Abstract
This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity (ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying the sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations improve the prediction of jet streams, strong winds, and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transport. In the Sichuan Basin, adaptive observations refine water vapor transport and adjust vortex dynamics. This research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction.
摘 要
本研究采用高分辨率数值模式模拟适应性观测试验,探讨了利用集合变换敏感性(ETS)方法进行适应性观测试验的有效性,以改进华南和四川盆地暴雨期间的降水预报。通过识别强降水事件发生前 42 小时关键天气系统位置的敏感区域,适应性观测有助于改善对急流、强风和切变线的预测,而这些对准确预报暴雨至关重要。这种改进体现在验证区域内的降水结构和位置精度上。在华南地区,目标观测通过改善水汽输送提高了降水预报。在四川盆地,适应性观测改进了水汽输送和低涡动力调整。这项研究强调了准确预测切变线和急流对预报这些地区强降水的重要性。总之,本研究发现,适应性观测提高了华南和四川盆地强降水结构和位置的降水预报能力,强调了其在业务数值天气预报中的潜在作用。
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Data Availability Statement. The European Center for Medium-Range Weather Forecasts 5th Reanalysis Dataset are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview. The Global Precipitation Measurement data are available at https://gpm.nasa.gov/data/directory. The THORPEX Interactive Grand Global Ensemble portal data are available at https://apps.ecmwf.int/datasets/data/tigge. The data products from the Global Forecast System model are available at https://rda.ucar.edu/datasets/ds084.1/dataaccess.
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Acknowledgements
This study was jointly supported by the Guangdong Province University Student Innovation and Entrepreneurship Project (580520049), the Guangdong Ocean University Scientific Research Startup Fund (R20021), and the Key Laboratory of Plateau and Basin Rainstorm and Drought Disasters in Sichuan Province Open Research Fund (SZKT201902).
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Article Highlights
• The ensemble transform sensitivity method helps identify the sensitive areas to improve rainfall forecasts in South China and the Sichuan Basin.
• Adaptive observations refine predictions of shear lines and the jet stream, which are crucial for rainfall forecasting.
• Targeted observation data assimilation proves superior to random in heavy rainfall prediction.
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He, L., Peng, W., Zhang, Y. et al. Comparison of Adaptive Simulation Observation Experiments of the Heavy Rainfall in South China and Sichuan Basin. Adv. Atmos. Sci. 41, 2173–2191 (2024). https://doi.org/10.1007/s00376-024-3114-1
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DOI: https://doi.org/10.1007/s00376-024-3114-1