Validation and Operational Implementation of the Navy Coastal Ocean Model Four Dimensional Variational Data Assimilation System (NCOM 4DVAR) in the Okinawa Trough

2016 ◽  
pp. 405-427
Author(s):  
Scott Smith ◽  
Hans Ngodock ◽  
Matthew Carrier ◽  
Jay Shriver ◽  
Philip Muscarella ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Okinawa Trough ◽  
Coastal Ocean ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
The Okinawa Trough ◽  
Coastal Ocean Model ◽  
Data Assimilation System ◽  
Assimilation System

Implementing a variational data assimilation system in an operational 1/4 degree global ocean model

2014 ◽  
Vol 141 (687) ◽  
pp. 333-349 ◽  
Author(s):  
Jennifer Waters ◽  
Daniel J. Lea ◽  
Matthew J. Martin ◽  
Isabelle Mirouze ◽  
Anthony Weaver ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Global Ocean ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Improving Variational Data Assimilation through Background and Observation Error Adjustments

2018 ◽  
Vol 146 (2) ◽  
pp. 485-501 ◽  
Author(s):  
Jann Paul Mattern ◽  
Christopher A. Edwards ◽  
Andrew M. Moore
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Observation Error ◽  
Observation Window ◽  
Error Covariance ◽  
Short Term Forecasting ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A procedure to objectively adjust the error covariance matrices of a variational data assimilation system is presented. It is based on popular diagnostics that utilize differences between observations and prior and posterior model solutions at the observation locations. In the application to a data assimilation system that combines a three-dimensional, physical–biogeochemical ocean model with large datasets of physical and chlorophyll a observations, the tuning procedure leads to a decrease in the posterior model-observation misfit and small improvements in short-term forecasting skill. It also increases the consistency of the data assimilation system with respect to diagnostics, based on linear estimation theory, and reduces signs of overfitting. The tuning procedure is easy to implement and only relies on information that is either prescribed to the data assimilation system or can be obtained from a series of short data assimilation experiments. The implementation includes a lognormal representation for biogeochemical variables and associated modifications to the diagnostics. Furthermore, the effect of the length of the observation window (number and distribution of observations) used to compute the diagnostics and the effect of neglecting model dynamics in the tuning procedure are examined.

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