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Kieran T. Bhatia and David S. Nolan

Murphy 1997 ; Wilks and Hamill 1995 ). Even with the obvious benefits of error predictions, to our knowledge there have been no studies applying such predictions to tropical cyclone intensity forecasts. Operationally, the National Hurricane Center (NHC) includes a wind speed probability graphic in their suite of products that provides users with an array of probabilities for different intensity outcomes. However, the percentage likelihood of the different intensity outcomes is based on a random

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Dale A. Lowry and Harry R. Glahn

VOL. 104, NO. 3 MONTHLY WEATHER REVIEW MARCH 1976An Operational Model for Forecasting Probability of Precipitation--PEATMO S PoP DALE A. L0w~y AND HARRY R. GLAHNTechniq,es Development Laboratory, National Weather Service NOAA, Silver Spring, Md. 20910 (Manuscript received 2 September 1975; in revised form 25 November 1975)ABSTRACT A dynamical-statistical model for use in

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Charles R. Sampson, Andrea B. Schumacher, John A. Knaff, Mark DeMaria, Edward M. Fukada, Chris A. Sisko, David P. Roberts, Katherine A. Winters, and Harold M. Wilson

decisions. Wallace (2008) found the TPU predictions to be biased toward longer lead times than the operational TC-CORs (which in our opinion is better than it being biased toward shorter lead times). One limitation of the TPU is that it is limited to tropical cyclones that are forecast to be >50 kt, something we hope to address with our TC-COR algorithm. The purpose of this paper is to describe an objective aid that provides TC-COR guidance for meteorologists to use when making recommendations to base

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Temple R. Lee, Michael Buban, David D. Turner, Tilden P. Meyers, and C. Bruce Baker

1. Introduction The High-Resolution Rapid Refresh (HRRR) model is an hourly updating convection-allowing model that is used for short-range weather forecasts ( Benjamin et al. 2016 ). Version 1 of the HRRR became operational for the coterminous United States in September 2014 and has been upgraded every two years since then, with version 2 of the HRRR (HRRRv2) becoming operational in August 2016, and HRRRv3 becoming operational in July 2018. Forecasts are available up to 18 h from

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Qian Wang, Qingqing Li, and Gang Fu

that is based on tools available to a forecaster would be desirable from an operational viewpoint. Klein et al. (2000) proposed a two-stage definition for ET in the western North Pacific from an observational perspective. With the loss of symmetric features of the cloud field, a TC is in the transformation stage, which completes when the storm becomes embedded in the baroclinic zone. The second stage of ET is called the reintensification stage, in which the transformed TC either reintensifies as a

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Michael J. Brennan and Sharanya J. Majumdar

System (GFS) deterministic model and the multimodel variable consensus model (TVCN). The TVCN is an average of at least two of the interpolated versions of the following models: the GFS, the Met Office Global Model, the Navy Operational Global Atmospheric Prediction System (NOGAPS), the Geophysical Fluid Dynamics Laboratory (GFDL) Hurricane Model, the navy version of the GFDL model (GFDN), the Hurricane Weather and Research Forecasting Model (HWRF), and the European Centre for Medium-Range Weather

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David R. Novak and Brian A. Colle

-range ensemble forecast (SREF) systems can provide objective information regarding the predictability of features by accounting for initial conditions and model uncertainty (e.g., Du et al. 2003 , 2004 , 2006 ; Grimit and Mass 2002 ; Eckel and Mass 2005 ; Jones et al. 2007 ; Suarez et al. 2012 ). For example, recent work has examined extratropical cyclone track performance (e.g., Charles and Colle 2009 ). However, current operational SREFs have horizontal grid spacing on the order of 20–40 km ( Du et

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Daniel J. Halperin, Andrew B. Penny, and Robert E. Hart

System (GFS) model is being considered for operational implementation, retrospective forecasts using the proposed version of the GFS were created for comparison with the current operational configuration of the model. These retrospective forecast datasets typically span at least three TC seasons and provide a unique opportunity to quantify how changes in model configuration impact forecast performance. Using retrospective forecasts of three different configurations of the GFS initialized over the

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Kelly Soich and Bernhard Rappenglueck

1. Introduction a. Background Atmospheric temperature prediction has improved escalating atmospheric modeling skill and provided high degrees of success in regional climate modeling. Prior to computer modeling, weather prediction methods utilized manual calculations to solve lengthy mathematical formulas forecasting atmospheric temperature on which to base operational decisions (i.e., optimal aircraft cruise altitude) ( Zhu et al. 2002 ). Advancements in computer technology allow atmospheric

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Inger-Lise Frogner, Ulf Andrae, Jelena Bojarova, Alfons Callado, Pau Escribà, Henrik Feddersen, Alan Hally, Janne Kauhanen, Roger Randriamampianina, Andrew Singleton, Geert Smet, Sibbo van der Veen, and Ole Vignes

clustering [e.g., mean sea level pressure (MSLP)] due to the increasing bias with forecast length in the ECMWF HRES forecasts used. One of the main drawbacks of the SLAF method is that the number of perturbations—and hence ensemble members—is limited by the length of the coarse-resolution deterministic forecast. In practice the ensemble size is limited to 10–12 perturbed members. The advantage of SLAF is that it offers an easy operational implementation with presumably already available deterministic

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