卫星遥感资料初始场对厄尔尼诺-南方涛动动力预测误差影响研究-海洋预报

卫星遥感资料初始场对厄尔尼诺-南方涛动动力预测误差影响研究

作者：姜华¹ 2 宋春阳¹ 2 张守文³ 袁智⁴ 穆博⁵ 向亮⁶ 谭晶¹ 2 黄勇勇¹ 2 何越¹ 2

单位：1. 卫星海洋环境监测预警全国重点实验室（国家海洋环境预报中心），北京 100081；2. 国家海洋环境预报中心自然资源部海洋灾害预报技术重点实验室，北京 100081；3. 南方海洋科学与工程广东省实验室（珠海），广东珠海 519082；4. 中国空间技术研究院遥感卫星总体部，北京 100086；5. 国家卫星海洋应用中心，北京 100081；6. 中国科学院海洋研究所，山东青岛 266071

关键词：厄尔尼诺-南方涛动预测误差卫星资料初始场动力试验多源卫星融合重构三维海温

分类号：P732.6

出版年·卷·期（页码）：2026·43·第四期（1-15）

摘要：

在国家海洋环境预报中心全球气候预测系统（NMEFC＿CESM）中分别同化海洋二号B卫星（HY-2B）和多源卫星融合两类海温资料建立初始场，构建天基厄尔尼诺/拉尼娜集合预测系统。为探讨卫星遥感资料初始场对厄尔尼诺-南方涛动（ENSO）预测误差的影响，通过改变同化数据源，设计了3类敏感性数值试验：第一类为同化海表温度和重构三维海温的对比试验；第二类试验是对同化所用的重构三维海温，设置经过和未经过海表高度订正的两组对比试验；第三类试验是将卫星资料初始场误差减半，测试其对ENSO预测误差的影响。试验结果表明：同化多源卫星融合海温资料构建的预测初始场精度高于HY-2B单星海温初始场，ENSO预测结果更稳定且更接近观测；仅同化表层海温、不同化次表层海温时，ENSO预测效果较差，即使降低初始场误差，也不会产生实质性的提升，表明次表层同化对于ENSO预测非常重要；采用经过海表高度订正的重构三维海温作为同化数据源，预测结果明显最优，表明开展海表面高度订正对于次表层海温重构具有关键作用；当初始场误差减小50%时，Niño3.4区域的12个月预测平均绝对误差降至0.5℃以下，预测准确率有效提升，针对预测误差较大的海温事件优化作用更为明显。

Based on the global climate prediction system NMEFC＿CESM of the National Marine Environmental Forecasting Center, this paper assimilates the HY-2B satellite and multi-source satellite remote sensing fusion types of sea surface temperature data to establish the initial field, and constructs a satellite-based El Niño/La Niña ensemble prediction system. To explore the influence of satellite remote sensing data initial field on the ENSO prediction error, three types of sensitivity numerical experiments were designed by changing the initial field: The first type includes the assimilation of sea surface temperature and the comparison experiment of assimilating reconstructed three-dimensional sea surface temperature; The second type is the assimilation of reconstructed three-dimensional sea surface temperature, with two groups of comparison experiments before and after sea surface height correction; The third type is to reduce the initial field error of satellite data by half, and test its impact on the ENSO prediction error. The experimental results show that the prediction initial field constructed by assimilating multi-source satellite fusion is more accurate than that constructed by the single-star sea surface temperature data of HY-2B, and the ENSO prediction results are more stable and closer to the observation; Only assimilating the surface sea temperature and differentiating the subsurface sea temperature, the effect of ENSO prediction is poor, even if the initial field error is reduced, it will not produce a substantive improvement, indicating the importance of subsurface assimilation for ENSO prediction; The prediction results using the reconstructed three-dimensional sea surface temperature corrected by sea surface height as the assimilation data source are significantly the best, indicating the importance of sea surface height correction for subsurface sea surface temperature reconstruction; When the initial field error is reduced by 50%, the 12-month average absolute error of Niño3.4 drops below 0.5 ℃, and the prediction accuracy significantly improves, especially for events with relatively large prediction errors..

参考文献：

[1] Chen Dake, Zebiak S E, Busalacchi A J, et al.An improved procedure for EI Niño forecasting:Implications for predictability[J].Science, 1995, 269(5231):1699-1702.
[2] Mcphaden M J.Genesis and evolution of the 1997-98 El Niño[J].Science, 1999, 283(5404):950-954.
[3] Ham Y G, Kim J H, Luo J J.Deep learning for multi-year ENSO forecasts[J].Nature, 2019, 573(7775):568-572, doi:10.1038/s41586-019-1559-7.
[4] Zhang Shouwen, Wang Jing, Jiang Hua, et al.Effects of Indian Ocean Dipole initialization on the forecasting of La Niña 1 year in advance[J].Climate Dynamics, 2023, 61(9/10):4661-4677, doi:10.1007/s00382-023-06816-5.
[5] Wang Jing, Zhang Shouwen, Jiang Hua, et al.Effects of 2019subsurface Indian Ocean initialization on the forecast of the 2020/2021 La Niña event[J].Climate Dynamics, 2023, 60(7/8):2419-2435, doi:10.1007/s00382-022-06442-7.
[6] Wen Jinhui, Zhang Shouwen, Jiang Hua, et al.Interplay of IndoPacific precursors in La Niña forecasting:IOD-ENSO phase coupling and thermodynamic drivers[J].Atmospheric Research,2026, 327:108359, doi:10.1016/j.atmosres.2025.108359.
[7] 李熠,陈幸荣,谭晶,等.基于CESM气候模式的ENSO后报试验[J].海洋学报, 2015, 37(9):39-50.Li Yi, Chen Xingrong, Tan Jing, et al.An ENSO hindcast experiment using CESM[J].Haiyang Xuebao, 2015, 37(9):39-50.
[8] 张守文,宋春阳,王辉,等.基于CESM预测系统对全球关键海区海温主要模态后报能力评估[J].海洋学报, 2018, 40(9):18-30.Zhang Shouwen, Song Chunyang, Wang Hui, et al.Evaluation of the hindcasting main SSTA modes of the global key regions based on the CESM forecasting system[J].Haiyang Xuebao, 2018, 40(9):18-30.
[9] Zhang Shouwen, Jiang Hua, Wang Hui.Assessment of the sea surface temperature predictability based on multimodel hindcasts[J].Weather and Forecasting, 2019, 34(6):1965-1977.
[10] Zhang Shouwen, Wang Hui, Jiang Hua, et al.Evaluation of ENSO prediction skill changes since 2000 based on multimodel hindcasts[J].Atmosphere, 2021, 12(3):365.
[11] Kirtman B P, Min D, Infanti J M, et al.The north American multimodel ensemble:Phase-1 seasonal-to-interannual prediction;phase-2 toward developing intraseasonal prediction[J].Bulletin of the American Meteorological Society, 2014, 95(4):585-601, doi:10.1175/BAMS-D-12-00050.1.
[12] Reynolds R W, Smith T M, Liu Chunying, et al.Daily highresolution-blended analyses for sea surface temperature[J].Journal of Climate, 2007, 20(22):5473-5496.
[13] 苗春生,程远,王坚红,等.中国风云卫星与海洋卫星近海SST资料融合技术及应用研究[J].地球科学进展, 2015, 30(10):1127-1143.Miao Chunsheng, Cheng Yuan, Wang Jianhong, et al.Data fusion of offshore SST from China FY and HY2 satellites and its application[J].Advances in Earth Science, 2015, 30(10):1127-1143.
[14] 张明琪,徐永生,张庆君,等.基于变分法评估卫星海面信息对重构三维温盐及声场特性的影响[J].海洋学报, 2023, 45(12):133-144, doi:10.12284/hyxb2023163.Zhang Mingqi, Xu Yongsheng, Zhang Qingjun, et al.Variational method of ocean three-dimensional thermohaline structure and its acoustic performance evaluation[J].Haiyang Xuebao, 2023, 45(12):133-144, doi:10.12284/hyxb2023163.
[15] 吴宇豪,徐永生,袁智,等.基于Stacking集成学习的日尺度海洋三维温度场重构[J].海洋预报, 2026, 43(2):1-10.Wu Yuhao, Xu Yongsheng, Yuan Zhi, et al.Reconstruction of daily-scale three-dimensional ocean temperature field based on Stacking integrated learning[J].Marine Forecasts, 2026, 43(2):1-10.
[16] 王喜冬,韩桂军,李威,等.利用卫星观测海面信息反演三维温度场[J].热带海洋学报, 2011, 30(6):10-17.Wang Xidong, Han Guijun, Li Wei, et al.Reconstruction of ocean temperature profile using satellite observations[J].Journal of Tropical Oceanography, 2011, 30(6):10-17.
[17] Su Hua, Huang Linjin, Li Wene, et al.Retrieving ocean subsurface temperature using a satellite-based geographically weighted regression model[J].Journal of Geophysical Research:Oceans,2018, 123(8):5180-5193.
[18] 黎文娥,苏华,汪小钦,等.多源卫星观测的全球海洋次表层温度异常信息提取[J].遥感学报, 2017, 21(6):881-891.Li Wene, Su Hua, Wang Xiaoqin, et al.Estimation of global subsurface temperature anomaly based on multisource satellite observations[J].Journal of Remote Sensing, 2017, 21(6):881-891.
[19] Su Hua, Zhang Feiyan, Teng Jianchen, et al.Reconstructing highresolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2024,218:389-404.
[20] Zhu Jieshun, Kim J, Vernieres G, et al.Next Generation Global Ocean Data Assimilation System(NG-GODAS):A new 40-year reanalysis and OSSE applications[C] //Proceedings of the 46th Climate Diagnostics and Prediction Workshop.College Park:NOAA/NCEP/CPC, 2022.
[21] Behringer D W.The global ocean data assimilation system(GODAS)at NCEP[C] //Proceedings of the 11th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans and Land Surface.San Antonio:American Meteorological Society, 2007.
[22] Huang Boyin, Liu Chunying, Banzon V F, et al.NOAA 0.25-degree daily optimum interpolation sea surface temperature(OISST), version 2.1[DS/OL].(2020-03-20).https://catalog.data.gov/dataset/noaa-0-25-degree-daily-optimum-interpolation-seasurface-temperature-oisst-version-2-1? from_hint=eyJxIjoiTk9BQSAxLzRcdTAwYjAgRGFpbHkgT3B0aW11bSBJbnRlcnBvbG F0aW9uIFNlYSBTdXJmYWNlIFRlbXBlcmF0dXJlIChPSVNTV CkifQ%3D%3D.
[23] Huang Boyin, Liu Chunying, Banzon V, et al.Improvements of the daily optimum interpolation sea surface temperature(DOISST)version 2.1[J].Journal of Climate, 2021, 34(8):2923-2939.
[24] Xiang Liang, Xu Yongsheng, Sun Haiwei, et al.Reconstruction of interior velocity in the southern pacific ocean using satellite and Argo data[J].IEEE Geoscience and Remote Sensing Letters,2025, 22:1500405.
[25] Yue Weihao, Xu Yongsheng, Xiang Liang, et al.Prediction of 3-D ocean temperature based on self-attention and predictive RNN[J].IEEE Geoscience and Remote Sensing Letters, 2024, 21:1501305.

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