An Artificial Intelligence-Based Enso Diversity Prediction Method

Abstract

The invention discloses an ENSO diversity prediction method based on artificial intelligence. The method uses the EOF decomposition method to extract the first three main modes from the SSTA observation data of the equatorial Pacific, and projects the CMIP6 historical simulation data on the three main modes. , three sets of PC values ​​were obtained; three sets of PC values ​​were used as forecast values, the SSTA data of the initial month and the two sea temperature data of the Tendency item were used as input values ​​for training, and the CMIP6 mode was used to train VGG‑11; the observation data was input into the training A good model can obtain the PC values ​​of three future moments, and combine them with the three main modes of EOF to reconstruct the SSTA spatial form of the equatorial Pacific region in the future. This method improves the forecasting skills of Central Pacific El Niño and breaks through the forecast bottleneck of previous dynamic models in the Central Pacific region. This method improves ENSO's forecasting skills, contributes to the forecasting and early warning of climate disasters, and helps reduce human and property losses.

Description

technical field [0001] The invention relates to the technical field of climate prediction, in particular to an ENSO diversity prediction method based on artificial intelligence. Background technique [0002] The El Niño-Southern Oscillation (ENSO) has a major impact on the global climate and can cause severe flooding. Therefore, improving ENSO forecasting skills is beneficial to disaster prevention and mitigation in all countries. Existing AI-based forecasting models refer to El Niño over the entire equatorial Pacific by forecasting the Nino3.4 index, but such forecasting techniques are not sufficient to solve El Niño forecasting problems. Because El Niño is manifested as sea surface temperature (SST) anomalies in the equatorial Pacific Ocean, and temperature anomalies have spatial diversity, the Nino3.4 index cannot show spatial diversity. In addition to the common eastern Pacific-type El Niño, there is also a central-Pacific-type El Niño in the spatial diversity of El Ni...

AI Technical Summary

Problems solved by technology

Existing artificial intelligence-based forecasting models refer to the El Niño phenomenon in the entire equatorial Pacific by forecasting the Nino3.4 index, but such forecasting techniques are not sufficient to solve the El Niño forecasting problem

Because El Niño is manifested as an anomaly of sea surface temperature (SST) in the equatorial Pacific, and there is spatial diversity in temperature anomalies, but the Nino3.4 index cannot show spatial diversity

Spatial diversity of El Niño In addition to the common East Pacific El Niño, there is also a Central Pacific El Niño, the impact of the two types on the global climate is quite different; The forecasting ability of the Central Pacific El Niño is not high, so the problem of forecasting the spatial type of El Niño has not been well resolved

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