Adaptive Coupled Physical and Biogeochemical Ocean Predictions: A Conceptual Basis

Abstract

Abstract. Physical and biogeochemical ocean dynamics can be intermittent and highly variable, and involve interactions on multiple scales. In general, the oceanic fields, processes and interactions that matter thus vary in time and space. For efficient forecasting, the structures and parameters of models must evolve and respond dynamically to new data injected into the executing prediction system. The conceptual basis of this adaptive modeling and corresponding computational scheme is the subject of this presentation. Specifically, we discuss the process of adaptive modeling for coupled physical and biogeochemical ocean models. The adaptivity is introduced within an interdisciplinary prediction system. Model-data misfits and data assimilation schemes are used to provide feedback from measurements to applications and modify the runtime behavior of the prediction system. Illustrative examples in Massachusetts Bay and Monterey Bay are presented to highlight ongoing progress.

Cooperative Filters and Control for Cooperative Exploration

“…It can be observed that the trajectory of the center of the formation is smoother than the actual level curve with spatial noise. In order to test our current algorithms on realistic ocean fields, we use a snapshot of the temperature field near Monterey Bay produced by the Harvard Ocean Prediction System (HOPS) [57]. This field reflects the temperature at 20 meters below sea surface on 00:00 GMT August 13th, 2003.…”

Collective Motion, Sensor Networks, and Ocean Sampling

“…For example, upwelling events in the ocean bring nutrient-rich, cold water from the sea bottom to the surface where phytoplankton, which need to consume iron but also need the sun for photosynthesis, can gather and grow. Accordingly, understanding the physical oceanography and how it couples with the biological dynamics is necessary for tackling a number of important open problems [1], [2].…”

“…Since reduced uncertainty implies better measurement coverage, we also refer to this as a coverage metric. In ongoing work [2], [35], [36], [37], information that can be inferred from a dynamical model is included into the metric used to control vehicles.…”

“…In this framework, the 2 Objective analysis is also commonly referred to as optimal interpolation. It was originally developed by Eliassen et al [14] in 1954 and independently reproduced and popularized by Gandin [15] in 1963. scalar field (e.g., temperature, salinity) observed at each point r and at each time t is viewed as a random variable T (r, t) or an ensemble of possible realizations.…”

“…The coupled physical and biological dynamics [1], [2] of the oceans have a major impact on the environment, from marine ecosystems to the global climate. In order to understand, model and predict these dynamics, oceanographers and ecologists seek measurements of temperature, salinity, flow and biological variables across a range of spatial and temporal scales [3], [4], [5].…”