If it can weather some challenges, AI can supercharge forecasting Premium

India turns to AI for weather forecasting, aiming to improve accuracy and timeliness in predicting extreme weather events.

Like it or not, it’s clear: every year, India must face down intense heat waves and erratic but also often intense bursts of rainfall. In a bid to find as many ways out of the consequences — or at least their ability to surprise governments — as possible, the country has turned to artificial intelligence (AI) for help with modelling and early warnings.

Traditional weather forecasting uses numerical weather prediction (NWP) models. Such models begin with physics equations that simulate atmospheric behaviour using the principles of fluid dynamics and thermodynamics. They process observational data from weather stations and satellites, including temperature and wind speed, and perform their complex and time-consuming calculations on supercomputers.

AI-based models start with the data instead. AI algorithms can ‘learn’ the relationships between some inputs and an output — e.g. a given set of wind, temperature, and humidity conditions on one hand and the formation of a cyclone on the other — or extract spatial and temporal patterns from large datasets. And they do this without prior knowledge of the underlying earth system processes. This makes AI particularly useful for applications that lack a complete theory.

For example, an AI model can explore hidden links between various earth system variables, such as air temperature, pressure and humidity or ocean temperature, salinity, and currents, to uncover cause-effect relationships existing physics-based models don’t capture. AI models can also factor in a wider range of input variables, whereas physics-based models use input variables that experts have traditionally considered to be relevant.

The Indian government joined the new international race to build such models when it announced ‘Mission Mausam’ in September 2024 with an allocation of ₹2,000 crore over two years. Its stated goals are to exponentially enhance the country’s weather and climate observations and to better understand modelling and forecasting for more accurate and timely services.

The Mission aims to do this by, inter alia, developing better earth system models and data-driven methods using AI. The Ministry of Earth Sciences has set up a dedicated AI and machine-learning (ML) centre to develop and test different techniques and models AI to improve short-range rain forecasts, develop high-resolution urban meteorological datasets, and explore these technologies for nowcasting rainfall and snow using data from Doppler radars.

Indian researchers are also making forays in the use of AI for weather prediction. For example, groups at the DST Centre of Excellence in Climate Modelling (CECM) at IIT-Delhi; the Indraprastha Institute of Information Technology, New Delhi; the Massachusetts Institute of Technology in the US; and the Japan Agency for Marine Earth Science and Technology have together developed a ML model to predict monsoon rainfall. The model uses data from 1901 to 2001 related to the Indian summer monsoon, and accounts for the influences of systems like the El Nino (a climate pattern that emerges due to unusual warming of surface waters in the eastern Pacific Ocean) and the Indian Ocean Dipole (IOD).