Deep learning for bias correction of MJO prediction

Hyemi Kim, Yoo‐Geun Ham, Young-Sik Joo, Seok‐Woo Son

Nature Communications · 25 May 2021

In one sentence. Deep learning bias correction of multi-model dynamical forecasts cuts MJO amplitude error by about 90% and phase error by 77% over four-week forecasts.

Abstract

Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

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