G. Tiwari, Indian Institute of Science Education and Research Bhopal, India

Tropical cyclones (TCs) are among the catastrophic natural hazards over the North Indian Ocean (NIO), and they are expected to become more frequent in the upcoming years. For reliable alerts and disaster warnings ahead of time, better forecasting of TC features such as track, landfall, intensity, rainfall, and so on is crucial. In the last couple of decades or so, the numerical weather prediction (NWP) models have become reliable significantly due to advancements in the understanding of the short-to-medium range weather systems, forecasting methodologies, as well as the quality of the observational datasets. However, these models are highly sensitive to the nature of input forcings. The sensitivity of initial and boundary conditions, made from Global Forecast System (GFS) and Final Analyses (FNL) datasets, is evaluated using the high-resolution Weather Research and Forecasting (WRF) model for two TCs over the North Indian Ocean (NIO), Tauktae (in May 2021) and Nivar (in November 2020). Model outputs from FNL forcing showed comparatively better results than GFS forcing. Further, using best initialization and forcing the stochastically perturbed physics-tendencies (SPPT) ensemble-mean approach along with digital filter initialization (DFI) is investigated for the simulation of Tauktae and Nivar. Compared with control runs, the track simulations in terms of the reduction in along-track (cross-track) errors for Tauktae and Nivar were improved by 68.8% (23.4%) and 28.2% (40.7%), respectively, in the DFI experiment. Further improvements were found in the SPPT-based ensemble mean experiments (DFI+SPPT) as the along-track (cross-track) errors, compared to control simulations, were reduced by 65.3% (27.7%) and 37% (54.1%), for Tauktae and Nivar, respectively. However, the DFI simulations showed a potential to improve the TCs’ track simulation but failed to reduce the error in intensity simulation. On the other hand, DFI+SPPT experiments improved the model's reliability in simulating TCs’ intensity considerably.