Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes
Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes
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使用数据驱动的振荡模式预测改进南亚季风降雨的次季节预测
DOI：
10.1073/pnas.2312573121
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Ghil, Michael
中科院分区：
文献类型：
--
作者：
Bach, Eviatar;Krishnamurthy, V.;Mote, Safa;Shukla, Jagadish;Sharma, A. Surjalal;Kalnay, Eugenia;Ghil, Michael
由于南亚季风降雨对农业、水资源供应和洪灾的影响，预测一个季节内南亚季风降水的时间和空间模式至关重要。季风季节内振荡(MISO)是一种强健的向北传播的模式，它决定了季风的活跃期和间歇期以及降水的大部分区域分布。然而，动力大气预报模式对这一模式的预测效果较差。数据驱动的MISO预测方法显示出更多的技巧，但只预测与MISO对应的降雨量部分，而不是全部降雨信号。在这里，我们结合了来自高分辨率大气模式的最先进的集合降水预报和MISO的数据驱动预报。详细大气模式的集合成员被投影到与MISO动力学相对应的较低维子空间，然后根据它们与数据驱动的MISO预报在该子空间中的距离进行加权。因此，我们在印度以及更广泛的季风区的降雨量预报方面取得了改进，提前时间为10至30天，这一间隔通常被认为是可预测性差距。在此时间范围内，降雨量预报的时间相关性提高了0.28。我们的结果证明了利用季节内振荡的可预测性来改进扩展范围预报的潜力；更广泛地说，它们指向了将动力预报和数据驱动预报结合起来进行地球系统预报的未来。
Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.
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影响因子：
23.8
作者：
Irrgang, Christopher;Boers, Niklas;Saynisch-Wagner, Jan
通讯作者：
Saynisch-Wagner, Jan
统计和动态长期大气预报：实验比较和混合
DOI：
--
发表时间：
1996
期刊：
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
Risbey JS;Squire DT;Black AS;DelSole T;Lepore C;Matear RJ;Monselesan DP;Moore TS;Richardson D;Schepen A;Tippett MK;Tozer CR
通讯作者：
Tozer CR
影响因子：
4.6
作者：
Krishnamurthy, V.;Shukla, J.
通讯作者：
Shukla, J.
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影响因子：
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作者：
A. Sahai;M. Kaur;S. Joseph;A. Dey;R. Phani;R. Mandal;R. Chattopadhyay
通讯作者：
R. Chattopadhyay