scholarly journals On space-time scaling of cumulated rainfall fields

1998 ◽  
Vol 34 (12) ◽  
pp. 3461-3469 ◽  
Author(s):  
Ignacio Rodriguez-Iturbe ◽  
Marco Marani ◽  
Paolo D'Odorico ◽  
Andrea Rinaldo
Keyword(s):  
Space Time ◽  
Time Scaling

Space-time characteristics of areal reduction factors and precipitation mechanisms

2020 ◽  
Author(s):  
Korbinian Breinl ◽  
Hannes Müller-Thomy ◽  
Günter Blöschl
Keyword(s):  
Rain Gauge ◽  
Space Time ◽  
Convective Precipitation ◽  
Convective Activity ◽  
Block Kriging ◽  
Rain Gauge Data ◽  
Time Scaling ◽  
Statistical Behavior ◽  
Gauge Data ◽  
Reduction Factors

<p>We link areal reduction factors (ARFs, the ratio of annual maxima catchment precipitation and point precipitation) to the dominating precipitation mechanisms in Austria (84,000km²), using a new efficient method of estimating ARFs based on block kriging. A better understanding of the precipitation mechanisms help assess the plausibility of the ARFs estimated, but ARFs likewise contribute to a better understanding of the precipitation mechanisms as they are a fingerprint of the spatial statistical behavior of extreme precipitation. Our main focus is on two sub-regions in the West and East of Austria, dominated by stratiform and convective precipitation, respectively. ARFs are estimated using rain gauge data with hourly resolution across five durations. ARFs decay faster with increasing area in regions of pronounced convective activity than in regions dominated by stratiform processes. Low ARF values are linked to increased lightening activity (as a proxy for convective activity), but low ARFs can likewise occur in areas of reduced lightning activity as, in summer, convective precipitation can occur everywhere in the country. ARFs tend to decrease with increasing return period, possibly because the contribution of convective precipitation is higher. Our analysis is a key component towards a better understanding of the hydrometeorology in the region, as the process links of the ARFs relate to the space-time scaling of floods.</p>

scholarly journals Cosmological perturbations in inflationary models with anisotropic space-time scaling in a Lifshitz background

2013 ◽  
Vol 88 (10) ◽  
Author(s):  
Mohsen Alishahiha ◽  
Hassan Firouzjahi ◽  
Kazuya Koyama ◽  
Mohammad Hossein Namjoo
Keyword(s):  
Space Time ◽  
Cosmological Perturbations ◽  
Anisotropic Space ◽  
Time Scaling ◽  
Inflationary Models

Conclusions: Richardson’s dreams

2019 ◽  
Author(s):  
Shaun Lovejoy
Keyword(s):  
Nonlinear Dynamics ◽  
Spatial Scales ◽  
Weather Prediction ◽  
A Priori ◽  
Space Time ◽  
Atmospheric Modeling ◽  
Time Scaling ◽  
Symmetry Principles ◽  
Big Picture ◽  
Numerical Process

From big to small, from fast to slow, we traveled through scales— through magnifications of billions in space and billions of billions in time. We looked at how the traditional scalebound approach singles out specific phenomena: structures at specific spatial scales with specific lifetimes. The approach attempts to understand each in a (scale) reductionist and (usually) deterministic manner. Yet it fails miserably to describe more than tiny portions of the actual variability, giving— at best— some qualitative insights. Viewing the big picture with the help of modern data, we saw that, quantitatively, the scalebound approach underestimates the variability by a factor of a million billion (Fig. 2.3A). The alternative is the scaling approach, which attempts to understand and model the atmosphere over wide ranges of scale. This approach is based on space– time scale symmetry principles. It describes statistically the synergy of nonlinear processes that act collectively over wide ranges of scale. To apply the idea in space, we needed to generalize the notion of scale itself (Chapter 3)— notably, to be able to account for the stratification caused by gravity. The appropriate notion of scale is one that emerges as a consequence of strong nonlinear dynamics, rather than being imposed a priori from without. Applying scaling in time, we found that the familiar weather– climate dichotomy was missing a key middle regime: from ten days to twenty years. It is a weather, macroweather, climate trichotomy. When it comes to real atmospheric modeling, scientists have long realized the limits of the scalebound approach. When they “really need to know,” they defer to NWP or GCMs, the embodiment of Richardson’s dream of “weather prediction by numerical process.” This is fortunate, because the NWPs and GCMs respect space– time scaling symmetries; without them, they would be hopelessly unrealistic. At least when used for their original purpose— weather prediction up to the ten- day deterministic predictability limit— respecting scaling allows them to be reasonably accurate.

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