Publications

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2019
Moore, AM, Martini MJ, Akella S, Arango HG, Balmaseda M, Bertino L, Ciavatta S, Cornuelle B, Cummings J, Frolov S, Lermusiaux P, Oddo P, Oke PR, Storto A, Teruzzi A, Vidard A, Weaver AT, Assimilation GOVD.  2019.  Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: A more complete picture of the state of the ocean. Frontiers in Marine Science. 6   10.3389/fmars.2019.00090   AbstractWebsite

Ocean data assimilation is increasingly recognized as crucial for the accuracy of real-time ocean prediction systems and historical re-analyses. The current status of ocean data assimilation in support of the operational demands of analysis, forecasting and reanalysis is reviewed, focusing on methods currently adopted in operational and real-time prediction systems. Significant challenges associated with the most commonly employed approaches are identified and discussed. Overarching issues faced by ocean data assimilation are also addressed, and important future directions in response to scientific advances, evolving and forthcoming ocean observing systems and the needs of stakeholders and downstream applications are discussed.

2011
Gawarkiewicz, G, Jan S, Lermusiaux PFJ, McClean JL, Centurioni L, Taylor K, Cornuelle B, Duda TF, Wang J, Yang YJ, Sanford T, Lien RC, Lee C, Lee MA, Leslie W, Haley PJ, Niiler PP, Gopalakrishnan G, Velez-Belchi P, Lee DK, Kim YY.  2011.  Circulation and Intrusions Northeast of Taiwan: Chasing and Predicting Uncertainty in the Cold Dome. Oceanography. 24:110-121. AbstractWebsite

An important element of present oceanographic research is the assessment and quantification of uncertainty. These studies are challenging in the coastal ocean due to the wide variety of physical processes occurring on a broad range of spatial and temporal scales. In order to assess new methods for quantifying and predicting uncertainty, a joint Taiwan-US field program was undertaken in August/September 2009 to compare model forecasts of uncertainties in ocean circulation and acoustic propagation, with high-resolution in situ observations. The geographical setting was the continental shelf and slope northeast of Taiwan, where a feature called the "cold dome" frequently forms. Even though it is hypothesized that Kuroshio subsurface intrusions are the water sources for the cold dome, the dome's dynamics are highly uncertain, involving multiple scales and many interacting ocean features. During the experiment, a combination of near-surface and profiling drifters, broad-scale and high-resolution hydrography, mooring arrays, remote sensing, and regional ocean model forecasts of fields and uncertainties were used to assess mean fields and uncertainties in the region. River runoff from Typhoon Morakot, which hit Taiwan August 7-8, 2009, strongly affected shelf stratification. In addition to the river runoff, a cold cyclonic eddy advected into the region north of the Kuroshio, resulting in a cold dome formation event. Uncertainty forecasts were successfully employed to guide the hydrographic sampling plans. Measurements and forecasts also shed light on the evolution of cold dome waters, including the frequency of eddy shedding to the north-northeast, and interactions with the Kuroshio and tides. For the first time in such a complex region, comparisons between uncertainty forecasts and the model skill at measurement locations validated uncertainty forecasts. To complement the real-time model simulations, historical simulations with another model show that large Kuroshio intrusions were associated with low sea surface height anomalies east of Taiwan, suggesting that there may be some degree of predictability for Kuroshio intrusions.

2010
Song, H, Hoteit I, Cornuelle BD, Subramanian AC.  2010.  An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter. Monthly Weather Review. 138:2825-2845.   10.1175/2010mwr2871.1   AbstractWebsite

A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF fromfully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a stationary background covariance matrix, as in the hybrid EnKF-three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF-3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF-3DVAR approach, depending on ensemble size and data coverage.