Sea Surface Temperature Prediction and Reconstruction Using Patch-Level Neural Network Representations
暂无分享,去创建一个
[1] Cédric Herzet,et al. Bilinear residual Neural Network for the identification and forecasting of dynamical systems , 2017, ArXiv.
[2] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[3] E. Lorenz. Atmospheric predictability experiments with a large numerical model , 1982 .
[4] D. Nychka. Data Assimilation” , 2006 .
[5] Ribana Roscher,et al. Sea Level Anomaly Prediction using Recurrent Neural Networks , 2017, ArXiv.
[6] C. Donlon,et al. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system , 2012 .
[7] John F. B. Mitchell,et al. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments , 2000 .
[8] Redouane Lguensat,et al. The Analog Data Assimilation , 2017 .
[9] Frank J. Wentz,et al. Global Microwave Satellite Observations of Sea Surface Temperature for Numerical Weather Prediction and Climate Research , 2005 .
[10] Paulo Nobre,et al. Variations of Sea Surface Temperature, Wind Stress, and Rainfall over the Tropical Atlantic and South America. , 1996 .
[11] S. Brunton,et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.
[12] V. Klemas,et al. Remote Sensing of Sea Surface Salinity: An Overview with Case Studies , 2011 .
[13] Redouane Lguensat,et al. Data-Driven Models for the Spatio-Temporal Interpolation of Satellite-Derived SST Fields , 2017, IEEE Transactions on Computational Imaging.
[14] John C. Ries,et al. Chapter 1 Satellite Altimetry , 2001 .
[15] Miao Sun,et al. Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation , 2019, Remote. Sens..