An Analog Offline EnKF for Paleoclimate Data Assimilation
Abstract An analog offline ensemble Kalman filter (AOEnKF) is proposed, which constructs ensemble priors from a control climate simulation for each assimilation time based on an analog criterion using proxy observations. Even though AOEnKF is an offline scheme and is therefore computationally econom...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2022-05-01
|
| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2021MS002674 |
| _version_ | 1828804656130686976 |
|---|---|
| author | Haohao Sun Lili Lei Zhengyu Liu Liang Ning Zhe‐Min Tan |
| author_facet | Haohao Sun Lili Lei Zhengyu Liu Liang Ning Zhe‐Min Tan |
| author_sort | Haohao Sun |
| collection | DOAJ |
| description | Abstract An analog offline ensemble Kalman filter (AOEnKF) is proposed, which constructs ensemble priors from a control climate simulation for each assimilation time based on an analog criterion using proxy observations. Even though AOEnKF is an offline scheme and is therefore computationally economical, it has the ability to capture “flow‐dependent” background error covariances that help spread observation information through climate fields. Extensive tests in the Lorenz05 model demonstrate that, compared to the online cycling EnKF (CEnKF), AOEnKF generates smaller posterior errors and requires much less computational cost. Compared to the commonly applied offline EnKF (OEnKF), AOEnKF has the advantages of having a more accurate prior ensemble mean and “flow‐dependent” background error covariances, even though the assimilation time scale is beyond significant forecast skill of the climate model. With varying ensemble sizes, sample sizes, observation error covariances and observing networks, AOEnKFs generally produce statistically significant error reduction relative to OEnKF, especially for larger sample sizes, increased observation uncertainties and sparser observing networks. The AOEnKF can be applied based on either the error of state variables from observations (AOEnKF_E) or the spatial correlation of state variables with observations (AOEnKF_C), with generally comparable results. |
| first_indexed | 2024-12-12T07:43:42Z |
| format | Article |
| id | doaj.art-179536a1a41042d4b41a89a2b8b7a766 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-12-12T07:43:42Z |
| publishDate | 2022-05-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-179536a1a41042d4b41a89a2b8b7a7662022-12-22T00:32:41ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662022-05-01145n/an/a10.1029/2021MS002674An Analog Offline EnKF for Paleoclimate Data AssimilationHaohao Sun0Lili Lei1Zhengyu Liu2Liang Ning3Zhe‐Min Tan4Key Laboratory of Mesoscale Severe Weather Ministry of Education, and School of Atmospheric Sciences Nanjing University Nanjing ChinaKey Laboratory of Mesoscale Severe Weather Ministry of Education, and School of Atmospheric Sciences Nanjing University Nanjing ChinaDepartment of Geography The Ohio State University Columbus OH USANational Key Laboratory for Virtual Geographic Environment Ministry of Education, and School of Geography Nanjing Normal University Nanjing ChinaKey Laboratory of Mesoscale Severe Weather Ministry of Education, and School of Atmospheric Sciences Nanjing University Nanjing ChinaAbstract An analog offline ensemble Kalman filter (AOEnKF) is proposed, which constructs ensemble priors from a control climate simulation for each assimilation time based on an analog criterion using proxy observations. Even though AOEnKF is an offline scheme and is therefore computationally economical, it has the ability to capture “flow‐dependent” background error covariances that help spread observation information through climate fields. Extensive tests in the Lorenz05 model demonstrate that, compared to the online cycling EnKF (CEnKF), AOEnKF generates smaller posterior errors and requires much less computational cost. Compared to the commonly applied offline EnKF (OEnKF), AOEnKF has the advantages of having a more accurate prior ensemble mean and “flow‐dependent” background error covariances, even though the assimilation time scale is beyond significant forecast skill of the climate model. With varying ensemble sizes, sample sizes, observation error covariances and observing networks, AOEnKFs generally produce statistically significant error reduction relative to OEnKF, especially for larger sample sizes, increased observation uncertainties and sparser observing networks. The AOEnKF can be applied based on either the error of state variables from observations (AOEnKF_E) or the spatial correlation of state variables with observations (AOEnKF_C), with generally comparable results.https://doi.org/10.1029/2021MS002674paleoclimate data assimilationensemble kalman filteranalog ensembleoffline assimilation |
| spellingShingle | Haohao Sun Lili Lei Zhengyu Liu Liang Ning Zhe‐Min Tan An Analog Offline EnKF for Paleoclimate Data Assimilation Journal of Advances in Modeling Earth Systems paleoclimate data assimilation ensemble kalman filter analog ensemble offline assimilation |
| title | An Analog Offline EnKF for Paleoclimate Data Assimilation |
| title_full | An Analog Offline EnKF for Paleoclimate Data Assimilation |
| title_fullStr | An Analog Offline EnKF for Paleoclimate Data Assimilation |
| title_full_unstemmed | An Analog Offline EnKF for Paleoclimate Data Assimilation |
| title_short | An Analog Offline EnKF for Paleoclimate Data Assimilation |
| title_sort | analog offline enkf for paleoclimate data assimilation |
| topic | paleoclimate data assimilation ensemble kalman filter analog ensemble offline assimilation |
| url | https://doi.org/10.1029/2021MS002674 |
| work_keys_str_mv | AT haohaosun ananalogofflineenkfforpaleoclimatedataassimilation AT lililei ananalogofflineenkfforpaleoclimatedataassimilation AT zhengyuliu ananalogofflineenkfforpaleoclimatedataassimilation AT liangning ananalogofflineenkfforpaleoclimatedataassimilation AT zhemintan ananalogofflineenkfforpaleoclimatedataassimilation AT haohaosun analogofflineenkfforpaleoclimatedataassimilation AT lililei analogofflineenkfforpaleoclimatedataassimilation AT zhengyuliu analogofflineenkfforpaleoclimatedataassimilation AT liangning analogofflineenkfforpaleoclimatedataassimilation AT zhemintan analogofflineenkfforpaleoclimatedataassimilation |