A numerical modeling study on highly intensified tropical cyclones of North Indian Ocean using MPAS-A

This study systematically evaluated the predictive performance of the Model for Prediction Across Scales-Atmosphere (MPAS-A) model for Highly Intensified Cyclonic Storms (HICS) over the North Indian Ocean (NIO) using GFS-FNL, GDAS, and ERA-5 datasets. ERA-5 consistently exhibited superior accuracy in HICS track predictions compared to GFS-FNL, particularly beyond 72 h. A detailed comparison of TC track simulations using GDAS and ERA-5 data revealed the superiority of GDAS at later forecast periods, with quantified track errors indicating a rightward bias and slower TC movement in both datasets. While GDAS outperformed ERA-5 beyond 72 h, ERA-5 showed superior intensity forecasting from 42 to 72 h. The study further analyzed the MPAS model’s ability to predict TC genesis against Indian Monsoon Data Assimilation and Analysis. The model demonstrated good agreement in cyclonic wind patterns, successfully replicating Vertical Integrated Moisture Transport (VIMT) and moisture conveyor belts crucial for TC development. Potential Vorticity (PV) on the 320 K isentropic surface highlighted the model’s capability to signify the oceanic moisture transfer for intensification. The vorticity budget analysis indicated horizontal advection to be crucial for some cases, while stretching and vertical advection dominated in others. The intensification was driven by a balance of horizontal advection and stretching, the latter being the main contributor. The model results with ERA-5 initial condition, identified key factors influencing intensification, viz., moisture concentration in the eyewall, mid to upper-level warming, and VIMT. However, challenges were noted in representing distinct characteristics of radial wind, temperature, tangential wind, diabatic heating, and also some vorticity budget terms. Nonetheless, the analysis provided valuable insights into dynamic and thermodynamic processes governing HICS intensification, along with indications for model refinement.