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

Assimilation of Satellite-retrieved Sea Ice Concentration and Prospects for September Predictions of Arctic Sea Ice

2020 ◽  
pp. 1-49
Author(s):  
Yong-Fei Zhang ◽  
Mitchell Bushuk ◽  
Michael Winton ◽  
Bill Hurlin ◽  
Xiaosong Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
Prediction System ◽  
Observational Error ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Concentration ◽  
Analysis Errors

AbstractThe current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea-ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice-ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SST) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (∼10%) from up to 3 times larger than this (∼30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from 5-daily to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.

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
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