skip to main content
10.1145/3678717.3691304acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
research-article

Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling: A Summary of Results

Published: 22 November 2024 Publication History

Abstract

Given coarser-resolution projections from global climate models or satellite data, the downscaling problem aims to estimate finer-resolution regional climate data, capturing fine-scale spatial patterns and variability. Downscaling is any method to derive high-resolution data from low-resolution variables, often to provide more detailed and local predictions and analyses. This problem is societally crucial for effective adaptation, mitigation, and resilience against significant risks from climate change. The challenge arises from spatial heterogeneity and the need to recover finer-scale features while ensuring model generalization. Most downscaling methods [21] fail to capture the spatial dependencies at finer scales and underperform on real-world climate datasets, such as sea-level rise. We propose a novel Kriging-informed Conditional Diffusion Probabilistic Model (Ki-CDPM) to capture spatial variability while preserving fine-scale features. Experimental results on climate data show that our proposed method is more accurate than state-of-the-art downscaling techniques.

References

[1]
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, and William Chan. 2021. WaveGrad: Estimating Gradients for Waveform Generation. In International Conference on Learning Representations (ICLR). PMLR, Virtual Conference, 1--10. https://openreview.net/forum?id=NsMLjcFaO8O
[2]
John A. Church, Peter U. Clark, Anny Cazenave, Jonathan M. Gregory, Svetlana Jevrejeva, Anders Levermann, Mark A. Merrifield, Glenn A. Milne, R. Steven Nerem, and Patrick D. Nunn. 2013. Sea Level Change. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom, 1137--1216. https://doi.org/10.1017/CBO9781107415324.026
[3]
Rein Haarsma et al. 2020. HighResMLP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR - description, model computational performance and basic validation. Geoscientific Model Development 13 (2020), 3507--3527. https://doi.org/10.5194/gmd-13-3507-2020
[4]
Andra J. Garner, Jeremy L. Weiss, Adam Parris, Robert E. Kopp, Radley M. Horton, Jonathan T. Overpeck, and Benjamin P. Horton. 2018. Evolution of 21st Century Sea Level Rise Projections. Earth's Future 6, 11 (2018), 1603--1615. https://doi.org/10.1029/2018EF000859
[5]
Estíbahz Gascón, Irina Sandu, Benoît Vannière, Linus Magnusson, Richard Forbes, Inna Polichtchouk, Annelize Van Niekerk, Birgit Sützl, Michael Maier-Gerber, Michail Diamantakis, Peter Bechtold, and Gianpaolo Balsamo. 2023. Advances Towards a Better Prediction of Weather Extremes in the Destination Earth Initiative. In EMS Annual Meeting 2023. Copernicus Meetings, Copernicus GmbH, Bratislava, Slovakia, 1--2. Abstract.
[6]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Networks. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 27. Curran Associates, Inc., Montreal, Canada, 2672--2680.
[7]
Jayant Gupta, Carl Molnar, Yiqun Xie, Joe Knight, and Shashi Shekhar. 2021. Spatial variability aware deep neural networks (svann): A general approach. ACM Transactions on Intelligent Systems and Technology (TIST) 12, 6 (2021), 1--21.
[8]
Benjamin D Hamlington, Alex S Gardner, Erik Ivins, Jan TM Lenaerts, JT Reager, David S Trossman, Edward D Zaron, Surendra Adhikari, Anthony Arendt, Andy Aschwanden, et al. 2020. Understanding of Contemporary Regional Sea-Level Change and the Implications for the Future. Reviews of Geophysics 58, 3 (2020), e2019RG000672. https://doi.org/10.1029/2019RG000672
[9]
Lucy Harris et al. 2022. A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts. Journal of Advances in Modeling Earth Systems 14 (2022), e2022MS003178. https://doi.org/10.1029/2022MS003178
[10]
Tim HJ Hermans, Jonathan Tinker, Matthew D Palmer, Caroline A Katsman, Bert LA Vermeersen, and Aimée BA Slangen. 2020. Improving sea-level projections on the Northwestern European shelf using dynamical downscaling. Climate Dynamics 54, 3 (2020), 1987--2011.
[11]
Hans Hersbach. 2000. Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting 15, 5 (2000), 559--570.
[12]
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS '20). Curran Associates Inc., Red Hook, NY, USA, Article 574, 12 pages.
[13]
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising Diffusion Probabilistic Models. In Advances in Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., Vancouver, Canada, 6840--6851.
[14]
Intergovernmental Panel on Climate Change (IPCC). 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Chapter 10: Linking Global to Regional Climate Change. https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter10.pdf Accessed: 2024-06-06.
[15]
M. Iturbide et al. 2020. An Update of IPCC Climate Reference Regions for Subcontinental Analysis of Climate Model Data: Definition and Aggregated Datasets. Earth System Science Data 12 (2020), 2959--2970. https://doi.org/10.5194/essd-12-2959-2020
[16]
Shanhu Jiang et al. 2020. Downscaling and projection of multi-CMIP5 precipitation using machine learning methods in the Upper Han River Basin. Atmospheric Research 247 (2020), 105156.
[17]
Yong-Yub Kim et al. 2021. Local Sea-level rise caused by climate change in the Northwest pacific marginal seas using dynamical downscaling. Frontiers in Marine Science 8 (2021), 620570.
[18]
Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In International Conference on Learning Representations (ICLR). PMLR, Banff, Canada, 1--10. Available at https://arxiv.org/abs/1312.6114.
[19]
Robert E. Kopp, Radley M. Horton, Christopher M. Little, Jerry X. Mitrovica, Michael Oppenheimer, D.J. Rasmussen, Benjamin H. Strauss, and Claudia Tebaldi. 2014. Probabilistic 21st and 22nd Century Sea-Level Projections at a Global Network of Tide-Gauge Sites. Earth's Future 2, 8 (2014), 383--406. https://doi.org/10.1002/2014EF000239
[20]
Jussi et. al Leinonen. 2020. Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network. IEEE Transactions on Geoscience and Remote Sensing 59, 9 (2020), 7211--7223.
[21]
X. Li et al. 2020. Performance of statistical and machine learning ensembles for daily temperature downscaling. Theoretical and Applied Climatology 140, 1 (2020), 1--17. https://doi.org/10.1007/s00704-019-03028-1
[22]
Yijun Lin and Yao-Yi Chiang. 2023. Modeling Spatially Varying Physical Dynamics for Spatiotemporal Predictive Learning. In Proceedings of the 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL). ACM, Hamburg, Germany, 98:1--98:11. https://doi.org/10.1145/3589132.3625648
[23]
Xiaoyu Liu et al. 2022. Downscaling of Climate Model Projections for Sea Level Rise Assessments: A Review. Journal of Geophysical Research: Oceans 127, 4 (2022), e2021JC018048. https://doi.org/10.1029/2021JC018048
[24]
Zhao-Jun Liu, Shoshiro Minobe, Yoshi N Sasaki, and Mio Terada. 2016. Dynamical downscaling of future sea level change in the western North Pacific using ROMS. Journal of Oceanography 72 (2016), 905--922.
[25]
Douglas Maraun, Martin Widmann, José M Gutiérrez, Radan Huth, Elke Hertig, Rasmus Benestad, Ole Roessler, Pedro M M Soares, José M Díaz-Navarro, Eva Enkelmann, et al. 2018. Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment. International Journal of Climatology 39, 9 (2018), 3692--3703.
[26]
Gordon McGranahan, Deborah Balk, and Bridget Anderson. 2007. The Rising Tide: Assessing the Risks of Climate Change and Human Settlements in Low Elevation Coastal Zones. Environment and Urbanization 19, 1 (2007), 17--37. https://doi.org/10.1177/0956247807076960
[27]
Daniel P McMillen. 2004. Geographically weighted regression: the analysis of spatially varying relationships.
[28]
Michael Oppenheimer et al. 2019. Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, Chapter 4. https://www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities/
[29]
Hannah S. Rabinowitz, Sophia Dahodwala, Sophie Baur, and Alison Delgado. 2023. Availability of State-level Climate Change Projection Resources for Use in Site-level Risk Assessment. Frontiers in Environmental Science 11 (2023), Article 1206039. https://doi.org/10.3389/fenvs.2023.1206039
[30]
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proceedings of the 31st International Conference on Machine Learning (ICML). PMLR, Beijing, China, 1278--1286.
[31]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer, Munich, Germany, 234--241. https://doi.org/10.1007/978-3-319-24574-4_28
[32]
Raina M Rutti, Fernando Garcia, and Marilyn M Helms. 2021. Entrepreneurship in Peru: a SWOT analysis. International Journal of Entrepreneurship and Small Business 42, 3 (2021), 369--396.
[33]
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P. Kingma. 2017. PixelCNN+ +: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. arXiv:1701.05517 [cs.LG] https://arxiv.org/abs/1701.05517
[34]
Aimee BA Slangen, Mark Carson, Caroline A Katsman, Roderik SW Van de Wal, Armin Köhl, LLA Vermeersen, and Detlef Stammer. 2014. Projecting twenty-first century regional sea-level changes. Climatic Change 124, 1 (2014), 317--332.
[35]
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep Unsupervised Learning Using Nonequilibrium Thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning (ICML). PMLR, Lille, France, 2256--2265.
[36]
Yang Song and Stefano Ermon. 2019. Generative Modeling by Estimating Gradients of the Data Distribution. In Advances in Neural Information Processing Systems. Curran Associates, Inc., Vancouver, Canada, 11918--11930.
[37]
M. Srinivasan and V. Tsontos. 2023. Satellite Altimetry for Ocean and Coastal Applications: A Review. Remote Sensing 15, 16 (2023), 3939.
[38]
Copernicus Climate Data Store. 2024. Climate Data. https://cds.climate.copernicus.eu/portfolio/dataset/satellite-sea-surface-temperature Accessed: 2024-05-28.
[39]
Copernicus Climate Data Store. 2024. Sea Level Gridded Data from Satellite Observations. https://cds.climate.copernicus.eu/portfolio/dataset/satellite-sea-level-global Accessed: 2024-05-28.
[40]
Claudia Teutschbein and Jan Seibert. 2012. Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale. Climate Dynamics 37, 9 (2012), 2087--2105.
[41]
USDA. 2012. USDA Plant Hardiness Zone Map. https://planthardiness.ars.usda.gov/. Accessed: 2021-04-26.
[42]
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. Pixel Recurrent Neural Networks. In Proceedings of the 33rd International Conference on Machine Learning (ICML) (ICML'16). JMLR.org, New York, NY, USA, 1747--1756.
[43]
Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani, and Auroop R. Ganguly. 2017. DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD '17). Association for Computing Machinery, New York, NY, USA, 1663--1672. https://doi.org/10.1145/3097983.3098004
[44]
Sergio M Vicente-Serrano et al. 2014. Improved statistical downscaling of climate scenarios using a three-step analogue regression downscaling method. Journal of Geophysical Research: Atmospheres 119, 17 (2014), 9539--9553.
[45]
Zhong Yi Wan, Ricardo Baptista, Yi fan Chen, John Anderson, Anudhyan Boral, Fei Sha, and Leonardo Zepeda-Núñez. 2023. Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models. arXiv:2305.15618 [cs.LG] https://arxiv.org/abs/2305.15618
[46]
Xiaolin Wang, Hui Wan, Shukun Jiao, Robert P. Allan, and Alison Pamment. 2021. Coastal Sea Level Changes and Extremes in a Warming Climate. Journal of Climate 34, 20 (2021), 8375--8393. https://doi.org/10.1175/JCLI-D-21-0171.1
[47]
Robbie A. Watt and Laura A. Mansfield. 2024. Generative Diffusion-based Downscaling for Climate. arXiv:2404.17752 [physics.ao-ph] https://arxiv.org/abs/2404.17752
[48]
Haomin Wen, Youfang Lin, Yutong Xia, Huaiyu Wan, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang. 2023. DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (Hamburg, Germany) (SIGSPATIAL '23). Association for Computing Machinery, New York, NY, USA, Article 60, 12 pages. https://doi.org/10.1145/3589132.3625614
[49]
Zhilun Zhou, Jingtao Ding, Yu Liu, Depeng Jin, and Yong Li. 2023. Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (Hamburg, Germany) (SIGSPATIAL '23). Association for Computing Machinery, New York, NY, USA, Article 91, 12 pages. https://doi.org/10.1145/3589132.3625641

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
October 2024
743 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Climate Science
  2. Diffusion Models
  3. Downscaling
  4. Generative AI
  5. GeoAI
  6. Geostatistics
  7. Kriging
  8. Remote Sensing

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • NSF

Conference

SIGSPATIAL '24
Sponsor:

Acceptance Rates

SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 65
    Total Downloads
  • Downloads (Last 12 months)65
  • Downloads (Last 6 weeks)46
Reflects downloads up to 14 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media