If it can weather some challenges, AI can supercharge forecasting

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 atleast their ability to surprise governments — as possible, the country has turned to artificial intelligence (Al) 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.

- GNS News Agency, April 28

Al-based models start with the data instead. Al 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 Al particularly useful for applications that lack a complete theory.For example, an Al 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. Al 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 22,000 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 Al. The Ministry of Earth Sciences has set up a dedicated Al and machine-learning (ML) centre to develop and test different techniques and models Al 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.