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Hui Liu and Jun Li

1. Introduction Forecasting rapid intensification [e.g., a decrease of 42 hPa in minimum sea level pressure (SLP) in less than 24 h; National Hurricane Center 2006 ] of tropical cyclones remains a critical challenge. In general, tropical cyclone development requires a favorable environment of moist air, thermo instability, low vertical wind shear, preexisting low-level perturbation, high sea surface temperature (SST), and high ocean heat content (OHC) in the vicinity of tropical cyclones (e

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A. Sankarasubramanian, Upmanu Lall, Naresh Devineni, and Susan Espinueva

prespecified level every year during the winter to accommodate the upcoming winter and spring peak flows. Several investigators have emphasized the importance of exploiting this improved hydrologic predictability to enhance the management of water and energy systems ( Carpenter and Georgakakos 2001 ; Hamlet et al. 2002 ; Voisin et al. 2006 ). The main intent of this study is to assess the utility of monthly updated probabilistic streamflow forecasts obtained from climate forecasts in improving

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Lei Qiao, Chris B. Zou, Carlos F. Gaitán, Yang Hong, and Renee A. McPherson

drought ( Maloney et al. 2014 ). The CMIP5 ensemble represents an opportunity to improve the understanding of climate change and its impacts on the environment. Uncertainties generated from climate model simulations greatly complicate interpretation of climate change–related studies. Output from a single climate model could be inherently biased and unrepresentative because of its fundamental structure (e.g., computational techniques, parameterizations); thus, an ensemble of simulations from multiple

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Thomas H. Jagger and James B. Elsner

1. Introduction Seasonal forecast models that predict U.S. hurricane activity by coastal region were developed in Elsner and Jagger (2006) . The models capture the historical distribution of hurricane counts and are widely used by the insurance and finance industries. They are based on the Poisson distribution, where the rate is conditioned on climate variables including the Southern Oscillation index (SOI), North Atlantic Ocean sea surface temperature (SST), the North Atlantic Oscillation

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Daniel S. Wilks

1. Introduction Seasonal forecasts are often made with linear multivariate statistical methods. Although the dynamics of the climate system are nonlinear, in practice it has been found that nonlinear statistical methods perform no better than the traditional linear statistics for seasonal prediction ( Tang et al. 2000 ; van den Dool 2007 ). Possibly this phenomenon occurs because the time averaging inherent in long-lead forecasting renders both the predictors and predictands Gaussian or quasi

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Thomas R. Saxen, Cynthia K. Mueller, Thomas T. Warner, Matthias Steiner, Edward E. Ellison, Eric W. Hatfield, Terri L. Betancourt, Susan M. Dettling, and Niles A. Oien

, as well as the commute to and from WSMR, include lightning, hail, strong winds, heavy rainfall, flash floods, and tornadoes. Safety regulations require that all range activities, especially those involving explosives, be coordinated with the WSMR meteorology office. During the summer months, the primary focus of the weather forecasters at WSMR is on thunderstorm activity and the associated hazards. For example, 97% of weather warnings issued during the months of June–August are related to

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Julia N. Paegle and Robert P. Wright

180 JOURNAL OF APPLIED METEOROLOGY VoLtmE14Forecast of Precipitation Probability Based on a Pattern Recognition Algorithm JtmI~ N. Dept. of Meleorology, University of Utah, Salt ?',ak~ City g4113ROBERT P. WRIGHT5t~ We~h~ Wing, ~ir ~ather S~, ~angley ~ir Forc~ B~, Va. ~33~5(Manuscript received 28 March 1974, ~ revised form 30 October 1974)ABSTRACT A nonparametric, statistical technique is

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Herbert D. Entrekin, James W. Wilson, and Keith D. Hage

based on advection; and, using detailed data analyses and a numericalphysical boundary-layer model, studies of methods for predicting changes due to non-advective processes. The second year of mesonet data was used as independent data for testing six forecast techniques. Of thesix techniques tested, three did not utilize mesonet data and were used for control purposes. The controlforecasts were prepared from persistence, from trend, and by experienced U. S. Weather Bureau terminalweather

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M. A. Shapiro and James J. O'Brien

limitations of available computers preclude the use of hemispheric or global fine-mesh forecasts, Hill developed a grid-telescoping technique bywhich an increasing density of computational grid pointsmay be introduced into a chosen geographical area ofinterest. The technique allows a hemispheric coarsemesh forecast to be used to specify time-variantboundary conditions for a fine-mesh, limited-areaforecast. Hill concluded that grid-telescoping givesrealistic boundary conditions on a fine-mesh, limitedarea

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Julia N. Paegle, Robert P. Wright, Klancy De Nevers, and Marilyn Keir

Biophysics and Computing, University of Utah, Salt Lake City 84112 1537 Emigration Street, Salt Lake City, Utah 84108 10 May 1977 and 26 August 1977 ABSTRACT A nonparametric technique based on a pattern recognition algorithm is used 'to forecast the probabilityof precipitation for 42 stations over the western United States. Mean-square errors or Brier scores betweenforecasted values and observed precipitation are obtained for an independent data sample

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