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FOAM
Background |
Ocean model |
Assimilation |
Computing resources |
References
| Background |
The Met Office has produced operational surface wave and storm surge forecasts for 2 decades. In the last 5 years, it has started to provide daily operational forecasts for the deep ocean using the Forecasting Ocean Assimilation Model (FOAM) system, and forecasts for the shelf seas using a model developed by POL. It is also developing an operational system for seasonal prediction.
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| Ocean model |
The FOAM ocean and sea-ice model is similar to HadOM3 (Gordon et al. 2000), except in the following respects. A third order accurate upwind interpolation scheme is used for advection of tracers (Holland et al. 1998) and the velocities used to advect momentum are calculated using the method of Webb (1995). A rigid-lid is used with a formulation which avoids the Killworth instability (Bell 2000, appendix A). The Brown & Campana (1978) pressure averaging technique is used in some configurations to increase the model timestep. River inflow is based on climatological data from the Global Rivers Data Center (GRDC). The nesting of models is one-way. It uses the Flow Relaxation scheme (Davies 1976) with the bathymetries of the models in the nesting region prescribed to be as similar as possible. In models of the Atlantic, a 1 Sv exchange flow through the Gibraltar Straits is specified using the scheme developed by Roberts (2003). Storkey (2004) provides a self-contained description of the model formulation.
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| Assimilation system |
Data assimilation is based on a new version of the analysis correction scheme originally devised by Lorenc et al. (1991) and implemented for FOAM by Bell et al. (2000). The new version (Bell et al. 2003) provides a sub-optimal approximation to a variant of 4D variational assimilation. Analysis steps are performed once per day. Each observation makes its full impact on the model on the day it arrives and on subsequent days is taken into account by giving additional weight to the model at the observation's location. Each analysis step consists of a number of iterations. On each iteration the observations are separated into groups which are easily related (thermal profiles, saline profiles, surface temperature, surface height). For each group of observations (e.g. the temperature profile data), increments are calculated first for the directly related model variables (e.g. the temperature fields). These increment fields are then used to calculate increments for less directly related model variables (e.g. the velocity fields) using hydrostatic and geostrophic balance relationships, water property conservation or statistical relationships. These balancing increments make the analysis multivariate. Increments are also made to the observations (Bratseth 1986) so that the iterations converge towards the statistically optimal analysis. The univariate components of the model error covariance are specified as the sum of two 3D error covariances, one describing the ocean mesoscale, the other large scales including atmospheric synoptic scales. These and the observation error covariances are estimated from statistics of observation minus model values obtained from hindcast assimilations. Altimeter data are assimilated by displacement of isopycnal surfaces (an extension of the Cooper & Haines 1996 scheme). A pressure correction technique (Bell et al. 2004b) is employed to improve the dynamical balance near the equator.
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| Computing resources |
The Met Office has an NEC SX6 with 30 nodes. The FOAM system currently uses about 1% of this resource.
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| References |
- Bell, M. J. 2000 An assessment of the value of semi-implicit schemes, semi-Lagrangian schemes and various grids for ocean dynamics. Ocean Applications Technical Note No. 25, 51 pp. Available from Met Office, UK.
- Bell, M. J., Forbes, R. M., Hines, A. 2000 Assessment of the FOAM global data assimilation system for real-time operational ocean forecasting. J. Mar. Sys., 25, 1-22.
- Bell, M. J., Hines, A., and Martin, M. J. 2003 Variational assimilation evolving individual observations and their error estimates. Ocean Applications Tech Note 32. Available from Met Office, UK
- Bell, M. J., M. J. Martin, and N. K. Nichols 2004 Assimilation of data into an ocean model with systematic errors near the equator. Quart. J. Roy. Metero. Soc., 130, 853-871.
- Bratseth, A. M., 1986 Statistical interpolation by means of successive corrections. Tellus, 38A, 439-447.
- Brown, J.A., Campana, K.A., 1978: An economical time-differencing system for numerical weather prediction. Mon. Wea. Rev., 106, 1125-1135.
- Cooper, M., and Haines, K., 1996: Altimetric assimilation with water property conservation. J. Geophys. Res., 101, C1, 1059-1077.
- Davies, H. C., 1976 A lateral boundary formulation for multi-level prediction models. Quart. J. Roy. Meteor. Soc., 102, 405-418.
- Gordon, C., Cooper, C., Senior, C.A., Banks, H., Gregory, J.M., Johns, T.C., Mitchell, J.F.B. and Wood, R.A., 2000: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics, 16, 147.168.
- Holland, W. R., J.C. Chow and F.O. Bryan 1998 Application of a third-order upwind scheme in the NCAR ocean model. J. Climate, 11, 1487-1493.
- Lorenc, A.C., Bell, R.S. and MacPherson, B., 1991: The Meteorological Office analysis correction data assimilation scheme. Quart. J. Roy. Meteor. Soc., 117, 59.89.
- Storkey D. 2004 Initial tuning of FOAM high-resolution models. Ocean Applications Tech. Note, 34. Available from Met Office, UK.
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