Research Progress on Ocean Intelligent Forecasting Based on Artificial Intelligence Technology

  • Fan WANG ,
  • Xudong ZHANG ,
  • Yibin REN ,
  • Yingjie LIU ,
  • Haoyu WANG ,
  • Xiaofeng LI
Expand
  • Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
WANG Fan, research areas include physical oceanography and artificial intelligence in oceanography. E-mail: fwang@qdio.ac.cn
LI Xiaofeng, research area includes artificial intelligence oceanography. E-mail: lixf@qdio.ac.cn

Received date: 2024-11-20

  Revised date: 2025-01-10

  Online published: 2025-04-02

Supported by

the Innovation Group Project of the National Natural Science Foundation of China(42221005)

Abstract

With the rapid accumulation of marine big data and the robust development of Artificial Intelligence (AI) technology, intelligent marine forecasting has shown greater precision and efficiency in this new era. Marine data can be categorized into point- and field-observation data based on the observation methods, providing foundational support for marine forecasting. Marine forecasting methods can be divided into three main types based on the characteristics of the dynamic marine processes and phenomena: point-to-point, field-to-point, and field-to-field forecasting. These forecasting approaches not only cover a variety of marine phenomena but also address different forecasting requirements. Through a case analysis, this study specifically introduces intelligent forecasting models and results for point-to-point internal solitary wave forecasting, field-to-point El Niño-Southern Oscillation (ENSO) forecasting, and field-to-field phenomena such as mesoscale eddies and sea ice. Finally, it explores the development directions for intelligent marine forecasting in the context of big data, suggesting that enhancing the integration of data-driven methods with physical mechanisms can improve forecast accuracy and real-time responsiveness, thereby providing technical support for marine environmental monitoring, disaster warning, and the sustainable use of marine resources.

Cite this article

Fan WANG , Xudong ZHANG , Yibin REN , Yingjie LIU , Haoyu WANG , Xiaofeng LI . Research Progress on Ocean Intelligent Forecasting Based on Artificial Intelligence Technology[J]. Advances in Earth Science, 2025 , 40(2) : 111 -125 . DOI: 10.11867/j.issn.1001-8166.2025.011

References

1 DRéVILLON M, BOURDALLé-BADIE R, DERVAL C, et al. The GODAE/Mercator-Ocean global ocean forecasting system: results, applications and prospects[J]. Journal of Operational Oceanography20081(1): 51-57.
2 BLOCKLEY E W, MARTIN M J, MCLAREN A J, et al. Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new global FOAM forecasts[J]. Geoscientific Model Development20147(6): 2 613-2 638.
3 GUO Huadong, LIANG Dong, CHEN Fang, et al. Big Earth data facilitates sustainable development goals[J]. Bulletin of Chinese Academy of Sciences202136(8): 874-884.
  郭华东, 梁栋, 陈方, 等. 地球大数据促进联合国可持续发展目标实现[J]. 中国科学院院刊202136(8): 874-884.
4 GANGOPADHYAY A, SCHMIDT A, AGEL L, et al. Multiscale forecasting in the western north Atlantic: sensitivity of model forecast skill to glider data assimilation[J]. Continental Shelf Research201363: S159-S176.
5 LERMUSIAUX P F J, SCHR?TER J, DANILOV S, et al. Multiscale modeling of coastal, shelf, and global ocean dynamics[J]. Ocean Dynamics201363(11): 1 341-1 344.
6 MIYAZAWA Y, VARLAMOV S M, MIYAMA T, et al. Assimilation of high-resolution sea surface temperature data into an operational nowcast/forecast system around Japan using a multi-scale three-dimensional variational scheme[J]. Ocean Dynamics201767(6): 713-728.
7 BURNETT W, HARPER S, PRELLER R, et al. Overview of operational ocean forecasting in the US navy: past, present, and future[J]. Oceanography201427(3): 24-31.
8 HAIDVOGEL D B, ARANGO H, BUDGELL W P, et al. Ocean forecasting in terrain-following coordinates: formulation and skill assessment of the regional ocean modeling system[J]. Journal of Computational Physics2008227(7): 3 595-3 624.
9 FRANCIS P A, JITHIN A K, EFFY J B, et al. High-resolution operational ocean forecast and reanalysis system for the Indian ocean[J]. Bulletin of the American Meteorological Society2020101(8): E1340-E1356.
10 CHAO Y H, HSU M K, CHEN H W, et al. Sieving nonlinear internal waves through path prediction[J]. International Journal of Remote Sensing200829(21): 6 391-6 402.
11 SIMMONS H, CHANG M H, CHANG Y T, et al. Modeling and prediction of internal waves in the South China Sea[J]. Oceanography201124(4): 88-99.
12 PACANOWSKI R C, PHILANDER S G H. Parameterization of vertical mixing in numerical models of tropical oceans[J]. Journal of Physical Oceanography198111(11): 1 443-1 451.
13 TROUW K J M, ZIMMERMANN N, MATHYS M, et al. Numerical modelling of hydrodynamics and sediment transport in the surf zone: a sensitivity study with different types of numerical models[J]. Coastal Engineering Proceedings2012(33). DOI:10.9753/icce.v33.sediment.23 .
14 WANG H Y, LI X F. Expanding Horizons: U-Net enhancements for semantic segmentation, forecasting, and super-resolution in ocean remote sensing[J]. Journal of Remote Sensing2024, 4. DOI: 10.34133/remotesensing.0196 .
15 CHEN G, HUANG B X, CHEN X Y, et al. Deep blue AI: a new bridge from data to knowledge for the ocean science[J]. Deep Sea Research Part I: Oceanographic Research Papers2022, 190. DOI: 10.1016/j.dsr.2022.103886 .
16 CHEN G, HUANG B X, YANG J, et al. Deep blue artificial intelligence for knowledge discovery of the intermediate ocean[J]. Frontiers in Marine Science2023, 9. DOI:10.3389/fmars.2022.1034188 .
17 ZHU X X, TUIA D, MOU L C, et al. Deep learning in remote sensing: a review[J]. IEEE Geoscience & Remote Sensing Magazine20185(4): 8-36.
18 SONNEWALD M, LGUENSAT R, JONES D C, et al. Bridging observations, theory and numerical simulation of the ocean using machine learning[J]. Environmental Research Letters202116(7). DOI:10.48550/arXiv.2104.12506 .
19 CHEN S L, HU C M, BARNES B B, et al. Improving ocean color data coverage through machine learning[J]. Remote Sensing of Environment2019222: 286-302.
20 LI X F, LIU B, ZHENG G, et al. Deep-learning-based information mining from ocean remote-sensing imagery[J]. National Science Review20207(10): 1 584-1 605.
21 RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics2019378: 686-707.
22 CAI S Z, MAO Z P, WANG Z C, et al. Physics-Informed Neural Networks (PINNs) for fluid mechanics: a review[J]. Acta Mechanica Sinica202137(12): 1 727-1 738.
23 GASHLER M S, ASHMORE S C. Modeling time series data with deep Fourier neural networks[J]. Neurocomputing2016188: 3-11.
24 HAN X H, LI X H, YANG J S, et al. Enhanced offshore wind speed forecasts along the US east coast: a deep learning framework leveraging NDBC buoy data[J]. Ocean-Land-Atmosphere Research2023, 2. DOI: 10.34133/olar.0031 .
25 ZHANG X D, LI X F. Satellite data-driven and knowledge-informed machine learning model for estimating global internal solitary wave speed[J]. Remote Sensing of Environment2022, 283. DOI: 10.1016/j.rse.2022.113328 .
26 ZHANG X D, LI X F, ZHENG Q A. A machine-learning model for forecasting internal wave propagation in the Andaman Sea[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing202114: 3 095-3 106.
27 QIAN P, FENG B, LIU X D, et al. Tidal current prediction based on a hybrid machine learning method[J]. Ocean Engineering2022, 260. DOI: 10.1016/j.oceaneng.2022.111985 .
28 ZHU Z C, WANG Z F, DONG C M, et al. Physics informed neural network modelling for storm surge forecasting: a case study in the Bohai Sea, China[J]. Coastal Engineering2025, 197. DOI: 10.1016/j.coastaleng.2024.104686 .
29 WANG J, BETHEL B J, XIE W H, et al. A hybrid model for significant wave height prediction based on an improved empirical wavelet transform decomposition and long-short term memory network[J]. Ocean Modelling2024, 189. DOI:10.1016/j.ocemod.2024.102367 .
30 SUN Y, HU P, LI S Q, et al. Regional storm surge forecast method based on a neural network and the coupled ADCIRC-SWAN model[J]. Advances in Atmospheric Sciences202542(1): 129-145.
31 DELAUNAY A, CHRISTENSEN H M. Interpretable deep learning for probabilistic MJO prediction[J]. Geophysical Research Letters202249(16). DOI: 10.1029/2022GL098566 .
32 QIN M J, DU Z H, HU L S, et al. Deep learning for multi-timescales Pacific decadal oscillation forecasting[J]. Geophysical Research Letters202249(6). DOI:10.1029/2021GL096479 .
33 HAM Y G, KIM J H, LUO J J. Deep learning for multi-year ENSO forecasts[J]. Nature2019573(7 775): 568-572.
34 ZHENG G, LI X F, ZHANG R H, et al. Purely satellite data-driven deep learning forecast of complicated tropical instability waves[J]. Science Advances20206(29). DOI: 10.1126/sciadv.aba1482 .
35 WANG C, LI X F, ZHENG G. Tropical cyclone intensity forecasting using model knowledge guided deep learning model[J]. Environmental Research Letters202419(2). DOI: 10.1088/1748-9326/ad1bde .
36 YANG N, LI X F. An individual motion-driven artificial intelligence method for precipitation forecasting using radar image sequencing[J]. IEEE Transactions on Geoscience and Remote Sensing2024, 62. DOI:10.1109/TGRS.2024.3439871 .
37 YANG N, WANG C, LI X F. Evaluation of precipitation forecasting methods and an advanced lightweight model[J]. Environmental Research Letters202419(9). DOI: 10.1088/1748-9326/ad661f .
38 FAN S T, XIAO N H, DONG S. A novel model to predict significant wave height based on long short-term memory network[J]. Ocean Engineering2020, 205. DOI: 10.1016/j.oceaneng.2020.107298 .
39 CUI Y Z, WU R H, ZHANG X, et al. Forecasting the eddying ocean with a deep neural network[J]. Nature Communications202516(1). DOI: 10.1038/s41467-025-57389-2 .
40 XIONG W, XIANG Y F, WU H, et al. AI-GOMS: large AI-driven global ocean modeling system[EB/OL]. 2023. [2025-01-04]. .
41 WANG X, WANG R Z, HU N Z, et al. XiHe: a data-driven model for global ocean eddy-resolving forecasting[EB/OL]. 2024. [2025-01-04]. .
42 YANG N, WANG C, ZHAO M H, et al. LangYa: revolutionizing cross-spatiotemporal ocean forecasting[EB/OL]. 2024. [2025-01-04]. .
43 QI J F, ZHANG L L, YIN B S, et al. Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: a multi-model ensemble machine learning approach[J]. Dynamics of Atmospheres and Oceans2023. DOI:10.1016/j.dynatmoce.2023.101403 .
44 COSTA M O, CAMPOS R M, GUEDES S C. Enhancing the accuracy of metocean hindcasts with machine learning models[J]. Ocean Engineering2023, 287. DOI:10.1016/j.oceaneng.2023.115724 .
45 ZHOU S Y, WANG J K, CAO Y H, et al. Improving the accuracy of global ECMWF wave height forecasts with machine learning[J]. Ocean Modelling2024, 192. DOI: 10.1016/j.ocemod.2024.102450 .
46 JIA Y G, TIAN Z C, SHI X F, et al. Deep-sea sediment resuspension by internal solitary waves in the northern South China Sea[J]. Scientific Reports20199(1). DOI: 10.1038/s41598-019-47886-y .
47 GUO D Q, AKYLAS T R, ZHAN P, et al. On the generation and evolution of internal solitary waves in the southern Red Sea[J]. Journal of Geophysical Research: Oceans2016121(12): 8 566-8 584.
48 GONG Y K, CHEN X E, XU J X, et al. An Internal Solitary Wave Forecasting Model in the Northern South China Sea (ISWFM-NSCS)[J]. Geoscientific Model Development202316(10): 2 851-2 871.
49 MENG J M, ZHANG H, SUN L N, et al. Remote sensing techniques for detecting internal solitary waves: a comprehensive review and prospects[J]. IEEE Geoscience and Remote Sensing Magazine202412(4): 46-78.
50 ALPERS W. Theory of radar imaging of internal waves[J]. Nature1985314: 245-247.
51 BAI X L, LI X F, LAMB K G, et al. Internal solitary wave reflection near Dongsha atoll, the South China Sea[J]. Journal of Geophysical Research: Oceans2017122(10): 7 978-7 991.
52 LI X F, JACKSON C R, PICHEL W G. Internal solitary wave refraction at Dongsha atoll, South China Sea[J]. Geophysical Research Letters201340(12): 3 128-3 132.
53 ZHANG X D, LI X F. Combination of satellite observations and machine learning method for internal wave forecast in the Sulu and Celebes Seas[J]. IEEE Transactions on Geoscience and Remote Sensing202159(4): 2 822-2 832.
54 LU K X, WANG J, ZHANG M. Study on prediction of internal solitary waves propagation in the southern Andaman Sea[J]. Journal of Oceanography202177(4): 607-613.
55 TIMMERMANN A, AN S I, KUG J S, et al. El Ni?o-Southern Oscillation complexity[J]. Nature2018559(7 715): 535-545.
56 HENSON C, MARKET P, LUPO A, et al. ENSO and PDO-related climate variability impacts on Midwestern United States crop yields[J]. International Journal of Biometeorology201761(5): 857-867.
57 HSIANG S M, MENG K C, CANE M A. Civil conflicts are associated with the global climate[J]. Nature2011476(7 361): 438-441.
58 HEANEY A K, SHAMAN J, ALEXANDER K A. El Ni?o-Southern Oscillation and under-5 diarrhea in Botswana[J]. Nature Communications201910(1). DOI: 10.1097/01.EE9.0000607268.40410.81 .
59 CANE M A, ZEBIAK S E, DOLAN S C. Experimental forecasts of El Ni?o[J]. Nature1986321: 827-832.
60 TANG Y M, ZHANG R H, LIU T, et al. Progress in ENSO prediction and predictability study[J]. National Science Review20185(6): 826-839.
61 HU J, WENG B, HUANG T Q, et al. Deep residual convolutional neural network combining dropout and transfer learning for ENSO forecasting[J]. Geophysical Research Letters202148(24). DOI: 10.1029/2021GL093531 .
62 WU R G, KIRTMAN B P, van den DOOL H. An analysis of ENSO prediction skill in the CFS retrospective forecasts[J]. Journal of Climate200922(7): 1 801-1 818.
63 CHEN D K, CANE M A, KAPLAN A, et al. Predictability of El Ni?o over the past 148 years[J]. Nature2004428(6 984): 733-736.
64 BARNSTON A G, TIPPETT M K, L’HEUREUX M L, et al. Skill of real-time seasonal ENSO model predictions during 2002-11: is our capability increasing?[J]. Bulletin of the American Meteorological Society201293(5): 631-651.
65 PENLAND C. A stochastic model of IndoPacific sea surface temperature anomalies[J]. Physica D: Nonlinear Phenomena199698(2/3/4): 534-558.
66 LIMA C H R, LALL U, JEBARA T, et al. Statistical prediction of ENSO from subsurface sea temperature using a nonlinear dimensionality reduction[J]. Journal of Climate200922(17): 4 501-4 519.
67 CAPOTONDI A, SARDESHMUKH P D. Optimal precursors of different types of ENSO events[J]. Geophysical Research Letters201542(22): 9 952-9 960.
68 WEBSTER P J, YANG S. Monsoon and ENSO: selectively interactive systems[J]. Quarterly Journal of the Royal Meteorological Society1992118(507): 877-926.
69 jan van OLDENBORGH G, BALMASEDA M A, FERRANTI L, et al. Did the ECMWF seasonal forecast model outperform statistical ENSO forecast models over the last 15 years?[J]. Journal of Climate200518(16): 3 240-3 249.
70 WANG H Y, HU S N, LI X F. An interpretable deep learning ENSO forecasting model[J]. Ocean-Land-Atmosphere Research2023, 2. DOI: 10.34133/olar.0012 .
71 WANG H Y, HU S N, GUAN C, et al. The role of sea surface salinity in ENSO forecasting in the 21st century[J]. NPJ Climate and Atmospheric Science2024, 7. DOI:10.1038/s41612-024-00763-6 .
72 CHELTON D B, GAUBE P, SCHLAX M G, et al. The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll[J]. Science2011334(6 054): 328-332.
73 DONG C M, MCWILLIAMS J C, LIU Y, et al. Global heat and salt transports by eddy movement[J]. Nature Communications2014, 5. DOI: 10.1038/ncomms4294 .
74 ZHANG Z G, WANG W, QIU B. Oceanic mass transport by mesoscale eddies[J]. Science2014345(6 194): 322-324.
75 FRENGER I, GRUBER N, KNUTTI R, et al. Imprint of southern ocean eddies on winds, clouds and rainfall[J]. Nature Geoscience20136: 608-612.
76 FRENGER I, MüNNICH M, GRUBER N. Imprint of southern ocean mesoscale eddies on chlorophyll[J]. Biogeosciences201815(15): 4 781-4 798.
77 LIU Q, LIU Y J, LI X F. Characteristics of physical and biochemical parameters within mesoscale eddies in the southern ocean[J]. Environmental Science2023. DOI:10.5194/bg-2023-39 .
78 ROBINSON A R, CARTON J A, MOOERS C N K, et al. A real-time dynamical forecast of ocean synoptic/mesoscale eddies[J]. Nature1984309: 781-783.
79 MASINA S, PINARDI N. Mesoscale data assimilation studies in the middle adriatic sea[J]. Continental Shelf Research199414(12): 1 293-1 310.
80 SHRIVER J F, HURLBURT H E, SMEDSTAD O M, et al. 1/32° real-time global ocean prediction and value-added over 1/16° resolution[J]. Journal of Marine Systems200765(1/2/3/4): 3-26.
81 HURLBURT H E, CHASSIGNET E P, CUMMINGS J A, et al. Eddy-resolving global ocean prediction[M]// Ocean modeling in an eddying regime. Washington, D.C.: American Geophysical Union, 2008: 353-381.
82 SHCHEPETKIN A F, MCWILLIAMS J C. The Regional Oceanic Modeling System (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model[J]. Ocean Modelling20059(4): 347-404.
83 SHCHEPETKIN A F, MCWILLIAMS J C. A method for computing horizontal pressure-gradient force in an oceanic model with a nonaligned vertical coordinate[J]. Journal of Geophysical Research: Oceans2003108(C3). DOI: 10.1029/2001JC001047 .
84 BLECK R. An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates[J]. Ocean Modelling20024(1): 55-88.
85 CHASSIGNET E P, SMITH L T, HALLIWELL G R, et al. North Atlantic simulations with the Hybrid Coordinate Ocean Model (HYCOM): impact of the vertical coordinate choice, reference pressure, and thermobaricity[J]. Journal of Physical Oceanography200333(12): 2 504-2 526.
86 GURVAN M, the NEMO team. NEMO ocean engine[M]. Scientific Notes of Climate Modelling Center2017.
87 JOHN S S, ARI S. MU-Net: modified U-Net architecture for automatic ocean eddy detection[J]. IEEE Geoscience and Remote Sensing Letters202219: 1-5.
88 LGUENSAT R, SUN M, FABLET R, et al. EddyNet: a deep neural network for pixel-wise classification of oceanic eddies[C]// IGARSS 2018-2018 IEEE international geoscience and remote sensing symposium. Valencia, Spain: IEEE, 2018: 1 764-1 767.
89 SUN X, ZHANG M, DONG J Y, et al. A deep framework for eddy detection and tracking from satellite sea surface height data[J]. IEEE Transactions on Geoscience and Remote Sensing202159(9): 7 224-7 234.
90 LIU Y J, ZHENG Q A, LI X F. Characteristics of global ocean abnormal mesoscale eddies derived from the fusion of sea surface height and temperature data by deep learning[J]. Geophysical Research Letters202148(17). DOI:10.1029/2021GL094772 .
91 MA C Y, LI S Q, WANG A N, et al. Altimeter observation-based eddy nowcasting using an improved conv-LSTM network[J]. Remote Sensing201911(7). DOI: 10.3390/rs11070783 .
92 DU Y L, HUANG J H, CHEN J S, et al. Enhanced transformer framework for multivariate mesoscale eddy trajectory prediction[J]. Journal of Marine Science and Engineering202412(10). DOI: 10.3390/jmse12101759 .
93 ZHOU Y, REN T, CHEN K R, et al. Graph-based memory recall recurrent neural network for mid-term sea-surface height anomaly forecasting[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing202417: 6 642-6 657.
94 WANG X N, WANG X G, YU M, et al. MesoGRU: deep learning framework for mesoscale eddy trajectory prediction[J]. IEEE Geoscience and Remote Sensing Letters1993, 19. DOI: 10.1109/LGRS.2021.3087835 .
95 WANG X G, LI C, WANG X N, et al. Spatio-temporal attention-based deep learning framework for mesoscale eddy trajectory prediction[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing202215: 3 853-3 867.
96 CHEN X Y, CHEN G, GE L Y, et al. Medium-range forecasting of oceanic eddy trajectory[J]. International Journal of Digital Earth202417(1). DOI:10.1080/17538947.2023.2300325 .
97 GE L Y, HUANG B X, CHEN X Y, et al. Medium-range trajectory prediction network compliant to physical constraint for oceanic eddy[J]. IEEE Transactions on Geoscience and Remote Sensing2023, 61. DOI: 10.1109/TGRS.2023.3298020 .
98 ZHANG X M, HUANG B X, CHEN G, et al. Global oceanic mesoscale eddies trajectories prediction with knowledge-fused neural network[J]. IEEE Transactions on Geoscience and Remote Sensing2024, 62. DOI:10.1109/TGRS.2024.3388040 .
99 CUI Y Z, WU R H, ZHANG X, et al. Forecasting the eddying ocean with a deep neural network[J]. Nature Communications202516(1). DOI: 10.1038/s41467-025-57389-2 .
100 KE Changqing, JIN Xin, SHEN Xiaoyi, et al. Comparison of Antarctic and Arctic seaice variations and their impact factors[J]. Chinese Journal of Polar Research202032(1): 1-12.
  柯长青, 金鑫, 沈校熠, 等. 南北极海冰变化及其影响因素的对比分析[J]. 极地研究202032(1): 1-12.
101 LI Xiaoming, ZHANG Qiang. Observation of Arctic Sea ice cover by spaceborne synthetic aperture radar[J]. Haiyang Xuebao201941(4): 145-146.
  李晓明, 张强. 星载合成孔径雷达北极海冰覆盖观测[J]. 海洋学报201941(4): 145-146.
102 ZHAO Jinping, SHI Jiuxin, WANG Zhaomin, et al. Arctic amplification produced by sea ice retreat and its global climate effects[J]. Advances in Earth Science201530(9): 985-995.
  赵进平, 史久新, 王召民, 等. 北极海冰减退引起的北极放大机理与全球气候效应[J]. 地球科学进展201530(9): 985-995.
103 LIU Jiping, LEI Ruibo, SONG Mirong, et al. Development and challenge of sea ice model adapting to rapid polar sea ice changes[J]. Transactions of Atmospheric Sciences202144(1): 12-25.
  刘骥平, 雷瑞波, 宋米荣, 等. 适应极地快速变化海冰模式的研发与挑战[J]. 大气科学学报202144(1): 12-25.
104 SHU Qi, QIAO Fangli, SONG Zhenya. The hindcast and forecast of Arctic sea ice from FIO-ESM[J]. Acta Oceanologica Sinica201335(5): 37-45.
  舒启, 乔方利, 宋振亚. 地球系统模式FIO-ESM对北极海冰的模拟和预估[J]. 海洋学报201335(5): 37-45.
105 YUAN X J, CHEN D K, LI C H, et al. Arctic sea ice seasonal prediction by a linear Markov model[J]. Journal of Climate201629(22): 8 151-8 173.
106 YANG Q H, MU L J, WU X R, et al. Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model[J]. Atmospheric Research2019227: 14-23.
107 REICHSTEIN M, CAMPS-VALLS G, STEVENS B, et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature2019566(7 743): 195-204.
108 CHI J, KIM H C. Prediction of Arctic sea ice concentration using a fully data driven deep neural network[J]. Remote Sensing20179(12). DOI: 10.3390/rs9121305 .
109 KIM Y J, KIM H C, HAN D, et al. Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks[J]. The Cryosphere202014(3): 1 083-1 104.
110 ANDERSSON T R, HOSKING J S, PéREZ-ORTIZ M, et al. Seasonal Arctic sea ice forecasting with probabilistic deep learning[J]. Nature Communications202112(1). DOI:10.1038/s41467-021-25257-4 .
111 REN Y B, LI X F, ZHANG W H. A data-driven deep learning model for weekly sea ice concentration prediction of the pan-Arctic during the melting season[J]. IEEE Transactions on Geoscience and Remote Sensing2022, 60. DOI: 10.1109/TGRS.2022.3177600 .
112 REN Y B, LI X F. Predicting the daily sea ice concentration on a subseasonal scale of the pan-Arctic during the melting season by a deep learning model[J]. IEEE Transactions on Geoscience and Remote Sensing2023, 61. DOI: 10.1109/TGRS.2023.3279089 .
113 ZHU Y C, ZHANG R H, MOUM J N, et al. Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations[J]. National Science Review20229(8). DOI:10.1093/nsr/nwac044 .
114 PARTEE S, ELLIS M, RIGAZZI A, et al. Using Machine Learning at scale in numerical simulations with SmartSim: an application to ocean climate modeling[J]. Journal of Computational Science2022, 62. DOI: 10.1016/j.jocs.2022.101707 .
115 SHEN D L, BAO S W, PIETRAFESA L J, et al. Improving numerical model predicted float trajectories by deep learning[J]. Earth and Space Science20229(9). DOI:10.1029/2022EA002362 .
116 BI K F, XIE L X, ZHANG H H, et al. Accurate medium-range global weather forecasting with 3D neural networks[J]. Nature2023619(7 970): 533-538.
Outlines

/