Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
173 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations (2404.06517v1)

Published 4 Apr 2024 in physics.comp-ph, cs.CV, cs.LG, physics.ao-ph, and stat.ML

Abstract: This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is trained to probabilistically forecast day-ahead precipitation. Nonetheless, it is stable for multi-month rollouts, which reveal a qualitatively realistic superposition of convectively coupled wave modes in the tropics. Cross-spectral analysis confirms successful generation of low frequency variations associated with the Madden--Julian oscillation, which regulates most subseasonal to seasonal predictability in the observed atmosphere, and convectively coupled moist Kelvin waves with approximately correct dispersion relationships. Despite secondary issues and biases, the results affirm the potential for a next generation of global diffusion models trained on increasingly sparse, and increasingly direct and differentiated observations of the world, for practical applications in subseasonal and climate prediction.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. Brian DO Anderson. Reverse-time diffusion equation models. Stochastic Processes and their Applications, 12(3):313–326, 1982.
  2. Rainnet v1. 0: a convolutional neural network for radar-based precipitation nowcasting. Geoscientific Model Development, 13(6):2631–2644, 2020.
  3. Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. arXiv preprint arXiv:2211.02556, 2022.
  4. Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead. arXiv preprint arXiv:2304.02948, 2023.
  5. Predicting the cloud patterns of the Madden-Julian oscillation through a low-order nonlinear stochastic model. Geophys. Res. Lett., August 2014. ISSN 0094-8276, 1944-8007. doi: 10.1002/2014GL060876.
  6. Prediff: Precipitation nowcasting with latent diffusion models. arXiv preprint arXiv:2307.10422, 2023.
  7. The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730):1999–2049, 2020.
  8. The global precipitation measurement mission. Bulletin of the American meteorological Society, 95(5):701–722, 2014.
  9. Ernest Hovmöller. The trough-and-ridge diagram. Tellus, 1(2):62–66, 1949.
  10. Nasa global precipitation measurement (gpm) integrated multi-satellite retrievals for gpm (imerg). Algorithm theoretical basis document (ATBD) version, 4(26):30, 2015.
  11. Gpm imerg final precipitation l3 half hourly 0.1 degree x 0.1 degree v06. Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: March 19, 2022., 2019. 10.5067/GPM/IMERG/3B-HH/06.
  12. Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research, 6(4), 2005.
  13. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565–26577, 2022.
  14. Zonal and vertical structure of the madden–julian oscillation. Journal of the atmospheric sciences, 62(8):2790–2809, 2005.
  15. The tropical rainfall measuring mission (trmm) sensor package. Journal of atmospheric and oceanic technology, 15(3):809–817, 1998.
  16. Graphcast: Learning skillful medium-range global weather forecasting. arXiv preprint arXiv:2212.12794, 2022.
  17. Predictability and forecasting. Springer, 2005.
  18. Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification. arXiv preprint arXiv:2304.12891, 2023.
  19. Seeds: Emulation of weather forecast ensembles with diffusion models. arXiv preprint arXiv:2306.14066, 2023.
  20. Observations of the 40–50-day tropical oscillation—a review. Monthly weather review, 122(5):814–837, 1994.
  21. Michael Maier-Gerber. A python package for the construction of the wheeler–kiladis space-time spectra. https://github.com/mmaiergerber/wk_spectra, 2018.
  22. Residual diffusion modeling for km-scale atmospheric downscaling. arXiv preprint arXiv:2309.15214, 2024.
  23. Forecasting tropical cyclones with cascaded diffusion models. arXiv preprint arXiv:2310.01690, 2023.
  24. Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11214, 2022.
  25. Gencast: Diffusion-based ensemble forecasting for medium-range weather. arXiv preprint arXiv:2312.15796, 2023.
  26. David A Randall. Beyond deadlock. Geophysical Research Letters, 40(22):5970–5976, 2013.
  27. Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878):672–677, 2021.
  28. Nigel Roberts. Assessing the spatial and temporal variation in the skill of precipitation forecasts from an nwp model. Meteorological Applications: A journal of forecasting, practical applications, training techniques and modelling, 15(1):163–169, 2008.
  29. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Monthly Weather Review, 136(1):78–97, 2008.
  30. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.
  31. Observations of a convectively coupled kelvin wave in the eastern pacific itcz. Journal of the Atmospheric Sciences, 59(1):30–53, 2002.
  32. Ace: A fast, skillful learned global atmospheric model for climate prediction. arXiv preprint arXiv:2310.02074, 2023.
  33. Can machines learn to predict weather? using deep learning to predict gridded 500-hpa geopotential height from historical weather data. Journal of Advances in Modeling Earth Systems, 11(8):2680–2693, 2019.
  34. Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. Journal of the Atmospheric Sciences, 56(3):374–399, 1999.
  35. Stratospheric wave disturbances propagating over the equatorial pacific. Journal of the Meteorological Society of Japan. Ser. II, 44(5):291–294, 1966.
  36. Four theories of the madden-julian oscillation. Reviews of Geophysics, 58(3):e2019RG000685, 2020. doi: https://doi.org/10.1029/2019RG000685. URL https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019RG000685. e2019RG000685 2019RG000685.
  37. The east pacific wavetrain: Its variability and impact on the atmospheric circulation in the boreal winter. Advances in Atmospheric Sciences, 29:471–483, 2012.
Citations (3)
View on Semantic Scholar

Summary

  • The paper introduces DiffObs, an autoregressive generative diffusion model that achieves both day-ahead and multi-month global precipitation forecasts.
  • It employs satellite observations and high-resolution data to effectively replicate complex atmospheric phenomena such as the Madden-Julian Oscillation and moist Kelvin waves.
  • The model demonstrates stable, realistic forecasting while simplifying data usage and leveraging advanced GPU computations to enhance efficiency.

Autoregressive Generative Diffusion Models for Global Precipitation Forecasting

Introduction

Machine learning-based forecasting systems are evolving rapidly, moving from short-term weather predictions to long-range climate forecasting. A particularly challenging aspect of this evolution is capturing the dynamics of tropical disturbances, which play a pivotal role in subseasonal to seasonal predictability of the Earth's atmosphere. This paper introduces a novel approach using an autoregressive generative diffusion model, named DiffObs, to forecast daily precipitation by learning from satellite observations. The model not only offers day-ahead forecasts but is also capable of stable multi-month predictions, showcasing a realistic representation of atmospheric wave modes, including the Madden-Julian Oscillation and moist Kelvin waves, which are crucial for understanding climate variability.

Methodology

The methodology section outlines the development of the DiffObs model, which extends existing diffusion model architectures without incorporating additional priors, focusing on estimating the probability distribution of future states based on immediate past observations. This approach allows for continuous predictions over extended periods by using each step's estimate as the initial condition for the next. The model avoids complex data requirements by training on single observational states - daily aggregated global precipitation data captured by satellites. This simplification, alongside computational optimizations, allows the model to be trained on high-resolution data using advanced GPU infrastructure, achieving significant performance in both accuracy and efficiency.

Experimental Setup

The research utilizes the Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, preprocessing it to suit the model's needs. It involves a significant period (2000-2021) of data, with a dedicated training and testing split. The model's ability to generate forecasts over various time frames and its probabilistic nature is rigorously tested against real-world observational data. This testing includes detailed analysis of atmospheric phenomena near the equator, showcasing the model's ability to reproduce complex weather patterns across multiple scales.

Results and Analysis

The results demonstrate DiffObs's capability to produce qualitatively accurate forecasts of daily global precipitation, including long-term stability and the realistic variability of equatorial wave structures over extended periods. The model successfully replicates key atmospheric dynamics, notably the Madden-Julian Oscillation and convectively coupled moist Kelvin waves, with approximately correct dispersion relationships. While some biases and shortcomings are acknowledged, such as a tendency towards overestimating precipitation in certain areas, these do not detract from the model's overall performance and its potential applicability in practical forecasting scenarios.

Implications and Future Directions

This research underscores the potential of generative diffusion models in atmospheric science, particularly for forecasting precipitation with global coverage. The model's ability to generate realistic weather patterns from limited observational data points to a promising direction for future development, especially in regions where ground-based observations are scarce. It also highlights the importance of continuous advancements in computational capabilities for training and deploying complex machine learning models. Looking forward, the findings suggest several avenues for further research, including the exploration of different atmospheric variables, integration with existing forecasting systems, and broader applications in climate science.

In summary, the development and application of the DiffObs model represent a significant step forward in the field of atmospheric sciences and climate modeling. By leveraging the latest advancements in machine learning and computational infrastructure, this research paves the way for more accurate and dynamic forecasting models, potentially transforming our approach to understanding and predicting global weather and climate patterns.