Saturday, March 23, 2019
Review of Prediction Models for yearly Hurricane CountsELserner, J. (2006). Prediction Models for Annual US Hurricane Counts. American Meteorological Society, 2935-3951.HURRICANESThis paper provides a Bayesian nest towards developing a prediction perplex for the occurrence of coastal hurricane employment based on historic hurricane data from 1851 to 2004 from US National ocean and Atmospheric Administration. A hurricane is defined as a tropical cycl ace with utmost sustained (1min) 10-m winds of 65kt (33 m s-1) or greater. 1A Hurricane grimefall occurs when a storm passes over land after originating in water. A hurricane can make more than one landfall. A landfall may occur even when the exact centre of unkept pressure remains offshore(eye) as the eyewall of the hurricane extends a radial distance of 50km. The belles-lettres review in the paper suggests a substantive effect of El Nino Southern Oscillations (ENSO) on the frequency of hurricanes forming over topics and a less significant effect over sub tropics. The North Atlantic Oscillation (NAO) in addition plays an important role in altering hurricane activity (Elsner 2003 Elsner et al. 2001 Jagger et al. 2001 Murnane et al 2000) has been stated.The hurricane observations considered in the model fulfills the following criteria1The storm hits the US chaste atleast erstwhile at hurricane intensity.2The storm is recorded in the US continent only except Hawaii, Puerto Rico, Virgin IslandsThe discrepancy associated with the available data of hurricanes is nigh the veritablety of the records for before 1899 ie the hurricane record from 1851-1898 are less certain than records available after 1899. The challenge here is to achieve such a model that gives accurate predictions even if t... ...June. Therefore the partial season count on excludes hurricanes of May (1 occurred) and June (19 occurred) from the total of 274 hurricanes from 1851 to 2004. A total of 20% data is eliminated from 274 hurricanes. illustration FOR ANNUAL HURRICANE COUNTPOISSON REGRESSION MODELh Poisson (lamdai )lamdai =exp(o+ Xi )Ln(lamdai)= o+ Xi o and define a specific model and are calculated on Bayesian approach. The model assumes the parameters (intercept and coefficient) to have a distribution and that inference is made by computer science the posterior probability density of the parameter conditioned on the find data.The Bayesian approach combines Prior belief f() and most frequent likelihood to give the posterior Densityf(h) proportional f(h/ ).f()The posterior density dialog about the belief of parameter values after considering the observed counts.