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.
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
31 citations as recorded by crossref.
- NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components H. Li et al.
- Ocean carbon sink assessment via temperature and salinity data assimilation into a global ocean biogeochemistry model F. Bunsen et al.
- Evaluating the Detection of Oceanic Mesoscale Eddies in an Operational Eddy-Resolving Global Forecasting System H. Mo et al.
- WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework C. Shao & L. Nerger
- Framework for an Ocean‐Connected Supermodel of the Earth System F. Counillon et al.
- Development of a flexible data assimilation system for a 3D unstructured-grid ocean model under Earth System Modeling Framework H. Yu et al.
- Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model M. Chang et al.
- Data assimilation of volcanic aerosol observations using FALL3D+PDAF L. Mingari et al.
- Robust reconstruction of glacier beds using transient 2D assimilation with Stokes S. Cook et al.
- Resolving the limits of MJO forecast skill: large-sample-based ensemble optimization in the IAP-CAS S2S model Y. Liu et al.
- Reconstruction of Baltic Gridded Sea Levels from Tide Gauge and Altimetry Observations Using Spatiotemporal Statistics from Reanalysis J. Elken et al.
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al.
- The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1) M. Buehner et al.
- A Python interface to the Fortran-based Parallel Data Assimilation Framework: pyPDAF v1.0.2 Y. Chen et al.
- Paleoclimate data assimilation with CLIMBER-X: An ensemble Kalman filter for the last deglaciation A. Masoum et al.
- Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System L. Mu et al.
- Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter Y. Li et al.
- The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case C. Shao & L. Nerger
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al.
- Strongly Coupled Data Assimilation of Ocean Observations Into an Ocean‐Atmosphere Model Q. Tang et al.
- Improving the estimation of thermospheric neutral density via two-step assimilation of in situ neutral density into a numerical model A. Corbin & J. Kusche
- Mitigating Model Error via a Multimodel Method and Application to Tropical Intraseasonal Oscillations J. Torchinsky & S. Stechmann
- EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters J. Bruggeman et al.
- Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model Y. Liu et al.
- Ensuring sustainable coastal fisheries under changing climate conditions and the scramble for fish resources G. Dinesen et al.
- Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers A. Penenko & E. Rusin
- Benefit of MAGIC and multipair quantum satellite gravity missions in Earth science applications J. Kusche et al.
- Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0 C. Sun et al.
- An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018 C. Yang et al.
- An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation J. Sun et al.
- Dynamical reconstruction of the upper-ocean state in the central Arctic during the winter period of the MOSAiC expedition I. Kuznetsov et al.
31 citations as recorded by crossref.
- NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components H. Li et al.
- Ocean carbon sink assessment via temperature and salinity data assimilation into a global ocean biogeochemistry model F. Bunsen et al.
- Evaluating the Detection of Oceanic Mesoscale Eddies in an Operational Eddy-Resolving Global Forecasting System H. Mo et al.
- WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework C. Shao & L. Nerger
- Framework for an Ocean‐Connected Supermodel of the Earth System F. Counillon et al.
- Development of a flexible data assimilation system for a 3D unstructured-grid ocean model under Earth System Modeling Framework H. Yu et al.
- Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model M. Chang et al.
- Data assimilation of volcanic aerosol observations using FALL3D+PDAF L. Mingari et al.
- Robust reconstruction of glacier beds using transient 2D assimilation with Stokes S. Cook et al.
- Resolving the limits of MJO forecast skill: large-sample-based ensemble optimization in the IAP-CAS S2S model Y. Liu et al.
- Reconstruction of Baltic Gridded Sea Levels from Tide Gauge and Altimetry Observations Using Spatiotemporal Statistics from Reanalysis J. Elken et al.
- HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model Q. Tang et al.
- The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1) M. Buehner et al.
- A Python interface to the Fortran-based Parallel Data Assimilation Framework: pyPDAF v1.0.2 Y. Chen et al.
- Paleoclimate data assimilation with CLIMBER-X: An ensemble Kalman filter for the last deglaciation A. Masoum et al.
- Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System L. Mu et al.
- Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter Y. Li et al.
- The Impact of Profiles Data Assimilation on an Ideal Tropical Cyclone Case C. Shao & L. Nerger
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al.
- Strongly Coupled Data Assimilation of Ocean Observations Into an Ocean‐Atmosphere Model Q. Tang et al.
- Improving the estimation of thermospheric neutral density via two-step assimilation of in situ neutral density into a numerical model A. Corbin & J. Kusche
- Mitigating Model Error via a Multimodel Method and Application to Tropical Intraseasonal Oscillations J. Torchinsky & S. Stechmann
- EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters J. Bruggeman et al.
- Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model Y. Liu et al.
- Ensuring sustainable coastal fisheries under changing climate conditions and the scramble for fish resources G. Dinesen et al.
- Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers A. Penenko & E. Rusin
- Benefit of MAGIC and multipair quantum satellite gravity missions in Earth science applications J. Kusche et al.
- Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0 C. Sun et al.
- An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018 C. Yang et al.
- An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation J. Sun et al.
- Dynamical reconstruction of the upper-ocean state in the central Arctic during the winter period of the MOSAiC expedition I. Kuznetsov et al.
Saved (final revised paper)
Latest update: 26 May 2026
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...