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Forecasting Northeastern Pacific tropical cyclone tracks using an analog scheme by Charles Joel Mauck

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Published by Naval Postgraduate School in Monterey, California .
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Subjects:

  • Meteorology

Book details:

ID Numbers
Open LibraryOL25327142M

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Abstract The Navy's EPANALOG (Northeastern Pacific Anlog Tropical Cyclone Tracker) forecast program is introduced. EPANALOG selects analog tropical cyclones from a year northeastern Pacific Ocea Author: J. D. Jarrell, C. J. Mauck, R. J. Renard. Enter the password to open this PDF file: Cancel OK. File name: . NEPHAT selects analog tropical cyclones from year Northeastern Pacific Ocean history. Each selected analog track is statistically adjusted for known differences between it and the recent history of the tropical cyclone being forecasted. The adjusted analog cyclone trajectories are then composited into a single forecast : Charles Joel III Mauck. MODHATR(best-trackbias)errors 39 9. MODHATR(best-trackbias)vsOFFICIALerrors: homogeneoussample, 40 MODHATR(best-trackbias)vsOFFICIALerrors: non-homogeneoussample, 41 MODHATR(operationalbias)vsOFFICIALerrors: 42 MODHATR(operationalbias)vsOFFICIALerrors: homogeneoussample, 43 .

The Navy's EPANALOG (Northeastern Pacifi.c Analog Tropical Cyclone Tracker) f~recast prog~am is introduced. EP ANALOG selects analog tropical cyclones from a year northeastern Pa~fi.c Ocean history. The selected analog tracks, statistically adjusted for position, vector motio~, an? date ~erences between them and the recent history of the tropical cyclone being forecast, are composited mto a smgle forecast track. The tropical cyclone life cycle model is divided into three intensity phases (): (I) formation stage to a named tropical storm (34 kt), (II) early intensification after becoming a named storm (intensification phase), and (III) the decay intensity forecast technique (to be described below) is evaluated to determine its accuracy during each intensity phase to provide guidance to the Cited by: This type of techniques is also known as extrapolation, as it extrapolates and averages the cyclone's current and recent-past movement (usually 6 to 12 h back in time) to produce a forecast of the cyclone's future track. Extrapolation techniques are the simplest among all track forecasting techniques Cited by:   The impact of two bogussing schemes on tropical cyclone (TC) forecasts is compared. One scheme for bogussing TCs into the initial conditions of the nonhydrostatic version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is proposed by NCAR and the Air Force Weather Agency (AFWA), and four Cited by:

The forecasting ability of the multimodel approach is evaluated using the best-track database of the tropical cyclones that occurred in the eastern and western North Pacific and South Indian Ocean.   In data mining, the k-nearest-neighbor algorithm is an algorithm for classifying objects based on other objects from some training set that are the closest in the feature this problem, the intensity of each snapshot of a TC is determined by averaging the intensity of its k nearest neighbors (NNs). The feature vector when comparing a query image with a candidate image has Cited by: 9.  Use the model itself to create (spin up) a cyclone vortex (GFDL). Operational Regional Models for TC Track Forecasting.  Geophysical Fluid Dynamics Laboratory Model (GFDL) Initialization spins up a vortex from a separate run of the model, which replaces GFS fields over the circulation of the Size: 7MB. Research and development (RD) advancements in tropical cyclone (TC) forecasts using ensemble methods 1 have been transferred into the operational forecasting community over the last few decades (Heming and Goerss ).For example, ensemble mean TC track forecasts have been widely used for operational TC track forecasting (Yamaguchi et al. , submitted to WMO by: