Data Assimilative Modeling of Marine Ecosystems
University of California, Santa Cruz
Marine ecosystem models generally combine numerical representations of basic processes like biological production and consumption with ocean circulation to simulate complex dynamics, usually governing photosynthesizers, their grazers and the inorganic nutrients required for their growth. These models are necessary simplifications of nature, and accurate marine ecosystem simulation is subject to many unavoidable errors, including in processes, organisms and in conditions used to initialize simulations.
This project focuses on methods that are used to rigorously constrain marine ecosystem models with observations of the natural system to better approximate the natural system. Specifically, we are focusing on four-dimensional variational and ensemble Kalman filter approaches, two methods that are commonly used in physical oceanography and numerical weather prediction.
Using these, we hope to produce accurate estimates of concentrations and fluxes that are critical to marine ecosystem dynamics in the California Current System and the ocean surrounding the Hawaiian Islands where observational programs can supply information to evaluate model output and benefit from model-produced estimates of fields that are difficult to measure directly.
CBIOMES Collaborators working with Chris Edwards
News from the UCSC Group
Jann Paul Mattern, Christopher A. Edwards, Christopher N. Hill (2019), Dual number-based variational data assimilation: Constructing exact tangent linear and adjoint code from nonlinear model evaluations, PLoS One, doi: 10.1371/journal.pone.0223131 Get the PDF [Requires...
by Helen Hill for CBIOMES CBIOMES investigators Paul Mattern and Chris Edwards present a technique that accurately approximates tangent linear and adjoint models for data assimilation applications based only on evaluations of...