Space-time correlations and the Taylor hypothesis behind forced detonations

2011 ◽  
Author(s):  
Luca Massa ◽  
Monika Chauhan ◽  
Frank Lu
Keyword(s):  
Space Time ◽  
Time Correlations ◽  
Taylor Hypothesis

scholarly journals Spatial and space-time correlations in systems of subpopulations with genetic drift and migration.

Genetics ◽  
1993 ◽  
Vol 133 (3) ◽  
pp. 711-727
Author(s):  
B K Epperson
Keyword(s):  
Population Genetics ◽  
Time Correlation ◽  
Space Time ◽  
Spatial Correlations ◽  
Gene Frequencies ◽  
Migration Patterns ◽  
Migration Rates ◽  
Space And Time ◽  
Time Correlations ◽  
And Migration

Abstract The geographic distribution of genetic variation is an important theoretical and experimental component of population genetics. Previous characterizations of genetic structure of populations have used measures of spatial variance and spatial correlations. Yet a full understanding of the causes and consequences of spatial structure requires complete characterization of the underlying space-time system. This paper examines important interactions between processes and spatial structure in systems of subpopulations with migration and drift, by analyzing correlations of gene frequencies over space and time. We develop methods for studying important features of the complete set of space-time correlations of gene frequencies for the first time in population genetics. These methods also provide a new alternative for studying the purely spatial correlations and the variance, for models with general spatial dimensionalities and migration patterns. These results are obtained by employing theorems, previously unused in population genetics, for space-time autoregressive (STAR) stochastic spatial time series. We include results on systems with subpopulation interactions that have time delay lags (temporal orders) greater than one. We use the space-time correlation structure to develop novel estimators for migration rates that are based on space-time data (samples collected over space and time) rather than on purely spatial data, for real systems. We examine the space-time and spatial correlations for some specific stepping stone migration models. One focus is on the effects of anisotropic migration rates. Partial space-time correlation coefficients can be used for identifying migration patterns. Using STAR models, the spatial, space-time, and partial space-time correlations together provide a framework with an unprecedented level of detail for characterizing, predicting and contrasting space-time theoretical distributions of gene frequencies, and for identifying features such as the pattern of migration and estimating migration rates in experimental studies of genetic variation over space and time.

scholarly journals On the computation of space-time correlations by large-eddy simulation

2004 ◽  
Vol 16 (11) ◽  
pp. 3859-3867 ◽  
Author(s):  
Guo-Wei He ◽  
Meng Wang ◽  
Sanjiva K. Lele
Keyword(s):  
Large Eddy Simulation ◽  
Space Time ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Time Correlations

scholarly journals Modeling the Variability of Drop Size Distributions in Space and Time

2007 ◽  
Vol 46 (6) ◽  
pp. 742-756 ◽  
Author(s):  
Gyu Won Lee ◽  
Alan W. Seed ◽  
Isztar Zawadzki
Keyword(s):  
Drop Size ◽  
Temporal Structure ◽  
Space Time ◽  
Cascade Model ◽  
Time Variability ◽  
Size Distributions ◽  
Precipitation Field ◽  
Stochastic Fluctuations ◽  
Taylor Hypothesis ◽  
Drop Size Distributions

Abstract The information on the time variability of drop size distributions (DSDs) as seen by a disdrometer is used to illustrate the structure of uncertainty in radar estimates of precipitation. Based on this, a method to generate the space–time variability of the distributions of the size of raindrops is developed. The model generates one moment of DSDs that is conditioned on another moment of DSDs; in particular, radar reflectivity Z is used to obtain rainfall rate R. Based on the fact that two moments of the DSDs are sufficient to capture most of the DSD variability, the model can be used to calculate DSDs and other moments of interest of the DSD. A deterministic component of the precipitation field is obtained from a fixed R–Z relationship. Two different components of DSD variability are added to the deterministic precipitation field. The first represents the systematic departures from the fixed R–Z relationship that are expected from different regimes of precipitation. This is generated using a simple broken-line model. The second represents the fluctuations around the R–Z relationship for a particular regime and uses a space–time multiplicative cascade model. The temporal structure of the stochastic fluctuations is investigated using disdrometer data. Assuming Taylor hypothesis, the spatial structure of the fluctuations is obtained and a stochastic model of the spatial distribution of the DSD variability is constructed. The consistency of the model is validated using concurrent radar and disdrometer data.

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