Mr. Saiprasanth Bhalachandran Profile

Saiprasanth Bhalachandran

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Proceedings Article | 23 October 2018 Paper

A low-wavenumber analysis of the environmental and vortex-scale variables responsible for rapid intensity changes in landfalling tropical cyclones

B. Saiprasanth, Z. Haddad, S. Hristova-Veleva, F. Marks

Proceedings Volume 10782, 1078208 (2018) https://doi.org/10.1117/12.2500290

KEYWORDS: Meteorology, Technetium, Stochastic processes, Microwave radiation, Statistical analysis, Environmental sensing, Principal component analysis, Convection, Humidity, Databases

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Forecasting rapid intensity changes in tropical cyclones (TCs) is hard as the factors responsible span many scales. External and internal dynamical and thermodynamical variables act simultaneously in a nonlinear fashion, either complementing, amplifying, inhibiting or not impacting the TC intensity at all. We try to address the following question: What is the relative importance of the external and vortex-scale variables that influence rapid intensity changes within a TC? Further, which of these variables must be prioritized from an observational standpoint? To answer these questions, a systematic analysis was conducted on a large number of representative TCs to make statistically significant conclusions using discriminant analyses of wavenumber (WN) - filtered fields, with a principal component analysis to detect over-fitting and identify the subset of variables (from the environment and the vortex) consistently correlated with rapid intensity change. Our analyses indicate that a small number of variables wield the most influence on TC rapid intensity changes. The most important variables within the vortex are the WN 0 of precipitation within the radius of maximum winds, the amplitudes of WN 1 of precipitation and the mid-level horizontal moisture flux convergence in the rain band region. Likewise, the most important environmental variables are the angle of the driest air from the shear vector and the magnitude of environmental wind shear. These variables must be prioritized in future observational and consequent data assimilation efforts.

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