A Deep-learning Real-time Bias Correction Method for Significant Wave Height Forecasts in the Western North Pacific

TL;DR

This paper introduces a deep-learning-based real-time bias correction method for significant wave height forecasts in the Western North Pacific, improving accuracy across seasons and weather conditions.

Contribution

It develops a novel spatiotemporal deep neural network with a pixel-switch loss function for dynamic bias correction of wave height forecasts.

Findings

Most effective correction in spring with 12.97-46.24% error reduction

Significant error reduction in winter by 13.79-38.95%

Improved forecasts under both normal and extreme weather conditions

Abstract

Significant wave height is one of the most important parameters characterizing ocean waves, and accurate numerical ocean wave forecasting is crucial for coastal protection and shipping. However, due to the randomness and nonlinearity of the wind fields that generate ocean waves and the complex interaction between wave and wind fields, current forecasts of numerical ocean waves have biases. In this study, a spatiotemporal deep-learning method was employed to correct gridded SWH forecasts from the ECMWF-IFS. This method was built on the trajectory gated recurrent unit deep neural network,and it conducts real-time rolling correction for the 0-240h SWH forecasts from ECMWF-IFS. The correction model is co-driven by wave and wind fields, providing better results than those based on wave fields alone. A novel pixel-switch loss function was developed. The pixel-switch loss function can dynamically fine-tune the pre-trained correction model, focusing on pixels with large biases in SWH forecasts. According to the seasonal characteristics of SWH, four correction models were constructed separately, for spring, summer, autumn, and winter. The experimental results show that, compared with the original ECMWF SWH predictions, the correction was most effective in spring, when the mean absolute error decreased by 12.972~46.237%. Although winter had the worst performance, the mean absolute error decreased by 13.794~38.953%. The corrected results improved the original ECMWF SWH forecasts under both normal and extreme weather conditions, indicating that our SWH correction model is robust and generalizable.