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Allan H. Murphy

Researcher at Oregon State University

Publications -  119
Citations -  10313

Allan H. Murphy is an academic researcher from Oregon State University. The author has contributed to research in topics: Consensus forecast & Probabilistic forecasting. The author has an hindex of 46, co-authored 119 publications receiving 9640 citations. Previous affiliations of Allan H. Murphy include National Oceanic and Atmospheric Administration & University of Michigan.

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A New Vector Partition of the Probability Score

TL;DR: In this article, a new vector partition of the probability, or Brier, score (PS) is formulated and the nature and properties of this partition are described, as well as the relationships between the terms in this partition and terms in the original vector partition.
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Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient

TL;DR: In this article, several skill scores are defined, based on the mean-square-error measure of accuracy and alternative climatological standards of reference, each of which is shown to possess terms involving 1) the coefficient of correlation between the forecasts and observations, 2) a measure of the nonsystematic (i.e., conditional) bias in the forecast, and 3) the systematic bias in forecasts.
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What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting

TL;DR: Three distinct types of goodness are identified in this paper: the correspondence between forecasters’ judgments and their forecasts, the correspondencebetween the forecasts and the matching observations, and the incremental economic and/or other benefits realized by decision makers through the use of the forecasts (type 3 goodness, or value).
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A General Framework for Forecast Verification

TL;DR: In this paper, a general framework for forecast verification based on the joint distribution of forecasts and observations is described, and two factorizations of the joint distributions are investigated: 1) the calibration-refinement factorization, which involves the conditional distributions of observations given forecasts and the marginal distributions of forecasts, and 2) the likelihood-base factorization.
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Time Series Models to Simulate and Forecast Wind Speed and Wind Power

TL;DR: In this paper, a general approach for modeling wind speed and wind power is described, which is based on the development of a model of wind speed, and values of wind power are estimated by applying the appropriate transformations to values of speed.