Amid the surge of extreme weather events globally, billions of dollars are pouring into developing cutting-edge weather forecasting models based on artificial intelligence (AI) and machine learning (ML). Leading tech giants, such as Google and IBM, are spearheading efforts for more precise and expedited forecasting.
In India, climate scientists have also begun experimenting with AI. In December 2023, Kiren Rijiju, a minister at the Ministry of Earth Sciences (MoES), said the department had established a virtual centre dedicated to developing and refining various AI and ML techniques for enhanced weather predictions.
There has since been considerable excitement around AI-based weather forecasting in the country. But there is a problem: lack of credible data.
Amitabha Bagchi, a computer science professor at the Indian Institute of Technology Delhi, explains that AI-based modelling, “extrapolates and builds scenarios based on the available data and past trends.” According to Bagchi, 95% of the development process of AI models revolves around data management, and robust data is critical to the process.
Compiling such data is a challenge in India, especially in the Himalayas, says Irfan Rashid, an assistant geoinformatics professor at the University of Kashmir. Rashid is working on a MoES project profiling 15 glacial lakes in Jammu & Kashmir and Ladakh to improve data collection in the Himalayan cryosphere (the frozen part of the Earth system), which could enhance AI predictions of glacial lake outburst floods (GLOFs).
The Geological Survey of India has recorded over 9,575 glaciers in the Himalayas yet detailed glaciological studies cover less than 30, he explains. This data scarcity undermines the development of AI-based early warning systems (EWS). “At present, if we want to know the volume of water in a glacial lake, there is no credible in-situ data. The data based on empirical models is associated with a high degree of uncertainty. Using such data to build an AI and ML based model may simulate scenarios/forecasts that might not be robust,” says Rashid.
His concerns are shared by Madhavan Nair Rajeevan, one of India’s top climate scientists and former earth sciences secretary. He notes the country’s data sets do not extend to the Himalayas, affecting the reliability of AI/ML predictions for the region’s complex terrain: “In India, we have good data sets on rainfall, temperature, humidity, wind speed etc, which are the basic meteorological parameters. However, we don’t have adequate data over the Himalayas and hardly any data to work on GLOFs,” says Rajeevan.
