Development of an Operational Data Assimilation Package Using NAAPS and NAVDAS

2009 ◽  
Author(s):  
Jianglong Zhang
Keyword(s):  
Data Assimilation ◽  
Operational Data

OPERATIONAL DATA ASSIMILATION IN MARINE MODELING: WATER LEVEL FORECASTING WITH REGULARISATION TECHNIQUES

2004 ◽  
pp. 1197-1204 ◽  
Author(s):  
JACOB TORNFELDT SØRENSEN ◽  
HENRIK MADSEN ◽  
HENRIK MADSEN
Keyword(s):  
Data Assimilation ◽  
Water Level ◽  
Operational Data

Short-Range Prediction Experiments with Operational Data Assimilation System for the Kuroshio South of Japan

2004 ◽  
Vol 60 (2) ◽  
pp. 269-282 ◽  
Author(s):  
Masafumi Kamachi ◽  
Tsurane Kuragano ◽  
Satoshi Sugimoto ◽  
Kumi Yoshita ◽  
Toshiyuki Sakurai ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Short Range ◽  
South Of Japan ◽  
The Kuroshio ◽  
Operational Data ◽  
Data Assimilation System ◽  
Assimilation System

Diagnosis and tuning of observational error in a quasi-operational data assimilation setting

2006 ◽  
Vol 132 (615) ◽  
pp. 543-565 ◽  
Author(s):  
Bernard Chapnik ◽  
Gerald Desroziers ◽  
Florence Rabier ◽  
Olivier Talagrand
Keyword(s):  
Data Assimilation ◽  
Observational Error ◽  
Operational Data

Operational Data Assimilation System for the Kuroshio South of Japan: Reanalysis and Validation

2004 ◽  
Vol 60 (2) ◽  
pp. 303-312 ◽  
Author(s):  
Masafumi Kamachi ◽  
Tsurane Kuragano ◽  
Hiroshi Ichikawa ◽  
Hirohiko Nakamura ◽  
Ayako Nishina ◽  
...  
Keyword(s):  
Data Assimilation ◽  
South Of Japan ◽  
The Kuroshio ◽  
Operational Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Physically-based data assimilation

2010 ◽  
Vol 3 (2) ◽  
pp. 517-540 ◽  
Author(s):  
G. Levy ◽  
M. Coon ◽  
G. Nguyen ◽  
D. Sulsky
Keyword(s):  
Data Assimilation ◽  
Simulation Experiment ◽  
Proof Of Concept ◽  
Model Skill ◽  
Physically Based ◽  
One Step ◽  
Assimilation Scheme ◽  
Operational Data ◽  
Physical Principles ◽  
Ice Model

Abstract. Ideally, a validation and assimilation scheme should maintain the physical principles embodied in the model and be able to evaluate and assimilate lower dimensional features (e.g., discontinuities) contained within a bulk simulation, even when these features are not directly observed or represented by model variables. We present such a scheme and suggest its potential to resolve or alleviate some outstanding problems that stem from making and applying required, yet often non-physical, assumptions and procedures in common operational data assimilation. As proof of concept, we use a sea-ice model with remotely sensed observations of leads in a one-step assimilation cycle. Using the new scheme in a sixteen day simulation experiment introduces model skill (against persistence) several days earlier than in the control run, improves the overall model skill and delays its drop off at later stages of the simulation. The potential and requirements to extend this scheme to different applications, and to both empirical and statistical multivariate and full cycle data assimilation schemes, are discussed.

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