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D. S. Wilks

1. Introduction Since the pioneering work of Barnett and Preisendorfer (1987) and Barnston (1994) , statistical seasonal forecasting primarily has been based on the relationships between sea surface temperatures (SSTs) and subsequent seasonal time averages of predictands such as near-surface temperatures. The physical basis for the predictive relationships captured by these statistical forecasting approaches is that the effects of slowly varying boundary conditions such as SSTs are

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J. V. Ratnam, Takeshi Doi, Willem A. Landman, and Swadhin K. Behera

SINTEX-F2 forecasts using both the dynamical and statistical downscaling methods. The technique of dynamical downscaling using a regional climate model has been widely used to improve the simulation of precipitation over South Africa ( Joubert et al. 1999 ; Tadross et al. 2006 ; Hansingo and Reason 2008 ; Kgatuke et al. 2008 ; MacKellar et al. 2009 ; Landman et al. 2009 ; Crétat et al. 2011 ; Ratnam et al. 2012 , 2013 , 2015 , 2016 ; Ratna et al. 2014 ; Diallo et al. 2015 ; Moalafhi et

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Jack W. Reed

360 JOURNAL OF APPLIED METEOROLOGYSome Notes on Forecasting of Winds Aloft by Statistical Methods~ JACK W. I~EED Sandia Corporation, Albuquerque, N. Mex. (Manuscript received 8 August 1966, in revised form 21 November 1966) ABSTRACT Winds aloft statistics from Eniwetok were used to derive a linear regression forecast technique thatverified satisfactorily in later application. Other studies showed that

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Thomas A. Gleeson

. Synoptic observations are regarded as samplings of the initial state. Probabilities ofinitial and forecast height values to occur in specified height intervals, are computed with graphical aids.These are limiting probabilities, determined by network density and initial synoptic analysis. They decreasewith time. The forecast procedure is described. Examples of probability forecasts and verifications are presented anddiscussed. The predictability of the graphical technique is examined quantitatively.1

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Verónica Torralba, Francisco J. Doblas-Reyes, Dave MacLeod, Isadora Christel, and Melanie Davis

challenges and support the development of sectorial climate services in Europe through the involvement of stakeholders in defining effective ways to develop climate information. This paper raises the limits associated with current seasonal forecast systems when used in wind energy applications. It focuses on the description of appropriate bias adjustment techniques to overcome some of these limits and to promote the use of climate forecast information on those occasions in which it can provide greater

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John A. Russo Jr., Isadore Enger, and Edna L. Sorenson

in Eastern Standard Time;midnight is 00, and 11:00 p.m. is 23. d is the day of theyear; 1 January is 1, and 31 December is 365. The useof these predictors is equivalent to fitting two harmonicsto explain both the diurnal and annual cycles of thepredictands.5. Control techniques The usefulness 6f a forecast technique may bepartially determined by its ability to surpass the forecast skill obtainable by persistence or climatology. Twocontrol techniques depicting the skill achievable bypersistence

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H. R. Glahn

: The design and analysis of an adaptivesystem for statistical classification. S.M. Thesis, MassachusettsInstitute of Technology, Cambridge, Mass., 60 pp. ' Hu, M. J., 1963: A trainable weather-forecasting s?tem.Tech. Rep. No. 6759-1, Contract No. AF33(616)-7726, S~stemsTheory Laboratory, Stanford University, Stanford, Calif., 19 pp.8 Duda, R. O., and I- W. Machanik, 1963: An adaptive prediction technique and its application to weather forecasting. Paperpresented at the Western Electronic Show

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Renaud Marty, Isabella Zin, Charles Obled, Guillaume Bontron, and Abdelatif Djerboua

statistical methods generally correct a substantial part of the bias. Multiple linear regression is a widely used method for generating high-resolution forecasts (e.g., Murphy 1999 ; Wilby et al. 2003 ). Other methods aim at reconstructing the subgrid spatiotemporal variability from NWP by providing PQPFs at spatiotemporal scales required by hydrological modeling. These include the Schaake shuffle of Clark et al. (2004) and the analog sorting technique proposed by Obled et al. (2002) . We have

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Richard H. Jones

conditions are observedwith error, there is information in the past forecasts which could increase the accuracy of the numericalpredictions. The techniques of control theory provide an optimal method for combining past forecasts withcurrent observations. This paper demonstrates the method on simulated non-linear time series.1. Introduction When a dynamical initial value problem containserrors in the observed initial conditions, the resultis a process which contains useful information in theentire past

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Stephen M. Leyton and J. Michael Fritsch

” system for producing short-term forecasts of ceiling and visibility. This system utilized a network of surface observations as predictors in a multiple linear regression technique. It was demonstrated that this approach provided more accurate short-term forecasts of ceiling and visibility than MOS when updated simultaneously. This result implies that an hourly updating observations-based system would produce even more accurate forecasts than statistical guidance for the hours between operational

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