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Estimating Monthly and Seasonal Distributions of Temperature and Precipitation Using the New CPC Long-Range Forecasts

William M. BriggsDepartment of Soil, Crop, and Atmospheric Sciences, Cornell University, Ithaca, New York

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Daniel S. WilksDepartment of Soil, Crop, and Atmospheric Sciences, Cornell University, Ithaca, New York

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Abstract

A method for transforming underlying climatological distributions for monthly and seasonal mean temperature and monthly and seasonal total precipitation, in a manner consistent with long-range forecasts by the U.S. Climate Prediction Center, is developed. These transformations are summarized as simple equations into which a user may substitute a forecast probability value and calculate the parameters of a conditional probability distribution. These distributions can then be used to evaluate probabilities associated with user-defined temperature and precipitation outcomes. Examples are given to show the case of use and interpretability.

Abstract

A method for transforming underlying climatological distributions for monthly and seasonal mean temperature and monthly and seasonal total precipitation, in a manner consistent with long-range forecasts by the U.S. Climate Prediction Center, is developed. These transformations are summarized as simple equations into which a user may substitute a forecast probability value and calculate the parameters of a conditional probability distribution. These distributions can then be used to evaluate probabilities associated with user-defined temperature and precipitation outcomes. Examples are given to show the case of use and interpretability.

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