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Martin Charron, Saroja Polavarapu, Mark Buehner, P. A. Vaillancourt, Cécilien Charette, Michel Roch, Josée Morneau, Louis Garand, Josep M. Aparicio, Stephen MacPherson, Simon Pellerin, Judy St-James, and Sylvain Heilliette

observations of the stratosphere were becoming available for operational assimilation by weather forecasting centers. Indeed, the Advanced Microwave Sounding Unit (AMSU-A) instrument—a microwave sounder that provides global coverage in 6 h because of its presence on several concurrent polar orbiting satellites—is now an important component of the current operational observing system ( Cardinali et al. 2004 ; Langland and Baker 2004 ). Though its horizontal coverage is excellent, vertically, the

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Carlee F. Loeser, Michael A. Herrera, and Istvan Szunyogh

papers that have investigated the performance of operational ensemble forecast systems along these lines (e.g., Molteni and Buizza 1999 ; Wei and Toth 2003 ; Buizza et al. 2005 ; Herrera et al. 2016 ). Perhaps the most important difference between these studies was that they chose the components of the state vector in the computation of the verification metrics differently. This paper presents the latest results of the investigations of our research group into the efficiency of global forecast

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Joseph R. Bocchieri and Harry R. Glahn

JUNE1976 JOSEPH R. BOCCHIERI AND HARRY R. GLAHN 691Verification and Further Development of an Operational Model for Forecasting the Probability of Frozen Precipitation JOSEPH R. BOCCHIERI AND HARRY R. GLAHNTechniques Development Laboratory, National Weather Service, NOAA, Silver Spring, Md. 20910(Manuscript recieved 10 November 1975, in revised form 24 February 1976)ABSTRACT An

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Justin McLay, Craig H. Bishop, and Carolyn A. Reynolds

the original, cycled global ET. Thus, for comparison, a not-cycled five-banded ET is also examined here, using the same method as in McLay and Reynolds (2009) , as follows: An archive of 1888 forecast perturbations was derived from a set of 32-member Navy Operational Global Atmospheric Prediction System (NOGAPS) ensembles initialized at 0000 UTC each day of the period 1 January–28 February 2006. For each of the fifty-nine 32-member ensembles in the period, 72-h forecast perturbations were

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Felipe Quintero, Witold F. Krajewski, and Marcela Rojas

1. Introduction In principle, distributed hydrological models allow river discharge forecasts at every channel in a drainage network. The spatial and temporal distribution of peak flows across the channels is a reflection of the interactions among the spatial and temporal variability of the model input (e.g., rainfall, snowmelt), the soil properties controlling the generation of overland flow, and the structure of the drainage network (e.g., Lu et al. 2017 ; Mantilla et al. 2006 ; Ayalew et

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Patrick A. Harr, Russell L. Elsberry, Timothy F. Hogan, and William M. Clune

456 WEATHER AND FORECASTING -OLUM-7Forecasts of North Pacific Maritime Cyclones with the Navy Operational Global Atmospheric Prediction System PATRICK A. HARR AND RUSSELL L. ELSBERRYDepartment of Meteorology, Naval Postgraduate School, Monterey, California TIMOTHY F, HOGANNaval Research Laboratory, Monterey, California WILLIAM M. CLUNE Fleet Numerical

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Willem A. Landman, David DeWitt, Dong-Eun Lee, Asmerom Beraki, and Daleen Lötter

1. Introduction The seasonal-to-interannual variability of rainfall and temperature anomalies over southern Africa is predictable (e.g., Klopper et al. 1998 ; Landman and Goddard 2002 ; Reason and Rouault 2005 , Tennant and Hewitson 2002 ). This knowledge led to the development of objective operational seasonal forecasting systems for South Africa, but only as recently as the 1990s (e.g., Jury 1996 ; Jury et al. 1999 ; Landman and Mason 1999 ; Mason 1998 ). Although the prediction

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Julie L. Demuth, Rebecca E. Morss, Jeffrey K. Lazo, and Douglas C. Hilderbrand

effort]. The PnC web page was found to be valued by its users for many reasons, chief among them that it provides accurate, up-to-date forecast information for a specific geographic area in usable formats both at a glance (i.e., the forecast icons) and in greater detail. 2 Fig . 1. Example PnC forecast. This real forecast served as the baseline for developing the experimental forecasts. Layout and content shown are how the PnC forecast web page was presented for over a decade up until 2 Jul 2012

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Daniel J. Halperin, Henry E. Fuelberg, Robert E. Hart, Joshua H. Cossuth, Philip Sura, and Richard J. Pasch

in analyzing the relative performance of multiple global models. Thus, we compare results from Environment Canada's Global Environmental Multiscale Model (CMC; Côté et al. 1998a , b ), the European Centre for Medium-Range Weather Forecasts global model ( ECMWF 2012 ), the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS; Kanamitsu 1989 ), the Navy Operational Global Atmospheric Prediction System (NOGAPS; Rosmond 1992 ), and the Met Office global model (UKMET

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James A. Renwick and Craig S. Thompson

1. Introduction Variations in the skill of operational numerical weather prediction (NWP) models arise in two main ways. First, technological improvements (faster computers, better models) have led to substantial and relatively steady improvements in skill over the last several decades ( Kalnay et al. 1998 ). Second, overlaid on the general upward trend in forecast skill are shorter-term variations related to the predictability of the atmospheric circulation. The chaotic nature of the

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