据悉，预测南亚季风一个季节内的降雨降雨的时空格局至关重要，因为它对农业、水资源供应和洪水有影响。季风季内振荡(MISO)是一种向北传播的模式，它决定了季风的活跃期和中断期以及降雨的大部分区域分布。然而，动力大气预报模型对这一模态的预测很差。数据驱动的MISO预测方法显示出更多的技巧，但只能预测MISO对应的部分降雨，而不是全部的降雨信号。
该研究小组人员将高分辨率大气模型的最先进的集合降水预报与MISO的数据驱动预报相结合。详细大气模式的集合成员被投射到与MISO动力学相对应的低维子空间上，然后根据它们与该子空间中数据驱动的MISO预报的距离进行加权。因此，研究人员在提前10到30天的时间内，改进了对印度以及更广泛的季风区的降雨预报，这一间隔通常被认为是可预测性的缺口。在此时间范围内，降雨预报的时间相关性提高了0.28。研究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测的潜力。更广泛地说，研究人员指出了地球系统预测的动态预测和数据驱动预测相结合的未来。
附：英文原文
Title: Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes
Author: Bach, Eviatar, Krishnamurthy, V., Mote, Safa, Shukla, Jagadish, Sharma, A. Surjalal, Kalnay, Eugenia, Ghil, Michael
Issue&Volume: 2024-4-1
Abstract: 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.
DOI: 10.1073/pnas.2312573121
Source: https://www.pnas.org/doi/abs/10.1073/pnas.2312573121