Investigating Probabilistic Forecasting of Tropical Cyclogenesis Over the North Atlantic Using Linear and Non-Linear Classifiers
Current numerical weather prediction models experience great difficulty in forecasting tropical cyclogenesis, primarily because of limitations of cloud parameterizations and observations. Forecasters have also struggled with the problem since they rely on the numerical models as an objective source of information. This research was performed with the aim of filling the void of objective guidance for tropical cyclogenesis. A new dataset of cloud clusters is created through the examination of infrared (IR) satellite imagery over the tropical Atlantic during the 1998-2001 hurricane seasons. Eight large-scale predictors of tropical cyclogenesis were then calculated from NCEP-NCAR Reanalysis dataset for each 6-hour interval of the cloud cluster life cycle extending back to 48 hours prior to genesis. Independent classifications were then performed on the entire dataset using both discriminant analysis (DA) and an artificial neural network (NN). The classifiers are fundamentally different from each other in that DA performs classifications based solely on linear trends in the predictors; the NN is potentially a more powerful classifier as it can find non-linear relationships in the data. The performance of each classifier was investigated through statistical scores and a series of case studies from the 1998-2001 Atlantic hurricane seasons. Tropical cyclogenesis is a rare event. Climatologically only about 15% of all cloud clusters develop into tropical depressions over the Atlantic Basin. The new cloud cluster database reflects that. 432 cloud clusters, of which 62 developed into tropical depressions, were tracked during the four seasons. Independent DA classifications show forecast skill over climatology. For the “prime” development season of August – October, the DA correctly forecast a higher percentage of clusters than climatology for all forecast periods. The most important predictors are latitude and the vertical shear structure. A comparison of DA forecasts with NN forecasts on the same dataset produced mixed results. The NN generally performed better with non-developing cloud clusters; however, there are indications that the NN suffers from over fitting to a greater degree than DA. An investigation of six case studies shows that both classifiers performed well in the majority of the cases. The DA appears to generalize much better than the NN in most cases. Danielle (1998) and a non-developing cluster (ND-6, 2000) brought to light several possible deficiencies in the statistical model. The large-scale predictors over-forecast genesis in a favorable shear environment, even if the thermodynamic environment is marginal. Also, the lack of any information on the convective structure of the cloud cluster will decrease forecast accuracy in some cases. Danielle (2000) developed explosively despite an unfavorable large-scale shear environment, perhaps due to mesoscale interactions that are not resolved in this model. Results suggest that this model has sufficient potential to be implemented as an objective forecast tool. Each predictor can easily be calculated from an analysis field that is routinely available to forecasters. The inclusion of mesoscale predictors, especially satellite derived temperature, moisture, and wind data, is thought to be an important next step for improvement of forecasts; especially since the current literature suggests that the important physical interactions for tropical cyclogenesis occur at the smaller scales.
School:The Ohio State University
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:tropical cyclogenesis neural network discriminant analysis cloud cluster statistical prediction atlantic basin cyclone formation
Date of Publication:01/01/2003