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Abstract
The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26–27 days (which competes with that obtained with most computationally demanding state-of-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated.
Highlights
The Madden–Julian oscillation (MJO)[1,2] is a major source of weather predictability on the sub-seasonal time scale[3,4,5] and has an important influence on the tropical weather[6]
We begin by computing COR and root-mean-squared error (RMSE) as a function of the forecast lead time, τ, for the two artificial neural networks (ANNs)
Using the standard value COR = 0.5 to define the prediction skill, we find a prediction skill of about 26–27 days for both ANNs, which is comparable to the best-known prediction skills obtained from most models[18], except ECMWF
Summary
The Madden–Julian oscillation (MJO)[1,2] is a major source of weather predictability on the sub-seasonal time scale[3,4,5] and has an important influence on the tropical weather[6]. The MJO is a major source of intraseasonal fluctuations in monsoon systems[7,8] and modulates the development of tropical cyclones[9]. 10,11) and its activity may affect El NiñoSouthern Oscillation (ENSO)[12]. For these reasons, many efforts have focused on forecasting the MJO3,13–20. The prediction skill of MJO is sensitive to the physics of the model and the quality of the initial conditions. Of the dynamical models considered in 2014 by Neena and coworkers[13], the ensemble-mean prediction skill is highest for the model of the European Centre for Medium-Range Weather
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