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Christopher D. Karstens, James Correia Jr., Daphne S. LaDue, Jonathan Wolfe, Tiffany C. Meyer, David R. Harrison, John L. Cintineo, Kristin M. Calhoun, Travis M. Smith, Alan E. Gerard, and Lans P. Rothfusz

likelihood of severe convective weather occurrence, all with the goal of enhancing forecaster situational awareness and extending warning lead time. With the continued emergence of analytical and predictive techniques, how can all of the aforementioned information be utilized by forecasters, particularly within the time constraints associated with warning decisions? The Forecasting a Continuum of Environmental Threats (FACETs; Rothfusz et al. 2014 ) project proposes a reinvention of the current NWS

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Matthew J. Onderlinde and Henry E. Fuelberg

forecast time (i.e., from 3 h before to 3 h after). This choice assumes that tornadoes during the 6-h period are related to conditions at the forecast time (the center of the 6-h interval). Each reported tornado that met these criteria and was located within 750 km of the TC’s center was used. Before using the regression technique, we computed cross correlations (not shown) between the potential predictors to ensure that the final equation would not be overfit to the data. The goodness of a regression

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Bjarne Hansen

visibility forecasts are expected to be accurate to within ¼ mi; and when significant changes of flight category are forecast, the forecast time of the change is expected to be accurate to within 1 h. The goal of the research described here is to develop a useful tool for operational forecasters to help them in predicting ceiling and visibility more efficiently and more accurately. b. Direct model output Operational forecasters have a number of objective techniques to help them to forecast ceiling and

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Robert Fawcett

1. Introduction In this paper, some standard forecast verification techniques commonly used in the practice of seasonal climate forecasting are explored within the context of some simple theoretical forecast models, with a view toward calculating their skill levels analytically. This approach permits the various verification techniques to be compared and contrasted within a controlled environment, without having to resort to very large Monte Carlo simulations. Skill scores are found to exhibit

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Ezio L. Mauri and William A. Gallus Jr.

-intensity severe (LS; green), and nonsevere (NS; blue) nocturnal bow echo events for the period 2010–18 used in the present study. The plotted points indicate the locations of the storm reports used for severe cases and the points closest to the apex of the bow for nonsevere. b. Statistical methods To analyze forecast parameters, several graphical and statistical techniques were employed. Means, medians, bias, interquartile distribution, box-and-whisker plots, and scatterplots were used to obtain additional

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Sung-Hun Kim, Il-Ju Moon, and Pao-Shin Chu

widely used in practice, which provide consistent and basic information. DeMaria and Kaplan (1994a) developed a Statistical Hurricane Intensity Prediction Scheme (SHIPS) combining statistical models and dynamical models. SHIPS, which is based on a multiregression technique, uses predictors estimated from a dynamical forecast model as well as climatological and persistence predictors. The scheme has been used for hurricane intensity guidance in the North Atlantic (NA) and eastern North Pacific (ENP

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Ting Ding and Zongjian Ke

by Karori and Zhang (2008) , but that seasonal prediction is not in the form of digits. Experimental season forecasts by the Pakistan Meteorological Department (PMD) began in 2009, which is a short period of time for operational forecasting and statistical validation. The comparison between the operational forecasts and the observation is not quantitative. No specific technique to validate the seasonal forecast is applied in the operational forecast by PMD. As a regional climate center in Asia

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Xinhua Liu, Kanghui Zhou, Yu Lan, Xu Mao, and Robert J. Trapp

to discuss whether the forecaster who uses it has the value of existence. Therefore, in sections 2 and 3 , threat scores between human forecasters and AI/machine learning techniques are compared. Furthermore, we describe the use of conceptual models for severe convective weather forecasting in China, which serve not only as a means for pattern recognition, but also as a reflection of the internal physical mechanisms and the interaction between different scales; thus, they help make the

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Xinhua Liu, Kanghui Zhou, Yu Lan, Xu Mao, and Robert J. Trapp

to discuss whether the forecaster who uses it has the value of existence. Therefore, in sections 2 and 3 , threat scores between human forecasters and AI/machine learning techniques are compared. Furthermore, we describe the use of conceptual models for severe convective weather forecasting in China, which serve not only as a means for pattern recognition, but also as a reflection of the internal physical mechanisms and the interaction between different scales; thus, they help make the

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Adam J. Clark, Andrew MacKenzie, Amy McGovern, Valliappa Lakshmanan, and Rodger A. Brown

, as well as performing systematic NWP model forecast evaluation, is likely related to several factors. First, the relatively coarse observing network makes identifying the precise location of the dryline difficult. For example, Hoch and Markowski (2005) used objective analyses of conventional, synoptic-scale surface observations obtained using a two-pass Barnes (1964) technique, and estimated that the density of observations allowed them to determine the maximum eastward extent of dryline

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