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Rachel G. Mauk
and
Jay S. Hobgood

Abstract

Tropical cyclones with nontropical characteristics are being identified more frequently over the North Atlantic Ocean in recent years. These systems present forecasting challenges because of their hybrid structure. The authors analyze environmental conditions preceding the formation of 20 late-season northeastern Atlantic tropical cyclones identified during the 1975–2005 seasons. A recent tropical storm, Grace (2009), is discussed as a case study. Seventeen of the 20 systems originated from nontropical systems (surface low, frontal weak, and frontal strong). Three tropical cyclones experienced nontropical influences during development despite originating from tropical waves. Ambient sea surface temperatures, relative vorticity, vertical temperature profiles, and wind shear are investigated to identify conditions conducive to tropical cyclone formation. Tropical cyclones developing from nontropical precursors form in environments distinct from the classical tropical cyclone environment. For 17 systems, sea surface temperatures are cooler than 26°C. Stability analysis suggests that convection is shallow. Wind shear decreases for the 850–300-hPa layer in comparison to the 850–200-hPa layer. Most systems still experience shear in excess of 8 m s−1 for the 850–300-hPa layer. It is suggested that late-season tropical cyclones in this region are shallower in vertical extent than typical tropical cyclones, which reduces the impact of strong wind shear in the 850–200-hPa layer.

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Christopher C. Hennon
,
Caren Marzban
, and
Jay S. Hobgood

Abstract

A binary neural network classifier is evaluated against linear discriminant analysis within the framework of a statistical model for forecasting tropical cyclogenesis (TCG). A dataset consisting of potential developing cloud clusters that formed during the 1998–2001 Atlantic hurricane seasons is used in conjunction with eight large-scale predictors of TCG. Each predictor value is calculated at analysis time. The model yields 6–48-h probability forecasts for genesis at 6-h intervals. Results consistently show that the neural network classifier performs comparably to or better than linear discriminant analysis on all performance measures examined, including probability of detection, Heidke skill score, and forecast reliability. Two case studies are presented to investigate model performance and the feasibility of adapting the model to operational forecast use.

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John G. W. Kelley
,
Jay S. Hobgood
,
Keith W. Bedford
, and
David J. Schwab

Abstract

A one-way coupled atmospheric–lake modeling system was developed to generate short-term, mesoscale lake circulation, water level, and temperature forecasts for Lake Erie. The coupled system consisted of the semi-operational versions of the Pennsylvania State University–National Center for Atmospheric Research three-dimensional, mesoscale meteorological model (MM4), and the three-dimensional lake circulation model of the Great Lakes Forecasting System (GLFS).

The coupled system was tested using archived MM4 36-h forecasts for three cases during 1992 and 1993. The cases were chosen to demonstrate and evaluate the forecasts produced by the coupled system during severe lake conditions and at different stages in the lake’s annual thermal cycle. For each case, the lake model was run for 36 h using surface heat and momentum fluxes derived from MM4’s hourly meteorological forecasts and surface water temperatures from the lake model. Evaluations of the lake forecasts were conducted by comparing forecasts to observations and lake model hindcasts.

Lake temperatures were generally predicted well by the coupled system. Below the surface, the forecasts depicted the evolution of the lake’s thermal structure, although not as rapidly as in the hindcasts. The greatest shortcomings were in the predictions of peak water levels and times of occurrence. The deficiencies in the lake forecasts were related primarily to wind direction errors and underestimation of surface wind speeds by the atmospheric model.

The three cases demonstrated both the potential and limitations of daily high-resolution lake forecasts for the Great Lakes. Twice daily or more frequent lake forecasts are now feasible for Lake Erie with the operational implementation of mesoscale atmospheric models such as the U.S. National Weather Service’s Eta Model and Rapid Update Cycle.

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