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Alexandra K. Anderson-Frey and Harold Brooks

Evaluating the success of a forecast is a necessary step in the development of a rigorous and useful forecast system; robust forecast evaluation can identify the situations in which the most substantial improvements can be made ( Brier and Allen 1950 ), and ideally also provides a roadmap for the application of those improvements. Choosing the metrics by which we evaluate forecasts, however, is a process that is far from trivial. Murphy (1993, hereafter M93) identifies three distinct types of

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James P. Jenrette

Practical procedures for forecasting areas of overcast skies and precipitation are presented. The forecast thickness field is relabeled as a forecast maximum precipitable water field. A forecast actual precepitable water field is then graphically subtracted from this field to derive a forecast saturation chart. The forecast saturation chart is then adjusted with the forecast vertical-motion field. Isopleths of critical values on the adjusted saturation chart outline forecast areas of overcast skies and areas of probable precipitation.

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Joseph Vederman and Bernard Dubofsky

Forecasting procedures are given so that a rapid 300-mb or 200-mb prognostic chart can be made from any valid 500-mb prognosis. Height relationships are given between the 500- and 300-mb levels and the 500- and 200-mb levels.

The 500-mb relative vorticity is also utilized to adjust for variations in tropopause height.

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Thomas M. Hamill, Gary T. Bates, Jeffrey S. Whitaker, Donald R. Murray, Michael Fiorino, Thomas J. Galarneau Jr., Yuejian Zhu, and William Lapenta

; improved chemistry and aerosol physics; improved estimates of the initial state estimate due to better data assimilation techniques; and improved couplings between the atmosphere and the land surface, cryosphere, ocean, and more. Nonetheless, judging from the pace of past improvements, medium-range forecast systematic errors will not become negligibly small within the next decade or two. For intermediate-resolution simulations such as those from current-generation global ensemble systems, users of

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Stéphane Vannitsem, John Bjørnar Bremnes, Jonathan Demaeyer, Gavin R. Evans, Jonathan Flowerdew, Stephan Hemri, Sebastian Lerch, Nigel Roberts, Susanne Theis, Aitor Atencia, Zied Ben Bouallègue, Jonas Bhend, Markus Dabernig, Lesley De Cruz, Leila Hieta, Olivier Mestre, Lionel Moret, Iris Odak Plenković, Maurice Schmeits, Maxime Taillardat, Joris Van den Bergh, Bert Van Schaeybroeck, Kirien Whan, and Jussi Ylhaisi

biases and inappropriate dispersion of ensemble forecasts requiring some sort of postprocessing in order to improve the forecast quality. Statistical postprocessing methods used for this purpose involve a wide range of correction techniques that can be appropriately developed for either deterministic or ensemble forecasts ( Wilks 2011 ; Vannitsem et al. 2018 ). The first applications and operational implementations of statistical corrections were based on simple linear regression techniques using

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Louis-Philippe Caron, Leon Hermanson, Alison Dobbin, Jara Imbers, Llorenç Lledó, and Gabriel A. Vecchi

similar multiannual forecasts based on climate model simulations. These climate simulations can be used either to replace the first step of a statistical forecast ( Vecchi et al. 2013 ; Caron et al. 2014 , 2015 ) (so-called hybrid forecasts) or to do without empirical models altogether ( Smith et al. 2010 ; Hermanson et al. 2014 ) (so-called dynamical forecasts). The latter technique involves directly tracking tropical cyclone–like disturbances in climate output using an automated detection and

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Amy McGovern, Kimberly L. Elmore, David John Gagne II, Sue Ellen Haupt, Christopher D. Karstens, Ryan Lagerquist, Travis Smith, and John K. Williams

Modern artificial intelligence (AI) techniques can aid forecasters on a wide variety of high-impact weather phenomena. Weather significantly impacts society for better and for worse. For example, severe weather hazards caused over $7.9 billion of property damage in 2015 ( National Oceanic and Atmospheric Administration/National Centers for Environmental Information 2016 ; CoreLogic 2016 ). The National Academies of Sciences, Engineering, and Medicine (2016) cites improving forecasting of

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Gregory W. Carbin, Michael K. Tippett, Samuel P. Lillo, and Harold E. Brooks

utility from the CFSv2 forecasts regarding favorable severe weather environments beyond 1 week by accumulating grid counts of SCP ≥ 1 and by applying a time-averaging technique to derive forecast anomalies as described here. CONCLUSIONS AND CONTINUING RESEARCH. The Chiclet Chart and accompanying maps of SCP areal coverage demonstrate the predictive skill of the CFSv2 for identifying severe weather events based on environments with large CAPE and strong vertical wind shear, as indicated by SCP values

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Steven M. Martinaitis, Jonathan J. Gourley, Zachary L. Flamig, Elizabeth M. Argyle, Robert A. Clark III, Ami Arthur, Brandon R. Smith, Jessica M. Erlingis, Sarah Perfater, and Benjamin Albright

NOAA/National Severe Storms Laboratory and National Weather Service forecasters evaluate new tools and techniques through real-time test bed operations for the improvement of flash flood detection and warning operations. Flooding is one of the deadliest weather-related phenomena in the United States. Over the 20-yr period from 1995 to 2014, flash flooding, river flooding, and coastal flooding combined contributed to an average of 77.3 fatalities per year. This was the third highest average

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Fuqing Zhang and Yonghui Weng

assimilation techniques capable of ingesting observations from an enhanced global network, and an exponential growth in computing resources. Hurricane intensity and structure are regulated somewhat by the large-scale environment, but are also strongly dependent on smaller-scale processes that are nonlinear and chaotic in nature (such as moist convection and inner-core dynamics), and thus harder to observe, resolve, and predict. F ig . 1. Evolution of yearly mean absolute errors at different forecast lead

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