Variational quality control of hydrographic profile data with non-Gaussian errors for global ocean variational data assimilation systems

2016 ◽  
Vol 104 ◽  
pp. 226-241 ◽  
Author(s):  
Andrea Storto
Keyword(s):  
Quality Control ◽  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Global Ocean ◽  
Profile Data ◽  
Non Gaussian

Implementing a variational data assimilation system in an operational 1/4 degree global ocean model

2014 ◽  
Vol 141 (687) ◽  
pp. 333-349 ◽  
Author(s):  
Jennifer Waters ◽  
Daniel J. Lea ◽  
Matthew J. Martin ◽  
Isabelle Mirouze ◽  
Anthony Weaver ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ocean Model ◽  
Variational Data Assimilation ◽  
Global Ocean ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Strongly Coupled Data Assimilation Experiments with Linearized Ocean–Atmosphere Balance Relationships

2018 ◽  
Vol 146 (4) ◽  
pp. 1233-1257 ◽  
Author(s):  
Andrea Storto ◽  
Matthew J. Martin ◽  
Bruno Deremble ◽  
Simona Masina
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Circulation Model ◽  
Variational Data Assimilation ◽  
Global Ocean ◽  
Background Error ◽  
Near Surface ◽  
Strongly Coupled ◽  
Coupled Data Assimilation ◽  
The Impact

Coupled data assimilation is emerging as a target approach for Earth system prediction and reanalysis systems. Coupled data assimilation may be indeed able to minimize unbalanced air–sea initialization and maximize the intermedium propagation of observations. Here, we use a simplified framework where a global ocean general circulation model (NEMO) is coupled to an atmospheric boundary layer model [Cheap Atmospheric Mixed Layer (CheapAML)], which includes prognostic prediction of near-surface air temperature and moisture and allows for thermodynamic but not dynamic air–sea coupling. The control vector of an ocean variational data assimilation system is augmented to include 2-m atmospheric parameters. Cross-medium balances are formulated either through statistical cross covariances from monthly anomalies or through the application of linearized air–sea flux relationships derived from the tangent linear approximation of bulk formulas, which represents a novel solution to the coupled assimilation problem. As a proof of concept, the methodology is first applied to study the impact of in situ ocean observing networks on the near-surface atmospheric analyses and later to the complementary study of the impact of 2-m air observations on sea surface parameters, to assess benefits of strongly versus weakly coupled data assimilation. Several forecast experiments have been conducted for the period from June to December 2011. We find that especially after day 2 of the forecasts, strongly coupled data assimilation provides a beneficial impact, particularly in the tropical oceans. In most areas, the use of linearized air–sea balances outperforms the statistical relationships used, providing a motivation for implementing coupled tangent linear trajectories in four-dimensional variational data assimilation systems. Further impacts of strongly coupled data assimilation might be found by retuning the background error covariances.

scholarly journals EnVAR for ICON-LAM: observations and quality control

2021 ◽  
Author(s):  
Mareike Burba ◽  
Sven Ulbrich ◽  
Stefanie Hollborn ◽  
Roland Potthast ◽  
Peter Knippertz
Keyword(s):  
Quality Control ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Future Development ◽  
Variational Data Assimilation ◽  
Limited Area ◽  
Deterministic Analysis ◽  
Ensemble Transform Kalman Filter ◽  
Nwp Model ◽  
Ensemble Transform

<p>The German Weather Service (DWD) introduces the regional NWP model ICON-LAM (ICON Limited Area Mode) in 2021 to replace the COSMO model. For the ICON-LAM data assimilation, a novel EnVAR (Ensemble VARiational data assimilation) setup is currently evaluated in comparison to the operational deterministic run of KENDA-LETKF (Local Ensemble Transform Kalman Filter). This requires special care as the observation handling differs for the global assimilation (via EnVAR) and the regional assimilation (KENDA). Furthermore, the variational quality control for the regional EnVAR may require a setup differing from the global setup. We will give an introduction to the observation processing in DWD's data assimilation framework (DACE).</p><p>For future development, we give an outlook on how a regional EnVAR can be used for a regional deterministic analysis by using a global ICON ensemble in combination with a regional deterministic ICON-LAM run. This is potentially of interest for DWD's partners with smaller computational capacities, because a regional EnVAR analysis is computationally less expensive than running a full KENDA ensemble assimilation cycle.</p>

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