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Andrew Cottrill, Harry H. Hendon, Eun-Pa Lim, Sally Langford, Kay Shelton, Andrew Charles, David McClymont, David Jones, and Yuriy Kuleshov

island Countries (SCOPIC) model, which is derived from statistical relationships between sea surface temperature (SST) variations, primarily those associated with El Niño–La Niña, and the local climate in the Pacific. He and Barnston (1996) have previously reported on similar statistically based seasonal forecasts for the Pacific region. Although these statistical forecasts have merit, especially in the Pacific region where El Niño–La Niña impacts are strong, the PASAP seasonal prediction project

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Christian Keil and George C. Craig

1. Introduction In recent years numerical weather prediction models have become more complex and have been applied on finer scales. These high-resolution models have the potential to forecast phenomena that are highly localized and episodic, as for instance warm season precipitation events. Unfortunately, traditional approaches for the validation of spatial forecasts, including convection forecasts and quantitative precipitation forecasts, are inadequate to meet current needs. Generally, these

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Michael C. Kochasic, William A. Gallus Jr., and Christopher J. Schaffer

1. Introduction Numerous probability of precipitation (PoP) forecast generation approaches exist, with the simplest approach considering the agreement between members of an ensemble prediction system (EPS). If there are 10 ensemble members, for example, with two showing precipitation exceeding a specified threshold amount, then the PoP for that threshold is 20%. This approach is referred to as the uncalibrated traditional approach (denoted Uncali_trad hereafter) in Schaffer et al. (2011

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Huaqing Cai and Robert E. Dumais Jr.

1. Introduction Traditional pixel-versus-pixel forecast verification scores based on contingency-table statistics have been widely used in forecast evaluations for many years, owing their popularity to the ease with which statistics are generated and forecasts can be compared. However, single-number scores such as the critical success index (CSI; Wilks 2006 ) offer little diagnostic information as to why a particular forecast may be good or bad and, thus, typically provide less useful

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Luying Ji, Xiefei Zhi, Clemens Simmer, Shoupeng Zhu, and Yan Ji

1. Introduction In the past two decades, numerical weather forecasting rapidly developed and—besides model improvements—evolved from traditional single deterministic forecasts to ensemble forecasting ( Gneiting et al. 2005 ; Bauer et al. 2015 ). Different forecast systems differ in their overall architecture, spatial resolution, choice of initial conditions, data assimilation technology, and physical parameterization schemes used in the numerical models. Multimodel ensemble (MME) forecasting

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Huixia He, Vitali E. Fioletov, David W. Tarasick, Thomas W. Mathews, and Craig Long

tropics. The scale was established from analysis of spectral UV data measured by Brewer spectrophotometers at Toronto, Ontario, Canada, between 1989 and 1992 ( Kerr and McElroy 1993 ; Kerr et al. 1994 ). In 1994 a UV index program was introduced in the United States by the National Weather Service (NWS; Long et al. 1996 ), which was replaced by the current forecast system in 2005. The UV index is now in operational use in more than 100 countries worldwide, including all of the countries of Europe

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Mei Hong, Dong Wang, Ren Zhang, Xi Chen, Jing-Jing Ge, and Dandan Yu

through changes in its position and intensity. In recent years, persistent anomalies in the WPSH have resulted in frequent severe meteorological disasters ( Tao and Wei 2006 ). As the WPSH plays such an important part in the East Asian climate system, many East Asian scientists have attempted to forecast its behavior ( Tao et al. 2001 , 42–47; Wang and Xue 2003 ; Xu et al. 2007 ). However, because the activity of the WPSH involves extremely complex processes ( Cao et al. 2002 ), it is very difficult

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Atoossa Bakhshaii and Roland Stull

1. Bias-corrected ensembles An ensemble of outputs from different numerical weather prediction (NWP) runs can provide information on forecast confidence, probability of different forecast outcomes, range of possible errors, predictability of the atmosphere, and other metrics. Although expensive ensemble runs are justified predominantly by their probabilistic output ( McCollor and Stull 2009 ), the most widely used metric is an average of the ensemble members. Except for rare events ( Hamill et

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Timothy W. Armistead

apparent misunderstanding of a property of H u , and three observations ( sections 2d – f ) are mixtures of well-considered opinions with which I do not agree and of further possible misunderstandings. I believe that none of the six observations rises to the level of being an undesirable property of Wagner’s measure. 2. The purportedly undesirable properties of H u a. Degrees of freedom Within the context of opining that H u is an incomplete measure of forecasting performance (see section 2f

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Steven A. Mauget and Jonghan Ko

; Kiladis and Diaz 1989 ), the state of those indices also persist or develop predictably over interseasonal time scales ( Rasmusson and Carpenter 1982 ; Trenberth and Shea 1987 ). As a result, they can be used as leading indicators in simple schemes to predict seasonal climate variation. In studying the effect of ENSO-related forecast information in agricultural management such simple forecasting methods are useful because they can be easily integrated into long-term management and cropping

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