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Daniel Hodyss, Elizabeth Satterfield, Justin McLay, Thomas M. Hamill, and Michael Scheuerer

techniques. While ensemble weather prediction systems have improved greatly, the predictions are still frequently affected by systematic errors, including biased ensemble mean forecasts and often an insufficiency of ensemble spread. Consequently, much attention has been paid in recent years to statistical postprocessing techniques, whereby the current guidance is adjusted based on relationships noted between past forecasts and observations/analyses. In many circumstances, the goal is to produce a

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Max A. Little, Patrick E. McSharry, and James W. Taylor

1. Introduction The source of most atmospheric rainwater is the sea, with rain forming when large droplets eventually become heavy enough to fall to the ground. Rainfall over land eventually flows back to the sea, completing the cycle ( Brutsaert 2005 ). Water is vital to life, but also immensely destructive: understanding the movement of atmospheric water is critical. Forecasting rainfall is therefore important in many disciplines, for example, economics and finance, hydrology, meteorology

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Alinede H. N. Maia, Holger Meinke, Sarah Lennox, and Roger Stone

.” We disagree. As explained by Mason (2004) , such negative scores are an inherent feature of RPSS and negative scores can occur even when true forecast skill exists. As the expected value of RPSS under the null hypothesis is influenced by the forecast system employed, empirical null distributions generated using Monte Carlo techniques can contain a high frequency of negative values ( Mason 2004 ), as shown in Fig. 2 . Such a negative bias of the RPSS expected value (in contrast to LEPS) does

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Maurice J. Schmeits and Kees J. Kok

the mean value of the cube root of the forecast precipitation from the mean value of the cube root of the observed precipitation amount, also with the nonzero precipitation observations as cases. The parameters W k , c 0 , and c 1 are estimated by the maximum likelihood technique ( Wilks 2006b ) from the training data. The log-likelihood function for the BMA model [Eq. (2) ] ( Sloughter et al. 2007 ) is maximized numerically using the so-called expectation-maximization (EM) algorithm

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Craig S. Schwartz, John S. Kain, Steven J. Weiss, Ming Xue, David R. Bright, Fanyou Kong, Kevin W. Thomas, Jason J. Levit, and Michael C. Coniglio

-spacing versions of the ARW-WRF model. KA08 observed that the 2-km configuration produced more realistic storm structures than the 4-km forecasts. Yet, using both objective and subjective verification ( Kain et al. 2003a ) techniques, KA08 concluded that both models provided virtually identical value in terms of next-day guidance to severe storm forecasters, as both configurations were remarkably similar in their representation of convective initiation, evolution, and mesoscale organizational mode. The two

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Haipeng Yu, Jianping Huang, and Jifan Chou

regard the current state as a repeat of the historical states. Combining analog states with a dynamical model is expected to be beneficial, so the so-called analog-dynamical approach has been developed. When the forecast state is regarded as a small disturbance superimposed on a historical analog field, statistical techniques can be used in combination with a dynamical forecast ( Chou 1979 ). By estimating the current tendency error with that of an analog state, a deviation equation is obtained in

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Christopher Davis, Wei Wang, Shuyi S. Chen, Yongsheng Chen, Kristen Corbosiero, Mark DeMaria, Jimy Dudhia, Greg Holland, Joe Klemp, John Michalakes, Heather Reeves, Richard Rotunno, Chris Snyder, and Qingnong Xiao

model output is instantaneous. However, the time step on the 4-km grid is 20 s. The fact that several time steps are needed to resolve temporal variations means that, at this resolution, instantaneous output should be roughly comparable to a 1-min average. Intensity and position forecasts from the AHW were verified against the best-track data from the National Oceanic and Atmospheric Administration (NOAA) National Hurricane Center (NHC) and were compared with several other forecast techniques for

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Chad M. Shafer, Andrew E. Mercer, Charles A. Doswell III, Michael B. Richman, and Lance M. Leslie

being tested. If the objective techniques prove to confirm many of the findings of this study, this will lead to greater confidence in the model’s ability to distinguish outbreak type. Moreover, verification of the forecasts is also planned to determine more quantitatively the association between forecast accuracy and the diagnosis of outbreak type. Before these techniques can be used in a forecast setting, additional research is required. Because the model appears to have more difficulty

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Morris A. Bender, Isaac Ginis, Robert Tuleya, Biju Thomas, and Timothy Marchok

the results, a major effort was undertaken over the next several years to develop a technique to insert a more realistic and model-consistent vortex into the global analysis. This new vortex initialization system was completed by 1991 ( Kurihara et al. 1993 ) and was later improved in 1994 ( Kurihara et al. 1995 ). The new GFDL system was tested on a limited number of cases from the 1991 Atlantic hurricane season using the NMC global analysis and forecast model [Aviation Model (AVN)] as the

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Hongwen Kang, Chung-Kyu Park, Saji N. Hameed, and Karumuri Ashok

1. Introduction Korea is located in East Asia and its climate is under the influence of the East Asia monsoon (EAM). In summer (June–August), the major rain-producing system is the monsoon subtropical front known as changma in Korea. More than 60% of the annual precipitation is concentrated during the changma period. The EAM often leads to severe disasters such as flooding or drought, which makes the forecast of the interannual variation of EAM important for the management of water resources

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