9.7 The role of moist processes on intrinsic predictability of Indian Ocean Cyclones

Wednesday, 7 August 2013: 5:00 PM
Multnomah (DoubleTree by Hilton Portland)
Sourav Taraphdar, PNNL, Richland, WA; and L. R. Leung, P. Mukhopadhyay, F. Zhang, S. Abhilash, and B. N. Goswami

The role of moist processes and upscale error cascade from cloud-scale processes affecting the intrinsic predictability of tropical cyclones (TCs) over the Indian region are investigated through high-resolution convection-permitting simulations. Consistent with past studies of extra-tropical cyclones, it is demonstrated that moist processes play a major role in forecast error growth which may ultimately limit the intrinsic predictability of the TCs. Small errors in the initial conditions may grow rapidly and cascades from smaller scales to the larger scales through strong diabatic heating and nonlinearities associated with moist convection. Results from a suite of twin perturbation experiments for four tropical cyclones suggest that the errors are significantly higher in cloud permitting resolutions with 3.3 km grid spacing compared to simulations at 3.3 km and 10 km grid spacing with parameterized convection. Convective parameterizations with prescribed convective time scales typically longer than the model time step allows the effects of microphysical tendencies to average out so convection responds to a smoother dynamical forcing. Without convective parameterizations, the finer-scale instabilities resolved at 3.3 km grid spacing and stronger vertical motion that results from the cloud microphysical parameterizations removing super-saturation at each model time step can ultimately feed the error growth in convection permitting simulations. This implies that careful considerations and/or improvements in cloud parameterizations are needed if numerical predictions are to be improved through increased model resolution. Rapid upscale error growth from convective scales may ultimately limit the intrinsic predictability of the TCs at all scales, which further supports the needs for probabilistic forecasts of these events, even at the mesoscales.
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