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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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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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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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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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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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Liew Juneng, Fredolin T. Tangang, Hongwen Kang, Woo-Jin Lee, and Yap Kok Seng

must be recomputed to derive a new empirical relationship between the predictands and predictors. Another significant advancement in seasonal climate forecasts over the past few decades was the use of compositing multiple GCM forecast techniques to obtain the multimodel ensemble (MME) forecast ( Krishnamurti et al. 1999 ; Palmer and Shukla 2000 ). The MME technique provides an effective way to handle any uncertainties among the GCMs. Combining the MME and downscaling have proven to have further

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Timothy DelSole and Jagadish Shukla

: Strategies for assessing skill and significance of screening regression models with emphasis on Monte Carlo techniques. J. Climate Appl. Meteor. , 23 , 1454 – 1458 . Lawley , N. D. , 1956 : Tests of significance for the latent roots of covariance and correlation matrices. Biometrika , 43 , 128 – 136 . Michaelson , J. , 1987 : Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor. , 26 , 1589 – 1600 . Montgomery , R. B. , 1940 : Report on the work of G. T

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Jie Feng, Ruiqiang Ding, Deqiang Liu, and Jianping Li

1998 ; Zhu et al. 2002 ). With this additional information, the quality of forecasts can be significantly enhanced. Initially, the use of ensemble techniques focused on random samples (Monte Carlo forecasting) as a description of the probability distribution of initial states ( Epstein 1969 ; Leith 1974 ). However, the atmosphere is an extremely complex system, which has a very high phase-space dimension; that is, the number of random samples must be sufficiently large. Consequently, the cost of

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