The present study uses the data collected from Cimel Sunphotometer of Aerosol Robotic Network（AERONET） for the period from January to December, 2012 over an urban site,Pretoria（PTR; 25.75°S, 28.28°E, 1449...The present study uses the data collected from Cimel Sunphotometer of Aerosol Robotic Network（AERONET） for the period from January to December, 2012 over an urban site,Pretoria（PTR; 25.75°S, 28.28°E, 1449 m above sea level）, South Africa. We found that monthly mean aerosol optical depth（AOD, τa） exhibits two maxima that occurred in summer（February） and winter（August） having values of 0.36 ± 0.19 and 0.25 ± 0.14,respectively, high-to-moderate values in spring and thereafter, decreases from autumn with a minima in early winter（June） 0.12 ± 0.07. The Angstrom exponents（α440–870） likewise,have its peak in summer（January） 1.70 ± 0.21 and lowest in early winter（June） 1.38 ± 0.26,while the columnar water vapor（CWV） followed AOD pattern with high values（summer） at the beginning of the year（February, 2.10 ± 0.37 cm） and low values（winter） in the middle of the year（July, 0.66 ± 0.21 cm）. The volume size distribution（VSD） in the fine-mode is higher in the summer and spring seasons, whereas in the coarse mode the VSD is higher in the winter and lower in the summer due to the hygroscopic growth of aerosol particles.The single scattering albedo（SSA） ranged from 0.85 to 0.96 at 440 nm over PTR for the entire study period. The averaged aerosol radiative forcing（ARF） computed using SBDART model at the top of the atmosphere（TOA） was- 8.78 ± 3.1 W/m2, while at the surface it was- 25.69 ± 8.1 W/m2 leading to an atmospheric forcing of ＋ 16.91 ± 6.8 W/m2, indicating significant heating of the atmosphere with a mean of 0.47 K/day.展开更多
This research concerns the estimation of latent linear or polychoric correlations from fuzzy frequency tables.Fuzzy counts are of particular interest to many disciplines including social and behavioral sciences and ar...This research concerns the estimation of latent linear or polychoric correlations from fuzzy frequency tables.Fuzzy counts are of particular interest to many disciplines including social and behavioral sciences and are especially relevant when observed data are classified using fuzzy categories—as for socioeconomic studies,clinical evaluations,content analysis,inter-rater reliability analysis—orwhen imprecise observations are classified into either precise or imprecise categories—as for the analysis of ratings data or fuzzy-coded variables.In these cases,the space of count matrices is no longer defined over naturals and,consequently,the polychoric estimator cannot be used to accurately estimate latent linear correlations.The aim of this contribution is twofold.First,we illustrate a computational procedure based on generalized natural numbers for computing fuzzy frequencies.Second,we reformulate the problem of estimating latent linear correlations from fuzzy counts in the context of expectation–maximization-based maximum likelihood estimation.A simulation study and two applications are used to investigate the characteristics of the proposed method.Overall,the results show that the fuzzy EM-based polychoric estimator is more efficient to deal with imprecise count data as opposed to standard polychoric estimators that may be used in this context.展开更多
基金supported through Africa Laser Centre (Pretoria) collaborative projectfunded NRF bi-lateral project(Grand UID:78682)
文摘The present study uses the data collected from Cimel Sunphotometer of Aerosol Robotic Network（AERONET） for the period from January to December, 2012 over an urban site,Pretoria（PTR; 25.75°S, 28.28°E, 1449 m above sea level）, South Africa. We found that monthly mean aerosol optical depth（AOD, τa） exhibits two maxima that occurred in summer（February） and winter（August） having values of 0.36 ± 0.19 and 0.25 ± 0.14,respectively, high-to-moderate values in spring and thereafter, decreases from autumn with a minima in early winter（June） 0.12 ± 0.07. The Angstrom exponents（α440–870） likewise,have its peak in summer（January） 1.70 ± 0.21 and lowest in early winter（June） 1.38 ± 0.26,while the columnar water vapor（CWV） followed AOD pattern with high values（summer） at the beginning of the year（February, 2.10 ± 0.37 cm） and low values（winter） in the middle of the year（July, 0.66 ± 0.21 cm）. The volume size distribution（VSD） in the fine-mode is higher in the summer and spring seasons, whereas in the coarse mode the VSD is higher in the winter and lower in the summer due to the hygroscopic growth of aerosol particles.The single scattering albedo（SSA） ranged from 0.85 to 0.96 at 440 nm over PTR for the entire study period. The averaged aerosol radiative forcing（ARF） computed using SBDART model at the top of the atmosphere（TOA） was- 8.78 ± 3.1 W/m2, while at the surface it was- 25.69 ± 8.1 W/m2 leading to an atmospheric forcing of ＋ 16.91 ± 6.8 W/m2, indicating significant heating of the atmosphere with a mean of 0.47 K/day.
文摘This research concerns the estimation of latent linear or polychoric correlations from fuzzy frequency tables.Fuzzy counts are of particular interest to many disciplines including social and behavioral sciences and are especially relevant when observed data are classified using fuzzy categories—as for socioeconomic studies,clinical evaluations,content analysis,inter-rater reliability analysis—orwhen imprecise observations are classified into either precise or imprecise categories—as for the analysis of ratings data or fuzzy-coded variables.In these cases,the space of count matrices is no longer defined over naturals and,consequently,the polychoric estimator cannot be used to accurately estimate latent linear correlations.The aim of this contribution is twofold.First,we illustrate a computational procedure based on generalized natural numbers for computing fuzzy frequencies.Second,we reformulate the problem of estimating latent linear correlations from fuzzy counts in the context of expectation–maximization-based maximum likelihood estimation.A simulation study and two applications are used to investigate the characteristics of the proposed method.Overall,the results show that the fuzzy EM-based polychoric estimator is more efficient to deal with imprecise count data as opposed to standard polychoric estimators that may be used in this context.
