A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions

Popov, Andrey; Sandu, Adrian

doi:10.5194/npg-26-109-2019

Cited by 14 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…How to coordinate the temporal and spatial scale discrepancies between different types of observations, as well as scale discrepancies between observations and model simulations, is also a major challenge. (c) Another incidental fact in multivariate DA is that the spurious correlation across different types of variables is typically more pronounced (Popov & Sandu, 2019; D. Zhang et al., 2016). Although some localization techniques alleviate spurious correlations, further efforts are needed to properly assign the degree of localization for each type of observation variable and account for the correlation changes spatiotemporally.…”

Section: Applications Of Land Data Assimilation In Estimating Land Su...mentioning

confidence: 99%

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

Li,

Liu,

et al. 2024

Reviews of Geophysics

View full text Add to dashboard Cite

Data assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in‐depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment‐scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory‐data land processes studies.

show abstract

Section: Applications Of Land Data Assimilation In Estimating Land Su...mentioning

confidence: 99%

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

Li,

Liu,

et al. 2024

Reviews of Geophysics

View full text Add to dashboard Cite

show abstract

“…Assuming (4.1) is ergodic (thus having a constant spatiotemporal measure of uncertainty on the manifold of the attractor), we compute the target covariance matrix P as the empirical covariance from 10,000 independent ensemble members run over 225 days in the system (where 0.05 time units corresponds to 6 hours), with an interval of 6 hours between snapshots. This system is known to have 13 positive Lyapunov exponents, with a Kaplan-Yorke dimension of about 27.1 (Popov and Sandu 2019).…”

Section: The Lorenz'96 Model (L96)mentioning

confidence: 99%

A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering

Popov

Sandu

Niño-Ruiz

et al. 2023

Tellus A: Dynamic Meteorology and Oceanography

Self Cite

View full text Add to dashboard Cite

The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localization, which takes advantage of the spatial locality of correlations among the model variables. This work proposes an approach to alleviate sampling errors that utilizes a locally averaged-in-time dynamics of the model, described in terms of a climatological covariance of the dynamical system. We use this covariance as the target matrix in covariance shrinkage methods, and develop a stochastic covariance shrinkage approach where synthetic ensemble members are drawn to enrich both the ensemble subspace and the ensemble transformation. We additionally provide for a way in which this methodology can be localized similar to the state-of-the-art LETKF method, and that for a certain model setup, our methodology significantly outperforms it.

show abstract

“…Alternatively, one can use adaptive localization schemes where the localization is adapted in view of the observations (and assumed errors). Examples of adaptive localization techniques include Anderson (2012), Zhen and Zhang (2014), Anderson (2016) and Popov and Sandu (2019). The computational cost of an adaptive localization is low and we emphasize that no training is required when using an adaptive strategy.…”

Section: Localization and Inflationmentioning

confidence: 99%

Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above

Lunderman

Morzfeld

Posselt

2021

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

Global Bayesian optimization (GBO) is a derivative-free optimization method that is used widely in the techindustry to optimize objective functions that are expensive to evaluate, numerically or otherwise. We discuss the use of GBO in ensemble data assimilation (DA), where the goal is to update the state of a numerical model in view of noisy observations. Specifically, we consider three tasks: (i) the estimation of model parameters; (ii) the tuning of localization and inflation in ensemble DA; (iii) doing both, i.e. estimating model parameters while simultaneously tuning the localization and inflation of the ensemble DA. For all three tasks, the GBO works 'offline', i.e. a set of 'training' observations are used within GBO to determine appropriate model or localization/inflation parameters, which are subsequently deployed within an ensemble DA system. Because of the offline nature of the technique, GBO can easily be combined with existing DA systems and it can effectively decouple (nearly) linear/Gaussian aspects of a problem from highly nonlinear/ non-Gaussian ones. We illustrate the use of GBO in simple numerical experiments with the classical Lorenz problems. Our main goals are to introduce GBO in the context of ensemble DA and to spark an interest in GBO and its uses for streamlining important tasks in ensemble DA.

show abstract

A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions

Cited by 14 publications

References 48 publications

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering

Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above

Contact Info

Product

Resources

About