Tropical Cyclone Basins Research Articles

Tropical Cyclone Forecast Errors and the Multimodal Bivariate Normal Distribution

Abstract This study focuses on the use of the bivariate normal distribution model to describe spatial distributions of tropical cyclone forecast errors. In this connection, it is found that forecast errors from the entire Atlantic tropical cyclone basin (Gulf of Mexico, Caribbean and North Atlantic) are multimodal and the fitting of these collective data to the usual unimodal bivariate normal distribution will be judged invalid by the usual statistical goodness-of-fit tests. While this is a recognized pitfall in classical statistics, it is often overlooked in meteorological application. The isolation of the clusters (components) and their statistical characteristics permit the issuance of forecast positions accompanied by more representative error ellipses. The study continues with a bivariate clustering analysis of a set of 979 tropical cyclone 24 h forecast errors for the Gulf of Mexico, Caribbean and North Atlantic. These errors were collected from the entire tropical cyclone basin without regard to se...

Trends in Forecasting the Tracks of Atlantic Tropical Cyclones

Errors in official 24 h forecasts of tropical cyclone motion over the Atlantic for the period 1954–80 are examined for the purpose of isolating any long-term trends in the data. It is shown that the magnitude of a forecast error has been primarily a function of forecast difficulty, i.e., how well storm motion adheres to climatology and persistence. Another contributing factor (in the negative partial correlation sense) is shown to be the storm's initial longitude—a measure of the adequacy of initial analyses. After adjusting 24 h forecast error for these two factors, it is shown that errors have declined gradually over the 27 years from near 124 n mi in 1954 to near 107 n mi in 1980—a 13.7% reduction. Most of the decline since the mid-1960s is attributed to better specification of initial storm motion through satellite imagery. Although the decline of forecast errors is encouraging, a disturbing aspect is that the rate of decline appears to have slowed in recent years. This leveling-off is attributed to a loss in the ability to assess environmental steering through mid-level analysis deficiencies that have been compounding since 1965 and, more recently, to a plateauing in the ability to obtain still better storm initial-motion vectors. To assure a continued monotonic decline in 24 h forecast errors, mid-level initial analysis (500 mb) over the essentially data-void tropical cyclone basins must be improved.

Statistical prediction of tropical storm motion over the Bay of Bengal and Arabian Sea

Statistical analog models for the prediction of tropical cyclone motion have been developed for all of the world's tropical cyclone basins. The popularity and success of these models is related to their ability to optimize the variance reducing potential of climatology and persistence, At the same time, they effectively recognize certain repetitious synoptic patterns without having to use fields of sometimes unavailable and often unreliable synoptic data over the tropics.
 The analog method can be simulated by the use of continuous function derived through the use of multivariate regression analysis. This method simulates all aspects of the analog process including the derivation of probability ellipses while avoiding certain operational problems inherent to the purely analog approach.
 This paper describes the derivation of such a model for the north Indian Ocean tropical cyclone basin. It is patterned after an earlier paper (Neumann and Randrianarison 1976) which describes the development of a similar model for the southwest Indian Ocean tropical cyclone basin.