Abstract: Linear parametric models are commonly assumed and used for unknown data-generating mechanisms. This study demonstrates the value of inferring statistics of meteorological and climatological time series by using a ...

Abstract: This study extends the authors? earlier work that addresses the importance of bootstrap methods in computing statistical characteristics of meteorological and climatological datasets. Subsampling confidence intervals for ...

Abstract: No abstracts available.

Abstract: A method for comprehensive statistical data analysis is presented. It includes a procedure for selection of segments within a data record, where data can be considered stationary, allowing the computation of statistical ...

Abstract: Low-order models (LOMs) arising in various popular fluid dynamic and atmospheric problems are shown to be equivalent to coupled three-mode nonlinear systems known in mechanics as Volterra gyrostats. The Volterra equations ...

Abstract: This study has employed the concept of bandpass filtering of aircraft field data that are obtained within convective planetary boundary layers, for the purpose of comparing the characteristic turbulence statistics with the ...

Abstract: Statistical issues of atmospheric data analysis are discussed that address the problem of stationarity and homogeneity of data and the problem of inadequate record lengths. Bandpass filtering of observational data is ...

Abstract: This study has addressed the sensitivity of large-eddy simulation (LES) models to domain size, grid resolution, and the number of large-eddy turnover times (t?) of integration, and their respective effects on the characteristic ...

Abstract: The development of physically sound low-order models (LOMs) of convective flow is discussed. LOMs are constructed in the form of coupled three-mode systems known in mechanics as Volterra gyrostats. This structure ensures ...