India’s governmental department believes that AI climate models can help improve forecasting, especially important for severe weather conditions like floods and droughts.
The Indian Meteorological Department plans to implement AI climate forecasting models to make extreme weather conditions forecasts more accurate with higher-quality and less expensive weather data.
According to the local official, K.S. Hosalikar, head of climate research and services at the India Meteorological Department (IMD), as quoted by Reuters, AI-based climate models and advisories will be particularly helpful for predicting severe weather changes like floods and droughts.
Currently, the IMD leverages mathematical models and supercomputers for its forecasts. AI incorporation could potentially make the process less expensive and generate higher-quality weather data.
Hosalikar explains that AI already helps alert the public about extreme weather like heat waves or disease outbreaks like malaria. However, the agency now plans to expand the scope of AI use and increase the number of weather observatories across local villages to receive higher-resolution forecasting data.
The Indian government is already testing the incorporation of AI into traditional weather forecasting and plans to conduct dedicated workshops and conferences to develop the idea.
For India, with its rapid weather changes, predicting extreme weather conditions is critical for both its inhabitants and the economy on the whole. India is the second-largest producer of rice, wheat and sugar. Weather changes severely affect agricultural production as well as the daily lives of 1.4 billion Indian citizens.
The agriculture sector, a cornerstone of the global economy, is increasingly using innovative technologies to improve productivity, food safety and consumer trust. Besides AI, the agriculture industry is utilizing blockchain to accurately record, track, and verify a product’s journey from the farm to the consumer’s table, improve food quality and safety, and build trustworthy and efficient supply chains.
