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Linked References

1. E. Klopper, C. H. Vogel, and W. A. Landman, “Seasonal climate forecasts-potential agricultural-risk management tools?” Climatic Change, vol. 76, no. 1-2, pp. 73–90, 2006. View at Publisher · View at Google Scholar · View at Scopus
2. A. Troccoli, “Seasonal climate forecasting,” Meteorological Applications, vol. 17, no. 3, pp. 251–268, 2010. View at Publisher · View at Google Scholar
3. J. Namias, “Remarks on the potential for long-range forecasting,” Bulletin of the American Meteorological Society, vol. 66, no. 2, pp. 165–173, 1985. View at Publisher · View at Google Scholar
4. National Research Council, “Assessment of intraseasonal to interannual climate prediction and predictability,” Technical Report, National Research Council, Washington, DC, USA, 2010.
5. T. C. Pagano, H. C. Hartmann, and S. Sorooshian, “Using climate forecasts for water management: Arizona and the 1997-1998 El Niño,” Journal of the American Water Resources Association, vol. 37, no. 5, pp. 1139–1153, 2001. View at Publisher · View at Google Scholar
6. K. Wernstedt and R. Hersh, “Climate forecasts in flood planning: promise and ambiguity,” Journal of the American Water Resources Association, vol. 38, no. 6, pp. 1703–1713, 2002. View at Scopus
7. S. Apipattanavis, F. Bert, G. Podestá, and B. Rajagopalan, “Linking weather generators and crop models for assessment of climate forecast outcomes,” Agricultural and Forest Meteorology, vol. 150, no. 2, pp. 166–174, 2010. View at Publisher · View at Google Scholar · View at Scopus
8. P. Block, “Tailoring seasonal climate forecasts for hydropower operations,” Hydrology and Earth System Sciences, vol. 15, no. 4, pp. 1355–1368, 2011. View at Publisher · View at Google Scholar · View at Scopus
9. A. H. Murphy and R. L. Winkler, “Probability forecasting in meterology,” Journal of the American Statistical Association, vol. 79, no. 387, pp. 489–500, 1984.
10. R. L. Winkler, J. Muñoz, J. L. Cervera et al., “Scoring rules and the evaluation of probabilities,” Test, vol. 5, no. 1, pp. 1–60, 1996. View at Publisher · View at Google Scholar · View at Scopus
11. T. Gneiting and A. E. Raftery, “Strictly proper scoring rules, prediction, and estimation,” Journal of the American Statistical Association, vol. 102, no. 477, pp. 359–378, 2007. View at Publisher · View at Google Scholar · View at Scopus
12. World Meteorological Organization, “Standardised Verification System (SVS) for Long-Range Forecasts (LRF),” Technical Report. New Attachment II-9 to the Manual on the GDPS WMO-No. 485, World Meteorological Organization, Geneva, Switzerland, 2002, volume 1.
13. I. J. Good, “Rational decisions,” Journal of the Royal Statistical Society B, vol. 14, no. 1, pp. 107–114, 1952.
14. M. S. Roulston and L. A. Smith, “Evaluating probabilistic forecasts using information theory,” Monthly Weather Review, vol. 130, no. 6, pp. 1653–1660, 2002. View at Publisher · View at Google Scholar
15. J. Bröcker and L. A. Smith, “From ensemble forecasts to predictive distribution functions,” Tellus A, vol. 60, no. 4, pp. 663–678, 2008. View at Publisher · View at Google Scholar
16. R. Benedetti, “Scoring rules for forecast verification,” Monthly Weather Review, vol. 138, no. 1, pp. 203–211, 2010. View at Publisher · View at Google Scholar · View at Scopus
17. S. V. Weijs, R. van Nooijen, and N. van de Giesen, “Kullback-Leibler divergence as a forecast skill score with classic reliability-resolution-uncertainty decomposition,” Monthly Weather Review, vol. 138, pp. 3387–3399, 2010. View at Publisher · View at Google Scholar
18. J. Tödter, New aspects of information theory in probabilistic forecast verification [M.S. thesis], Goethe University, Frankfurt, Germany, 2011.
19. S. V. Weijs and N. van de Giesen, “Accounting for observational uncertainty in forecast verification: an information-theoretical view on forecasts, observations, and truth,” Monthly Weather Review, vol. 139, no. 7, pp. 2156–2162, 2011. View at Publisher · View at Google Scholar · View at Scopus
20. R. Peirolo, “Information gain as a score for probabilistic forecasts,” Meteorological Applications, vol. 18, no. 1, pp. 9–17, 2011. View at Publisher · View at Google Scholar · View at Scopus
21. R. E. Livezey, K. Y. Vinnikov, M. M. Timofeyeva, R. Tinker, and H. M. van den Dool, “Estimation and extrapolation of climate normals and climatic trends,” Journal of Applied Meteorology and Climatology, vol. 46, no. 11, pp. 1759–1776, 2007. View at Publisher · View at Google Scholar · View at Scopus
22. P. C. D. Milly, J. Betancourt, M. Falkenmark et al., “Climate change: stationarity is dead: whither water management?” Science, vol. 319, no. 5863, pp. 573–574, 2008. View at Publisher · View at Google Scholar · View at Scopus
23. N. Y. Krakauer, “Estimating climate trends: application to United States plant hardiness zones,” Advances in Meteorology, vol. 2012, Article ID 404876, 9 pages, 2012. View at Publisher · View at Google Scholar
24. D. S. Wilks, “Diagnostic verification of the Climate Prediction Center long-lead outlooks, 1995–9,” Journal of Climate, vol. 13, no. 13, pp. 2389–2403, 2000. View at Publisher · View at Google Scholar
25. E. A. O'Lenic, D. A. Unger, M. S. Halpert, and K. S. Pelman, “Developments in operational long-range climate prediction at CPC,” Weather and Forecasting, vol. 23, no. 3, pp. 496–515, 2008. View at Publisher · View at Google Scholar
26. R. E. Livezey and M. M. Timofeyeva, “The first decade of long-lead U.S. seasonal forecasts,” Bulletin of the American Meteorological Society, vol. 89, no. 6, pp. 843–854, 2008. View at Publisher · View at Google Scholar · View at Scopus
27. P. Peng, A. Kumar, M. S. Halpert, and A. G. Barnston, “An analysis of CPC's operational 0.5-month lead seasonal outlooks,” Weather and Forecasting, vol. 27, no. 4, pp. 898–917, 2012. View at Publisher · View at Google Scholar
28. S. Kullback and R. A. Leibler, “On information and sufficiency,” Annuals of Mathematical Statistics, vol. 22, no. 1, pp. 79–86, 1951. View at Publisher · View at Google Scholar
29. T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley, New York, NY, USA, 1991.
30. B. Ahrens and A. Walser, “Information-based skill scores for probabilistic forecasts,” Monthly Weather Review, vol. 136, no. 1, pp. 352–363, 2008. View at Publisher · View at Google Scholar · View at Scopus
31. G. W. Brier, “Verification of forecasts expressed in terms of probability,” Monthly Weather Review, vol. 78, pp. 1–3, 1950. View at Publisher · View at Google Scholar
32. A. H. Murphy, “A note on the ranked probability score,” Journal of Applied Meteorology, vol. 10, no. 1, pp. 155–156, 1971. View at Publisher · View at Google Scholar
33. W. A. Müller, C. Appenzeller, F. J. Doblas-Reyes, and M. A. Liniger, “A debiased ranked probability skill score to evaluate probabilistic ensemble forecasts with small ensemble sizes,” Journal of Climate, vol. 18, no. 10, pp. 1513–1523, 2005. View at Publisher · View at Google Scholar · View at Scopus
34. P. Heidke, “Berechnung des Erfolges und der Güte der Windstärkevorhersagen im Sturmwarnungsdienst,” Geografiska Annaler, vol. 8, pp. 301–349, 1926. View at Publisher · View at Google Scholar
35. V. Naumov and O. Martikainen, “Exponentially weighted simultaneous estimation of several quantiles,” World Academy of Science, Engineering and Technology, vol. 8, pp. 563–568, 2007.
36. J. Huang, H. M. van den Dool, and A. G. Barnston, “Long-lead seasonal temperature prediction using optimal climate normals,” Journal of Climate, vol. 9, no. 4, pp. 809–817, 1996. View at Publisher · View at Google Scholar
37. A. A. Bradley, S. S. Schwartz, and T. Hashino, “Sampling uncertainty and confidence intervals for the Brier score and Brier skill score,” Weather and Forecasting, vol. 23, no. 5, pp. 992–1006, 2008. View at Publisher · View at Google Scholar · View at Scopus
38. D. S. Wilks, “Sampling distributions of the Brier score and Brier skill score under serial dependence,” Quarterly Journal of the Royal Meteorological Society, vol. 136, no. 653, pp. 2109–2118, 2010. View at Publisher · View at Google Scholar · View at Scopus
39. A. G. Barnston, S. Li, S. J. Mason, D. G. Dewitt, L. Goddard, and X. Gong, “Verification of the first 11 years of IRI's seasonal climate forecasts,” Journal of Applied Meteorology and Climatology, vol. 49, no. 3, pp. 493–520, 2010. View at Publisher · View at Google Scholar · View at Scopus
40. A. H. Murphy, “Forecast verification: its complexity and dimensionality,” Monthly Weather Review, vol. 119, no. 7, pp. 1590–1601, 1991. View at Publisher · View at Google Scholar
41. S. V. Weijs, G. Schoups, and N. Van De Giesen, “Why hydrological predictions should be evaluated using information theory,” Hydrology and Earth System Sciences, vol. 14, no. 12, pp. 2545–2558, 2010. View at Publisher · View at Google Scholar · View at Scopus
42. J. D. Brown, J. Demargne, D. J. Seo, and Y. Liu, “The Ensemble Verification System (EVS): a software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations,” Environmental Modelling and Software, vol. 25, no. 7, pp. 854–872, 2010. View at Publisher · View at Google Scholar · View at Scopus