Extreme Weather Events Forecasting with AI

Brief Context

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.

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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