The planet is surpassing historic heating records at an alarming rate. The year 2024 was the hottest ever recorded, continuing a decade-long trend of unprecedented global temperatures. All of the past ten years have now ranked among the hottest on record. This warming trend manifests in intensified heat within the coupled ocean-atmosphere systems, which supercharge tropical cyclones (TCs) in the Bay of Bengal and Arabian Sea.

Rapid intensification (RI) of TCs poses a significant forecasting challenge. These storms can develop quickly and follow unexpected, complex tracks, often resulting in inaccurate predictions. The consequences are severe: unchecked casualties, damage, and economic loss. For example, Cyclone Titli in 2018 struck India’s east coast and, despite accurate early warnings, caused over 50 fatalities due to post-landfall flooding and landslides.

At the 52nd Session of the WMO/ESCAP Panel on Tropical Cyclones, the role of artificial intelligence (AI) in advancing the UN’s "Early Warnings for All" (EW4All) initiative by 2027 was discussed. While progress is underway, vulnerable countries continue to suffer disproportionately.

Two key challenges have emerged:

1. Even in countries with Multi-Hazard Early Warning System (MHEWS) capacities, there are gaps among the four essential pillars: risk knowledge, forecasting and detection, warning dissemination, and timely response.
2. Traditionally, floods and storms caused the greatest losses. However, extreme heat has now emerged as a leading cause of weather-related fatalities.

AI: A potential game changer for EW4All

AI holds transformative potential to address critical data and modeling gaps within EW4All. RI remains among the most difficult phenomena to forecast due to its complex and rapidly evolving nature. Traditional methods—numerical weather prediction and statistical models—often fall short. In contrast, AI models show promise in improving RI prediction accuracy by integrating complex environmental and structural data.

The technologies powering AI-enabled EW4All are advancing at a remarkable pace. Key innovations include segmentation algorithms, intelligent satellites capable of detecting image changes, drone-based mapping and sensing, digital twins, crowdsourced and automated image analysis, and multilingual natural language processing. A particularly notable development is the emergence of drone-enabled communication networks and fully automated drone swarms, which are revolutionizing real-time data collection and dissemination by integrating seamless connectivity into emergency response systems.

Five ways AI can scale early warning systems

Artificial Intelligence is poised to significantly scale the effectiveness of early warning systems in five critical ways. First, AI can enhance disaster risk knowledge by addressing data gaps, especially in underserved or data-scarce areas. For example, tools like FloodSENS use machine learning to reconstruct flood zones under cloud cover by combining digital elevation models with water flow algorithms. 

Second, AI improves forecasting capabilities through advanced predictive analytics and real-time data assessments, delivering more accurate and timely warnings. Third, AI streamlines the dissemination of information by consolidating severe weather data and optimizing alert delivery across multiple channels and languages. Fourth, AI enables tailored communication, customizing alerts based on individual users’ location, language, and behavior to boost the chances of prompt and appropriate action.

 Finally, AI supports scenario simulation, which enhances emergency preparedness and enables responsive decision-making. A case in point is Google Research’s AI model, which predicts riverine flooding up to seven days in advance across more than 80 countries, including those with limited data availability.

Challenges to ethical AI integration

While promising, AI must be deployed responsibly.  Despite these advancements, the ethical integration of AI into early warning systems remains a critical challenge. AI tools must uphold rigorous standards for data governance, bias mitigation, cybersecurity, and ethical integrity. Ensuring transparency in algorithms and establishing clear accountability mechanisms are essential to building public trust in AI-generated alerts. Moreover, the risk of false alarms or inaccurate warnings can undermine credibility and must be minimized through robust validation and testing processes. Finally, the deployment of AI-driven solutions often requires substantial technical and financial resources—resources that are frequently unavailable in the very regions where early warnings are most urgently needed. The energy-intensive nature of many AI models further exacerbates this disparity, placing additional strain on already limited infrastructure in developing countries and raising concerns about sustainability and equitable access.

Strengthening regional cooperation

The India Meteorological Department’s Regional Specialized Meteorological Centre (RSMC), operating under the Panel on Tropical Cyclones (PTC), monitors cyclones across the Northern Indian Ocean, from Oman to Myanmar. In collaboration with 13 PTC member states, the RSMC is modernizing MHEWS frameworks and integrating AI-based forecasting techniques.

A key innovation is the use of AI to monitor and predict Tropical Cyclone Heat Potential (TCHP)- a crucial factor in forecasting cyclone intensity. Incorporating AI into regional cooperation frameworks enhances EW4All efforts in cyclone-prone areas.