HR: 08:00h
AN: A11G-01 INVITED    [Abstracts]
TI: The promise and the challenges of the coming age of cloud resolving tropical cyclone prediction
AU: * Tripoli, G
EM: tripoli@aos.wisc.edu
AF: University of Wisconsin - Madison, 1225 West Dayton Street, Madison, WI 53706, United States
AB: Over the last two decades cloud resolving tropical cyclone models have evolved from an ambitious research project to the realm of real time numerical TC prediction systems. The increase in resolution has resulted in the ability to simulate internal storm features such as multiple and asymmetric eye wall systems, rain bands, outflow structures, variable sea states and so on. However, although model resolution has dramatically increased, model physics and initialization techniques have evolved much less. In early generations TC prediction models, numerous aspects of model physics such as the evolving sea state, microphysics processes and radiative processes could be treated less precisely because they coupled only weakly with imprecise model dynamics. These couplings become more significant in cloud resolving applications and result in the need for improvements both in the physics representations and the ability to provide observations of new key variables such as liquid, ice and aerosol structures and concentrations, initial sea state and so on. Perhaps most critical, is the serious and unprecedented (in numerical weather prediction) data gap between the space-time scales of the controlling internal features represented numerically and the capability of all current and perhaps all projected observation systems to provide information to initialize and correct through assimilation a deterministic prediction of these features. The data gap also reduces the ability to verify the structures simulated. The limits of deterministic predictability depend directly on the space-time scales of the features in question while the potential for deterministic analysis depends on the space-time fidelity of the data collection system. As deterministic predictability vanishes at small space-time thresholds, the cloud resolving prediction becomes probabilistic. Just as climate models place no significance in the space time location of a single transient cold front in a 500 year prediction, the new high resolution TC models may have limited or no skill predicting the timing and location of certain small scale features such as a convective plume. Predictability will depend on the time since critical data input was available to define these features independently from the model prediction as well as their dependence on more predictable features of the simulation, such as topographical interactions. The application of high resolution cloud resolving TC models to the intensity prediction problem has the potential to result in a significant period of deterministic prediction capability at meso-beta and meso-gamma scales, which can be quite useful, particularly as the TC makes landfall. These issues of probabilistic analysis and prediction and the data and physics required to maximize deterministic outcomes in the new age of cloud resolving TC prediction will be discussed.
DE: 0545 Modeling (4255)
DE: 3310 Clouds and cloud feedbacks
DE: 3314 Convective processes
DE: 3329 Mesoscale meteorology
DE: 3354 Precipitation (1854)
SC: Atmospheric Sciences [A]
MN: 2007 Fall Meeting