Development of tropical disturbances like this one is difficult to predict
The formation of a tropical cyclone is difficult to predict accurately because of several reasons: there is a small
amount of observational data in areas where tropical cyclones form, the small-scale nature of the important
processes is unable to be resolved in present day numerical models, and there is still a lack of adequate understanding
of the physics of development. However, it has long been well known that there are several larger-scale factors that
affect the probability of formation, such as sea surface temperature, vertical wind shear, and the humidity of the
atmosphere (e.g. Gray 1968). It is thus not too surprising that statistical approaches to tropical cyclone
formation ("tropical cyclogenesis") which approach the problem from the synoptic scale have shown more skill than
dynamical models (Hennon and Hobgood 2003, Schumacher et al. 2009).
Our research group at UNC Asheville is continuing to improve upon earlier efforts at tropical cyclogenesis prediction.
We approach the problem from a "cloud cluster centered" view by tracking potential tropical cyclones and calculating a
series of predictors that have been shown to be correlated with development. We have used an
automated, objective cloud cluster tracker, to compile thousands of cloud cluster
case studies that can be used for cyclogenesis studies.
Related Journal Articles:
Related Conference Papers:
- Hennon, C.C., P.P. Papin, C.M. Zarzar, J.R. Michael, J.A. Caudill, C.R. Douglas,
W.C. Groetsema, J.H. Lacy, Z.D. Maye, J.L. Reid, M.A. Scales, and M.D. Talley, 2013:
Tropical Cloud Cluster Climatology, Variability, and Genesis Productivity.
Journal of Climate, 26, 3046-3066.
- Hennon, C.C., C.N. Helms, K.R. Knapp, and A. Bowen, 2011:
An Objective Algorithm for Detecting and Tracking Tropical Cloud Clusters: Implications for Tropical
Cyclogenesis Prediction.Journal of Atmospheric and Oceanic Technology,28, 1007-1018.
- Papin, P.P., 2011:
Using the Rossby Radius of Deformation as a Forecasting Tool for Tropical Cyclogenesis.
Preprints, 25th National Conference on Undergraduate Research, Ithaca College,
- Hennon, C.C., C. Marzban, and J.S. Hobgood, 2005:
Improving Tropical Cyclogenesis Statistical Model Forecasts through the Application of a
Neural Network Classifier. Weather and Forecasting, 20, 1073-1083.
- Hennon, C.C., and J.S. Hobgood, 2003:
Forecasting Tropical Cyclogenesis over the Atlantic Basin Using Large-Scale Data.
Monthly Weather Review, 131, 2927-2940.
- Helms, C., C.C. Hennon, and K.R. Knapp, 2008:
An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in
Geostationary Infrared Imagery.28th Conference on Hurricanes and Tropical Meteorology, (Orlando FL),
American Meteorological Society.
- Hennon, C.C., 2004: A statistical model for forecasting tropical cyclogenesis over
the Atlantic Basin. 26th Conference on Hurricanes and Tropical Meteorology,
American Meteorological Society, Boston, MA, pp. 76-77.
- Helms, C., C.C. Hennon, and K. Knapp, 2009:
Augmenting Global Best Track Databases with Tropical Cloud Clusters. IBTrACS Workshop,
National Climatic Data Center, Asheville NC.
- Helms, C., C.C. Hennon, and K. Knapp, 2008:
An Objective Algorithm for the Identification of Convective Tropical Cloud Clusters in Geostationary
Infrared Imagery.28th Conference on Hurricanes and Tropical Meteorology, (Orlando FL),
American Meteorological Society.
- Gray, W.M., 1968: Global view of the origin of tropical disturbances and storms. Monthly
- Schumacher, A.B., M. DeMaria, and J.A. Knaff, 2009: Objective estimation of the 24-hour probability
of tropical cyclone formation. Weather and Forecasting, 24, 456-471.