Going Beyond Models: A Statistical Method for Predicting the Madden Julian Oscillation

Going Beyond Models: A Statistical Method for Predicting the Madden Julian Oscillation

By Meriem Krouma

Going Beyond Models: A Statistical Method for Predicting the Madden Julian Oscillation

The Madden Julian Oscillation (MJO) is the major source of fluctuation in tropical weather on weekly to monthly timescales [1]. The MJO can be characterised as an eastward moving pulse of cloud and rainfall near the equator that typically recurs every 30 to 60 days [1]. The MJO is one of the sources of predictability in the subseasonal lead time (2 to 4 weeks) [2]. Due to its important role in driving meteorological variables in the tropic and in the extratropics [3], many studies have focused on predicting the MJO. Most of those studies have used numerical models [2].

In this work [4], we propose a statistical and probabilistic method to forecast the amplitude of the MJO based on atmospheric circulation analogs and a stochastic weather generator (SWG) [4,5,6]. Our objective is to forecast the amplitude of the MJO and its indices for a lead time of 2 to 4 weeks [4]. We configured our model (the Analog-SWG) to forecast the MJO using analogs of the geopotential at 500 hPa over the Indian Ocean. This choice is one of the main results of our work [4].

In addition, our model provides a forecast of the MJO up to 40 days in advance and yields competitive skills compared to numerical weather forecasts and machine learning forecasts up to 40 days [4].

[1] Marshall, A. G., Hendon, H. H., and Hudson, D.: Visualizing and verifying probabilistic forecasts of the Madden-Julian Oscillation: PROBABILISTIC MJO FORECASTS, Geophys. Res. Lett., 43, 12278–12286, https://doi.org/10.1002/2016GL071423, 2016.

[2] Vitart, F., et al.: The Subseasonal to Seasonal (S2S) Prediction Project Database, Bulletin of the American Meteorological Society. (2017)

[3] Cassou, C.: Intraseasonal interaction between the Madden-Julian Oscillation and the North Atlantic Oscillation, Nature, 455, 523–527, https://doi.org/10.1038/nature07286, 2008

[4] Krouma, M., Silini, R., and Yiou, P.: Ensemble forecast of an index of the Madden–Julian Oscillation using a stochastic weather generator based on circulation analogs, Earth Syst. Dynam., 14, 273–290, https://doi.org/10.5194/esd-14-273-2023, 2023.

[5] Krouma, M., Yiou, P., Déandreis, C., and Thao, S.: Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation, Geosci. Model Dev. (2022). https://doi.org/10.5194/gmd-15-4941-2022

[6] Yiou, P. and Déandréis, C.: Stochastic ensemble climate forecast with an analogue model, Geoscientific Model Development. (2019). https://doi.org/10.5194/gmd-12-723-2019,2019.

See also :

Ensemble weather predictability with stochastic weather generator based on analogues of circulation