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Tal Boneh, Gary T. Weymouth, Peter Newham, Rodney Potts, John Bally, Ann E. Nicholson, and Kevin B. Korb

of which, however, lack an explicit forecast decision component. An early example, the Hailfinder system ( Abramson et al. 1996 ) was developed and constructed using domain expert knowledge to predict severe weather phenomena, but was never tested. The Sydney, New South Wales, Australia, harbor sea-breeze BN ( Kennett et al. 2001 ) was tested but never used operationally. Cano et al. (2004) looked at data-mining methods to automatically construct BNs for meteorological applications, but again

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Xiaosong Yang, Timothy DelSole, and Hua-Lu Pan

of a model. Given the significant benefits that might follow from improving the skill of an operational weather forecast model, it is important to investigate whether an empirical correction method can indeed improve forecast skill. The study of DelSole et al. (2008) was based on the Center for Ocean–Land–Atmosphere Studies, version 3.2, (COLAv3.2) model, which is not an operational weather forecast model. In addition, the analyses used to verify the forecasts were generated by a data

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Peitao Peng, Arun Kumar, Michael S. Halpert, and Anthony G. Barnston

procedure is applied to the outlooks for the first (0.5 month) lead time. It is intended to help inform the user community about the past performance of the operational seasonal outlooks, providing guidance on the potential utility of the real-time forecast information for decision making processes. The skill assessments are also useful for the producers of the seasonal outlooks, informing them of potential systematic biases in the forecast tools in order that they may focus on improving such weaknesses

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Aaron J. Hill, Gregory R. Herman, and Russ S. Schumacher

challenge in accurately forecasting the phenomena. Due to the very small spatial scales associated with severe weather, it is often exceedingly difficult to model dynamically with operational weather models. Production of large hail involves a plethora of very small-scale microphysical processes that are necessarily parameterized in numerical models. The microphysical simplifications involved to hasten production of operational model output, including bulk rather than bin schemes (e.g., Khain et al

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Jennifer C. Roman, Gonzalo Miguez-Macho, Lee A. Byerle, and Jan Paegle

uncertainty and continues to probe the limitation to deterministic weather prediction due to inadequate observation of relatively large scales of the atmosphere. Other global model studies attempting to prioritize the relative contributions of initial errors, boundary errors, and model errors to total forecast error include work by Reynolds et al. (1994) and Hacker et al. (2003) . Simmons and Hollingsworth (2002) show substantial improvement in forecast accuracy in global operational models over the

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Katie A. Wilson, Pamela L. Heinselman, and Ziho Kang

differences occurred as a result of the sensitivity of the MultiMatch method to how individual forecasters interacted with the user interface, tackled technological glitches in WarnGen, and approached tasks differently. The findings from this experiment suggest that applications of eye-tracking and qualitative research methods together could be useful for other avenues of operational meteorology research. With new types of data and products being introduced to forecasters often, there are opportunities to

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Gary P. Ellrod and John A. Knox

in operational forecasts of TI. For the combined dataset, the improvement in DTI over TI is approximately 50% for POD y and is better by a factor of 5 for TSS. POD n was slightly worse for DTI for all three datasets, indicating that DTI has a slight tendency to overforecast CAT. Relative improvements for other threshold values (not shown) were similar, although the best verification metrics for DTI (based on the TSS value) were obtained using the threshold value of 4. The results for December

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Feng Nai, Jami Boettcher, Christopher Curtis, David Schvartzman, and Sebastián Torres

is comparable to that of the WSR-88D ( Lei et al. 2015 ). To properly balance system cost and performance, it is important for decision-makers to understand the impact of not meeting one or more cost-driving threshold requirements on forecasters’ interpretation of radar data. One potential cost-driving requirement that has significant operational impact is the spatial resolution. The spatial resolution (measured in the azimuth, elevation, and range dimensions) is tied to the radar’s ability to

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Luciana Bertotti, Luigi Cavaleri, Layla Loffredo, and Lucio Torrisi

1. Purpose of the work Nettuno is a high-resolution local-scale wind and wave forecast system operational in the Mediterranean Sea. Several parallel systems are available in this area, although often not on the whole basin. Some of these results are public domain (within limits), but a thorough analysis of the related performance is not a frequent product. A notable exception is the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) intercomparison exercise focused on

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Fuqing Zhang, Y. Qiang Sun, Linus Magnusson, Roberto Buizza, Shian-Jiann Lin, Jan-Huey Chen, and Kerry Emanuel

predictability limit of multiscale midlatitude weather assuming a perfect model with nearly perfect initial conditions? 2) How much longer can the practical predictability be increased by reducing initial-condition uncertainties to different degree of accuracy? a. Model details 1) ECMWF/IFS model The IFS control and ensemble forecasts presented herein uses the latest upgrade (cycle 41r2) of ECMWF, the highest-resolution-ever (~9 km) global operational NWP model. More details of this model upgrade can be

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