Academic Journal
A Hybrid Gain Analog Offline EnKF for Paleoclimate Data Assimilation
| Title: | A Hybrid Gain Analog Offline EnKF for Paleoclimate Data Assimilation |
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| Authors: | Haohao Sun, Lili Lei, Zhengyu Liu, Liang Ning, Zhe‐Min Tan |
| Source: | Journal of Advances in Modeling Earth Systems, Vol 16, Iss 1, Pp n/a-n/a (2024) |
| Publisher Information: | American Geophysical Union (AGU), 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Physical geography LCC:Oceanography |
| Subject Terms: | paleoclimate data assimilation, hybrid gain approach, ensemble Kalman filter, analog ensemble, offline assimilation, Physical geography, GB3-5030, Oceanography, GC1-1581 |
| More Details: | Abstract For Paleoclimate data assimilation (PDA), a hybrid gain analog offline ensemble Kalman filter (HGAOEnKF) is proposed. It keeps the benefits of the analog offline ensemble Kalman filter (AOEnKF) that constructs analog ensembles from existing climate simulations with joint information of the proxies. The analog ensembles can provide more accurate prior ensemble mean and “flow‐dependent” error covariances than randomly sampled ensembles. HGAOEnKF further incorporates the benefits of static prior error covariances that better capture large‐scale error correlations and mitigate sampling errors than the sample prior error covariances, through a hybrid gain approach within an ensemble framework. Observing system simulation experiments are conducted for various data assimilation methods, using ensemble simulations from the Community Earth System Model‐Last Millennium Ensemble Project. Results show that using the static prior error covariances estimated from a sufficiently large sample set is beneficial for the traditional offline ensemble Kalman filter (OEnKF) and AOEnKF. HGAOEnKF method is superior to the OEnKF and AOEnKF with and without static prior error covariances, especially for the reconstruction of extreme events. The advantages of HGAOEnKF over OEnKF and AOEnKF with and without static prior error covariances are persistent with varying sample sizes and presence of model errors. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1942-2466 |
| Relation: | https://doaj.org/toc/1942-2466 |
| DOI: | 10.1029/2022MS003414 |
| Access URL: | https://doaj.org/article/4a006093e3a64b52a1fb13afca496189 |
| Accession Number: | edsdoj.4a006093e3a64b52a1fb13afca496189 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 19422466 |
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| DOI: | 10.1029/2022MS003414 |
| Published in: | Journal of Advances in Modeling Earth Systems |
| Language: | English |