Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar M.; Hoteit, Ibrahim

doi:10.1007/s10236-017-1064-1

Cited by 25 publications

(50 citation statements)

References 66 publications

(106 reference statements)

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“…Version c65x of the MITgcm was employed to simulate the baroclinic tides in the Red Sea. Considering the significant spatial and temporal variability, the tide simulation was conducted based on an ocean circulation model with realistic forcing that was successfully used to describe the overturning circulation (Yao, Hoteit, Pratt, Bower, Zhai, et al, ; Yao, Hoteit, Pratt, Bower, et al, ), seasonal variability (Zhan et al, ), and kinetic energy budget (Zhan et al, ) of mesoscale eddies and ensemble data assimilation (Toye et al, ) in the Red Sea.…”

Section: Model Configurationmentioning

confidence: 99%

Baroclinic Tides Simulation in the Red Sea: Comparison to Observations and Basic Characteristics

et al. 2018

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The baroclinic tides in the Red Sea are simulated using a three‐dimensional, nonhydrostatic, high‐resolution Massachusetts Institute of Technology general circulation model. Various observations have been used to validate the simulation results. A good match between the model results and observations from five tidal gauges has been obtained. Tidal amplitude and phase data from 21 tidal stations present high correlation coefficients and low deviations with the model results. Comparisons between model and Oregon State University Tidal Inversion Software data suggest consistent results, with only small discrepancies at the locations of the amphidromic points. Tidal currents from four mooring observations are in good agreement with the simulation results, with discrepancies appearing in shallow areas and those with complex bottom topography. Based on the simulation results, the basic characteristics of baroclinic tides in the Red Sea are analyzed. The properties of barotropic tides, and distribution of the forcing function parameter, indicate that the baroclinic tides are generated mainly in four areas: the Bab‐el‐Mandeb (BAM) Strait, the southern Red Sea, the Gulf of Suez, and the Strait of Tiran. This is confirmed by the spatial distributions of baroclinic tidal kinetic energy and energy flux. The properties of the conversion rate from barotropic tides to baroclinic tides, and the divergence of baroclinic energy flux, further reveal quantitatively that the southern Red Sea features the most of the generated baroclinic energy. The majority of the baroclinic energy disappears within the four areas, either dissipating due to friction and bottom drag or converting back to barotropic energy.

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Section: Model Configurationmentioning

confidence: 99%

Baroclinic Tides Simulation in the Red Sea: Comparison to Observations and Basic Characteristics

et al. 2018

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“…MITgcm was integrated to DART by Hoteit et al (2013) and was recently applied in the Red Sea by Toye et al (2017). MITgcm and DART run as separate executables with no modifications to each code.…”

Section: Mitgcm-data Assimilation Research Testbed (Mitgcm-dart)mentioning

confidence: 99%

“…Several other ocean data assimilation projects are designing their assimilation systems based on various flavors of EnKF. Some examples are Towards an Operational Prediction system for the North Atlantic European coastal Zones (TOPAZ: Bertino et al, 2008), the European COastal sea and Operational observing and Prediction system (ECCOP: Nerger et al, 2005), the POSEIDON monitoring, forecasting and information system for the Greek Seas (Korres et al, 2010), the global ocean DA system at the Global Modeling and Assimilation Office (GMAO: Keppenne et al, 2005), and the Red Sea ocean assimilation system (Toye et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…It has been developed at the National Center of Atmospheric Research (NCAR) and is now used for research and operational weather forecasting problems (Zubrow et al, 2008;Aksoy et al, 2009;Dowell and Wicker, 2009, to cite but a few). The MITgcm-DART system has been tested for LC predictability in the GoM by Hoteit et al (2013), and for predicting the circulation of the Red Sea by Toye et al (2017).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of 4DVAR and EnKF state estimates and forecasts in the Gulf of Mexico

Gopalakrishnan

Hoteit

Cornuelle

et al. 2019

Quart J Royal Meteoro Soc

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An experiment is conducted to compare four‐dimensional variational (4DVAR) and ensemble Kalman filter (EnKF) assimilation systems and their predictability in the Gulf of Mexico (GoM) using the Massachusetts Institute of Technology general circulation model (MITgcm). The quality of the ocean‐state estimates, forecasts, and the contribution of ensemble prediction are evaluated. The MITgcm–Estimating the Circulation and Climate of the Ocean (ECCO) 4DVAR (MITgcm–ECCO) and the MITgcm–Data Assimilation Research Testbed (DART) EnKF (MITgcm–DART) systems were used to compute two‐month hindcasts (March–April, 2010) by assimilating satellite‐derived along‐track sea‐surface height (SSH) and gridded sea‐surface temperature (SST) observations. The estimates from both methods at the end of the hindcast period were then used to initialize forecasts for two months (May–June, 2010). This period was selected because a loop current (LC) eddy (Eddy Franklin: Eddy‐F) detachment event occurred at the end of May 2010, immediately after the Deepwater Horizon (DwH) oil spill. Despite some differences between the setups, both systems produce analyses and forecasts of comparable quality and both solutions significantly outperformed model persistence. A reference forecast initialized from the 1/12° Hybrid Coordinate Ocean Model (HYCOM)/NCODA global analysis also performed well. The EnKF experiments for sensitivity to filter parameters showed enhanced predictability when using more ensemble members and stronger covariance localization, but not for larger inflation. The EnKF experiments varying the number of assimilation cycles showed enhanced short‐term (long‐term) predictability with fewer (more) assimilation cycles. Additional hindcast and forecast experiments at other times of significant LC evolution showed mixed performance of both systems, which depends strongly on the background state of the GoM circulation. The present work demonstrates a practical application of both assimilation methods for the GoM and compares them in a limited number of realizations. The overall conclusion showing improved short‐term (long‐term) predictability for EnKF (4DVAR) carries an important caveat that the results from this study are specific to a few 4DVAR and EnKF LC eddy separation experiments in the GoM and cannot be generalized to conclude the relative performance of both methods, especially in other applications. However, some of the concepts and methods should carry over to other applications.

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“…The method is illustrated using an assimilated ensemble dataset of velocity fields of the Red Sea. The full description of the assimilation system from which the dataset was generated can be found in (Toye et al, 2017 of N e = 50 realizations. In an EnKF, the realizations are independently sampled after every analysis step and do not therefore exhibit well defined correspondences to the realizations at the previous analysis steps (Hoteit et al, 2015;Höllt et al, 2015).…”

Section: Ensemble Datasetmentioning

confidence: 99%

Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea

et al. 2018

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Lagrangian tracking of passive tracers in a stochastic velocity field within a sequential ensemble data assimilation framework is challenging due to the exponential growth in the number of particles. This growth arises from describing the behavior of velocity over time as a set of possible combinations of the different realizations, before and after each assimilation cycle. This paper addresses the problem of efficiently advecting particles in stochastic flow fields, whose statistics are prescribed by an underlying ensemble, in a parallel computational framework (openMP). To this end, an efficient algorithm for forward and backward tracking of passive particles in stochastic flowfields is presented. The algorithm, which employs higher order particle advection schemes, presents a mechanism for controlling the growth in the number of particles. The mechanism uses an adaptive binning procedure, while conserving the zeroth, first and second moments of probability (total probability, mean position, and variance). The adaptive binning process offers a tradeoff between speed and accuracy by limiting the number of particles to a desired maximum. To validate our method, we conducted various forward and backward particles tracking experiments within a realistic high-resolution ensemble assimilation setting of the Red Sea, focusing on the effect of the maximum number of particles, the time step, the variance of the ensemble, the travel time, the source location, and history of transport.

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Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

Cited by 25 publications

References 66 publications

Baroclinic Tides Simulation in the Red Sea: Comparison to Observations and Basic Characteristics

Baroclinic Tides Simulation in the Red Sea: Comparison to Observations and Basic Characteristics

Comparison of 4DVAR and EnKF state estimates and forecasts in the Gulf of Mexico

Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea

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