Description of task
This work package will prepare data assimilation systems for production of fields of arctic key variables combining information from observations and models.
A set of data assimilation systems around regional NWP (HIRLAM/HIROMB) and ocean-sea ice climate (MI-IM/MIPOM, TOPAZ or Fram Strait model, NAOSIM) models using different data assimilation techniques (OI, SEIK, EnOI or EnKF, 3D Var, 4D Var) are prepared for production of atmospheric, oceanic and sea ice fields that combine information from models and observations. The individual activities benefit from development of models or assimilation methods in WP4.1 and WP4.2:
1. The operational coupled ocean-sea ice forecast model MI-IM/MIPOM is embedded in a SEIK (singular evolutive interpolated Kalman filter) assimilation scheme. The scheme uses OSI-SAF ice concentration and hydrographical observations and delivers analysed fields of ice concentration and thickness. Observations of ice thickness made available during the project is used to validate the model and to further develop the assimilation scheme so as to include such data.
2. Temperature/sound speed profiles or direct acoustic measurements from the Fram Strait array, will be assimilated close to real time. The model and the assimilation method are selected depending on the insights from WP4.2: Either ensemble optimal interpolation (EnOI) or or ensemble Kalman filter (EnKF) will be applied to the higher resolution Fram Strait model or EnKF to TOPAZ.
3. The feasibility of a regional Arctic atmosphere-ocean-sea ice analysis is demonstrated with a combined optimal interpolation (OI)/3D variational assimilation (3DVar) scheme for the coupled modelling system HIRLAM/HIROMB used for operational NWP. The combination OI/4D variational assimilation (4DVar) will be evaluated on short assimilation periods. The corresponding two assimilation systems are set up and the observations are prepared, in particular the radiances from space born microwave instruments for assimilation into the atmosphere component.
Building on the adjoint ocean-sea ice model ADNAOSIM generated in WP4.1, a prototype of a 4D variational data assimilation system will be set up. A cost function quantifying the misfit between model and observations is implemented. The observations encompass both DAMOCLES and non-DAMOCLES data. The adjoint code for the cost function is implemented.
