Teams from IIT-Delhi and IIIT-Delhi have created AI-driven monsoon models that surpass conventional forecasting methods

How Artificial Intelligence Can Help Forecast Weather

Data-Driven Predictions: Unlike physics-based models that depend on set equations, AI uses historical data to identify intricate patterns that can be used to forecast heatwaves, cyclones, or rainfall.

For instance, IIT Delhi's monsoon machine learning model outperformed conventional predictions with an accuracy of 61.9% from 2002 to 2022.

Faster, Scalable Forecasts: AI models are perfect for nowcasting and real-time warnings because they can provide short-term forecasts more rapidly and with less computing expense.

Improved Extreme Prediction: AI aids in capturing nonlinear relationships between variables, which is helpful in forecasting sporadic and abrupt weather phenomena like tornadoes and flash floods.

Hybrid modeling improves forecast accuracy and interpretability by combining the advantages of AI technologies and physics-based models.

Difficulties with AI-Powered Weather Prediction

Problems with Data Scarcity and Quality: Long-term, clean, high-resolution meteorological records are crucial. There may not be enough or consistent historical data.

For instance, inadequate sensor coverage in remote areas causes gaps in several Indian meteorological datasets.

Absence of Interdisciplinary Talent: Deep partnerships may be limited by the fact that climate scientists may lack AI/ML knowledge and ML engineers frequently lack a meteorological foundation.

Black Box Nature: Because AI models are opaque, it might be challenging to communicate their results to meteorologists or policymakers.

Infrastructure Restrictions: Due to a lack of computational or technological capacity locally, the majority of forecasters rely on model outputs from outside organizations.

Issues with Verification and Trust: Without thorough validation, model predictions may result in missed or false alarms, which could erode public confidence.

Path Ahead:

• Create Hybrid Weather Institutes: Set up specialized facilities that combine AI and meteorology under one roof to facilitate smooth cooperation. For instance, the Ministry-backed AI-Climate Center at IITM Pune is currently up and running.
• Improve Data Systems: Integrate and standardize historical and real-time data from satellites, ground sensors, and Doppler radars.
• Building Capacity: Educate a new group of earth system science-trained engineers and meteorologists who are proficient in AI/ML.
• Customization of the Model: Create AI models for hyperlocal forecasts that are adapted to India's varied climate zones and topography.
• Public-Private Partnership: Involve government agencies, academic institutions, and entrepreneurs in the joint development and implementation of validated AI models.

India's weather forecasting could be completely transformed by AI, particularly in terms of handling extreme events. But it takes a combination of solid data, knowledgeable labor, and institutional innovation. AI has the potential to play a key role in India's climate resilience strategy with careful cooperation.