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Gary P. Ellrod and David I. Knapp

150 WEATHER AND FORECASTING VOLUME7FORECASTING TECHNIQUESAn Objective Clear-Air Turbulence Forecasting Technique: Verification and Operational Use GARY P. ELLRODSatellite Applications Laboratory (NOAA/NESDIS), Washington, D.C. DAVID I. KNAPPAir Force Global Weather Central (AFGWC), Offutt Air Force Base, Nebraska6 June 1991 and 12 September 1991 An objective technique for forecasting clear

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Tom H. Durrant, Diana J. M. Greenslade, Ian Simmonds, and Frank Woodcock

these changes is desired. One such technique is the operational consensus forecast (OCF) scheme of Woodcock and Engel (2005) . In its complete form, OCF combines forecasts derived from a multimodel ensemble to produce an improved real-time forecast at locations where recent observations are available. Component model biases and weighting factors are derived from a training period of previous model forecasts and verifying observations. The next real-time OCF forecast is a weighted average of the set

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Adrienne Tivy, Bea Alt, Stephen Howell, Katherine Wilson, and John Yackel

coincident El Niño and positive North Atlantic Oscillation (NAO) episodes. The goal of this study is to improve seasonal or long-range (3-month lead) forecasts for the spring opening of the shipping route through Hudson Bay. A predictive model is developed using multiple linear regression techniques. More than 1500 time series representing dominant modes in Northern Hemisphere atmospheric and oceanic variability, local climate, and sea ice conditions are tested as potential predictors and are described

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Yulia R. Gel

application of the discussed approaches to the bias correction of 48-h MM5 forecasts of surface temperature in the Pacific Northwest. The proposed methods are compared with each other and the grid-based “obs-based” method of Wedam et al. (2005) in terms of mean absolute error (MAE). In addition, section 3 reports the results on the “improve to hurt” statistics of the proposed techniques, that is, the number of cases when the bias has been removed or added at a site of interest. The paper concludes

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Jonathan M. Wilkinson

; Ebert 2009 ; Clark et al. 2010 ) and a range of neighborhoods from 12 to 96 km. Lynn et al. (2015) found that the ETS increased with increasing neighborhood size. However, as far as can be ascertained from the literature, no measure has been able to show what value such forecasts have above large-scale indices. In this manuscript, a new technique for determining the accuracy of lightning forecasts is developed and illustrated using UKV model lightning forecasts with the McCaul et al. (2009

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Yunsung Hwang, Adam J. Clark, Valliappa Lakshmanan, and Steven E. Koch

operational concepts for managing strategic traffic flow, including examination of how improved weather data can aid traffic management initiatives efficiently ( Song et al. 2008 ). For short-term prediction of convection for route-planning applications, frequently updating high-resolution forecasts of convection are needed (i.e., nowcasts). To address this need, since about the early 1990s, various nowcasting techniques have been developed that rely on extrapolation (EXT) of observed convection as

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Astrid Suarez, Heather Dawn Reeves, Dustan Wheatley, and Michael Coniglio

forecasts include (a) a 12-km grid-spaced deterministic forecast, (b) a 3-km grid-spaced deterministic forecast, (c) a 12-km traditional ensemble forecast, and (d) a 12-km EnKF-based ensemble forecast. Although the accuracy and/or reliability of any forecast system cannot be gauged from a single forecast, this case study reveals some of the strengths of the EnKF technique, as well as potential problems that may be unique to the wintertime environment. This paper is organized as follows. An overview of

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Chermelle Engel and Elizabeth E. Ebert

; Tapp et al. 1986 ; Krishnamurti et al. 1999 ; Wilson and Vallée 2002 ), gene-expression programming ( Bakhshaii and Stull 2009 ), ensemble Kalman filter methods ( Cheng and Steenburgh 2007 ), and regime matching ( Greybush and Haupt 2008 ). Regression and gene-expression-programming techniques may remove more model and representativeness error but require years and almost a year, respectively, of stable forecast and observed paired information, and such information is not currently available for

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Keith F. Brill and Matthew Pyle

interest in revealing the behavior of the CPR relative to the benchmarks. High-resolution models often produce stochastically reasonable distributions of heavy precipitation, but fail to achieve proper placement of the accumulation areas relative to verifying observations. In fact, such placement errors have motivated other verification treatments known as spatial techniques (e.g., Gilleland et al. 2009 ; Mesinger 2008 ; Davis et al. 2006 , among others). Here, verification of 36-h forecasts of

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James O. Pinto, Dan L. Megenhardt, Tressa Fowler, and Jenny Colavito

datasets are available during which model formulations have not been changing. The current state of rapid model development cycles makes it difficult to maintain stable regression relationships between model forecasted fields and observations, especially for the diagnosis of infrequently occurring events (e.g., LIFR conditions). Machine learning (e.g., Rasp and Lerch 2018 ) and analog ensemble (e.g., Delle Monache et al. 2011 ) techniques have also been shown to be effective in developing

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