The seamless forecast approach of subseasonal to seasonal scale variability has been succeeding in the forecast of multiple meteorological scales in a uniform framework. In this paradigm, it is hypothesized that reduction in initial error in dynamical forecast would help to reduce forecast error in extended lead-time up to 2-3 weeks. This is tested in a version of operational extended range forecasts based on Climate Forecast System version 2 (CFSv2) developed at Indian Institute of Tropical Meteorology (IITM), Pune. Forecast skills are assessed to understand the role of initial errors on the prediction skill for MJO. A set of lowest and highest initial day error (LIDE & HIDE) cases are defined and the error-growth for these categories are analysed for the strong MJO events during May to September (MJJAS). The MJO forecast initial errors are categorized and defined using the well-known multivariate MJO index introduced by Wheeler &Hendon (2004). The probability distribution of bivariate RMSE and error growth evolution (first order difference of index error for each successive lead days) with respect to extended range lead-time are used as metrics in this analysis. The result showed that initial error is not showing any influence in the skill of model after a lead time of 7-10 days and the error growth remains the same for both set of errors. A rapid error growth evolution of same order is seen for both the classified cases. Further the physical attribution of these errors is studied and found that the errors originate from the events with initial phase in Western Pacific and Indian Ocean. The spatial distribution of OLR and the zonal winds also confirms the same. The study emphasises the importance of better representation of MJO phases especially over Indian ocean in the model to improve the MJO prediction rather than focusing primarily on the initial condition
"La Ola-MJO": a public-friendly nickname for the Madden-Julian Oscillation
IRD (French national Institute of Research for sustainable Development)（法国国家可持续发展研究研究所）；Université de Polynésie Française (UPF)（法属波利尼西亚大学）；GEPASUD laboratory, Université de Polynésie Française (UPF)（法属波利尼西亚大学 GEPASUD 实验室）；Leibniz Centre for Tropical Marine Research (ZMT)（莱布尼茨热带海洋研究中心）
The influence of the Madden Julian Oscillation (MJO) on the precipitation extremes in Indonesia during the rainy season (October to April) has been evaluated using the daily station rain gauge data and the gridded Asian Precipitation Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) from 1987 to 2017 for different phases of the MJO. The results show that MJO significantly modulates the frequency of extreme precipitation events in Indonesia, with the magnitude of the impact varying across regions. Specifically, the convectively active (suppressed) M…
We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden-Julian Oscillation. The Madden-Julian Oscillation is a multi-scale pattern within the tropical atmosphere that has been extensively studied over the past decades, which makes it an ideal test case to ensure the interpretability methods can recover the current state of knowledge regarding its spatial structure. The neural networks can, indeed, reproduce the c…
This study examines the influence of Madden-Julian Oscillation (MJO) on Indonesian precipitation during the extended boreal summer (May to September). The MJO is one of the dominant intraseasonal variabilities that influence the extreme precipitation in the tropics, especially in Indonesia. The episodes of intense precipitation (95th percentiles) during active MJO phases from 1998-2015 are evaluated using the daily precipitation datasets from the gridded Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) and several station rain…
A more predictable Madden-Julian Oscillation index derived from Koopman spectral analysis
Courant Institute School of Mathematics, Computing, and Data Science, New York University（纽约大学库朗数学科学研究所）；Computing and Mathematical Sciences, California Institute of Technology（加州理工学院计算与数学科学系）
The Madden-Julian Oscillation (MJO) has been studied using its daily RMM1, RMM2 and VPM1, VPM2 principal components' indices. A spectral analysis of the raw indices indicates presence of a noise presumably contributed by the random high-frequency waves. A soft simple filter - 3 day running average, has been applied to filter out this high-frequency noise (the filter has been chosen in accordance with the observationally known properties of these waves). Power spectrum of the smoothed indices exhibits a stretched exponential decay $E(f) \propto \exp-(f/f_0)^{1/2}$ characteristic to the Hamilton…
Key role of the Madden-Julian Oscillation on tropical and subtropical humid heat and heatwaves
Université de la Polynésie Française (UPF)（法属波利尼西亚大学）；UMR241 SECOPOL laboratory (IRD/UPF/IFREMER/ILM)（SECOPOL联合研究实验室）；Institut de Recherche pour le Développement (IRD)（法国发展研究中心）；Institute for Marine and Antarctic Studies, University of Tasmania（塔斯马尼亚大学海洋与南极研究所）；Australian Research Council Centre of Excellence for the Weather of the 21st Century, University of Tasmania（塔斯马尼亚大学21世纪天气卓越中心）；Université de Toulouse, LEGOS (CNES/CNRS/IRD/UT3)（图卢兹大学LEGOS实验室）；GePaSud Laboratory（GePaSud实验室）；Leibniz Centre for Tropical Marine Research, ZMT（莱布尼茨热带海洋研究中心）
The prediction of the Madden-Julian Oscillation (MJO), a massive tropical weather event with vast global socio-economic impacts, has been infamously difficult with physics-based weather prediction models. Here we construct a machine learning model using reservoir computing technique that forecasts the real-time multivariate MJO index (RMM), a macroscopic variable that represents the state of the MJO. The training data was refined by developing a novel filter that extracts the recurrency of MJO signals from the raw atmospheric data and selecting a suitable time-delay coordinate of the RMM. The …
