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Mana Inoue, Alexander D. Fraser, Neil Adams, Scott Carpentier, and Helen E. Phillips

here are not suitable for use with a simple conversion to RH ice , particularly during summertime (i.e., when most flight operations are conducted) when sea ice extent is low. Nevertheless, such a conversion should be investigated if these techniques are to be used farther inland. Inspection of the mean RH bias profile (RH NWP − RH sonde ) shows that the lower-troposphere NWP forecast RH values are drier than observed across all three stations, for times of ≥6 oktas ( Fig. 1 ). A dry bias of up to

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Edward A. O’Lenic, David A. Unger, Michael S. Halpert, and Kenneth S. Pelman

. A method to objectively combine, or “consolidate,” four forecast tools for surface temperature and precipitation was implemented in late 2006. This technique resulted in a substantial increase in the skill of hindcasts, in comparison with official outlooks made operationally over the same 10-yr period (1995–2005). We now briefly describe the forecast tools. The climate forecast system (CFS, implemented 2004; see Table 1 ) is a “one tier”, fully coupled, dynamical model of the global oceans

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Brian C. Ancell

-h forecasts and nearly as good as forecasts run after assimilating the observations themselves at 24-h forecast time. Madaus and Hakim (2015) extended this technique (termed ensemble forecast adjustment) to a more operational framework within ensemble systems using both European Center for Medium-Range Weather Forecasts (ECMWF) and Canadian Meteorological Centre (CMC) global models. By adjusting 12–30-h forecasts based on surface pressure observations at 6-h forecast time, they found

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Shoupeng Zhu, Xiefei Zhi, Fei Ge, Yi Fan, Ling Zhang, and Jianyun Gao

). The intelligent prediction of such events is of great importance in preventing and mitigating disruptive impacts of natural disasters. Short- to medium-range forecasts have been improved in recent decades as a result of advances in equipments (e.g., radar and satellite) and techniques (e.g., data assimilation and computing resources), more comprehensive understanding of weather processes, and the development of numerical weather prediction models. Climate predictions have also been improved in

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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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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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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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