scholarly journals Relationship between snow cover and atmospheric circulation, central North America, winter 1988

1997 ◽  
Vol 25 ◽  
pp. 347-352 ◽  
Author(s):  
Chris Derksen ◽  
Kkevin Misurak ◽  
Ellsworth Ledrew ◽  
Joe Piwowar ◽  
Barry Goodison
Keyword(s):  
Correlation Analysis ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Canonical Correlation Analysis ◽  
Principal Components ◽  
Canonical Correlation ◽  
Snow Water Equivalent ◽  
Principal Component ◽  
Original Data ◽  
Snow Cover Extent

The stochastic relationships between terrestrial snow water equivalent (SWE) and measures of the atmospheric circulation were investigated for the Canadian Prairies and the American Great Plains for the winter of 1988. Snow-cover extent, derived from EASE-grid SSM/I satellite data, and griddcd atmospheric data from the National Meteorological Center were averaged at five day intervals. Principal components analysis (PCA) were performed for the time series of SSM/I snow-cover imagery as well as for 700 mb geopotential height and temperature, 500 mb height and 700–500 mb thickness. Canonical correlation analysis of the derived principal component weights was used to identify relationships between atmospheric variables and SWE. Results of the PCA indicate that a high degree of variance in upper air variables (>75%) can be explained by the first three principal components, while the first three SWE components account for over 90% of the variance in the original data. Results of the canonical correlation analysis show positive relationships between snow-cover accumulation and a meridional pressure distribution pattern, while snow ablation is linked to a zonal atmospheric pressure pattern.

scholarly journals Relationship between snow cover and atmospheric circulation, central North America, winter 1988

1997 ◽  
Vol 25 ◽  
pp. 347-352 ◽  
Author(s):  
Chris Derksen ◽  
Kkevin Misurak ◽  
Ellsworth Ledrew ◽  
Joe Piwowar ◽  
Barry Goodison
Keyword(s):  
Correlation Analysis ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Canonical Correlation Analysis ◽  
Principal Components ◽  
Canonical Correlation ◽  
Snow Water Equivalent ◽  
Principal Component ◽  
Original Data ◽  
Snow Cover Extent

The stochastic relationships between terrestrial snow water equivalent (SWE) and measures of the atmospheric circulation were investigated for the Canadian Prairies and the American Great Plains for the winter of 1988. Snow-cover extent, derived from EASE-grid SSM/I satellite data, and griddcd atmospheric data from the National Meteorological Center were averaged at five day intervals. Principal components analysis (PCA) were performed for the time series of SSM/I snow-cover imagery as well as for 700 mb geopotential height and temperature, 500 mb height and 700–500 mb thickness. Canonical correlation analysis of the derived principal component weights was used to identify relationships between atmospheric variables and SWE. Results of the PCA indicate that a high degree of variance in upper air variables (>75%) can be explained by the first three principal components, while the first three SWE components account for over 90% of the variance in the original data. Results of the canonical correlation analysis show positive relationships between snow-cover accumulation and a meridional pressure distribution pattern, while snow ablation is linked to a zonal atmospheric pressure pattern.

scholarly journals Expression reflects population structure

2018 ◽  
Author(s):  
Brielin C Brown ◽  
Nicolas L. Bray ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Population Structure ◽  
African Americans ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Gene Expression Data ◽  
Principal Components ◽  
Canonical Correlation ◽  
Principal Component ◽  
Expression Data

AbstractPopulation structure in genotype data has been extensively studied, and is revealed by looking at the principal components of the genotype matrix. However, no similar analysis of population structure in gene expression data has been conducted, in part because a naïve principal components analysis of the gene expression matrix does not cluster by population. We identify a linear projection that reveals population structure in gene expression data. Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis. Futhermore, we analyze the variance of each gene within the projection matrix to determine which genes significantly influence the projection. We identify thousands of significant genes, and show that a number of the top genes have been implicated in diseases that disproportionately impact African Americans.Author SummaryHigh dimensional, multi-modal genomics datasets are becoming increasingly common, which warrants investigation into analysis techniques that can reveal structure in the data without over-fitting. Here, we show that the coupling of principal component analysis to canonical correlation analysis offers an efficient approach to exploratory analysis of this kind of data. We apply this method to the GEUVADIS dataset of genotype and gene expression values of European and Yoruban individuals, finding as-of-yet unstudied population structure in the gene expression values. Moreover, many of the top genes identified by our method have been previously implicated in diseases that disproportionately impact African Americans.

scholarly journals Analysis of Soil-Vegetation Interrelationships in a South-Southern Secondary Forest of Nigeria

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
D. D. Eni ◽  
A. I. Iwara ◽  
R. A. Offiong
Keyword(s):  
Organic Matter ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
State Government ◽  
Canonical Correlation ◽  
Secondary Forest ◽  
Principal Component ◽  
Tree Density ◽  
Tree Size ◽  
Canonical Variate

Soil-vegetation interrelationships in a secondary forest of South-Southern Nigeria were studied using principal component analysis (PCA) and canonical correlation analysis (CCA). The grid system of vegetation sampling was employed to randomly collect vegetation and soil data from fifteen quadrats of 10 m × 10 m. PCA result showed that exchangeable sodium, organic matter, cation exchange capacity, exchangeable calcium, and sand content were the major soil properties sustaining the regenerative capacity and luxuriant characteristics of the secondary forest, while tree size and tree density constituted the main vegetation parameters protecting and enriching the soil for its continuous support to the vegetation after decades of anthropogenic disturbance (food crop cultivation and illegal logging activities) before its acquisition and subsequent preservation by the Cross River State government in 2003. In addition, canonical correlation analysis showed result similar to PCA, as it indicated a pattern of relationship between soil and vegetation. The only retained canonical variate revealed a positive interrelationship between organic matter and tree size as well as an inverse relationship between organic matter and tree density. These extracted soil and vegetation variables are indeed significantly important in explaining soil-vegetation interrelationships in the highly regenerative secondary forest.

Trends in Snow Cover and Related Quantities at Weather Stations in the Conterminous United States

2015 ◽  
Vol 28 (19) ◽  
pp. 7518-7528 ◽  
Author(s):  
Noah Knowles
Keyword(s):  
United States ◽  
Correlation Analysis ◽  
Snow Cover ◽  
Canonical Correlation Analysis ◽  
Great Plains ◽  
Canonical Correlation ◽  
Snow Depth ◽  
The United States ◽  
Eastern Half ◽  
Precipitation Trends

Abstract Trend tests, linear regression, and canonical correlation analysis were used to quantify changes in National Weather Service Cooperative Observer (COOP) snow depth data and derived quantities, precipitation, snowfall, and temperature over the study period 1950–2010. Despite widespread warming, historical trends in snowfall and snow depth are generally mixed owing to competing influences of trends in precipitation. Trends toward later snow-cover onset in the western half of the conterminous United States and earlier onset in the eastern half and a widespread trend toward earlier final meltoff of snow cover combined to produce trends toward shorter snow seasons in the eastern half of the United States and in the west and longer snow seasons in the Great Plains and southern Rockies. The annual total number of days with snow cover exhibited a widespread decline. Monthly trend patterns show the dominant influence of temperature trends on occurrence of snow cover in the warmer snow-season months and a combination of temperature and precipitation trends in the colder months. A canonical correlation analysis indicated that most trends presented here took hold in the 1970s, consistent with the temporal pattern of global warming during the study period.

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