Co-developed by UC Berkeley's William Boos, an artificial intelligence-based weather model delivered a timely prediction of a stalled monsoon this season, helping farmers decide when to plant their crops.
Artificial intelligence is revolutionizing weather prediction around the world, as evidenced by the successful prediction this spring of a delayed onset of the monsoon in northeastern India.
The prediction gave millions of smallholder farmers the option of postponing planting to take better advantage of the rains or to plant different crops. Based on a preliminary phone survey, many farmers adjusted their planting as a result.
This AI-based weather model — a collaboration between the University of California, Berkeley, and the University of Chicago — paves the way for much better forecasts for hundreds of millions of farmers across the tropics and global south whose livelihoods depend on timing crop planting with the monsoon’s arrival. Nearly two-thirds of the world’s population live in regions of the tropics impacted by monsoon rains, whose arrival each year is being affected by climate change.
“This program harnesses the revolution in AI-based weather forecasting to predict the arrival of continuous rains, empowering farmers to plan agricultural activities with greater confidence and manage risks. We look forward to continuing to improve this effort in future years,” said Pramod Kumar Meherda, additional secretary at the Indian Ministry of Agriculture and Farmers’ Welfare.
The success of this AI prediction project — the largest targeted dissemination of AI weather forecasts to date — required a herculean effort by atmospheric scientists, AI experts, India’s Ministry of Agriculture and Farmers’ Welfare and a global nonprofit that supports smallholder farmers. Key to these predictions were daily climate data compiled and made publicly available by the U.S. National Oceanic and Atmospheric Administration.
To make the actual predictions, UChicago AI expert Pedram Hassanzadeh teamed up with Berkeley atmospheric scientist William Boos to evaluate and use global AI weather models that were developed independently by Google and the European Centre for Medium-range Weather Forecasts (ECMWF). Both of those models have been trained on 40 years of global climate data. To localize the models to India and correct biases in their predictions, the UC Berkeley and UChicago teams used statistics from 100 years of rainfall data from the India Meteorological Department.
The monsoon-onset forecasts, which differed for different regions, were delivered weekly to about 38 million farmers across 13 states in central and northeastern India — most of the core monsoon zone. These forecasts provided predictions up to four weeks in advance for the arrival of monsoon rains in particular regions, something that had not been done before in 150 years of monsoon forecasting, Boos said. Current numerical models, based on the physics of the atmosphere, typically provide reasonably accurate rainfall predictions no more than five days out.
When the monsoon hit southern India in early June, the AI-based model predicted that it would stop temporarily, something that was not predicted by any other available forecast. That’s what actually happened — it stalled for 20 days.
“Demonstrating that the long lead-time precipitation forecasts made by these AI models are of practical use in a tropical region where people live is a major step forward — no one really knew that before we did this work,” said Boos, a UC Berkeley professor of earth and planetary science.