Sea surface height data reconstruction via inter and intra layer features based on dual attention

[1]  Ralph R. Martin,et al.  Attention mechanisms in computer vision: A survey , 2021, Computational Visual Media.

[2]  Ronan Fablet,et al.  Joint Calibration and Mapping of Satellite Altimetry Data Using Trainable Variational Models , 2021, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Zhiqiang Wei,et al.  Unsupervised Deep Multi-Similarity Hashing With Semantic Structure for Image Retrieval , 2021, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  J. Klinck,et al.  Data-driven reconstruction reveals large-scale ocean circulation control on coastal sea level , 2021, Nature Climate Change.

[5]  Xiangmin Xu,et al.  Spatiotemporal and frequential cascaded attention networks for speech emotion recognition , 2021, Neurocomputing.

[6]  Hui Xiong,et al.  Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting , 2020, AAAI.

[7]  G. Manucharyan,et al.  A Deep Learning Approach to Spatiotemporal Sea Surface Height Interpolation and Estimation of Deep Currents in Geostrophic Ocean Turbulence , 2020, Journal of Advances in Modeling Earth Systems.

[8]  Tao Zhang,et al.  Residual scale attention network for arbitrary scale image super-resolution , 2020, Neurocomputing.

[9]  Ronan Fablet,et al.  Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations , 2020, Remote. Sens..

[10]  Gang Xiao,et al.  A novel time series forecasting model with deep learning , 2020, Neurocomputing.

[11]  J. Fasullo,et al.  Origin of interannual variability in global mean sea level , 2020, Proceedings of the National Academy of Sciences.

[12]  Maxime Ballarotta,et al.  DUACS DT2018: 25 years of reprocessed sea level altimetry products , 2019, Ocean Science.

[13]  Guoqi Han,et al.  Reconstruction of the Surface Inshore Labrador Current from SWOT Sea Surface Height Measurements , 2019, Remote. Sens..

[14]  D. Menemenlis,et al.  Ocean‐Scale Interactions From Space , 2019, Earth and Space Science.

[15]  Miao Sun,et al.  Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation , 2019, Remote. Sens..

[16]  Huimin Lu,et al.  CONet: A Cognitive Ocean Network , 2019, IEEE Wireless Communications.

[17]  Jungang Yang,et al.  An evaluation of sea surface height assimilation using along-track and gridded products based on the Regional Ocean Modeling System (ROMS) and the four-dimensional variational data assimilation , 2018, Acta Oceanologica Sinica.

[18]  Sangram Ganguly,et al.  Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version , 2018, IJCAI.

[19]  Bertrand Chapron,et al.  Improving Mesoscale Altimetric Data From a Multitracer Convolutional Processing of Standard Satellite-Derived Products , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Wei Cao,et al.  DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[21]  Sangram Ganguly,et al.  DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution , 2017, KDD.

[22]  E. Gibney.  Space-weather forecast to improve with European satellite , 2017, Nature.

[23]  C. Tanajura,et al.  Impact on oceanic dynamics from assimilation of satellite surface height anomaly data into the Hybrid Coordinate Ocean Model Ocean Model (HYCOM) over the Atlantic Ocean , 2016, Oceanology.

[24]  Bellie Sivakumar,et al.  Neural network river forecasting through baseflow separation and binary-coded swarm optimization , 2015 .

[25]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[26]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[27]  Zhenya Song,et al.  High efficient parallel numerical surface wave model based on an irregular quasi-rectangular domain decomposition scheme , 2014, Science China Earth Sciences.

[28]  Lee-Lueng Fu,et al.  On the Transition from Profile Altimeter to Swath Altimeter for Observing Global Ocean Surface Topography , 2014 .

[29]  A. Pascual,et al.  Improvement of coastal and mesoscale observation from space: Application to the northwestern Mediterranean Sea , 2013 .

[30]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[31]  Thomas M. Smith,et al.  Reconstruction of near‐global annual precipitation using correlations with sea surface temperature and sea level pressure , 2009 .

[32]  Adrian E. Raftery,et al.  Weather Forecasting with Ensemble Methods , 2005, Science.

[33]  F. Qiao,et al.  Wave‐induced mixing in the upper ocean: Distribution and application to a global ocean circulation model , 2004 .

[34]  R. Kasperson,et al.  Sustainability Science , 2019, Critical Skills for Environmental Professionals.

[35]  G. Dibarboure,et al.  Mesoscale Mapping Capabilities of Multiple-Satellite Altimeter Missions , 1999 .

[36]  Thomas M. Smith,et al.  Reconstruction of Historical Sea Surface Temperatures Using Empirical Orthogonal Functions , 1996 .

[37]  Allan R. Robinson,et al.  Assimilation of altimeter Eddy fields in a limited-area quasi-geostrophic model , 1987 .

[38]  W. Zhuang,et al.  Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[39]  C. Wunsch,et al.  Ocean Circulation Kinetic Energy: Reservoirs, Sources, and Sinks , 2009 .

[40]  J. Beckers,et al.  Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature , 2005 .

[41]  Rainer Bleck,et al.  An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates , 2002 .