Estimating Ocean Observation Impacts on Coupled Atmosphere‐Ocean Models Using Ensemble Forecast Sensitivity to Observation (EFSO)

Autor: Chu‐Chun Chang, Tse‐Chun Chen, Eugenia Kalnay, Cheng Da, Safa Mote
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Geophysical Research Letters, Vol 50, Iss 20, Pp n/a-n/a (2023)
Druh dokumentu: article
ISSN: 1944-8007
0094-8276
DOI: 10.1029/2023GL103154
Popis: Abstract Ensemble Forecast Sensitivity to Observation (EFSO) is a technique that can efficiently identify the beneficial/detrimental impacts of every observation in ensemble‐based data assimilation (DA). While EFSO has been successfully employed on atmospheric DA, it has never been applied to ocean or coupled DA due to the lack of a suitable error norm for oceanic variables. This study introduces a new density‐based error norm incorporating sea temperature and salinity forecast errors, making EFSO applicable to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2‐LETKF and investigated the impact of ocean observations under a weakly coupled DA framework. By removing the detrimental ocean observations detected by EFSO, the CFSv2 forecasts were significantly improved, showing the validation of impact estimation and the great potential of EFSO to be extended as a data selection criterion.
Databáze: Directory of Open Access Journals
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