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Doug McCollor and Roland Stull

. Previous studies ( Stensrud and Skindlov 1996 ; Mao et al. 1999 ; Eckel and Mass 2005 ; Stensrud and Yussouf 2005 ) have shown how straightforward moving-average techniques can reduce systematic error in DMO. In the study presented in this paper, moving-average and other related postprocessing techniques are applied to daily maximum and minimum temperature forecasts and daily quantitative precipitation forecasts (QPFs). These sensible weather element forecasts of temperature and precipitation are

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BANNER I. MILLER, ELBERT C. HILL, and PETER P. CHASE

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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Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, and Alan F. Blumberg

predictions. Therefore, researchers have proposed different methodologies to improve the forecast models. Cheng and Steenburgh (2007) , Gel (2007) , Glahn and Lowry (1972) , Ott et al. (2004) , and Houtekamer and Mitchell (2001) present several postprocessing techniques such as model output statistics (MOS), running-mean bias removal, and Kalman filtering. MOS is a statistical method that generates a better forecast by using a multiple linear regression model. However, this methodology needs a long

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William A. Gallus Jr.

have been shown to be inconsistent with the subjective impressions of forecasters ( Chapman et al. 2004 ). In an effort to provide more informative measures of forecast performance that better reflect the quality of these finer-grid forecasts, several new spatial verification techniques have been proposed including neighborhood or fuzzy verification, scale decomposition, object-based verification, and field verification approaches [see Casati et al. (2008) and Gilleland et al. (2009) for

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Tom H. Durrant, Frank Woodcock, and Diana J. M. Greenslade

the model grid. Interpolation of this output to specific locations may result in systematic biases due to unresolved local effects ( Engel and Ebert 2007 ). Postprocessing techniques aim to reduce these systematic biases. The widely used model output statistics (MOS), for example, uses multiple linear regression based on model output and previous observations to provide improved forecasts at specific locations ( Glahn and Lowry 1972 ). A major drawback to MOS is the long training dataset required

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Zewdu T. Segele, Michael B. Richman, Lance M. Leslie, and Peter J. Lamb

; Charney and Shukla 1981 ; Xue and Shukla 1993 ; Clark and Arritt 1995 ; Clark et al. 2001 ). The surface boundary focus of the present Ethiopian study is SST. However, El Niño–Southern Oscillation (ENSO)-related “predictability barrier” in Northern Hemisphere spring (e.g., Goswami and Shukla 1991 ; Webster and Yang 1992 ; Webster et al. 1998 ) can pose a major challenge to providing seasonal rainfall forecasts two or more months in advance in the tropics ( Goddard et al. 2001 ; Korecha and

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Alexander Kann, Christoph Wittmann, Yong Wang, and Xulin Ma

procedure A variety of methods exists for statistical adaptation of the direct model output of ensemble forecasts. Focusing especially on severe weather, the parameters of primary interest for statistical calibration are precipitation, 10-m wind speed, and 2-m temperature. As the observed relative frequency of precipitation is characterized by a high degree of skewness, logistic regression techniques are found to be adequate for many applications ( Hamill et al. 2008 ). In case of wind speed

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Baiquan Zhou, Panmao Zhai, and Ruoyun Niu

techniques like analog methods can easily establish nonlinear relationships between large-scale variables and local variables ( Fernández and Sáenz 2003 ). For KISAM, the vertical velocity that actually regulates the production of the precipitation forecast is the average vertical velocity obtained from the NCEP–NCAR reanalysis of the three most analogous historical records. Therefore, the improved capture of the ascending motion related to precipitation production in KISAM explains its superior

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Haiganoush K. Preisler and Anthony L. Westerling

techniques with piecewise polynomials to estimate the probabilities of interest as functions of explanatory variables (predictors; see appendices A and B for further details). The explanatory variables used were monthly average temperature, (forecast from previous months), PDSI value in the previous month, maximum PDSI in the last 12 months, and values of Niño and PDO in addition to location (latitude, longitude) and month. The use of piecewise polynomials, rather than logistic regression with linear

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Frank Woodcock and Diana J. M. Greenslade

wind forcings and spatial resolutions, while some include data assimilation and some include shallow-water physics. These different configurations generate errors that vary between the models and thereby enhance the likelihood of improved forecasts from a consensus of bias-corrected model forecasts—the success of compositing techniques depends in part upon the extent to which these errors are random and out of phase. 3. Method The 24-h model forecasts of H s were generated at the observation

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