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    The Effects of Gridding Algorithms on the Statistical Moments and Their Trends of Daily Surface Air Temperature

    Source: Journal of Climate:;2015:;volume( 028 ):;issue: 023::page 9188
    Author:
    Cavanaugh, Nicholas R.
    ,
    Shen, Samuel S. P.
    DOI: 10.1175/JCLI-D-14-00668.1
    Publisher: American Meteorological Society
    Abstract: his paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network?Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT?s higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations.
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      The Effects of Gridding Algorithms on the Statistical Moments and Their Trends of Daily Surface Air Temperature

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    contributor authorCavanaugh, Nicholas R.
    contributor authorShen, Samuel S. P.
    date accessioned2017-06-09T17:11:26Z
    date available2017-06-09T17:11:26Z
    date copyright2015/12/01
    date issued2015
    identifier issn0894-8755
    identifier otherams-80833.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4223769
    description abstracthis paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network?Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT?s higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations.
    publisherAmerican Meteorological Society
    titleThe Effects of Gridding Algorithms on the Statistical Moments and Their Trends of Daily Surface Air Temperature
    typeJournal Paper
    journal volume28
    journal issue23
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-14-00668.1
    journal fristpage9188
    journal lastpage9205
    treeJournal of Climate:;2015:;volume( 028 ):;issue: 023
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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