Data Assimilation in Oceanography: Current Status and New Directions

Hoteit, Ibrahim; Luo, Xiaodong; Bocquet, Marc; Köhl, Armin; Ait‐El‐Fquih, Boujemaa

doi:10.17125/gov2018.ch17

Cited by 28 publications

(40 citation statements)

References 245 publications

(14 reference statements)

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“…It is worth mentioning that the MPD based on which the IAPexp ensembles were generated was designed such that the ensemble members do not deviate much from each other (please see supporting information Text S1). The high resolution features are still present in the ensemble members, but they are smeared in the ensemble mean, which is taken here as the final estimate (forecast and analysis) in our EnKF system, formulated around Gaussian forecast (and noise) distributions (Hoteit et al, 2018). This Gaussian framework may be less adequate for IAPexp given the enhanced degree of mixing due to the random parameterizations.…”

Section: Impact Of Perturbed Model Physicsmentioning

confidence: 99%

Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

et al. 2020

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The Ensemble Adjustment Kalman Filter of the Data Assimilation Research Testbed is implemented to assimilate observations of satellite sea surface temperature, altimeter sea surface height, and in situ ocean temperature and salinity profiles into an eddy‐resolving 4 km Massachusetts Institute of Technology general circulation model of the Red Sea. We investigate the impact of three different ensemble generation strategies: (1) Iexp—uses ensemble of ocean states to initialize the model on 1 January 2011 and inflates filter error covariance by 10%; (2) IAexp—adds ensemble of atmospheric forcing to Iexp; and (3) IAPexp—adds perturbed model physics to IAexp. The assimilation experiments are run for 1 year, starting from the same initial ensemble and assimilating data every 3 days. Results demonstrate that the Iexp mainly improved the model outputs with respect to assimilation‐free Massachusetts Institute of Technology general circulation model run in the first few months, before showing signs of dynamical imbalances in the ocean estimates, particularly in the data‐sparse subsurface layers. The IAexp yielded substantial improvements throughout the assimilation period with almost no signs of imbalances, including the subsurface layers. It further well preserved the model mesoscale features resulting in an improved forecast for eddies, both in terms of intensity and location. Perturbing model physics in IAPexp slightly improved the forecast statistics and also the placement of basin‐scale eddies. Increasing hydrographic coverage further improved the results of IAPexp compared to IAexp in the subsurface layers. Switching off multiplicative inflation in IAexp and IAPexp leads to further improvements, especially in the subsurface layers.

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Section: Impact Of Perturbed Model Physicsmentioning

confidence: 99%

Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

et al. 2020

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“…Operational centers should improve their capability to provide feedback in near real-time regarding the QC classifications of individual observations based on forecasts made using those observations. From a fundamental standpoint, most of the approaches used for characterizing uncertainty in Ocean DA methods are predicated on the principles of Bayes' theorem (Hoteit et al, 2018). A common assumption is that errors are Gaussiandistributed and that the time evolution of the errors is linear.…”

Section: Connecting Ocean Data Assimilation With Ocean Observing Effortsmentioning

confidence: 99%

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

et al. 2019

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Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of oceanobserving data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.

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“…Data assimilation combines observations and dynamical models to determine the "best" estimates of geophysical states of interest (Reichle, 2008;Edwards et al, 2015;Hoteit et al, 2018). Nowadays, data assimilation is a well-established field with a broad range of Bayesian estimation methods that can be classified into two groups: variational (optimization) methods that seek to maximize the joint (over time) posterior distribution by fitting the model's trajectory to available observations by adjusting a well-chosen set of control parameters (Dimet and Talagrand, 1986), and sequential methods that follow a probabilistic framework in which the estimation problem is split into successive cycles of alternating forecast and analysis steps (Künsch, 2001).…”

Section: Introductionmentioning

confidence: 99%

A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter

Ait‐El‐Fquih

Hoteit

2020

Quart J Royal Meteoro Soc

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An adaptive covariance inflation scheme is proposed for the ensemble Kalman filter (EnKF) to mitigate the loss of ensemble variance. Adaptive inflation methods are mostly based on a Bayesian approach, which considers the inflation factor as a random variable with a given prior probability distribution and then combines it with the inflation likelihood through Bayes' rule to obtain its posterior distribution. In this work, we introduce a numerical implementation of this generic Bayesian approach that uses a particle filter (PF) to compute a Monte Carlo approximation of the inflation posterior distribution. To alleviate the sample attrition issue, the proposed PF employs an artificial dynamical model for the inflation factor based on the well‐known smoothing‐kernel West and Liu model. The positivity constraint on the inflation factor is further imposed through an inverse‐Gamma transition density, with parameters that suggest analytical expressions. The resulting PF–EnKF scheme is straightforward to implement, and can use different numbers of particles in its EnKF and PF components. Numerical experiments are conducted with the Lorenz‐96 model to demonstrate the effectiveness of the proposed method under various experimental scenarios.

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Data Assimilation in Oceanography: Current Status and New Directions

Cited by 28 publications

References 245 publications

Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter

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