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Mark E. Struthwolf

160 WEATHER AND FORECASTING VOLUIvlE 10Forecasting Maximum Temperatures through Use of an Adjusted 850- to 700-mb Thickness Technique MARK E. STRUTHWOLFMeteorology Division, U.S. Army Dugway Proving Ground, Dugway, Utah15 June 1993 and 6 October 1994ABSTRAC'F A new technique is discussed for forecasting maximum daily surface temperatures at Dugway Proving Ground(DPG), Utah, a non

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Baiquan Zhou and Panmao Zhai

improve the forecast accuracy of PEP. The analog method (AM), the simplest statistical downscaling technique available, can establish nonlinear relationships between variables straightforwardly ( Fernández and Sáenz 2003 ). AM has two main advantages when predicting precipitation: it is favorable for hydrological studies on account of using the observed weather patterns and retaining the spatial covariance structure of the local-scale weather and, when constructing forecast scenarios, AM does not need

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Xiping Zhang and Hui Yu

a similar real-time technique of TC track correction for the EPS from ECMWF and studied the influence of position errors. They both did their research under the premise that errors at short and following lead times are related; however this premise has been left unproven. In other studies ( He et al. 2015 ; H. B. Zhang et al. 2015 ), a Kalman filter and a bias-removed ensemble mean were employed to conduct TC forecast consensus experiments in a deterministic way while only the ensemble mean of

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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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Liang Hu, Elizabeth A. Ritchie, and J. Scott Tyo

measure the wind field or central pressure, techniques have been developed starting with the Dvorak technique ( Dvorak 1975 ), to estimate TC intensity based on cloud pattern detection. While the Dvorak technique is still widely used in operational forecast centers, it is time consuming, subjective, and relies heavily on the expertise of the analyst, which can result in widely varying estimates of intensity from the same satellite observations ( Velden et al. 1998 ). More recently the advanced Dvorak

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Liang Hu, Elizabeth A. Ritchie, and J. Scott Tyo

measure the wind field or central pressure, techniques have been developed starting with the Dvorak technique ( Dvorak 1975 ), to estimate TC intensity based on cloud pattern detection. While the Dvorak technique is still widely used in operational forecast centers, it is time consuming, subjective, and relies heavily on the expertise of the analyst, which can result in widely varying estimates of intensity from the same satellite observations ( Velden et al. 1998 ). More recently the advanced Dvorak

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Yuxuan Yang, Lifeng Zhang, Bin Zhang, Wei You, Mingyang Zhang, and Binpeng Xie

and Chou 2006 ; Tian et al. 2008 ; Wang et al. 2010 ). The EVA method adopted in this paper, referred to as POD-4DEnVar, was proposed by Tian et al. (2008 , 2009 , 2011 ) and Tian and Feng (2015 ) and is based on proper orthogonal decomposition (POD) and ensemble forecasting. This method applies the POD technique to the 4DVar framework, and the observation perturbations are decomposed so as to extract the standard orthogonal basis of the forecast bias in a four-dimensional (4D) space, which

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Paul J. Roebber

temperature and precipitation and that, in specific circumstances, this human intervention can add considerably to the value of those forecasts. While these and other studies have helped to clarify some outstanding questions regarding the forecast process, an issue that has not yet been addressed is the large-scale regime dependence of forecast technique; that is, given a profile of forecast information (e.g., 850-hPa temperature, dewpoint temperature, surface wind speed, etc., hereafter called cues

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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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Fan Han and Istvan Szunyogh

1. Introduction In a pair of papers, Keil and Craig (2007 , 2009 ; hereafter KC07 and KC09 , respectively) introduced a morphing-based, nonparametric optical flow technique ( Marzban et al. 2009 ) for the verification of precipitation forecasts. Their technique was most recently used by Geiß (2015) to examine the forecast cases of the Mesoscale Verification Intercomparison over Complex Terrain (MesoVICT) research project ( Dorninger et al. 2013 ). In an earlier paper ( Han and Szunyogh

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