Page 101 - 应用声学2019年第4期
P. 101
第 38 卷 第 4 期 笪良龙等: 海洋水声环境敏感区诊断与适应性观测研究进展 561
tion and influence experiment to identify sensitive areas targeted observation[J]. Journal of Geophysical Research
for target observations on ETKF method[J] . Transactions C: Oceans, 2013, 118(2): 869–884.
of Atmospheric Sciences, 2014, 37(6): 749–757. [26] 邹广安. 黑潮大弯曲路径预报研究中的目标观测问题 [D]. 青
[18] Bishop C H, Toth Z. Ensemble transformation and adap- 岛: 中国科学院海洋研究所, 2015.
tive observations[J]. Journal of the Atmospheric Sciences, [27] Li Y, Peng S, Liu D. Adaptive observation in the South
1999, 56(11): 1748–1765. China Sea using CNOP approach based on a 3-D ocean
[19] Hamill T M, Snyder C. Using improved background error circulation model and its adjoint model[J]. Journal of Geo-
covariances from an ensemble Kalman filter for adaptive physical Research Oceans, 2014, 119(12): 8973–8986.
observations[J]. Monthly Weather Review, 2000, 130(6): [28] Peng S Q, Xie L. Effect of determining initial condi-
1552–1572. tions by four-dimensional variational data assimilation
[20] 田伟红. 集合变换卡尔曼滤波方法在集合预报和适应性观测 on storm surge forecasting[J]. Ocean Modelling, 2006,
中的初步应用 [D]. 北京: 中国气象科学研究院, 2006. 14(1/2): 1–18.
[21] 马旭林. 基于集合卡尔曼变换 (ETKF) 理论的适应性观测研 [29] Peng S Q, Xie L, Pietrafesa L J. Correcting the errors
究与应用 [D]. 南京: 南京信息工程大学, 2008. in the initial conditions and wind stress in storm surge
[22] 孙国栋, 穆穆, 段晚锁, 等. 条件非线性最优扰动 (CNOP): 简 simulation using an adjoint optimal technique[J]. Ocean
介与数值求解 [J]. 气象科技进展, 2016, 6(6): 6–14. Modelling, 2007, 18(3/4): 175–193.
Sun Guodong, Mu Mu, Duan Wansuo, et al. Conditional [30] 笪良龙, 熊张浩, 过武宏. 海洋温度场稳定性与可预报性研
nonlinear optimal perturbation: introduction and numer- 究 [J]. 海洋技术学报, 2015, 34(1): 55–61.
ical computation[J]. Advances in Meteorological Science Da Lianglong, Xiong Zhanghao, Guo Wuhong. Analysis of
and Technology, 2016, 6(6): 6–14. stability and predictability for ocean temperature field[J].
[23] 谭晓伟. CNOP 新算法研究及其在目标观测中的应用检 Journal of Marine Technology, 2015, 34(1): 55–61.
验 [D]. 北京: 中国科学院大气物理研究所, 2009. [31] 刘敬一. 基于 CNOP 的适应性观测敏感区诊断方法研究 [D].
[24] Mu M, Yu Y S, Xu H, et al. Similarities between optimal 青岛: 海军潜艇学院, 2017.
precursors for ENSO events and optimally growing initial [32] 崔宝龙, 笪良龙, 过武宏. 基于集合卡尔曼变换的东中国海声
errors in El Niño predictions[J]. Theoretical and Applied 学敏感区判定方法 [J]. 应用声学, 2018, 37(6): 895–903.
Climatology, 2014, 115(3/4): 416–469. Cui Baolong, Da Lianglong, Guo Wuhong. Identifying
[25] Wang Q, Mu M, Dijkstra H A. The similarity between op- acoustic sensitive area of the East China Sea based on
timal precursor and optimally growing initial error in pre- ensemble transform Kalman filter[J]. Journal of Applied
diction of Kuroshio large meander and its application to Acoustics, 2018, 37(6): 895–903.