BAYESIAN CONVOLUTIONAL NEURAL NETWORK (BCNN)
- SCI & TECH
News: What is INCOIS’s new
product to forecast El Nino and La Nina conditions?
What's in the news?
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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.
Bayesian Convolutional Neural Network (BCNN):
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It is a variant of the Convolutional Neural Network.
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It uses advanced technologies including Artificial Intelligence (AI),
deep learning, and machine learning (ML).
Developed by - Indian National
Centre for Ocean Information Services (INCOIS).
Objective:
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The tool forecasts the onset of
El Niño and La Niña phases of the El Niño Southern Oscillation (ENSO).
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It predicts these climate patterns up to 15 months in advance.
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According to the bulletin issued on June 5, it is highly likely (70-90%
probability) that La Niña conditions will develop from July to September and
persist until February 2025.
Working:
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The model’s predictive capabilities leverage the connection between
these phases and gradual oceanic changes, coupled with atmospheric
interactions.
Operational Details:
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The model calculates predictions based on the Niño3.4 index value.
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This index averages sea surface temperature (SST) anomalies in the
central equatorial Pacific region, spanning from 5°N to 5°S and 170°W to 120°W.
Significance:
1. Early Warning System:
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Provides early forecasts by analyzing oceanic variations and their
atmospheric effects, offering valuable lead time for preparedness and planning.
2. Advancement in ENSO:
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The Bayesian Convolutional Neural Network represents a significant
advancement in ENSO prediction technology.
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It aids in better understanding and preparation for climate variability
linked to ocean-atmosphere interactions.