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Context: Indian National Centre for Ocean Information Services (INCOIS) developed the Bayesian Convolutional Neural Network (BCNN) using AI to predict El Niño and La Niña conditions up to 15 months in advance.

Details

• The Indian National Centre for Ocean Information Services (INCOIS) has developed a new forecasting product called the Bayesian Convolutional Neural Network (BCNN) to predict the emergence of El Niño and La Niña conditions up to 15 months in advance.
• This new product incorporates advanced technologies such as Artificial Intelligence (AI), deep learning, and machine learning (ML) to enhance the accuracy and lead time of ENSO (El Niño Southern Oscillation) forecasts.

Understanding ENSO

• ENSO is a climate phenomenon characterized by fluctuations in the sea surface temperatures of the central and eastern tropical Pacific Ocean, coupled with changes in the overlying atmospheric circulation. It occurs irregularly every 2-7 years and has three phases:
• El Niño: Warmer than average sea surface temperatures in the central and eastern Pacific.
• La Niña: Cooler than average sea surface temperatures in the central and eastern Pacific.
• Neutral: Conditions when sea surface temperatures are close to average.
• These phases of ENSO influence global atmospheric circulation patterns, which in turn affect weather patterns worldwide. In India, El Niño conditions typically lead to a weak monsoon and intense heat waves, while La Niña conditions often result in a strong monsoon.

Bayesian Convolutional Neural Network (BCNN)

Technology and Methodology

• AI and Deep Learning: BCNN integrates AI techniques with deep learning methodologies to analyze historical oceanic and atmospheric data. This combination allows the model to learn complex patterns and relationships within the data.
• Enhanced Forecasting: Unlike traditional statistical or dynamic models, BCNN extends the forecast lead time for El Niño and La Niña conditions to up to 15 months. This is significantly longer than previous models, which typically provided forecasts up to six to nine months in advance.

Data Utilization

• One of the primary challenges in forecasting ENSO phases is the scarcity of sufficient historical data, especially for oceanic and sea temperature records.
• INCOIS addressed this challenge by incorporating data from historical simulations (1850-2014) provided by the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). These simulations enriched the training dataset used to develop the BCNN model, thereby improving its accuracy and reliability.

Development and Testing

• The BCNN model took approximately eight months to develop, which included rigorous testing phases to ensure its robustness and accuracy.
• The BCNN model predicted a high probability (70-90%) of La Niña conditions emerging during July-September and continuing until February 2025.

Significance and Impact

• The introduction of the BCNN model by INCOIS marks a significant advancement in climate forecasting capabilities, particularly for ENSO-related phenomena. By leveraging AI and deep learning, the model not only extends forecast lead times but also enhances the accuracy of predictions crucial for global weather patterns and regional climate impacts.

Conclusion

• BCNN represents an important effort in integrating cutting-edge technologies with climate science, offering policymakers, meteorologists, and researchers valuable insights into long-term climate trends and potential impacts on agriculture, water resources, and disaster management. Its development highlights India's commitment to leveraging technological innovation to address complex environmental challenges.

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Source:

Indian Express

PRACTICE QUESTION Q. What technology does the Bayesian Convolutional Neural Network (BCNN) utilize? A) Artificial Intelligence (AI) B) Augmented Reality (AR) C) Virtual Reality (VR) D) Blockchain Answer: A