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Stevie Roquelaure and Thierry Bergot

be very careful if the user cannot tolerate the losses related to high false alarm rates. 3. The Local Ensemble Prediction System a. LEPS construction Ensemble prediction techniques are designed to estimate the level of confidence of a forecast. Theoretically, the goal is to make an explicit computation (through the Liouville equations) of the probability density function (pdf) of a forecast out of the pdf of the initial state ( Ehrendorfer 1994 ). Multiple perturbed initial states, derived from

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Rebecca E. Morss and F. Martin Ralph

, it is important not only to improve forecast quality, but also to improve forecasters’ understanding of user needs and to formally incorporate more value-related considerations into the forecasting process, for example, by developing and implementing more user-relevant verification techniques. A limitation of the results in section 3a is that they are based on data from 2001, before the NDFD and IFPS were implemented. Although our results cannot address how the NDFD and IFPS have affected the

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Charles R. Sampson, John A. Knaff, and Edward M. Fukada

change was due in part to the lack of success and opportunity to form SCON forecasts noted in section 3 , and the large amount of staff required for training and use of this process. JTWC decided to shift the analysis and forecast emphasis toward intensity and wind field determination. An objective and automated technique that employed the SCON methodology, which but used all available NWP models and extended to 120 h, might be appropriate for use in operations. Acknowledgments The authors would

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Jonathan D. W. Kahl

downscaling of model output ( Fovell and Cao 2014 ); and (ii) observational wind data contain reporting artifacts that hinder the development and verification of gust forecast models ( Harris and Kahl 2017 ). Current gust forecasting techniques include physical approaches involving features of boundary layer turbulence, and statistical models employing extreme value statistics or underlying assumptions about wind distribution as a function of predictor variables ( Sheridan 2011 , 2018 ). One statistical

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John Kaplan, Christopher M. Rozoff, Mark DeMaria, Charles R. Sampson, James P. Kossin, Christopher S. Velden, Joseph J. Cione, Jason P. Dunion, John A. Knaff, Jun A. Zhang, John F. Dostalek, Jeffrey D. Hawkins, Thomas F. Lee, and Jeremy E. Solbrig

that compose the 10-yr (i.e., 2004–13) sample. For example, the developmental data from the periods 1995–2003 and 2005–12 are used to rederive the 2004 RI models. The RI models derived by the leave-one-year-out cross-validation technique are then rerun for each of the 10 yr in the 2004–13 sample period using the archived operational NCEP forecast fields, GOES imagery, and NHC initial storm intensity and location data for that period. A probabilistic and deterministic verification of those cross

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Laura X. Huang, George A. Isaac, and Grant Sheng

integrated techniques focus on integrated nowcasting with forecasts from a single NWP model. Recently, NWP models, model output statistics (MOS), updateable model output statistics (UMOS), and ensembles with different spatial and temporal resolutions have become available for many areas. When instrumentation and technology are sufficient to satisfy the high-density, high spatial and temporal resolution data requirements for nowcasting, and if traditional NWP model forecasts and modeling techniques for

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John P. Cangialosi, Eric Blake, Mark DeMaria, Andrew Penny, Andrew Latto, Edward Rappaport, and Vijay Tallapragada

look at the details of how the models perform in different regions and environments. Table 1. Composite intensity skill and the frequency of superior performance of NHC’s official forecast against Decay-SHIFOR5 averaged over the entire forecast period by decade. Multilayer dynamical models are generally, if not always, “late” models, meaning the 1200 UTC model (for example) is not available until after NHC’s deadline to release the 1200 UTC forecast package at 1500 UTC. Fortunately, a technique

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Wentao Li, Qingyun Duan, and Quan J. Wang

(BSS), and relative operating characteristic (ROC) score. The sampling uncertainty for the verification metrics was estimated by the stationary block bootstrap technique ( Politis and Romano 1994 ) in EVS. The stratified probability integral transform (PIT) diagram was used in this study to show the reliability of different precipitation amounts. Specifically, all the samples for verification were stratified into three strata by the 85% and 95% quantiles of the raw forecast mean. The PIT histograms

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Joseph J. J. James, Chen Ling, Christopher D. Karstens, James Correia Jr., Kristin Calhoun, Tiffany Meyer, and Daphne LaDue

, these hazards can be tracked and projected into the future, and while accounting for uncertainty in those projections, used to create a PHI object (polygon, freehand or ellipse). These added capabilities, including probabilistic information, were put to the test as an improvement to deterministic warning polygons for both forecasters and end users, such as emergency managers and TV broadcasters ( Karstens et al. 2018 ). a. The PHI prototype tool Within the PHI prototype tool, there are both

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Rita D. Roberts, Amanda R. S. Anderson, Eric Nelson, Barbara G. Brown, James W. Wilson, Matthew Pocernich, and Thomas Saxen

significantly small fraction of events [i.e., they are “rare” events in comparison to the large number of storm extrapolation forecasts; Roberts et al. (2007) ]. Assessment of skill is problematic because of the rarity of such events ( Stephenson et al. 2008 ). As the day progresses, it is difficult to discern any appreciable difference in CSI values between the different techniques because storm extrapolation nowcasts dominate the statistics over the ANC domain where many storms exist simultaneously. b

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