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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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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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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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Lin Dong and Fuqing Zhang

models per se, but are instead combinations of forecasts from multiple models ( Cangialosi and Franklin 2014 ). Therefore, the performance of a consensus model is determined by two factors: the consensus technique and the consensus components. Consensus technique refers to how the weight of each component is assigned, and can be divided into two categories: equal weights and unequal weights. Equal weights are calculated by a simple arithmetic average, whereas unequal weights are determined using more

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Sheng-Lun Tai, Yu-Chieng Liou, Juanzhen Sun, Shao-Fan Chang, and Min-Chao Kuo

, a cloud-scale model formulated using the Cartesian coordinate system and a warm rain microphysical process, is adopted as a forward forecast model ( Sun and Crook 1997 ). Using this prognostic model as a constraint and applying the 4DVAR technique, VDRAS is able to find an optimal initial state that minimizes a cost function ( J ), which measures the distances between the model predictions and the observations, and can be written as In (1) , the summation is conducted over space ( σ ) and time

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Theodore W. Funk

548 WEATHER AND FORECASTING VOLUME6FORECASTING TECHNIQUESForecasting Techniques Utilized by the Forecast Branch of the National Meteorological Center During a Major Convective Rainfall Event THEODORE W. FUNK*NWS/NMC /Meteorological Operations Division/Forecast Branch, Washington, D. C.28 February 1991 and 10 July 1991 Meteorologists within the Forecast Branch (FB) of the

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Anu Simon, Andrew B. Penny, Mark DeMaria, James L. Franklin, Richard J. Pasch, Edward N. Rappaport, and David A. Zelinsky

during HFIP ( Cangialosi and Franklin 2016 ). To extend these improvements in NWP skill, multimodel consensus postprocessing techniques can also be applied. Multimodel consensus forecast guidance is widely used in many operational weather forecasting centers. The National Hurricane Center (NHC) relies on various consensus aids to help improve TC track and intensity forecasts. Goerss (2000) found that a simple, equally weighted average of several models consistently outperforms each of the

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Lynette van Schalkwyk and Liesl L. Dyson

Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data ( Kalnay et al. 1996 ) to investigate the synoptic circulation patterns that lead to fog over the northeastern part of the United States, focusing specifically on patterns resulting in fog in the New York region. After using clustering techniques to obtain more information about the nature of fog at three different airports in Finland, 40-yr European Centre for Medium-Range Weather Forecasts

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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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