Artificial intelligence is transforming the landscape of disaster risk management, serving as an end-to-end bridge across climate observation, forecasting, and response, experts revealed at COP31.
However, scientists warn that AI's potential remains constrained by persistent gaps in surface and atmospheric data collection.
According to the Earth Information Day briefing on Sunday ahead of the COP31 in Turkey, AI applications are now operating across all four pillars of multi-hazard early warning systems.
They are: disaster risk knowledge, detection and forecasting, warning dissemination, and preparedness and response.
Experts say AI is discovering risk data, building exposure baselines, and tracking loss and damage. It enables AI-driven weather predictions, flood and cyclone forecasting, impact-based modeling, and real-time nowcasting. Optimizing emergency resources and automating anticipatory actions are also triggered by AI.
"AI allows for end-to-end solutions, connecting all four pillars into interoperable, people-centered warning systems," the report said.
However, AI models require accurate observational data to generate reliable forecasts.
Current compliance with the Global Basic Observing Network (GBON) stands at just 11 percent worldwide.
While 65 WMO (World Meteorological Organization) members achieve surface-level GBON compliance as of Q3 2026, upper-air monitoring compliance has stagnated at near 32 members, leaving large observational blind spots across parts of Africa, Asia, and oceanic regions.
Technical initiatives like the Systematic Observations Financing Facility (SOFF) are currently supporting 32 percent of target countries to close meteorological observation gaps, ensuring AI algorithms receive the high-resolution input required to save lives.
The UN-backed Early Warnings for All (EW4All) initiative aims to cover everyone on Earth with timely, life-saving alerts by 2027.
Its AI Sub-Group, convened by the International Telecommunication Union (ITU) with WMO, UNDRR, and IFRC, integrates artificial-intelligence tools across the all pillars of early-warning systems.
Working with governments, tech firms, and communities, the group pilots machine-learning models that fuse satellite, radar, social-media, and IoT data to sharpen hazard forecasts and send population-specific alerts in near real time.
