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Andrew E. Mercer, Chad M. Shafer, Charles A. Doswell III, Lance M. Leslie, and Michael B. Richman

1995 ) and the probability of detection ( Wilks 1995 ) were used to evaluate the classification performance. Bayesian neural networks (BNNs; MacKay 1992 ) produced the largest Heidke skill score values, although the BNN suffered from significant false-alarm ratios ( Wilks 1995 ), which can be problematic for tornado forecasting. SVMs minimized this false-alarm ratio and only decreased the Heidke skill score slightly, so it was chosen as the best method. Other techniques were tested in T05

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652MONTHLY WEATHER REVIEW Vol. 97, No. 9UDC 551.509.313A TECHNIQUE OF OBJECTIVE ANALYSIS AND INITIALIZATION FOR THE PRIMITIVE FORECAST EQUATIONS TAKASHI NlTTA and JOHN B. HOVERMALE *National Meteorological Center, Weather Bureau, ESSA, Washington D.C.AB!7RACTA technique .of initialization for the primitive forecast equations is presented. The method consists of a march-ing prediction scheme performed in such a manner that the large-scale solution remains approximately steady

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W. E. Baker, R. Atlas, M. Halem, and J. Susskind

1544 MONTHLY WEATHER REVIEW VOLUME 112A Case Study of Forecast Sensitivity to Data and Data Analysis Techniques W. E. BAKER, R. ATLAS, M. HALEM AND J. SUSSKINDLaboratory for Atmospheric Sciences, NASA-Goddard $lmce Flight Center, Greenbelt, MD 20771(lvlanuscript received 5 July 1983, in final form 30 March 1984)ABSTRACT In this study we examine the sensitivity of forecast skill to

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Le Bao, Tilmann Gneiting, Eric P. Grimit, Peter Guttorp, and Adrian E. Raftery

1. Introduction Forecasts of wind direction have varied and important uses, ranging from air pollution management to aircraft and ship routing and recreational boating. However, wind direction is an angular variable that takes values on the circle, as opposed to other weather quantities, such as temperature, quantitative precipitation, or wind speed, which are linear variables that take values on the real line. As a result, traditional postprocessing techniques for forecasts from numerical

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Peter A. Stamus, Frederick H. Carr, and David P. Baumhefner

JANUARY 1992 STAMUS ET AL. 149Application of a Scale-Separation Verification Technique to Regional Forecast Models PETER A. STAMUS*NOAA Forecast Systems Laboratory, Boulder, Colorado FREDERICK H. CARRSchool of Meteorology, University of Oklahoma, Norman, Oklahoma DAVID P. BAUMHEFNERNational Center for Atmospheric Research, * Boulder, Colorado

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Maria E. B. Frediani, Thomas M. Hopson, Joshua P. Hacker, Emmanouil N. Anagnostou, Luca Delle Monache, and Francois Vandenberghe

outages, such as when areas of high wind speed are predicted at the incorrect location. The outage prediction model currently operates with regional deterministic NWP forecasts, and the potential to obtain ensemble wind speed predictions (thus accounting for weather variability) to drive the power outage prediction model at no additional computational cost motivated the development of this analog technique. With wind speed being the most important nonstatic variable in outage prediction model, the

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Mei Xu, David J. Stensrud, Jian-Wen Bao, and Thomas T. Warner

1. Introduction Most of the perturbation techniques developed for generating medium-range ensemble forecasts have concentrated on synoptic-scale weather systems over the midlatitudes that are associated with regions of baroclinic instability ( Palmer et al. 1992 ; Toth and Kalnay 1993 ; Buizza 1997 ; Houtekamer and Lefaivre 1997 ). This is a temporal and spatial scale that is well suited for numerical weather prediction, since numerical models are skillful in predicting baroclinic wave

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Ryan D. Torn and Gregory J. Hakim

. Previous studies on initial condition sensitivity have involved using the adjoint of a linearized forecast model. Adjoint sensitivity and singular vector analyses for extratropical cyclones emphasize structures in the lower troposphere, which have large vertical tilts and are not always obviously related to the major synoptic features (e.g., Errico and Vukicevic 1992 ; Langland et al. 1995 ; Rabier et al. 1996 ; Zou et al. 1998 ; Hoskins et al. 2000 ). Difficulties with these techniques include

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Thomas D. Keenan and Michael Fiorino

986 MONTHLY WEATHER REVIEW VOLUME II5Development and Testing of Statistical Tropical Cyclone Forecasting Techniques for the Southern Hemisphere THOMAS D. K.EENANBureau of Meteorology Research Centre, Me/bourne, Australia MICHAEL FIORINO*Naval Environmental Prediction Research Facility, Monterey, CA 93943(Manuscript received 3 June 1986, in final form 15

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Nina Schuhen, Thordis L. Thorarinsdottir, and Tilmann Gneiting

1998 ; Grimit and Mass 2002 ), ensemble forecasts tend to be biased, and typically they are underdispersed ( Hamill and Colucci 1997 ), in that the ensemble spread is too small to be realistic. Furthermore, differing spatial resolutions of the forecast grid and the observation network may need to be reconciled. To address these shortcomings, various techniques for the statistical postprocessing of ensemble model output have been developed ( Wilks and Hamill 2007 ), including ensemble model output

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