Bayesian Convolutional Neural Network (BCNN)

About

- BCNN is a new product developed to predict the emergence of El Niño and La Niña conditions up to 15 months in advance.
- BCNN uses the latest technologies such as Artificial Intelligence (AI), deep learning, and machine learning (ML) to improve forecasts related to the ENSO phases.
- The model’s prediction relies on the fact that El Niño or La Niña are connected to the slow oceanic variations and their atmospheric coupling, which gives sufficient lead time to issue early forecasts.
- It calculates the Nino3.4 index value which is used to determine the different phases of ENSO and makes the forecast.

The index value is obtained by averaging the sea surface temperature (SST) anomaly in the central equatorial Pacific, extending from 5°N to 5°S, and 170°W to 120°W.

The Niño 3.4 index typically uses a 5-month running mean, and El Niño or La Niña events are defined when the Niño 3.4 SSTs exceed +/- 0.4C for a period of six months or more.

It took eight months to develop the BCNN model and it was put through several testing phases.

Comparison with existing models

There are largely two kinds of weather models used for forecasting.
- Statistical model: It generates forecasts based on various information sets received from different countries and regions.
- Dynamic model: It involves a 3D mathematical simulation of the atmosphere done using High Performance Computers (HPC). The dynamic model is much more accurate than the statistical model.

The BCNN is a combination of the dynamic model with AI.
This helps it forecast the emergence of El Niño and La Niña conditions with a 15-month lead time. The other models which can give a prediction up to six to nine months in advance.

What is the prediction?

According to a recent prediction, La Niña conditions would emerge during July-September (probability 70-90%) and continue till February 2025.

Why in news?

Indian National Centre for Ocean Information Services (INCOIS) has developed a new product known as Bayesian Convolutional Neural Network (BCNN) to predict the emergence of El Niño and La Niña conditions which are different phases of El Niño Southern Oscillation (ENSO) up to 15 months in advance.

El Nino Southern Oscillation (ENSO)

ENSO 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.
It can alter the global atmospheric circulation, which, in turn, influences weather across the world.
ENSO occurs in irregular cycles of 2-7 years and has three different phases
- El Nino (warm): In the El Niño phase the wind systems weaken, leading to lesser displacement of warmer waters. As a result, the eastern side of the Pacific becomes warmer than usual. In India, El Niño conditions usually lead to a weak monsoon and intense heat waves.
- La Nina (cool): During La Niña events, trade winds are even stronger than usual, pushing more warm water toward Asia. In India, La Niña conditions result in a strong monsoon.
- Neutral: In the neutral phase, the eastern side of the Pacific Ocean (near the northwestern coast of South America) is cooler than the western side (near the Philippines and Indonesia). This is due to the prevailing wind systems that move from east to west, sweeping the warmer surface waters towards the Indonesian coast. The relatively cooler waters from below come up to replace the displaced water.

What is the El Niño–Southern Oscillation (ENSO) in a nutshell? | NOAA Climate.gov

INCOIS -Indian National Centre for Ocean Information Services

INCOIS was established as an autonomous body in 1999 under the Ministry of Earth Sciences (MoES) and is a unit of the Earth System Science Organisation (ESSO).
It is mandated to provide the best possible ocean information and advisory services to society, industry, government agencies and the scientific community through sustained ocean observations and constant improvements through systematic and focussed research.

Activities of INCOIS

Provides round-the-clock monitoring and warning services for the coastal population on tsunamis, storm surges, high waves, etc. through the in-house Indian Tsunami Early Warning Centre (ITEWC).
Provides daily advisories to fisher folk to help them easily locate areas of abundant fish in the ocean while saving on both fuel and time used to search for the same.
Deploys and maintains a suite of Ocean Observing Systems in the Indian Ocean to collect data on various oceanic parameters to understand the processes in the ocean and to predict their changes.

Sources

Today Current Affairs Upsc Exam Syllabus & Previous Yearpaper.