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- Author or Editor: A. J. Chambers x
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Abstract
Conditional sampling and averaging techniques are used to obtain statistics of convectively-driven quasi-ordered structures at a height of 4 m within the atmospheric surface layer. The fraction of time 'y occupiedby these structures, and their frequency of occurrence I can depend on detection criteria parameters, suchas the threshold and hold time. The effect of these parameters on 'y and f is investigated for two conditionalsampling techniques. Both techniques indicate that y decreases continuously with increasing threshold,whereas there is a region in which I is independent of both parameters. When the parameters are suitablyselected, reasonable agreement for both 'y and f can be obtained between the techniques. This agreementdoes not depend on whether the velocity or the temperature fluctuation is used as the basis of detection forone of the techniques.
Abstract
Conditional sampling and averaging techniques are used to obtain statistics of convectively-driven quasi-ordered structures at a height of 4 m within the atmospheric surface layer. The fraction of time 'y occupiedby these structures, and their frequency of occurrence I can depend on detection criteria parameters, suchas the threshold and hold time. The effect of these parameters on 'y and f is investigated for two conditionalsampling techniques. Both techniques indicate that y decreases continuously with increasing threshold,whereas there is a region in which I is independent of both parameters. When the parameters are suitablyselected, reasonable agreement for both 'y and f can be obtained between the techniques. This agreementdoes not depend on whether the velocity or the temperature fluctuation is used as the basis of detection forone of the techniques.
Abstract
Central New York State, located at the intersection of the northeastern United States and the Great Lakes basin, is impacted by snowfall produced by lake-effect and non-lake-effect snowstorms. The purpose of this study is to determine the spatiotemporal patterns of snowfall in central New York and their possible underlying causes. Ninety-three Cooperative Observer Program stations are used in this study. Spatiotemporal patterns are analyzed using simple linear regressions, Pearson correlations, principal component analysis to identify regional clustering, and spatial snowfall distribution maps in the ArcGIS software. There are three key findings. First, when the long-term snowfall trend (1931/32–2011/12) is divided into two halves, a strong increase is present during the first half (1931/32–1971/72), followed by a lesser decrease in the second half (1971/72–2011/12). This result suggests that snowfall trends behave nonlinearly over the period of record. Second, central New York spatial snowfall patterns are similar to those for the whole Great Lakes basin. For example, for five distinct regions identified within central New York, regions closer to and leeward of Lake Ontario experience higher snowfall trends than regions farther away and not leeward of the lake. Third, as compared with precipitation totals (0.02), average air temperatures had the largest significant (ρ < 0.05) correlation (−0.56) with seasonal snowfall totals in central New York. Findings from this study are valuable because they provide a basis for understanding snowfall patterns in a region that is affected by both non-lake-effect and lake-effect snowstorms.
Abstract
Central New York State, located at the intersection of the northeastern United States and the Great Lakes basin, is impacted by snowfall produced by lake-effect and non-lake-effect snowstorms. The purpose of this study is to determine the spatiotemporal patterns of snowfall in central New York and their possible underlying causes. Ninety-three Cooperative Observer Program stations are used in this study. Spatiotemporal patterns are analyzed using simple linear regressions, Pearson correlations, principal component analysis to identify regional clustering, and spatial snowfall distribution maps in the ArcGIS software. There are three key findings. First, when the long-term snowfall trend (1931/32–2011/12) is divided into two halves, a strong increase is present during the first half (1931/32–1971/72), followed by a lesser decrease in the second half (1971/72–2011/12). This result suggests that snowfall trends behave nonlinearly over the period of record. Second, central New York spatial snowfall patterns are similar to those for the whole Great Lakes basin. For example, for five distinct regions identified within central New York, regions closer to and leeward of Lake Ontario experience higher snowfall trends than regions farther away and not leeward of the lake. Third, as compared with precipitation totals (0.02), average air temperatures had the largest significant (ρ < 0.05) correlation (−0.56) with seasonal snowfall totals in central New York. Findings from this study are valuable because they provide a basis for understanding snowfall patterns in a region that is affected by both non-lake-effect and lake-effect snowstorms.
