Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4305-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4305-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
16 Sep 2020
Development and technical paper |
| 16 Sep 2020
| 16 Sep 2020
Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
Longjiang Mu
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
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Cited
23 citations as recorded by crossref.
- Paleoclimate data assimilation with CLIMBER-X: An ensemble Kalman filter for the last deglaciation A. Masoum et al. 10.1371/journal.pone.0300138
- Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System L. Mu et al. 10.1029/2022MS003176
- Evaluating the Detection of Oceanic Mesoscale Eddies in an Operational Eddy-Resolving Global Forecasting System H. Mo et al. 10.3390/jmse11122343
- WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework C. Shao & L. Nerger 10.5194/gmd-17-4433-2024
- Framework for an Ocean‐Connected Supermodel of the Earth System F. Counillon et al. 10.1029/2022MS003310
- Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter Y. Li et al. 10.3390/rs15071852
- The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case C. Shao & L. Nerger 10.3390/rs16020430
- Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model M. Chang et al. 10.1007/s00703-024-01015-1
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- Strongly Coupled Data Assimilation of Ocean Observations Into an Ocean‐Atmosphere Model Q. Tang et al. 10.1029/2021GL094941
- Improving the estimation of thermospheric neutral density via two-step assimilation of in situ neutral density into a numerical model A. Corbin & J. Kusche 10.1186/s40623-022-01733-z
- Mitigating Model Error via a Multimodel Method and Application to Tropical Intraseasonal Oscillations J. Torchinsky & S. Stechmann 10.1137/22M152551X
- Data assimilation of volcanic aerosol observations using FALL3D+PDAF L. Mingari et al. 10.5194/acp-22-1773-2022
- Robust reconstruction of glacier beds using transient 2D assimilation with Stokes S. Cook et al. 10.1017/jog.2023.26
- Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers A. Penenko & E. Rusin 10.3390/math10234522
- Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0 C. Sun et al. 10.5194/gmd-14-2635-2021
- An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018 C. Yang et al. 10.5194/gmd-15-1155-2022
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al. 10.5194/gmd-17-3559-2024
- An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation J. Sun et al. 10.5194/gmd-15-4805-2022
- Dynamical reconstruction of the upper-ocean state in the central Arctic during the winter period of the MOSAiC expedition I. Kuznetsov et al. 10.5194/os-20-759-2024
- Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review S. Zhang et al. 10.1007/s00382-020-05275-6
- Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting? E. Hunke et al. 10.1007/s40641-020-00162-y
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
20 citations as recorded by crossref.
- Paleoclimate data assimilation with CLIMBER-X: An ensemble Kalman filter for the last deglaciation A. Masoum et al. 10.1371/journal.pone.0300138
- Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System L. Mu et al. 10.1029/2022MS003176
- Evaluating the Detection of Oceanic Mesoscale Eddies in an Operational Eddy-Resolving Global Forecasting System H. Mo et al. 10.3390/jmse11122343
- WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework C. Shao & L. Nerger 10.5194/gmd-17-4433-2024
- Framework for an Ocean‐Connected Supermodel of the Earth System F. Counillon et al. 10.1029/2022MS003310
- Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter Y. Li et al. 10.3390/rs15071852
- The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case C. Shao & L. Nerger 10.3390/rs16020430
- Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model M. Chang et al. 10.1007/s00703-024-01015-1
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- Strongly Coupled Data Assimilation of Ocean Observations Into an Ocean‐Atmosphere Model Q. Tang et al. 10.1029/2021GL094941
- Improving the estimation of thermospheric neutral density via two-step assimilation of in situ neutral density into a numerical model A. Corbin & J. Kusche 10.1186/s40623-022-01733-z
- Mitigating Model Error via a Multimodel Method and Application to Tropical Intraseasonal Oscillations J. Torchinsky & S. Stechmann 10.1137/22M152551X
- Data assimilation of volcanic aerosol observations using FALL3D+PDAF L. Mingari et al. 10.5194/acp-22-1773-2022
- Robust reconstruction of glacier beds using transient 2D assimilation with Stokes S. Cook et al. 10.1017/jog.2023.26
- Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers A. Penenko & E. Rusin 10.3390/math10234522
- Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0 C. Sun et al. 10.5194/gmd-14-2635-2021
- An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018 C. Yang et al. 10.5194/gmd-15-1155-2022
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al. 10.5194/gmd-17-3559-2024
- An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation J. Sun et al. 10.5194/gmd-15-4805-2022
- Dynamical reconstruction of the upper-ocean state in the central Arctic during the winter period of the MOSAiC expedition I. Kuznetsov et al. 10.5194/os-20-759-2024
3 citations as recorded by crossref.
- Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review S. Zhang et al. 10.1007/s00382-020-05275-6
- Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting? E. Hunke et al. 10.1007/s40641-020-00162-y
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
Latest update: 29 Jun 2024
Short summary
Data assimilation combines observations with numerical models to get an improved estimate of the model state. This work discusses the technical aspects of how a coupled model that simulates the ocean and the atmosphere can be augmented by data assimilation functionality provided in generic form by the open-source software PDAF (Parallel Data Assimilation Framework). A very efficient program is obtained that can be executed on high-performance computers.
Data assimilation combines observations with numerical models to get an improved estimate of the...
