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John A. Knaff, Charles R. Sampson, and Kate D. Musgrave

combination of current TC conditions, environmental conditions, and information about the current IR structure to forecast the probability of various intensification rates. In development, we use analyses (i.e., perfect prog) of environmental conditions. In applications, environmental conditions are based on forecasts. We also use two statistical methods to create forecasts from which we construct a two-member consensus forecast. The two methods are a linear discriminant analysis and logistic regression

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Kevin R. Petty and Jay S. Hobgood

Ocean (ENP) basin over that provided by climatology and persistence models. Toward this objective, we investigate the use of shear and thermal variables in statistical forecasts of TC intensity change. This goal is achieved using a methodology analogous to that of DeMaria and Kaplan (1994b , 1999) . They used multiple linear regression analysis to produce SHIPS for the Atlantic basin and later for the ENP. 2. Data Best-track data from NHC are used in this study as the source of nonsynoptic

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John R. Colquhoun and Philip A. Riley

than 200 tornado proximity soundings. Soundings were obtained from two sources: the University of Missouri and from analyses. The surface to 600 hPa wind shear, streamwise vorticity, storm velocity, and storm-relative environmental helicity are shown to be best correlated with tornado intensity. A multiple regression analysis produced an equation relating tornado intensity to the lifted index and the surface to 600 hPa wind shear. This relationship could be used operationally to predict tornado

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Paul J. Roebber

mean values and standard deviations as a function of identified regime. These data are consistent with the expectations stated above for temperature conditions at ALB during the HI (LI) regime, averaging 4.0°C above (2.1°C below) climatology. A number of approaches are possible for studying forecast methodology. One approach that could be used would be to make the default assumption that the forecast baseline is provided by MOS. Under this assumption, one could perform a regression analysis between

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Emily K. Grover-Kopec and J. Michael Fritsch

to study bird migration patterns find echoes caused by precipitation noisy to their investigations. Previous studies have used a variety of techniques to detect false echoes. Methods to eliminate ground clutter include the incorporation of regression filters ( Torres and Zrnić 1999 ; Sachidananda and Zrnić 2000 ) and a staggered pulse-repetition-time transmission. Others have detected false data with neural networks ( Grecu and Krajewski 2000 ; Krajewski and Vignal 2001 ) and statistical

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Yijia Hu, Yimin Zhu, Zhong Zhong, and Yao Ha

winter and spring during 1966–97 are calculated to find the physical predictors that have close associations with the MOD. Last, a physics-based statistical forecast model for MOD is established using the multiple linear regression method. The predictors are the YIs of variables closely related to MODYI based on the second step. The output of this model is MODYI. The MOD itself can be calculated from the MODYI. We chose the period from 1966 to 1997 to carry out the correlation analysis and to

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John Kaplan, Christopher M. Rozoff, Mark DeMaria, Charles R. Sampson, James P. Kossin, Christopher S. Velden, Joseph J. Cione, Jason P. Dunion, John A. Knaff, Jun A. Zhang, John F. Dostalek, Jeffrey D. Hawkins, Thomas F. Lee, and Jeremy E. Solbrig

season while a SHIPS-RII developed for the eastern North Pacific basin was first implemented for the 2006 hurricane season. Over the next few years, new versions of the SHIPS-RII that included additional RI predictors and more sophisticated statistical methods were developed and the current linear-discriminant analysis version of the SHIPS-RII described in KDK10 was implemented operationally at the NHC prior to the 2008 hurricane season. Although the SHIPS-RII is currently an operational

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Kristen Kehrer, Brian Graf, and William P. Roeder

lightning from GPS PW was first developed in 2002 ( Mazany et al. 2002 ). This model used binary logistic regression to predict the probability of lightning at Spaceport Canaveral using GPS PW, the K index to incorporate atmospheric instability, and the largest value from the network of 31 surface electric field mills at Spaceport Canaveral to include the electric signal from developing thunderstorms. The output of the model is a lightning index between 0 (lightning) and 1 (no lightning) that

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Stefano Mariani, Christophe Accadia, Nazario Tartaglione, Marco Casaioli, Marco Gabella, Silas Chr Michaelides, and Antonio Speranza

particular the use of TRMM PR observations for the improvement of the precipitation analysis fields and its use in NWP verification study. Moreover, as discussed later, alternative observations from space, like total column water vapor (TCWV), can be used to perform a model evaluation over otherwise in situ data-void regions. The island of Cyprus, located in the eastern Mediterranean Sea (see Fig. 1 ), was an optimal test site for the project’s activities. Cyprus is one of the few European lands covered

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Baxter E. Vieux and Philip B. Bedient

particular importance to the forecaster is the presence of meteorological factors, such as lifting mechanisms, and sources of moisture-causing deep convection and widespread rainfall, coupled with hydrologic variables such as antecedent soil moisture conditions and spatial superposition of heavy rainfall over a particular catchment. However, both meteorological and hydrologic factors must be present for flooding to occur in response to heavy rainfall. Senesi et al. (1996) describe the analysis of a

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