Data assimilation of physical and chlorophyll a observations in the California Current System using two biogeochemical models

2017 ◽  
Vol 109 ◽  
pp. 55-71 ◽  
Author(s):  
Jann Paul Mattern ◽  
Hajoon Song ◽  
Christopher A. Edwards ◽  
Andrew M. Moore ◽  
Jerome Fiechter
Keyword(s):  
Data Assimilation ◽  
Chlorophyll A ◽  
Current System ◽  
California Current ◽  
California Current System ◽  
Biogeochemical Models

Application of 4D-Variational data assimilation to the California Current System

2009 ◽  
Vol 48 (1-3) ◽  
pp. 69-92 ◽  
Author(s):  
G. Broquet ◽  
C.A. Edwards ◽  
A.M. Moore ◽  
B.S. Powell ◽  
M. Veneziani ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Current System ◽  
California Current ◽  
Variational Data Assimilation ◽  
California Current System

scholarly journals Weak constraint four-dimensional variational data assimilation in a model of the California Current System

2016 ◽  
Vol 2 (2) ◽  
pp. 171-192 ◽  
Author(s):  
William J. Crawford ◽  
Polly J. Smith ◽  
Ralph F. Milliff ◽  
Jerome Fiechter ◽  
Christopher K. Wikle ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ocean Circulation ◽  
Hierarchical Modeling ◽  
Current System ◽  
California Current ◽  
Model Errors ◽  
Strong Constraint ◽  
California Current System ◽  
Twin Model ◽  
Error Covariance

Abstract. A new approach is explored for computing estimates of the error covariance associated with the intrinsic errors of a numerical forecast model in regions characterized by upwelling and downwelling. The approach used is based on a combination of strong constraint data assimilation, twin model experiments, linear inverse modeling, and Bayesian hierarchical modeling. The resulting model error covariance estimates Q are applied to a model of the California Current System using weak constraint four-dimensional variational (4D-Var) data assimilation to compute estimates of the ocean circulation. The results of this study show that the estimates of Q derived following our approach lead to demonstrable improvements in the model circulation estimates and isolate regions where model errors are likely to be important and that have been independently identified in the same model in previously published work.

scholarly journals Reconstruction of Diffusion Coefficients and Power Exponents from Single Lagrangian Trajectories

Fluids ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 111
Author(s):  
Leonid M. Ivanov ◽  
Collins A. Collins ◽  
Tetyana Margolina
Keyword(s):  
Black Sea ◽  
Diffusion Coefficients ◽  
Current System ◽  
Mean Square Displacement ◽  
California Current ◽  
The Black Sea ◽  
Mean Square ◽  
California Current System ◽  
The Mean ◽  
Non Gaussian

Using discrete wavelets, a novel technique is developed to estimate turbulent diffusion coefficients and power exponents from single Lagrangian particle trajectories. The technique differs from the classical approach (Davis (1991)’s technique) because averaging over a statistical ensemble of the mean square displacement (<X2>) is replaced by averaging along a single Lagrangian trajectory X(t) = {X(t), Y(t)}. Metzler et al. (2014) have demonstrated that for an ergodic (for example, normal diffusion) flow, the mean square displacement is <X2> = limT→∞τX2(T,s), where τX2 (T, s) = 1/(T − s) ∫0T−s(X(t+Δt) − X(t))2 dt, T and s are observational and lag times but for weak non-ergodic (such as super-diffusion and sub-diffusion) flows <X2> = limT→∞≪τX2(T,s)≫, where ≪…≫ is some additional averaging. Numerical calculations for surface drifters in the Black Sea and isobaric RAFOS floats deployed at mid depths in the California Current system demonstrated that the reconstructed diffusion coefficients were smaller than those calculated by Davis (1991)’s technique. This difference is caused by the choice of the Lagrangian mean. The technique proposed here is applied to the analysis of Lagrangian motions in the Black Sea (horizontal diffusion coefficients varied from 105 to 106 cm2/s) and for the sub-diffusion of two RAFOS floats in the California Current system where power exponents varied from 0.65 to 0.72. RAFOS float motions were found to be strongly non-ergodic and non-Gaussian.

Sign in / Sign up

Export Citation Format

Share Document