Marine data assimilation
Marine data assimilation is a vital component in the production of Met Office ocean products. By assimilating observations we aim to bring ocean models closer to reality.
Data assimilation greatly improves the ability of ocean models to produce accurate forecasts and meaningful reanalyses. The marine data assimilation group is responsible for the maintenance and improvement of the data assimilation component of Met Office ocean models.
Temperature and salinity profiles, sea surface temerature (SST), sea surface height (SSH), and sea ice concentration observations are all assimilated into Met Office ocean model systems on a daily basis. Much of the data comes from satellites with various satellites providing SSH measurements, SST observations coming from the Group for High Resolution Sea Surface Temperature (GHRSST) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Faciility on Ocean and Sea Ice (OSI-SAF) providing sea ice concentration data. A wide variety of surface and profile in-situ measurements, including from the Argo programme, are also used. All data are quality controlled and bias corrected before assimilation.
Assimilation within the Forecast Ocean Assimilation Model (FOAM) systems is carried out using the 3DVar assimilation scheme.
Key aims
- To improve the accuracy of ocean model forecasts by the use of data assimilation.
- To continually improve our assimilation methods.
- To continually improve the representation and processing of errors in both models and observations.
- To ensure operational ingestion of ocean data into Met Office FOAM systems.
- To aid in the construction of data assimilation systems for coupled models and seamless prediction.
Current projects
- Developing and implementing coupled ocean/atmosphere data assimilation.
- Developing an operational system to produce global analyses of diurnal SST.
- Improving assimilation in the shelf seas by including more data types.
- Improving assimilation in the deep ocean through error covariance improvements.
- Demonstrating the impact of various data types on ocean and coupled ocean/atmosphere analyses and forecasts.
