Eastward propagating Madden–Julian Oscillation (MJO) is a dominant mode of the intraseasonal variability and hence a potential source of intraseasonal predictability. Therefore, advancing MJO prediction using state-of-the-art dynamical model is of utmost importance for improving intraseasonal prediction. The prediction skill and predictability of MJO are assessed using 44 members ensemble hindcast (16 years data; 2001–2016) of CFSv2 based extended range prediction (ERP) system of IITM as well as 10 member ensemble hindcast (16 years data; 2001–2016) of ECMWF S2S dataset. The MJO is diagnosed using a newly developed Extended Empirical Orthogonal Function (EEOF) analysis. Near equatorial (15° S–15° N) model anomaly fields are projected onto the leading pair of observed eigen modes. The leading pair of observed eigen modes are obtained based on the EEOF analysis of the combined field of zonal wind at 200 hPa (U200), zonal wind at 850 hPa (U850) and velocity potential at 200 hPa (chi200). Model forecasted principal components (PCs) are quantitatively compared with observed PCs using bivariate correlation coefficient and root mean square error (RMSE). We find that MJO could be predicted up to around 22 days (around 31 days) for IITM ERP system (ECMWF S2S dataset) as measured by anomaly correlation coefficient remains larger than 0.5 and RMSE remains lower than 1.4. This prediction skill is quite low compared to potential predictability, which is estimated as more than 40 days both for IITM-ERP and ECMWF system. MJO prediction skill varies with initial MJO phase, particularly at the longer lead. This variation is more significant for the ECMWF system. Model (both for IITM-ERP and ECMWF) predicted amplitude drops at a faster rate and phase propagation speed for almost all initial phase is slower and amplitude is weaker compared to the observation. It could be concluded that even the state-of-the-art models [IITM-ERP (basically NCEP CFSv2) and ECMWF] are also not free from systematic errors/biases. Hence, there is an enormous space for improving MJO prediction skill by reducing these errors/biases in the dynamical model and error in the initial condition.

中文翻译：

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使用IITM扩展范围预测系统的MJO预测技巧以及与ECMWF S2S的比较

向东传播的马登-朱利安振荡 (MJO) 是季节内变率的主要模式，因此是季节内可预测性的潜在来源。因此，使用最先进的动力学模型推进 MJO 预测对于改进季节内预测至关重要。使用基于 CFSv2 的 IITM 扩展范围预测 (ERP) 系统的 44 个成员集合后报（16 年数据；2001-2016）以及 10 个成员集合后报（16 年数据；2001-2016 年）评估 MJO 的预测技能和可预测性) ECMWF S2S 数据集。MJO 使用新开发的扩展经验正交函数 (EEOF) 分析进行诊断。近赤道 (15° S–15° N) 模型异常场被投影到观察到的本征模式的前导对上。观测到的先导模式对是基于 200 hPa (U200) 纬向风、850 hPa (U850) 纬向风和 200 hPa (chi200) 速度势组合场的 EEOF 分析获得的。使用双变量相关系数和均方根误差 (RMSE) 将模型预测的主成分 (PC) 与观察到的 PC 进行定量比较。我们发现，通过异常相关系数测量的 IITM ERP 系统（ECMWF S2S 数据集）可以预测 MJO 最多约 22 天（约 31 天），而 RMSE 仍低于 1.4。与潜在的可预测性相比，这种预测技能相当低，IITM-ERP 和 ECMWF 系统估计超过 40 天。MJO 预测技能随初始 MJO 阶段而变化，尤其是在较长的领先阶段。这种变化对于 ECMWF 系统更为重要。模型（对于 IITM-ERP 和 ECMWF）预测的幅度下降速度更快，几乎所有初始相位的相位传播速度都比观测值更慢，幅度更弱。可以得出的结论是，即使是最先进的模型 [IITM-ERP（基本上是 NCEP CFSv2）和 ECMWF] 也并非没有系统错误/偏差。因此，通过减少动力学模型中的这些误差/偏差和初始条件中的误差来提高 MJO 预测技能有巨大的空间。可以得出的结论是，即使是最先进的模型 [IITM-ERP（基本上是 NCEP CFSv2）和 ECMWF] 也并非没有系统错误/偏差。因此，通过减少动力学模型中的这些误差/偏差和初始条件中的误差，提高 MJO 预测技能有巨大的空间。可以得出的结论是，即使是最先进的模型 [IITM-ERP（基本上是 NCEP CFSv2）和 ECMWF] 也并非没有系统错误/偏差。因此，通过减少动力学模型中的这些误差/偏差和初始条件中的误差来提高 MJO 预测技能有巨大的空间。