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Peter C. McIntosh, Andrew J. Ash, and Mark Stafford Smith

benefits to their enterprises. Part of this skepticism is due to poor links between the forecast information and the timing and type of decisions made in agriculture. Also the failure to incorporate the probabilistic nature of forecasts into decision making leads to a lack of faith in their reliability. New forecasting techniques must therefore be developed in conjunction with industry needs; because these needs vary by industry and location, a collaborative approach is necessary. In addition, these

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Jeffrey L. Anderson

1518JOURNAL OF CLIMATEVOLUME 9A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations JEv'fmE- L. ANDERSONGFDL/Princeton University, Princeton, New Jersey(Manuscript received 23 March 1995, in final form 17 November 1995)ABSTRACT The binned probability ensemble (BPE) technique is presented as a method for producing forecasts of theprobability distribution of a variable using an ensemble of numerical model integrations. The ensemble

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Willem A. Landman and Lisa Goddard

future behavior of the climate system; namely, a purely empirical-statistical approach and a dynamical approach using the first principles of the processes governing the climate system, the latter has received far less investigation as a seasonal forecasting technique for southern Africa. However, recently the emphasis of research for the region has begun to shift toward the use of more sophisticated forecast schemes involving the use of dynamical models based on first principles. These dynamical

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Chris K. Folland, Andrew W. Colman, David P. Rowell, and Mike K. Davey

Oscillation–climate relationships for New Zealand. Int. J. Climatol., 15, 1365–1386. Nicholls, N., 1984: The stability of empirical long-range forecast techniques: A case study. J. Climate Appl. Meteor., 23, 143–147. ——, 1999: Cognitive illusions, heuristics, and climate prediction. Bull. Amer. Meteor. Soc., 80, 1385–1397. Potts, J. M., C. K. Folland, I. Jolliffe, and D. Sexton, 1996: Revised“LEPS” scores for assessing climate model simulations and long-range forecasts. J. Climate, 9

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Vincent Y. S. Cheng, George B. Arhonditsis, David M. L. Sills, William A. Gough, and Heather Auld

climate models resolve large-scale atmospheric conditions, which are then incorporated in severe weather forecasting tools to predict the occurrence of a regional tornadic event from hours to days (outbreak) in advance. Interestingly, most of the predictive attempts in the literature aimed at associating parameters that characterize the nearly immediate (hourly) atmospheric conditions with the occurrence of tornadic events ( Brooks et al. 1994 ; Rasmussen and Blanchard 1998 ; Thompson et al. 2003

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Akiyo Yatagai, T. N. Krishnamurti, Vinay Kumar, A. K. Mishra, and Anu Simon

) and NOAA ( Saha et al. 2005 ), Seoul National University (SNU) in Korea ( Kug et al. 2005 ), and the University of Hawaii ( Fu and Wang 2001 ). Each model consists of different sets of atmospheric or oceanic GCMs. Forecast precipitation data from the four GCMs were converted to monthly precipitation for the seven years. CGCM results do not reflect the dynamic field in a year. Hence, the climate superensemble technique ( Yun et al. 2003 , 2005 ) uses empirical orthogonal function (EOF) analysis to

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Lawrence Greischar and Stefan Hastenrath

, N., 1984: The stability of empirical long-range forecast techniques: A case study. J. Climate Appl. Meteor., 23, 143–147. ——, 1985: Towards the prediction of major Australian droughts. Aust. Meteor. Mag., 33, 161–166. Ward, M. N., and C. K. Folland, 1991: Prediction of seasonal rainfall in the North Nordeste of Brazil using eigenvectors of sea surface temperature. Int. J. Climatol., 11, 711–743. ——, A. Colman, M. Davey, and C. Folland, 1994: Multiple regression and discriminant

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Di Tian, Christopher J. Martinez, Wendy D. Graham, and Syewoon Hwang

( Troccoli et al. 2008 ). While such statistical models are black-box models and their applications are restricted by locations, they can give comparable and even higher skill for some regions and are much less expensive and more straightforward than dynamical models. While GCM P and T2M forecasts have coarse resolution and suffer from systematic errors, there are several techniques that can be used to compensate for these limitations. Statistical or dynamical downscaling procedures can to be employed

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Anthony G. Barnston and Huug M. van den Dool

) regarding negative correlationskills as zero, or 3 ) using a forecast verification measure other than correlation such as root-mean-square error. When the correlation skill score degeneracy is acknowledged and treated appropriately, cross-validation remainsan effective and valid technique for estimating predictive skill for independent data.1. Introduction The desire to accurately quantify statistical forecastskill has existed among meteorologists and oceanographers for many years. Estimates of

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Barnaby S. Love, Adrian J. Matthews, and Gareth J. Janacek

on the first two EOFs of equatorially averaged OLR and 850- and 200-hPa zonal wind. These EOFs were projected onto daily maps of OLR, from which the annual cycle and a component of the interannual variability had been subtracted. Hence, the necessity for time filtering was reduced and the resulting principal component time series could be calculated in real time. A seasonally varying lagged linear regression technique was then applied to produce real-time forecasts of the two MJO EOFs ( http

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