Adaptive ensemble optimal interpolation for efficient data assimilation in the red sea

Toye, Habib; Zhan, Peng; Sana, Furrukh; Sanikommu, Sivareddy; Raboudi, Naila Mohammed Fathi; Hoteit, Ibrahim

doi:10.1016/j.jocs.2021.101317

Cited by 3 publications

(1 citation statement)

References 67 publications

(116 reference statements)

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“…Many studies mentioning the computational requirements of the EnOI technique, emphasize the need for only a single model simulation as its main computational benefit [ 17 , 42 , 45 ]. In practice, we find that it is very easy to parallelize model simulations across different cluster computer nodes, so that most, if not all, model simulations can easily be performed simultaneously, and an ensemble regeneration is not out of the question.…”

Section: Discussionmentioning

confidence: 99%

Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation

Mattern,

Edwards

2023

PLoS ONE

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Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable covariance localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is computationally suitable for advanced ecosystem models. We test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables, coupled to a physical circulation model for the California Current System. In these tests, our 4dEnOI reference implementation performs similarly well to the 4dVar benchmark in lowering the model observation misfit. We show that the 4dEnOI results depend heavily on covariance localization generally, and benefit from variable localization in particular, when it is applied to reduce the coupling strength between the physical and biogeochemical model and the biogeochemical variables. The 4dEnOI results can be further improved by small modifications to the algorithm, such as multiple 4dEnOI iterations, albeit at additional computational cost.

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Section: Discussionmentioning

confidence: 99%

Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation

Mattern,

Edwards

2023

PLoS ONE

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Insights from very‐large‐ensemble data assimilation experiments with a high‐resolution general circulation model of the Red Sea

Sanikommu,

Raboudi,

El Gharamti

et al. 2024

Quart J Royal Meteoro Soc

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Ensemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from an ensemble of samples of ocean states, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long‐range correlations resulting from the limited‐size ensembles imposed by computational burden constraints. Such ad‐hoc methods were found to be not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of one‐year‐long ensemble experiments with a fully realistic EnKF‐DA system in the Red Sea using tens ‐to thousands of ensemble members. The system assimilates satellite and in‐situ observations and accounts for model uncertainties by integrating a 4‐km‐resolution ocean model with European Center for Medium Range Weather Forecast (ECMWF) atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. OceanOur results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensembles leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by: (i) amplified spurious long‐range correlations produced by the low‐rank nature of the ECMWF atmospheric forcing ensemble; and (ii) non‐Gaussianity generated by the perturbed internal physical parameterization schemes. Large‐ensemble forcing fields and non‐Gaussian DA methods might be needed to get full benefits from large ensembles in ocean DA.

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Assimilating water level observations with the ensemble optimal interpolation scheme into a rainfall‐runoff‐inundation model: A repository‐based dynamic covariance matrix generation approach

Khaniya,

Tachikawa,

Sayama

2024

J Flood Risk Management

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Although conceptually attractive, the use of ensemble data assimilation methods, such as the ensemble Kalman filter (EnKF), can be constrained by intensive computational requirements. In such cases, the ensemble optimal interpolation scheme (EnOI), which works on a single model run instead of ensemble evolution, may offer a sub‐optimal alternative. This study explores different approaches of dynamic covariance matrix generation from predefined state vector repositories for assimilating synthetic water level observations with the EnOI scheme into a distributed rainfall‐runoff‐inundation model. Repositories are first created by storing open loop state vectors from the simulation of past flood events. The vectors are later sampled during the assimilation step, based on their closeness to the model forecast (calculated using vector norm). Results suggest that the dynamic EnOI scheme is inferior to the EnKF, but can improve upon the deterministic simulation depending on the sampling approach and the repository used. Observations can also be used for sampling to increase the background spread when the system noise is large. A richer repository is required to reduce analysis degradation, but increases the computation cost. This can be resolved by using a sliced repository consisting of only the vectors with norm close to the model forecast.

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Adaptive ensemble optimal interpolation for efficient data assimilation in the red sea

Cited by 3 publications

References 67 publications

Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation

Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation

Insights from very‐large‐ensemble data assimilation experiments with a high‐resolution general circulation model of the Red Sea

Assimilating water level observations with the ensemble optimal interpolation scheme into a rainfall‐runoff‐inundation model: A repository‐based dynamic covariance matrix generation approach

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