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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.

Verdy, A, Mazloff MR, Cornuelle BD, Kim SY.  2014.  Wind-driven sea level variability on the California coast: An adjoint sensitivity analysis. Journal of Physical Oceanography. 44:297-318.   10.1175/jpo-d-13-018.1   AbstractWebsite

Effects of atmospheric forcing on coastal sea surface height near Port San Luis, central California, are investigated using a regional state estimate and its adjoint. The physical pathways for the propagation of nonlocal [O(100 km)] wind stress effects are identified through adjoint sensitivity analyses, with a cost function that is localized in space so that the adjoint shows details of the propagation of sensitivities. Transfer functions between wind stress and SSH response are calculated and compared to previous work. It is found that (i) the response to local alongshore wind stress dominates on short time scales of O(1 day); (ii) the effect of nonlocal winds dominates on longer time scales and is carried by coastally trapped waves, as well as inertia-gravity waves for offshore wind stress; and (iii) there are significant seasonal variations in the sensitivity of SSH to wind stress due to changes in stratification. In a more stratified ocean, the damping of sensitivities to local and offshore winds is reduced, allowing for a larger and longer-lasting SSH response to wind stress.

Miller, AJ, Cornuelle BD.  1999.  Forecasts from fits of frontal fluctuations. Dynamics of Atmospheres and Oceans. 29:305-333.   10.1016/s0377-0265(99)00009-3   AbstractWebsite

A primitive equation ocean model is fit with strong constraints to non-synoptic hydrographic surveys in an unstable frontal current region, the Iceland-Faeroe Front. The model is first initialized from a time-independent objective analysis of non-synoptic data (spanning 2 to 6 days). A truncated set of eddy-scale basis functions is used to represent the initial error in temperature, salinity, and velocity. A series of model integrations, each perturbed with one basis function for one dependent variable in one layer, is used to determine the sensitivity to the objective-analysis initial state of the match to the non-synoptic hydrographic data. A new initial condition is then determined from a generalized inverse of the sensitivity matrix and the process is repeated to account for non-linearity. The method is first tested in 'identical twin' experiments to demonstrate the adequacy of the basis functions in representing initial condition error and the convergence of the method to the true solution. The approach is then applied to observations gathered in August 1993 in the Iceland-Faeroe Front. Model fits are successful in improving the match to the true data, leading to dynamically consistent evolution scenarios. However, the forecast skill (here defined as the variance of the model-data differences) of the model runs from the optimized initial condition is not superior to less sophisticated methods of initialization, probably due to inadequate initialization data. The limited verification data in the presence of strong frontal slopes may not be sufficient to establish Forecast skill, so that it must be judged subjectively or evaluated by other quantitative measures. (C) 1999 Elsevier Science B.V. All rights reserved.