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Transforming Observations of Ocean Temperature with a Deep Convolutional Residual Regressive Neural Network (2306.09987v1)

Published 16 Jun 2023 in physics.ao-ph and cs.LG

Abstract: Sea surface temperature (SST) is an essential climate variable that can be measured via ground truth, remote sensing, or hybrid model methodologies. Here, we celebrate SST surveillance progress via the application of a few relevant technological advances from the late 20th and early 21st century. We further develop our existing water cycle observation framework, Flux to Flow (F2F), to fuse AMSR-E and MODIS into a higher resolution product with the goal of capturing gradients and filling cloud gaps that are otherwise unavailable. Our neural network architecture is constrained to a deep convolutional residual regressive neural network. We utilize three snapshots of twelve monthly SST measurements in 2010 as measured by the passive microwave radiometer AMSR-E, the visible and infrared monitoring MODIS instrument, and the in situ Argo dataset ISAS. The performance of the platform and success of this approach is evaluated using the root mean squared error (RMSE) metric. We determine that the 1:1 configuration of input and output data and a large observation region is too challenging for the single compute node and dcrrnn structure as is. When constrained to a single 100 x 100 pixel region and a small training dataset, the algorithm improves from the baseline experiment covering a much larger geography. For next discrete steps, we envision the consideration of a large input range with a very small output range. Furthermore, we see the need to integrate land and sea variables before performing computer vision tasks like those within. Finally, we see parallelization as necessary to overcome the compute obstacles we encountered.

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References (82)
  1. Global hydrological cycles and world water resources. science, 313(5790):1068–1072, 2006.
  2. How do atmospheric rivers form? Bulletin of the American Meteorological Society, 96(8):1243–1255, 2015.
  3. NH Saji and TJCR Yamagata. Possible impacts of indian ocean dipole mode events on global climate. Climate Research, 25(2):151–169, 2003.
  4. The esa climate change initiative: Satellite data records for essential climate variables. Bulletin of the American Meteorological Society, 94(10):1541–1552, 2013.
  5. The orbiting carbon observatory-2 early science investigations of regional carbon dioxide fluxes. Science, 358(6360):eaam5745, 2017.
  6. Contributions to accelerating atmospheric co2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proceedings of the national academy of sciences, 104(47):18866–18870, 2007.
  7. Four billion people facing severe water scarcity. Science advances, 2(2):e1500323, 2016.
  8. The brisbane declaration and global action agenda on environmental flows (2018). Frontiers in Environmental Science, 6:45, 2018.
  9. Rebecca E Tharme. A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River research and applications, 19(5-6):397–441, 2003.
  10. Towards a global river health assessment framework. 2022.
  11. People need freshwater biodiversity. Wiley Interdisciplinary Reviews: Water, page e1633, 2023.
  12. Global biodiversity scenarios for the year 2100. science, 287(5459):1770–1774, 2000.
  13. Global threats to human water security and river biodiversity. nature, 467(7315):555–561, 2010.
  14. Collaborative modeling with fine-resolution data enhances flood awareness, minimizes differences in flood perception, and produces actionable flood maps. Earth’s Future, 8(1):e2019EF001391, 2020.
  15. Third-party certification in the global agrifood system. Food policy, 30(3):354–369, 2005.
  16. Climate risk management. Annual Review of Earth and Planetary Sciences, 49:95–116, 2021.
  17. A high-resolution earth observations and machine learning-based approach to forecast waterborne disease risk in post-disaster settings. Climate, 10(4):48, 2022.
  18. Course-based undergraduate research to advance environmental education, science, and resource management. Frontiers in Ecology and the Environment, 20(7):431–440, 2022.
  19. The role of the biological pump in the global carbon cycle understanding an imperative for ocean science. Oceanography, 27(3):10–16, 2014.
  20. The role of coastal plant communities for climate change mitigation and adaptation. Nature Climate Change, 3(11):961–968, 2013.
  21. Mesoscale iron enrichment experiments 1993-2005: synthesis and future directions. Science, 315(5812):612–617, 2007.
  22. Southern ocean deep-water carbon export enhanced by natural iron fertilization. Nature, 457(7229):577–580, 2009.
  23. Enhanced carbonate dissolution:: a means of sequestering waste co2 as ocean bicarbonate. Energy Conversion and Management, 40(17):1803–1813, 1999.
  24. Carbon and other biogeochemical cycles. Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 11.
  25. Christopher G Piecuch. River effects on sea-level rise in the río de la plata<? xmltex\\\backslash\break?> estuary during the past century. Ocean Science, 19(1):57–75, 2023.
  26. Samrhitha A Akash Sriram. Nvidia briefly joins $1 trillion valuation club — reuters.com. https://www.reuters.com/technology/nvidia-sets-eye-1-trillion-market-value-2023-05-30/. [Accessed 06-Jun-2023].
  27. Discerning watershed response to hydroclimatic extremes with a deep convolutional residual regressive neural network. Hydrology, 10(6):116, 2023.
  28. Albert Larson. A clearer view of earth’s water cycle via neural networks and satellite data. Nature Reviews Earth & Environment, 3(6):361–361, 2022.
  29. The regional oceanic modeling system (roms): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9(4):347–404, 2005.
  30. A conservative, multi-dimensional advection transport algorithm with application to the simulation of oceanic flows. Ocean Modelling, 10(1-2):27–47, 2006.
  31. Optimum image formation for spaceborne microwave radiometer products. IEEE Transactions on Geoscience and remote sensing, 54(5):2763–2779, 2015.
  32. Resolution enhancement of spaceborne scatterometer data. IEEE Transactions on Geoscience and Remote Sensing, 31(3):700–715, 1993.
  33. Peter J Minnett. A brief history of sea surface temperature measurements. Bulletin of the American Meteorological Society, 100(5):S3–S6, 2019.
  34. David K Woolf. Satellite observations of sea surface temperature. Remote sensing of environment, 114(3):531–547, 2007.
  35. Christopher J Merchant. Sea surface temperature and its relevance to climate change studies. Remote Sensing, 4(6):1872–1901, 2012.
  36. Passive microwave remote sensing of the ocean: An overview. Oceanography from Space: Revisited, pages 13–33, 2010.
  37. Climate impacts of the atlantic multidecadal oscillation. Geophysical Research Letters, 33(17), 2006.
  38. Decadal variation of wintertime sea surface temperature in the taiwan strait. Journal of Marine Science and Technology, 21(7):15, 2013.
  39. Warming of the world ocean, 1955–2003. Geophysical research letters, 32(2), 2005.
  40. Coral reefs under rapid climate change and ocean acidification. Science, 318(5857):1737–1742, 2007.
  41. Marine species distribution shifts on the us northeast continental shelf under continued ocean warming. Progress in Oceanography, 153:24–36, 2017.
  42. Satellite imagery of sea surface temperature cooling in the wake of hurricane edouard (1996). Monthly Weather Review, 125(10):2716–2721, 1997.
  43. Cyclone phailin enhanced the productivity following its passage: evidence from satellite data. Current Science, 106(3):360–361, 2014.
  44. Spatial and temporal variability and connectivity of the marine environment of the south sandwich islands, southern ocean. Deep Sea Research Part II: Topical Studies in Oceanography, 198:105057, 2022.
  45. Impacts of climate change on marine top predators: advances and future challenges, 2015.
  46. Satellite remote sensing and the marine biodiversity observation network. Oceanography, 34(2):62–79, 2021.
  47. Phytoplankton phenology in the global ocean. Ecological Indicators, 14(1):152–163, 2012.
  48. The greening and wetting of the sahel have leveled off since about 1999 in relation to sst. Remote Sensing, 12(17):2723, 2020.
  49. Claire L Parkinson. Aqua: An earth-observing satellite mission to examine water and other climate variables. IEEE Transactions on Geoscience and Remote Sensing, 41(2):173–183, 2003.
  50. In situ validation of sea surface temperatures from the gcom-w 1 amsr 2 rss calibrated brightness temperatures. Journal of Geophysical Research: Oceans, 120(5):3567–3585, 2015.
  51. Assimilating retrievals of sea surface temperature from viirs and amsr2. Journal of Atmospheric and Oceanic Technology, 33(2):361–375, 2016.
  52. Improved viirs and modis sst imagery. Remote Sensing, 8(1):79, 2016.
  53. Evaluation of sea surface temperature from the hy-2 scanning microwave radiometer. IEEE Transactions on Geoscience and Remote Sensing, 55(3):1372–1380, 2016.
  54. Estimating land and sea surface temperature from cross-calibrated chinese gaofen-5 thermal infrared data using split-window algorithm. IEEE Geoscience and Remote Sensing Letters, 17(3):509–513, 2019.
  55. The advanced microwave scanning radiometer for the earth observing system (amsr-e), nasda’s contribution to the eos for global energy and water cycle studies. IEEE Transactions on Geoscience and Remote Sensing, 41(2):184–194, 2003.
  56. Global microwave satellite observations of sea surface temperature for numerical weather prediction and climate research. Bulletin of the American Meteorological Society, 86(8):1097–1116, 2005.
  57. Remote sensing systems AQUA AMSR-E. Available online at www.remss.com/missions/amsr, 2014. [Accessed 07 June 2023].
  58. Jiawei Zhuang. xesmf documentation. 2019.
  59. An overview of modis capabilities for ocean science observations. IEEE Transactions on Geoscience and Remote Sensing, 36(4):1250–1265, 1998.
  60. NASA/JPL. Modis aqua level 3 sst thermal ir monthly 4km daytime v2019.0, 2020. Accessed 07 June 2023.
  61. NASA/JPL. Modis aqua level 3 sst thermal ir monthly 4km nighttime v2019.0, 2020. Accessed 07 June 2023.
  62. In situ–based reanalysis of the global ocean temperature and salinity with isas: Variability of the heat content and steric height. Journal of Climate, 29(4):1305–1323, 2016.
  63. A survey of transfer learning. Journal of Big data, 3(1):1–40, 2016.
  64. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  65. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.
  66. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 40(7):1–9, 2010.
  67. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems, 29, 2016.
  68. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  69. Neural networks and the bias/variance dilemma. Neural computation, 4(1):1–58, 1992.
  70. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
  71. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929–1958, 2014.
  72. Shuffleblock: Shuffle to regularize convolutional neural networks. In 2022 National Conference on Communications (NCC), pages 36–41. IEEE, 2022.
  73. The book of why: the new science of cause and effect. Basic books, 2018.
  74. Judea Pearl. Direct and indirect effects. In Probabilistic and causal inference: The works of Judea Pearl, pages 373–392. 2022.
  75. Rapid coastal forest decline in florida’s big bend. Remote Sensing, 10(11):1721, 2018.
  76. Tracking cholera in coastal regions using satellite observations 1. JAWRA Journal of the American Water Resources Association, 46(4):651–662, 2010.
  77. Saltwater intrusion into coastal aquifers in the contiguous united states—a systematic review of investigation approaches and monitoring networks. Science of the Total Environment, page 155641, 2022.
  78. Christopher G Piecuch. Likely weakening of the florida current during the past century revealed by sea-level observations. Nature Communications, 11(1):3973, 2020.
  79. Mean squared error: Love it or leave it? a new look at signal fidelity measures. IEEE signal processing magazine, 26(1):98–117, 2009.
  80. Image quality assessment for magnetic resonance imaging. arXiv preprint arXiv:2203.07809, 2022.
  81. Meta-learning pinn loss functions. Journal of Computational Physics, 458:111121, 2022.
  82. NASA/JPL. Ghrsst level 4 ostia global historical reprocessed foundation sea surface temperature analysis produced by the uk meteorological office, 2023. Accessed 08 June 2023.
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