A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary Tests
Abstract A four‐dimensional ensemble‐variational (4DEnVar) data assimilation (DA) system was developed based on the global forecast system of the Global/Regional Assimilation and Prediction System (GRAPES‐GFS). Instead of using the adjoint technique, this system utilizes a dimension‐reduced projecti...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2022-07-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2021MS002737 |
| _version_ | 1828432792814354432 |
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| author | Shujun Zhu Bin Wang Lin Zhang Juanjuan Liu Yongzhu Liu Jiandong Gong Shiming Xu Yong Wang Wenyu Huang Li Liu Yujun He Xiangjun Wu |
| author_facet | Shujun Zhu Bin Wang Lin Zhang Juanjuan Liu Yongzhu Liu Jiandong Gong Shiming Xu Yong Wang Wenyu Huang Li Liu Yujun He Xiangjun Wu |
| author_sort | Shujun Zhu |
| collection | DOAJ |
| description | Abstract A four‐dimensional ensemble‐variational (4DEnVar) data assimilation (DA) system was developed based on the global forecast system of the Global/Regional Assimilation and Prediction System (GRAPES‐GFS). Instead of using the adjoint technique, this system utilizes a dimension‐reduced projection (DRP) technique to minimize the cost function of the standard four‐dimensional variational (4DVar) DA. It dynamically predicts ensemble background error covariance (BEC) and realizes the explicit flow‐dependence of BEC in the variational configuration. An inflation technique based on a linear combination of analysis increments and balanced random perturbations, is utilized to overcome the problem of underestimation of BEC matrix (B‐matrix) during the assimilation cycle. To mitigate the spurious correlations in the ensemble B‐matrix caused by the insufficient ensemble members, an ensemble‐sample‐based subspace localization method is utilized. In order to evaluate the new system, single‐point observation experiments (SOEs) and observing system simulation experiments (OSSEs) were conducted with sounding and cloud‐derived wind data based on GRAPES‐GFS. The explicit flow‐dependent characteristic of the 4DEnVar system using a localized ensemble covariance was verified in the SOEs. In the OSSEs, the ensemble mean analysis of 4DEnVar outperforms the analysis of 4DVar. The deterministic forecast initialized from the 4DEnVar ensemble mean analysis has better performance in the short‐range forecasts, better (worse) performance in the early (late) period of the medium‐range forecasts in the Northern Extratropics, and opposite performance in the Southern Extratropics, and exhibits slightly worse effects in the Tropics. Moreover, the ensemble mean forecast initialized by the 4DEnVar system has higher forecast skills. |
| first_indexed | 2024-12-10T18:25:53Z |
| format | Article |
| id | doaj.art-96f434fa312a4a15b4c93e27f481cc21 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-12-10T18:25:53Z |
| publishDate | 2022-07-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-96f434fa312a4a15b4c93e27f481cc212022-12-22T01:38:05ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662022-07-01147n/an/a10.1029/2021MS002737A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary TestsShujun Zhu0Bin Wang1Lin Zhang2Juanjuan Liu3Yongzhu Liu4Jiandong Gong5Shiming Xu6Yong Wang7Wenyu Huang8Li Liu9Yujun He10Xiangjun Wu11Department of Earth System Science Tsinghua University Beijing ChinaDepartment of Earth System Science Tsinghua University Beijing ChinaCMA Earth System Modeling and Prediction Centre China Meteorological Administration Beijing ChinaState Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing ChinaCMA Earth System Modeling and Prediction Centre China Meteorological Administration Beijing ChinaCMA Earth System Modeling and Prediction Centre China Meteorological Administration Beijing ChinaDepartment of Earth System Science Tsinghua University Beijing ChinaDepartment of Earth System Science Tsinghua University Beijing ChinaDepartment of Earth System Science Tsinghua University Beijing ChinaDepartment of Earth System Science Tsinghua University Beijing ChinaState Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing ChinaCMA Earth System Modeling and Prediction Centre China Meteorological Administration Beijing ChinaAbstract A four‐dimensional ensemble‐variational (4DEnVar) data assimilation (DA) system was developed based on the global forecast system of the Global/Regional Assimilation and Prediction System (GRAPES‐GFS). Instead of using the adjoint technique, this system utilizes a dimension‐reduced projection (DRP) technique to minimize the cost function of the standard four‐dimensional variational (4DVar) DA. It dynamically predicts ensemble background error covariance (BEC) and realizes the explicit flow‐dependence of BEC in the variational configuration. An inflation technique based on a linear combination of analysis increments and balanced random perturbations, is utilized to overcome the problem of underestimation of BEC matrix (B‐matrix) during the assimilation cycle. To mitigate the spurious correlations in the ensemble B‐matrix caused by the insufficient ensemble members, an ensemble‐sample‐based subspace localization method is utilized. In order to evaluate the new system, single‐point observation experiments (SOEs) and observing system simulation experiments (OSSEs) were conducted with sounding and cloud‐derived wind data based on GRAPES‐GFS. The explicit flow‐dependent characteristic of the 4DEnVar system using a localized ensemble covariance was verified in the SOEs. In the OSSEs, the ensemble mean analysis of 4DEnVar outperforms the analysis of 4DVar. The deterministic forecast initialized from the 4DEnVar ensemble mean analysis has better performance in the short‐range forecasts, better (worse) performance in the early (late) period of the medium‐range forecasts in the Northern Extratropics, and opposite performance in the Southern Extratropics, and exhibits slightly worse effects in the Tropics. Moreover, the ensemble mean forecast initialized by the 4DEnVar system has higher forecast skills.https://doi.org/10.1029/2021MS002737four‐dimensional ensemble‐variational data assimilationDRP‐4DVarGRAPES‐GFSflow‐dependent |
| spellingShingle | Shujun Zhu Bin Wang Lin Zhang Juanjuan Liu Yongzhu Liu Jiandong Gong Shiming Xu Yong Wang Wenyu Huang Li Liu Yujun He Xiangjun Wu A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary Tests Journal of Advances in Modeling Earth Systems four‐dimensional ensemble‐variational data assimilation DRP‐4DVar GRAPES‐GFS flow‐dependent |
| title | A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary Tests |
| title_full | A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary Tests |
| title_fullStr | A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary Tests |
| title_full_unstemmed | A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary Tests |
| title_short | A Four‐Dimensional Ensemble‐Variational (4DEnVar) Data Assimilation System Based on GRAPES‐GFS: System Description and Primary Tests |
| title_sort | four dimensional ensemble variational 4denvar data assimilation system based on grapes gfs system description and primary tests |
| topic | four‐dimensional ensemble‐variational data assimilation DRP‐4DVar GRAPES‐GFS flow‐dependent |
| url | https://doi.org/10.1029/2021MS002737 |
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