Soon, cyclones over the BoB can be forecast 2-3 days in advance

Visakhapatnam: In what could become a potential game changer for cyclone early-warning system, a new scientific study suggests that cyclones over the Bay of Bengal could be forecast up to three days earlier than is currently possible. The advance warning comes from a novel forecasting approach that integrates atmospheric conditions with subsurface ocean signals, which shows that the ocean often provides clear precursors to cyclone formation well before the atmosphere responds.Currently, cyclone genesis over the North Indian Ocean is tracked using a Genesis Potential Index (GPI) that relies mainly on atmospheric indicators such as wind circulation, humidity, vertical wind shear, and temperature instability. This method typically offers only about a day's lead time — adequate for short-range alerts but insufficient for large-scale evacuations and advanced disaster preparedness in densely populated coastal regions.The study, conducted by researchers from Andhra University in Visakhapatnam and the Indian National Centre for Ocean Information Services (INCOIS) in Hyderabad, attributes this limitation to the under-representation of oceanic drivers in existing models. The Bay of Bengal, which generates nearly 80 percent of North Indian Ocean cyclones, is particularly influenced by heat stored beneath the sea surface. This subsurface heat acts as a sustained energy reservoir, enabling rapid intensification once atmospheric conditions turn favourable.The research team — comprising Dr P Suneeta, Dr TVS Udaya Bhaskar, and Dr E Pattabhi Rama Rao from the ocean data management group at INCOIS, and Prof SSVS Ramakrishna and Prof CV Naidu from the department of meteorology and oceanography at Andhra University — developed and tested two modified versions of the GPI. While the conventional index, known as GPIK, uses only atmospheric parameters, the enhanced versions — GPIS and GPIS1 — integrate Upper Ocean Heat Content (UOHC) and Sea Surface Height (SSH). UOHC measures the total thermal energy stored in the upper layers of the ocean, not just at the surface, while SSH rises when warm water expands, indicating deeper and more energetic warm pools beneath the surface.Dr Suneeta said the study analysed four post-monsoon cyclones that formed between 2016 and 2022 — Kyant, Bulbul, Burevi, and Mandous — to evaluate how much these ocean parameters improve forecasting skill. "Each cyclone was monitored for five days prior to its official genesis, allowing a direct comparison of how early different indices detected favourable conditions," said the INCOIS scientist.Across all four cyclones, a consistent pattern emerged. The traditional atmospheric-only index was often late or inconsistent, whereas the ocean-integrated indices detected early warning signals sooner and more clearly. "Incorporating ocean parameters into cyclone genesis forecasting represents a major step forward for the Bay of Bengal region. With further testing and operational integration, the approach could help India move from short-notice cyclone alerts to earlier, anticipatory preparedness, reducing both human and economic losses in one of the world's most cyclone-prone regions," she added.The study's methods are key to its results. Using high-resolution atmospheric data (ERA5) and ocean data (AVISO/CMEMS), indices were calculated hourly for greater accuracy than older, coarser models. GPIS builds upon the traditional GPIK by adding UOHC, while GPIS1 goes further by including squared SSH to better capture subsurface heat effects. Tests showed GPIS1 consistently performed best, with lower errors and stronger correlations, especially in cyclones like Kyant and Mandous. The researchers stress that ocean metrics such as D26 depth, UOHC, and SSH often give earlier signals than atmospheric indicators, particularly in the post-monsoon season.Cyclones are complex systems influenced by both atmospheric and oceanic processes, the study noted. Some storms respond more strongly to subsurface heat, while others depend on atmospheric instability and wind patterns. Because of this variability, the researchers recommend a multi-index forecasting framework, where ocean-enhanced indices complement existing operational models rather than replacing them.