Correcting nonstationary sea surface temperature bias in NCEP CFSv2 using Ensemble-based Neural Networks

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN Journal of Atmospheric and Oceanic Technology Pub Date : 2023-05-17 DOI:10.1175/jtech-d-22-0066.1
Ziying Yang, Jiping Liu, Chaoyuan Yang, Yongyun Hu
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Abstract

Sea surface temperature (SST) forecast products from the NCEP Climate Forecast System (CFSv2) that are widely used in climate research and prediction have nonstationary bias. In this study, we develop single (ANN1) and three hidden layers (ANN3) neural networks and examine their ability to correct the SST bias in the NCEP CFSv2 extended seasonal forecast starting from July in the extratropical Northern Hemisphere. Our results show that the ensemble-based ANN1 and ANN3 can reduce the uncertainty associated with parameters assigned initially and dependence on random sampling. Overall, ANN1 reduces RMSE of the CFSv2 forecasted SST substantially by 0.35°C (0.34°C) for the testing (training) data and ANN3 further reduces RMSE relatively by 0.49°C (0.47°C). Both the ensemble-based ANN1 and ANN3 can significantly reduce the spatial and temporal varying bias of the CFSv2 forecasted SST in the Pacific and Atlantic Oceans, and ANN3 shows better agreement with the observation than that of ANN1 in some subregions.
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基于集成的神经网络校正NCEP CFSv2非平稳海面温度偏差
NCEP气候预报系统(CFSv2)中广泛应用于气候研究和预测的海面温度预报产品具有非平稳偏差。在这项研究中,我们开发了单层(ANN1)和三隐层(ANN3)神经网络,并检验了它们在北半球温带7月开始的NCEP CFSv2扩展季节预报中校正SST偏差的能力。我们的结果表明,基于系综的ANN1和ANN3可以降低与初始分配的参数相关的不确定性和对随机采样的依赖性。总体而言,对于测试(训练)数据,ANN1将CFSv2预测SST的均方根误差大幅降低0.35°C(0.34°C),ANN3将均方根误差相对降低0.49°C(0.47°C)。基于系综的ANN1和ANN3都可以显著降低太平洋和大西洋CFSv2预测海温的空间和时间变化偏差,在某些亚区域,ANN3与ANN1的观测结果更为一致。
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来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
期刊介绍: The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.
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