Toward improved tropical cyclone intensity forecasts : probabilistic prediction, predictability, and the role of verification

Over the past two decades, deterministic predictions of tropical cyclone (TC) intensity consistently scored poorly in mean absolute error (MAE) verification, despite the concurrent advancement of TC modeling and observing capabilities. Given the importance of understanding this situation for the future of TC intensity prediction, the "TC intensity prediction problem" is examined here on two fronts: (1) the role of verification in driving the forecast system development process, and (2) the inherent limit of predictability under the extant TC observing network. Verification is first examined from a theoretical perspective. It is shown that the use of certain summary measures of probabilistic forecast performance in the forecast system development process should be favored, because those summary measures promote production of theoretically-optimal predictions. However, the choice of a summary measure for verification of deterministic forecasts is arbitrary, since theoretically-optimal predictions cannot be produced by a deterministic forecast system. It is also demonstrated that the summary measure used in development of TC intensity forecast systems, MAE, does not necessarily drive development of a deterministic dynamical model toward the true system dynamics. A dynamical model should instead be developed in the context of ensemble prediction. Within the current operational environment of deterministic TC intensity prediction, it is shown that MAE provides a very limited view of forecast quality relative to the joint distribution of forecasts and observations. Analysis of the joint distribution reveals the profound influence of MAE-driven TC intensity forecast system development on the quality of operational predictions. Furthermore, the joint distribution inspires an information-theoretic summary measure with appealing properties.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Earth, Atmospheric, and Planetary Sciences.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Moskaitis, Jonathan Robert

Advisor dc:contributor.advisor

• Kerry A. Emanuel.

• Earth, Atmospheric, and Planetary Sciences.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/47846

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/47846