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

and are considered supplementarily in a “significant severe” weather class ( Hales 1988 ; Edwards et al. 2015 ). Collectively, these hazards have inflicted more than 1100 fatalities and $36.4B in damages across the contiguous United States (CONUS) in 2010–18 ( NWS 2018 ). While inherently dangerous and damaging phenomena, accurate severe weather forecasts can increase preparedness and help mitigate inclement weather losses. The hazards associated with severe weather are further encumbered by the

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Charles R. Sampson, Paul A. Wittmann, Efren A. Serra, Hendrik L. Tolman, Jessica Schauer, and Timothy Marchok

1. Introduction Intense tropical cyclones (TCs, also known as hurricanes and typhoons) have tremendous impact on U.S. naval vessels due to the high seas associated with the strong winds. The worst naval disaster in U.S. history was the result of Typhoon Cobra on 18 December 1944 ( Drury and Clavin 2007 ), in which three ships broke up and sank with their crews. Although these accidents should decrease due to improved TC forecasting ( Rappaport et al. 2009 ), there will continue to be periodic

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Daniel S. Wilks, Charles J. Neumann, and Miles B. Lawrence

1. Introduction The landfall location of a tropical cyclone is an important element of its damage potential, and forecasts of landfall location are critical components of information used by disaster preparedness officials and coastal residents to prepare for a threatened tropical cyclone strike, even though serious storm damage often also occurs well away from the landfall location of the storm center. The U.S. National Hurricane Center (NHC) issues forecast advisories for future tracks of

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Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, and Alan F. Blumberg

1. Introduction An ensemble prediction system combines several individual forecasts to produce an overall combined, and hopefully better, forecast. These ensemble prediction systems are commonly used in weather prediction. Leutbecher and Palmer (2008) discuss the sources of uncertainties in weather forecasting as well as the importance of ensemble forecasts compared to a single forecast. Mass et al. (2002) show that higher-resolution forecast models do not necessarily produce accurate

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J. V. Ratnam, Takeshi Doi, and Swadhin K. Behera

1. Introduction Seasonal forecasting of precipitation in the austral summer months, from December to February (DJF), is beneficial for the agro-based regions of northern Australia (landmass to the north of 25°S). During DJF, northern parts of Australia experience monsoon climate ( Wheeler and McBride 2005 ; Hendon et al. 2012 ) with reversal of low-level winter winds from easterlies to westerlies and increased precipitation. In fact, northern Australia receives most of its annual rainfall

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Keith F. Brill and Matthew Pyle

1. Introduction This note systematically applies the bias sensitivity analysis method of Brill (2009) to the eight conditional probabilities associated with binary (dichotomous “yes” or “no”) forecasts and examines implications for random or near-random changes to forecasts. The critical performance ratio (CPR; Brill 2009 ) quantifies the sensitivity of a performance measure for binary forecasts to either increasing or decreasing the frequency bias. The CPR is the minimum increase in

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

classified according to weather conditions at stations in various provinces and cities. These conceptual models are mainly based on characteristics of the 500-mb pattern (1 mb = 1 hPa), and occasionally on the 850-mb pattern, and then subdivided according to the type of severe convective weather ( Zhang 2011 ). These conceptual models help the forecaster quickly understand and identify the larger-scale patterns associated with severe convective weather and guide the subsequent forecast process. However

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

classified according to weather conditions at stations in various provinces and cities. These conceptual models are mainly based on characteristics of the 500-mb pattern (1 mb = 1 hPa), and occasionally on the 850-mb pattern, and then subdivided according to the type of severe convective weather ( Zhang 2011 ). These conceptual models help the forecaster quickly understand and identify the larger-scale patterns associated with severe convective weather and guide the subsequent forecast process. However

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Antony Millner

1. Introduction It has long been recognized that seasonal forecasts hold tremendous potential value for managing climate risks ( Mjelde et al. 1998 ; Messina et al. 1999 ; Palmer 2002 ). Despite this widely accepted assertion, relatively little of that potential value is extracted by actual forecast users ( Rayner et al. 2005 ; Vogel and O’Brien 2006 ), often despite an increase in forecast skill over the past decade ( Saha et al. 2006 ). There is growing awareness in the forecasting

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

level so low as to impede or prevent aircraft operations ( ICAO 2007 ). Fog forecasts are critical for airlines and have significant economic consequences. A single unforecast fog event at a major Australian airport can lead to a serious safety hazard, cause passenger inconvenience, and cost millions of dollars ( Leigh 1995 ). About 20% of fog events at Melbourne Airport, Melbourne, Victoria, Australia, were not forecast in the 1999–2005 period leading up to the current study ( Newham et al. 2009

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