A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting

Bach, Eviatar; Ghil, Michael

doi:10.1029/2022ms003123

Cited by 10 publications

(3 citation statements)

References 118 publications

(218 reference statements)

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“…As machine learning forecasts of weather and climate continue to improve, we envision the integration of dynamical and data-driven forecasts for both real-time prediction and data assimilation. Recent work on multi-model ensemble Kalman filters (35) provides a versatile method for this application.…”

Section: Discussionmentioning

confidence: 99%

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Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes

Bach

Krishnamurthy

Shukla

et al. 2023

Preprint

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<p>Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode which determines the active and break phases of the monsoon, and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the rainfall portion corresponding to MISO.</p> <p>Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO using a novel method. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics, and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve significant improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10&#8211;30 day lead times, an interval that is generally considered as a predictability gap. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point towards a future of combining dynamical and data-driven forecasts for Earth system prediction.</p>

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Section: Discussionmentioning

confidence: 99%

“…† However, to our knowledge, this was never implemented. Recent work (34)(35)(36) suggested a multi-model data assimilation-based approach for similar problems.…”

Section: Prediction Using Dynamical and Data-driven Forecastsmentioning

confidence: 99%

Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes

Bach

Krishnamurthy

Shukla

et al. 2023

Preprint

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“…Subsequently, three different predictive models are utilized to prognosticate shallow water data in the reduced latent space, followed by comparisons of their prediction performance. Moreover, Bach and Ghil propose that through the amalgamation of model forecasts with observational metrics, the data assimilation algorithm can rectify their discrepancies, thereby enhancing the model's predictive prowess [3]. Finally, the experimental results demonstrate that prediction values are congruent with actual observations, which accentuates the resilience and effectiveness of this comprehensive methodology.…”

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confidence: 92%

Applying Latent Data Assimilation to a Fluid Dynamics Problem

2023

Env Sci Climate Res

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Shallow water equations are extensively considered in the domains of oceans, atmospheric modeling, and engineering research, which play significant roles in floods and tsunami governance [1]. Nonetheless, the accurate prediction of shallow water behaviors is regarded as an arduous undertaking, particularly when confronted with multi-dimensional data and potential errors within the model. To address these challenges and improve the accuracy of forecasts, this study employs an integrated approach, involving dimensionality reduction methods, deep learning architectures, and data assimilation techniques. Indeed, Reduced-order modeling facilitates the conversions of high-dimensional data, extracting important features and attenuating the complexity of problems [2]. Subsequently, three different predictive models are utilized to prognosticate shallow water data in the reduced latent space, followed by comparisons of their prediction performance. Moreover, Bach and Ghil propose that through the amalgamation of model forecasts with observational metrics, the data assimilation algorithm can rectify their discrepancies, thereby enhancing the model's predictive prowess [3]. Finally, the experimental results demonstrate that prediction values are congruent with actual observations, which accentuates the resilience and effectiveness of this comprehensive methodology. Its potential to accurately forecast shallow water data holds the applicability and referential significance in preventing storm swell and other meteorological events.

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