Extreme Weather Events Forecasting with AI

Syllabus: GS1/ Geography

Context

• With rising extreme weather events, Artificial Intelligence (AI) is emerging as a transformative tool to improve prediction accuracy beyond traditional models.

Traditional Model of Weather Prediction

• Traditional weather forecasting uses numerical weather prediction (NWP) models. 
• The model simulates atmospheric processes using equations of fluid dynamics and thermodynamics. 
• These physics-based models input observational data from satellites, radars, and weather stations and require high-performance supercomputers for computation.

Prediction of Weather with AI Models

• Unlike traditional weather models that rely on the laws of physics, AI-based models begin with data. 
• These models use machine learning algorithms to identify patterns and learn relationships between input variables—such as temperature, humidity, wind speed—and resulting weather events like cyclones or heavy rainfall. 
• They do this without any prior knowledge of the physical processes that govern the Earth’s atmosphere.

Advantages of AI Models in Weather Forecasting

• Ability to Use Big Data: AI models can process massive datasets from satellites, radars, weather stations, and even social media, allowing them to detect subtle signals and trends.
• Handling of Nonlinear Systems: AI models have the potential to uncover hidden patterns and nonlinear cause-effect relationships among Earth system variables that physics-based models may overlook.
• Adaptability to Local Conditions: AI allows for region-specific models that account for local geographical, topographical, and climatic factors, improving forecast relevance.
• Real-time Forecasting: AI is capable of rapid “nowcasting” — forecasting weather within the next few hours — which is crucial for disaster preparedness and urban planning.

Challenges in AI-Based Weather Forecasting

• Complexity: Weather systems  require sophisticated models to capture their dynamic nature.
• Human Resource Gap: There is a lack of professionals with interdisciplinary expertise in both meteorology and AI/ML.
  • This hampers the development and deployment of high-quality models.
• Inadequate Sensor Network: The diverse topography of India necessitates regionally tailored models, but this is hindered by gaps in meteorological infrastructure, leading to poor data availability.
• Climate Change: AI models trained on today’s climate data may become less effective in a warmer future, as the atmospheric system continues to evolve due to climate change.
• Data-Related Issues: AI models require large, high-quality datasets to train effectively. However, these are compromised by sensor errors, inconsistencies in format, and spatial-temporal gaps in the data, especially in remote regions.
• Black Box Nature of AI Models: AI systems, particularly deep learning models, operate as “black boxes”, meaning their decision-making processes are opaque.
  • This hinders trust and interpretability, especially among non-experts and operational meteorologists.

Weather Prediction in India

• India, at present, depends on satellite data and computer models for weather prediction. The Indian Meteorological Department (IMD) uses the INSAT series of satellites and supercomputers.
• In India three satellites, INSAT-3D, INSAT-3DR and INSAT-3DS are used mainly for meteorological observations. 
• Forecasters use satellite data around cloud motion, cloud top temperature, and water vapor content that help in rainfall estimation, weather forecasting, and tracking cyclones.

Initiatives taken to improve the efficiency

• Mission Mausam: It was launched to upgrade the capabilities of India’s weather department in forecasting, modelling, and dissemination. The objectives of the mission are;
  • Develop Cutting Edge Weather Surveillance Technologies & Systems
  • Implement Next-generation radars, and satellites with advanced instrument payloads
  • Develop improved earth system models, and data-driven methods (use of AI/ML).
• The ‘National Monsoon Mission’ was set out in 2012 to move the nation over to a system that relies more on real-time, on-the-ground data gathering.
• The IMD is also increasingly using Doppler radars to improve efficiency in predictions. The number of Doppler radars has increased from 15 in 2013 to 37 in 2023. 
  • Doppler radars are used to predict rainfall in the immediate vicinity, making predictions more timely and accurate.
• The Ministry of Agriculture & Farmers Welfare have initiated the weather information network and data system (WINDS) under which more than 200,000 ground stations will be installed, to generate long-term, hyper-local weather data. 

Source: TH