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    A Cautionary Note on Artificial Examples of EOFs 

    Source: Journal of Climate:;2003:;volume( 016 ):;issue: 007:;page 1084
    Author(s): Jolliffe, Ian T.
    Publisher: American Meteorological Society
    Abstract: A recent paper used a simple artificial example to illustrate the shortcomings of EOFs and rotated EOFs in identifying underlying physical modes. The example raises at least as many questions as it answers. This note ...
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    Uncertainty and Inference for Verification Measures 

    Source: Weather and Forecasting:;2007:;volume( 022 ):;issue: 003:;page 637
    Author(s): Jolliffe, Ian T.
    Publisher: American Meteorological Society
    Abstract: When a forecast is assessed, a single value for a verification measure is often quoted. This is of limited use, as it needs to be complemented by some idea of the uncertainty associated with the value. If this uncertainty ...
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    Evaluating Rank Histograms Using Decompositions of the Chi-Square Test Statistic 

    Source: Monthly Weather Review:;2008:;volume( 136 ):;issue: 006:;page 2133
    Author(s): Jolliffe, Ian T.; Primo, Cristina
    Publisher: American Meteorological Society
    Abstract: Rank histograms are often plotted to evaluate the forecasts produced by an ensemble forecasting system?an ideal rank histogram is ?flat? or uniform. It has been noted previously that the obvious test of ?flatness,? the ...
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    Proper Scores for Probability Forecasts Can Never Be Equitable 

    Source: Monthly Weather Review:;2008:;volume( 136 ):;issue: 004:;page 1505
    Author(s): Jolliffe, Ian T.; Stephenson, David B.
    Publisher: American Meteorological Society
    Abstract: Verification is an important part of any forecasting system. It is usually achieved by computing the value of some measure or score that indicates how good the forecasts are. Many possible verification measures have been ...
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    Comments on “Discussion of Verification Concepts in Forecast Verification: A Practitioner’s Guide in Atmospheric Science” 

    Source: Weather and Forecasting:;2005:;volume( 020 ):;issue: 005:;page 796
    Author(s): Jolliffe, Ian T.; Stephenson, David B.
    Publisher: American Meteorological Society
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    Comments on “H. L. Wagner’s Unbiased Hit Rate and the Assessment of Categorical Forecasting Accuracy” 

    Source: Weather and Forecasting:;2014:;volume( 029 ):;issue: 003:;page 763
    Author(s): Jolliffe, Ian T.; Stephenson, David B.
    Publisher: American Meteorological Society
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    Spatial Weighting and Iterative Projection Methods for EOFs 

    Source: Journal of Climate:;2009:;volume( 022 ):;issue: 002:;page 234
    Author(s): Baldwin, Mark P.; Stephenson, David B.; Jolliffe, Ian T.
    Publisher: American Meteorological Society
    Abstract: Often there is a need to consider spatial weighting in methods for finding spatial patterns in climate data. The focus of this paper is on techniques that maximize variance, such as empirical orthogonal functions (EOFs). ...
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    Calibration of Probabilistic Forecasts of Binary Events 

    Source: Monthly Weather Review:;2009:;volume( 137 ):;issue: 003:;page 1142
    Author(s): Primo, Cristina; Ferro, Christopher A. T.; Jolliffe, Ian T.; Stephenson, David B.
    Publisher: American Meteorological Society
    Abstract: Probabilistic forecasts of atmospheric variables are often given as relative frequencies obtained from ensembles of deterministic forecasts. The detrimental effects of imperfect models and initial conditions on the quality ...
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    Equitability Revisited: Why the “Equitable Threat Score” Is Not Equitable 

    Source: Weather and Forecasting:;2009:;volume( 025 ):;issue: 002:;page 710
    Author(s): Hogan, Robin J.; Ferro, Christopher A. T.; Jolliffe, Ian T.; Stephenson, David B.
    Publisher: American Meteorological Society
    Abstract: In the forecasting of binary events, verification measures that are ?equitable? were defined by Gandin and Murphy to satisfy two requirements: 1) they award all random forecasting systems, including those that always issue ...
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