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    Uncertainty in Ranking the Hottest Years of U.S. Surface Temperatures 

    Source: Journal of Climate:;2013:;volume( 026 ):;issue: 017:;page 6323
    Author(s): Guttorp, Peter; Kim, Tae Yen
    Publisher: American Meteorological Society
    Abstract: anking years based on statistical estimates of regional and temporal averages is subject to uncertainty. This uncertainty can in fact be quite substantial and can be described by the rank distribution of an ensemble of ...
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    Incorporating Spatial Dependence and Atmospheric Data in a Model of Precipitation 

    Source: Journal of Applied Meteorology:;1994:;volume( 033 ):;issue: 012:;page 1503
    Author(s): Hughes, James P.; Guttorp, Peter
    Publisher: American Meteorological Society
    Abstract: Nonhomogeneous hidden Markov models (NHMM) provide a method of relating synoptic atmospheric measurements to precipitation occurrence at a network of rain gauge stations. In previous work it was assumed that, conditional ...
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    A Markov Chain Model of Tornadic Activity 

    Source: Monthly Weather Review:;2003:;volume( 131 ):;issue: 012:;page 2941
    Author(s): Drton, Mathias; Marzban, Caren; Guttorp, Peter; Schaefer, Joseph T.
    Publisher: American Meteorological Society
    Abstract: Tornadic activity in four U.S. regions is stochastically modeled based on data on tornado counts over the years 1953?98. It is shown that tornadic activity on a given day is mostly affected by the activity on the previous ...
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    Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction 

    Source: Monthly Weather Review:;2009:;volume( 138 ):;issue: 005:;page 1811
    Author(s): Bao, Le; Gneiting, Tilmann; Grimit, Eric P.; Guttorp, Peter; Raftery, Adrian E.
    Publisher: American Meteorological Society
    Abstract: Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ...
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    Assessing the Uncertainty in Projecting Local Mean Sea Level from Global Temperature 

    Source: Journal of Applied Meteorology and Climatology:;2014:;volume( 053 ):;issue: 009:;page 2163
    Author(s): Guttorp, Peter; Januzzi, Alex; Novak, Marie; Podschwit, Harry; Richardson, Lee; Sowder, Colin D.; Zimmerman, Aaron; Bolin, David; Särkkä, Aila
    Publisher: American Meteorological Society
    Abstract: he process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned ...
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