scholarly journals Improved Turbulent Air–Sea Flux Bulk Parameters for Controlling the Response of the Ocean Mixed Layer: A Sequential Data Assimilation Approach

2009 ◽  
Vol 26 (3) ◽  
pp. 538-555 ◽  
Author(s):  
Sergey Skachko ◽  
Jean-Michel Brankart ◽  
Frédéric Castruccio ◽  
Pierre Brasseur ◽  
Jacques Verron
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Mixed Layer ◽  
Sensible Heat Flux ◽  
Ocean Model ◽  
Global Ocean ◽  
Model Parameters ◽  
Sequential Data ◽  
Bulk Parameters ◽  
Sequential Data Assimilation

Abstract Bulk formulations parameterizing turbulent air–sea fluxes remain among the main sources of error in present-day ocean models. The objective of this study is to investigate the possibility of estimating the turbulent bulk exchange coefficients using sequential data assimilation. It is expected that existing ocean assimilation systems can use this method to improve the air–sea fluxes and produce more realistic forecasts of the thermohaline characteristics of the mixed layer. The method involves augmenting the control vector of the assimilation scheme using the model parameters that are to be controlled. The focus of this research is on estimating two bulk coefficients that drive the sensible heat flux, the latent heat flux, and the evaporation flux of a global ocean model, by assimilating temperature and salinity profiles using horizontal and temporal samplings similar to those to be provided by the Argo float system. The results of twin experiments show that the method is able to correctly estimate the large-scale variations in the bulk parameters, leading to a significant improvement in the atmospheric forcing applied to the ocean model.

TREATMENT OF THE ERROR DUE TO UNRESOLVED SCALES IN SEQUENTIAL DATA ASSIMILATION

2011 ◽  
Vol 21 (12) ◽  
pp. 3619-3626 ◽  
Author(s):  
ALBERTO CARRASSI ◽  
STÉPHANE VANNITSEM
Keyword(s):  
Dynamical System ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Large Scale ◽  
Model Error ◽  
Environmental Modeling ◽  
Sequential Data ◽  
Chaotic Dynamical System ◽  
Error Covariance ◽  
Sequential Data Assimilation

In this paper, a method to account for model error due to unresolved scales in sequential data assimilation, is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. This approach is tested in the context of a low order chaotic dynamical system. The results show that the filter skill is significantly improved by implementing the proposed scheme for the treatment of the unresolved scales.

Sequential data assimilation for single-molecule FRET photon-counting data

2015 ◽  
Vol 142 (21) ◽  
pp. 214115 ◽  
Author(s):  
Yasuhiro Matsunaga ◽  
Akinori Kidera ◽  
Yuji Sugita
Keyword(s):  
Data Assimilation ◽  
Single Molecule ◽  
Photon Counting ◽  
Sequential Data ◽  
Single Molecule Fret ◽  
Counting Data ◽  
Sequential Data Assimilation

Evaluating Effects of Observational Data Assimilation in General Ocean Circulation Model by Ensemble Kalman Filtering: Numerical Experiments with Synthetic Observations

2021 ◽  
pp. 50-66
Author(s):  
V. N. Stepanov ◽  
◽  
Yu. D. Resnyanskii ◽  
B. S. Strukov ◽  
A. A. Zelen’ko ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Observational Data ◽  
Ocean Circulation ◽  
Numerical Experiments ◽  
Turbulent Viscosity ◽  
Bottom Topography ◽  
Synthetic Data ◽  
Circulation Model ◽  
Ocean Model ◽  
Global Ocean

The quality of simulation of model fields is analyzed depending on the assimilation of various types of data using the PDAF software product assimilating synthetic data into the NEMO global ocean model. Several numerical experiments are performed to simulate the ocean–sea ice system. Initially, free model was run with different values of the coefficients of horizontal turbulent viscosity and diffusion, but with the same atmospheric forcing. The model output obtained with higher values of these coefficients was used to determine the first guess fields in subsequent experiments with data assimilation, while the model results with lower values of the coefficients were assumed to be true states, and a part of these results was used as synthetic observations. The results are analyzed that are assimilation of various types of observational data using the Kalman filter included through the PDAF to the NEMO model with real bottom topography. It is shown that a degree of improving model fields in the process of data assimilation is highly dependent on the structure of data at the input of the assimilation procedure.

Sign in / Sign up

Export Citation Format

Share Document