As climate change leads to more frequent and intense extreme precipitation events, accurately predicting rainfall during the flood season has become increasingly critical.
As climate change leads to more frequent and intense extreme precipitation events, accurately predicting rainfall during the flood season has become increasingly critical.
A recent study has employed machine learning (ML) algorithms to address the nonlinear challenges faced by traditional models in predicting flood season rainfall, resulting in significant improvements in accuracy. The findings were published in Advances in Atmospheric Sciences.
Current predictions for flood season rainfall rely largely on outputs from climate system numerical models, which often contain systematic biases. To correct these outputs and reduce errors, researchers traditionally combine historical observational data with statistical methods.
This approach, known as the dynamical-statistical method, has its limitations. Prediction errors from numerical models tend to grow nonlinearly over time, and traditional correction methods, which primarily rely on linear approaches, struggle to effectively address these errors.
Read more at Institute of Atmospheric Physics, Chinese Academy of Sciences
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