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Buo-Fu Chen, Boyo Chen, Hsuan-Tien Lin, and Russell L. Elsberry

experts at TAFB and SAB differed by 20 kts in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 kt lower than either of them.” In summary, most of the current techniques for TC intensity estimation rely upon feature-engineering to transform low-level satellite imagery into high-level human-constructed features. Even for the most experienced meteorologists and forecasters, it is still hard to identify if a feature is suitable for intensity regression for all TCs in

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Eric Metzger and Wendell A. Nuss

utilized operationally by the NWS Forecast Office (NWSFO) in Huntsville since 2003 ( Darden et al. 2010 ), where forecasters note a sudden increase in total lightning activity prior to the onset of severe weather. These lightning jumps occurred as much as 30 min prior to the occurrence of severe weather ( Darden et al. 2010 ), confirming earlier studies by Williams et al. (1999) and Goodman et al. (2005) . The observations from these prior studies and the Huntsville site led to the development of

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Cristina Primo, Christopher A. T. Ferro, Ian T. Jolliffe, and David B. Stephenson

perfect forecasting models and perfect ensembles, observations behave like draws from the ensemble distribution and relative frequencies will make good forecasts. In practice, however, models are imperfect ( Ferranti et al. 2002 ) and ensemble generation techniques do not sample randomly from the probability distribution of initial-condition uncertainty ( Hamill et al. 2000 , 2003 ; Wang and Bishop 2003 ). Various techniques have therefore been proposed for improving such probabilistic forecasts

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Binbin Zhou and Jun Du

new diagnostic fog-forecasting method compared to a conventional method used in current practice; the second goal is to examine the forecast skill level of current operational NWP models in predicting fog with various approaches, including ensemble technique, multimodel approach, and the increase in ensemble size; and the last goal is to compare the performances of a single-model-based ensemble and multimodel-based ensembles, as well as to examine the impacts of ensemble size on probabilistic

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Andrew R. Lawrence and James A. Hansen

forecast’s utility is its relatively small size. This paper presents an approach to increase forecast ensemble size using lagged ensemble forecasts that have been transformed to account for all observations that have become available since the forecasts were launched. This transformed lagged ensemble forecasting (TLEF) technique is equally valid for single-model and multimodel ensembles, but the technique introduced here places emphasis on the single-model case in the context of an idealized model

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W. T. Yun, L. Stefanova, and T. N. Krishnamurti

. (1999) . Various ensemble methods have been used to reduce climate noise in model prediction, such as a lagged ensemble forecasting method introduced by Hoffman and Kalnay (1983) , breeding techniques by Toth and Kalnay (1993) , or a singular vector method by Buizza and Palmer (1995) . Ensemble techniques are routinely used at operational weather forecasting centers ( Molteni et al. 1996 ; Buizza et al. 1998 ; Toth and Kalnay 1997 ; Houtekamer et al. 1996 ; Stephenson and Doblas-Reyes 2000

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David R. Novak, Keith F. Brill, and Wallace A. Hogsett

public misinterpretations of probability of precipitation forecasts have been documented (e.g., Joslyn et al. 2009 ). This article proposes an objective technique using percentiles from a probability distribution function (PDF) to determine forecast snowfall ranges consistent with the risk tolerance of users. This technique is dynamic, with the resultant ranges varying based on the spread of ensemble forecasts. Furthermore, this technique allows users to choose the risk tolerance, quantified as the

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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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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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