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contributor authorMurphy, Allan H.
contributor authorBrown, Barbara G.
contributor authorChen, Yin-Sheng
date accessioned2017-06-09T14:42:59Z
date available2017-06-09T14:42:59Z
date copyright1989/12/01
date issued1989
identifier issn0882-8156
identifier otherams-2509.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4161834
description abstractA diagnostic approach to forecast verification is described and illustrated. This approach is based on a general framework for forecast verification. It is ?diagnostic? in the sense that it focuses on the fundamental characteristics of the forecasts, the corresponding observations, and their relationship. Three classes of diagnostic verification methods are identified: 1) the joint distribution of forecasts and observations and conditional and marginal distributions associated with factorizations of this joint distribution; 2) summary measures of these joint, conditional, and marginal distributions; and 3) performance measures and their decompositions. Linear regression models that can be used to describe the relationship between forecasts and observations are also presented. Graphical displays are advanced as a means of enhancing the utility of this body of diagnostic verification methodology. A sample of National Weather Service maximum temperature forecasts (and observations) for Minneapolis, Minnesota, is analyzed to illustrate the use of this methodology. Graphical displays of the basic distributions and various summary measures are employed to obtain insights into distributional characteristics such as central tendency, variability, and asymmetry. The displays also facilitate the comparison of these characteristics among distributions?for example, between distributions involving forecasts and observations, among distributions involving different types of forecasts, and among distributions involving forecasts for different seasons or lead times. Performance measures and their decompositions are shown to provide quantitative information regarding basic dimensions of forecast quality such as bias, accuracy, calibration (or reliability), discrimination, and skill. Information regarding both distributional and performance characteristics is needed by modelers and forecasters concerned with improving forecast quality. Some implications of these diagnostic methods for verification procedures and practices are discussed.
publisherAmerican Meteorological Society
titleDiagnostic Verification of Temperature Forecasts
typeJournal Paper
journal volume4
journal issue4
journal titleWeather and Forecasting
identifier doi10.1175/1520-0434(1989)004<0485:DVOTF>2.0.CO;2
journal fristpage485
journal lastpage501
treeWeather and Forecasting:;1989:;volume( 004 ):;issue: 004
contenttypeFulltext


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