AI Algorithm Used to Improve Predictability of Indian Summer Monsoons: How it Works

The newly devised algorithm is capable of increasing predictability of the Indian Summer Monsoons 18 months before the season begins.

AI Algorithm Used to Improve Predictability of Indian Summer Monsoons: How it Works

Photo Credit: Pixabay/ Geralt

The new algorithm indicates that the potential skill of ISMR is maximum, 18 months before the ISMR season

Highlights
  • This could facilitate skillful forecast of the ISMR in time
  • PDA is made using a single ocean-related variable
  • The potential skill of ISMR at 18-month lead is 0.87
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A newly devised algorithm powered by artificial intelligence can help increase the predictability of the Indian Summer Monsoons (ISMR), 18 months ahead of the season.

The algorithm called predictor discovery algorithm (PDA) made using a single ocean-related variable could facilitate skillful forecast of the ISMR in time for making effective agricultural and other economic plans for the country, according to the ministry of science and technology.

Scientists at the Institute of Advanced Study in Science and Technology (IASST), Guwahati, an autonomous institute of department of science and technology (DST), along with their collaborators have found that the widely used sea surface temperature (SST) is inadequate for calculation of long-lead prediction of ISMR. This, they found was because the potential skill of ISMR estimated by the predictor discovery algorithm (PDA) using SST-based predictors was low at all the lead months.

The team consisting of IASST, Indian Institute of Tropical Meteorology (IITM), Pune, and Cotton University, Guwahati, devised a predictor discovery algorithm (PDA) that generates predictor at any lead month by projecting the ocean thermocline depth (D20) over the entire tropical belt between 1871 and 2010 onto the correlation map between ISMR and D20 over the same period.

The new algorithm indicates that the potential skill of ISMR is maximum (0.87, highest being 1.0), 18 months before the ISMR season. At any lead month, the predictability of the annual variability of ISMR depends on the degree of regularities in the annual variability of its drivers.

With the newly discovered basis of long-lead ISMR predictability in place, Devabrat Sharma (IASST), Santu Das (IASST), Subodh K. Saha (IITM), and B N Goswami (Cotton University) were able to make 18-month lead forecast of ISMR between 1980 to 2011 with an actual skill of 0.65 using a machine learning-based ISMR prediction model.

According to the statement of the ministry, the success of the model was based on the ability of artificial intelligence (AI) to learn the relationship between ISMR and tropical thermocline patterns from 150 years of simulations by 45 physical climate models and transferring that learning to actual observations between 1871 and 1974.

As the potential skill of ISMR at 18-month lead is 0.87, there is still considerable scope in improving the model.


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