2 scenario, whereas it is least for COMMIT scenario.

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“…In this case, downscaling aims at estimating statistical characteristics of a variable at a small temporal scale (short accumulation time), given observations with a long accumulation time. This approach -sometimes called temporal downscaling -assumes, for instance, that accumulated rainfall has scaling properties, which imply that statistical moments computed at different accumulation times are related (Perica and Foufoula-Georgiou, 1996;Deidda, 2000).…”

Section: Bridging Time and Space Scalesmentioning

confidence: 99%

Extreme events: dynamics, statistics and prediction

Ghil

Yiou

Hallegatte

et al. 2011

Nonlin. Processes Geophys.

228

167

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Abstract. We review work on extreme events, their causes and consequences, by a group of European and American researchers involved in a three-year project on these topics. The review covers theoretical aspects of time series analysis and of extreme value theory, as well as of the deterministic modeling of extreme events, via continuous and discrete dynamic models. The applications include climatic, seismic and socio-economic events, along with their prediction.Two important results refer to (i) the complementarity of spectral analysis of a time series in terms of the continuous and the discrete part of its power spectrum; and (ii) the need for coupled modeling of natural and socio-economic systems. Both these results have implications for the study and prediction of natural hazards and their human impacts.

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Linkage of scaling and thermodynamic parameters of rainfall: Results from midlatitude mesoscale convective systems

Cited by 87 publications

References 22 publications

Multifractal properties of embedded convective structures in orographic precipitation: toward subgrid-scale predictability

Multifractal properties of embedded convective structures in orographic precipitation: toward subgrid-scale predictability

Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine

Extreme events: dynamics, statistics and prediction

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