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In 2005 the Royal Meteorological Society commissioned a study to examine current issues regarding the quality fitness for purpose of commercial weather forecasts in the United Kingdom. UK commercial weather forecast providers and users were consulted by means of online questionnaires, interviews, visits and an open workshop discussion. Results from this consultation uncovered significant deficiencies in the methodologies and in the communication of forecast quality assessments, a lack of open dialogue and transparency in the industry, and that some users may be indifferent to forecast quality. Descriptive or worded forecasts cannot be assessed objectively. However, suitable quality assessment methods are available for nearly all types of quantitative forecasts identified in the consultation. The crucial importance of choosing proper quality assessment metrics, the impact of their statistical properties on results and the need to estimate the statistical significance of quality assessment results were exemplified by means of four case studies, one of which is presented in this paper. The findings from this study have led to a set of practical recommendations aiming to establish the discipline and rigour that are necessary for achieving best practice in the quality assessment of weather forecasts. Specific recommendations were also made to the Royal Meteorological Society to set up a special commission that would promote a sense of community within the industry, and to run an accreditation scheme that would encourage best practice on a voluntary basis. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

In weather and climate forecasting, hedging is said to occur whenever a forecasters judgement and the forecast differ, and it is usually taken as evident that hedging is undesirable. Forecasts are often judged by computing a verification measure or score. A number of different scores is available in most circumstances, and to choose between them, various desirable properties of scores have been defined. It is generally accepted that it is undesirable to use a score for which hedging can improve the score or its expected value. Three ‘desirable’ properties of scores are linked to the idea that hedging should be avoided, namely propriety, equitability and consistency. It is fair to say that none of these properties is fully understood. The aim of this article is to provide some clarification and new insights, as well as some historical background. Nearly as many questions are raised as are answered. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is a dimension‐reducing tool that replaces the variables in a multivariate data set by a smaller number of derived variables. Dimension reduction is often undertaken to help in interpreting the data set but, as each principal component usually involves all the original variables, interpretation of a PCA can still be difficult. One way to overcome this difficulty is to select a subset of the original variables and use this subset to approximate the principal components. This article reviews a number of techniques for choosing subsets of the variables and examines their merits in terms of preserving the information in the PCA, and in aiding interpretation of the main sources of variation in the data. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An earlier paper (Jolliffe IT, Sarria-Dodd DE. 1994. International Journal of Climatology 14: 71–76) investigated the problem of deciding when the wet season has started in tropical and sub-tropical climates. In particular, methodology based on linear discriminant analysis was developed for using data from the current season to make the decision, rather than relying only on information from previous seasons. It was shown, for three stations in eastern Africa, that the methodology was potentially valuable. The present study is much larger, using data from 24 stations, covering a range of annual rainfall totals, in western Africa. It is confirmed that linear discriminant analysis can indeed be useful in detecting when the wet season has started, and hence in deciding when to plant crops. As well as being a larger analysis than that reported previously, the present study also extends the previous work by investigating an alternative definition of the start of the wet season and by including ‘date’ as a potential explanatory variable. Copyright © 2001 Royal Meteorological Society [ABSTRACT FROM AUTHOR]