Ensemble forecast of an index of the Madden–Julian Oscillation using a stochastic weather generator based on circulation analogs

Abstract

Abstract. The Madden–Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the tropical region. The MJO affects precipitation over highly populated areas, especially around southern India. Therefore, predicting its phase and intensity is important as it has a high societal impact. Indices of the MJO can be derived from the first principal components of zonal wind and outgoing longwave radiation (OLR) in the tropics (RMM1 and RMM2 indices). The amplitude and phase of the MJO are derived from those indices. Our goal is to forecast these two indices on a sub-seasonal timescale. This study aims to provide an ensemble forecast of MJO indices from analogs of the atmospheric circulation, computed from the geopotential at 500 hPa (Z500) by using a stochastic weather generator (SWG). We generate an ensemble of 100 members for the MJO amplitude for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using probabilistic scores and deterministic skill scores. According to score-based criteria, we find that a reasonable forecast of the MJO index could be achieved within 40 d lead times for the different seasons. We compare our SWG forecast with other forecasts of the MJO. The comparison shows that the SWG forecast has skill compared to ECMWF forecasts for lead times above 20 d and better skill compared to machine learning forecasts for small lead times.

Improving subseasonal forecast of precipitation in Europe by combining a stochastic weather generator with dynamical models

“…The ROC curve is a valuable tool for quantifying discrimination skill, which measures the model's capacity to differentiate between binary classes (Fawcett, 2006; Krouma et al ., 2023). The area under the curve (AUC) is a metric used to quantify this discrimination skill.…”

“…SWGs have a good capacity to simulate the behaviour of the climate variables (Ailliot et al ., 2015). They have been used to forecast weather and climate variables such as temperature (Yiou & Déandréis, 2019), precipitation (Krouma et al ., 2022), the Madden–Julian oscillation (Krouma et al ., 2023), and the North Atlantic oscillation. SWGs have a low computing cost compared with numerical models (Ailliot et al ., 2015).…”

Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere

“…The MJO is regarded as the dominant mode of subseasonal (intraseasonal) variability in the tropics that couples with organized convective activity (Jones et al, 2004; Krouma et al, 2023; Liess et al, 2005; Neena et al, 2014; Pobon & Dorado, 2008; Pohl et al, 2007; Pohl & Camberlin, 2014; Sultan et al, 2009; Tam & Lau, 2005; Wang et al, 2011) and represents a primary source of predictability in the intraseasonal timescales and modulates and influences different scales of atmospheric and oceanic variability from the tropics to the extratropics (Wang et al, 2018). According to Vitart et al (2017), the S2S project models can skillfully predict the MJO up to 3–4 weeks ahead.…”

Extreme heat wave sampling and prediction with analog Markov chain and comparisons with deep learning

“…On the other hand, for predicting certain types of large-scale patterns, analog forecasting may require less resources and can be competitive with or superior to NWP on time scales longer than Lyapunov time, such as subseasonal-to-seasonal scales (Van den Dool et al, 2003; Cohen et al, 2019) and especially long-range predictions. The examples include prediction of ENSO (Ding et al, 2019; Wang et al, 2020) and Madden–Julian oscillation (Krouma et al, 2023).…”