BAYESIAN CONVOLUTIONAL NEURAL NETWORK (Syllabus: GS Paper 1 – Geography)
Context: Indian National Centre for Ocean Information Services (INCOIS) has developed a Bayesian Convolutional Neural Network to predict the emergence of El Niño and La Niña conditions they are different phases of El Niño Southern Oscillation (ENSO) up to 15 months in advance.
Bayesian Convolutional Neural Network
- About: It is the new product uses the latest technologies such as Artificial Intelligence (AI), deep learning, and machine learning (ML) to improve forecasts related to the ENSO phases.
- Focus: To enhance forecasts related to ENSO phases (El Niño Southern Oscillation).
- Model Operation: BCNN operates by leveraging the relationship between El Niño or La Niña and the slow oceanic variations coupled with atmospheric conditions.
- Mechanism: The model predicts ENSO phases by calculating the Niño3.4 index value.
ENSO
- About: It is a climate phenomenon which involves changes in the temperature of waters in the central and eastern tropical Pacific Ocean, coupled with fluctuations in the overlying atmosphere.
- Found: It occurs in irregular cycles of 2-7 years and has three different phases — warm (El Niño), cool (La Niña), and neutral.
oIn India, while El Niño conditions usually lead to a weak monsoon and intense heatwaves, La Niña conditions result in a strong monsoon.
Comparison of BCNN with Existing Weather Models
- Types of Existing Weather Models:
oThere are two main types of weather models: statistical models and dynamic models.
oStatistical models rely on diverse datasets from various sources for generating forecasts.
oDynamic models utilize 3D mathematical simulations of the atmosphere using High Performance Computers (HPC).
üDynamic models generally provide more accurate forecasts compared to statistical models due to their detailed simulations.
- Forecasting Abilities of BCNN: It is capable of forecasting the onset of El Niño and La Niña conditions with a lead time of 15 months.
oIn contrast, other existing models typically provide predictions up to six to nine months in advance.
