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540MONTHLY WEATHER REVIEWVal. 96, No. 8A REVISED TECHNIQUE FOR FORECASTING HURRICANE MOVEMENT BY STATISTICAL METHODSBANNER 1. MILLER*, ELBERT C. HILL**, and PETER P. CHASE**National Hurricane Research Laboratory and **National Hurricane Center, ESSA, Miami, Fla.ABSTRACTThe NHC-64 statistical equations for predicting the movement of hurricanes have been in operational use for 4 yr.These equations have continued to perform well. Following the 1966 hurricane season, however, it

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Andrew J. Condon, Y. Peter Sheng, and Vladimir A. Paramygin

which can be important. As pointed out by Rego and Li (2009) and Jelesnianski (1972) , neglecting the forward speed and angle of approach may not be appropriate as there is a “critical motion relative to a coast that gives the highest possible surge.” Additionally the technique does not account for tides and wave setup, which can contribute significantly to the surge and inundation. This paper addresses the rapid generation of high-resolution probabilistic inundation forecasts. The optimal storm

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Bruce A. Veenhuis

and Loughe 1998 ). The ensemble members sample the various sources of error that degrade NWP forecasts. To quantify the error in the underlying analysis, the ensemble members are initialized with perturbed initial conditions. Over the years, a range of perturbation techniques have been proposed including the breeding method ( Toth and Kalnay 1997 ), singular vectors ( Palmer et al. 1998 ), and the ensemble transform Kalman filter ( Wang and Bishop 2003 ). The numerical model itself is also a

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September 1968E. Lee Geraldson649A COMPARISON OF THE ACCURACY OF OBJECTIVE TECHNIQUES FOR FORECASTING TYPHOON MOVEMENT DURING 1967E. LEE GERALDSONJoint Typhoon Warning Center, GuamABSTRACTIn the past, many objective techniques have been evaluated for severe tropical cyclones in the Atlantic, butlittle has been done along this line in the Pacific. A computer program was developed at the Joint Typhoon WarningCenter, to verify 10 separate 24-hr. forecast techniques. During the course of the 1967

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depression are made for periods up t o 36 hours using observed700-mb. charts and initial and 12-hr. forecast vertical motion charts from the JNWP Unit's thermotropic model.Results show that moisture can be successfully forecast by this technique. Through establishment of a relationshipbetween 700-mb. dewpoint depression, instantaneous vertical velocity, and large-scale weather, a forecast scheme isdevised using forecast 700-mb. dewpoint depression and forecast vertical motion from the thermotropic model

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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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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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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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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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Sim D. Aberson, Sharanya J. Majumdar, Carolyn A. Reynolds, and Brian J. Etherton

statistically significant GFS track forecast error reduction of up to 25%, thereby yielding larger improvements than were possible by assimilating all available surveillance data. The deficiencies of symmetric sampling were attributed to suboptimal data assimilation schemes and their impact near targets that were bisected or otherwise not fully sampled. Advanced techniques, such as the ensemble transform Kalman filter (ETKF; Bishop et al. 2001 ) and singular vectors (SVs; Palmer et al. 1998 ), have

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