INDIA: New AI Algorithm can predict Indian Summer Monsoons (ISMR). Scientists at the Institute of Advanced Study in Science and Technology (IASST), Guwahati, have developed an algorithm that can predict Indian Summer Monsoons with increased accuracy and 18 months ahead of time.
The Predictor Discovery Algorithm (PDA), which uses a single ocean-related variable, was created by a team of researchers from IASST, Indian Institute of Tropical Meteorology (IITM), Pune, and Cotton University, Guwahati.
According to the Ministry of Science and Technology, the PDA algorithm could facilitate skillful forecasting of the ISMR, which could help farmers and businesses make effective plans for the country’s economy.
It was discovered that the commonly employed sea surface temperature (SST) was insufficient for the long-lead prediction of ISMR. The team found that the potential skill of Indian Summer Monsoons estimated by PDA using SST-based predictors was low at all the lead months.
This was addressed by the researchers who developed the PDA method, which projects the ocean thermocline depth (D20) throughout the entire tropical belt between 1871 and 2010 onto the correlation map between ISMR and D20 over the same time period.
This technique creates a predictor at any lead month. According to the new formula, 18 months before the ISMR season, the potential skill of ISMR is at its highest (0.87, with 1.0 being the highest).
The researchers were able to forecast the ISMR throughout an 18-month period between 1980 and 2011 using a machine learning-based ISMR prediction model, with an actual skill of 0.65.
The model’s success was based on artificial intelligence (AI)’s capacity to transfer its learning from 150 years of simulations by 45 physical climate models to actual observations between 1871 and 1974, revealing the association between Indian Summer Monsoons and tropical thermocline patterns.
This breakthrough in AI technology is expected to bring significant benefits to the Indian economy, particularly in the agriculture sector, which relies heavily on the monsoon season. With the potential for further improvements in the model, researchers hope to continue developing and refining the algorithm to increase its accuracy and precision.
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