Andrew Lorenc Head of Data Assimilation & Ensembles Numerical Weather Prediction Met Office, UK

1. Data Assimilation – WGNE etc. WOAP August 2006 Andrew Lorenc Head of Data Assimilation & Ensembles Numerical Weather Prediction Met Office, UK

2. Data Assimilation – Summary (2005) • Growing field: • 0 increase in Met Office R&D effort (1999-2005) • 7%/year researchers (WMO DA Symposia, 1999-2005) • 100%/year computer power (Met Office, 2000-5) • 115%/year operational data volume (Met Office, 2000-6) • NWP • 4D-Var is most popular (for those who can afford it) • Fitting model to observations • DA for ObsSystem cal-val well established • DA for model development increasing • Assimilation products • Good fields are sufficient for some users • More information in ob-model assimilation diagnostics • Problems & Issues

3. Problems and Issues • Management: • Data volume & diversity • System complexity • Resources • Collaboration between operations & research • Scientific: • Error modelling • Efficient use of all obs, allowing for all errors • Representing uncertainty • Nonlinear models, non-Gaussian errors

4. WGNE: extracts from TOR • development of atmospheric models for weather prediction and climate studies • atmospheric physics processes, boundary layer processes and land surface processes in models • variability and predictability • data assimilation for numerical weather and climate predictions, and estimation of derived climatological quantities • exchange of information through publications, workshops and meetings

5. Steady improvement in forecasts

9. S.Hem. Z500 T+24 rms v analyses 3DVar+ATOVS 4D-Var Radiances+Cov FGAT+Cov ATOVS NOAA16+AMSU-B Model+Cov 2nd ATOVS New stats 12hr 4D-Var Higher res.

10. N.Hem. Z500 T+24 rms v analyses 3DVar+ATOVS 4D-Var Radiances+Cov FGAT+Cov ATOVS NOAA16+AMSU-B Model+Cov 2nd ATOVS New stats 12hr 4D-Var Higher res.

11. Relative scores 2003-5 + dates of 4D-Var implementation 4D-Var implementation

12. THORPEX – DA OS WG (Mar’06) • ATReC2003: value of targeted obs is ~twice normal, but overall impact is marginal & does not justify cost of deploying targeted obs on demand. • It remains important to make significant progress on the assimilation of satellite data. • Model error needs to be taken into account, but it is not obvious how. Links with multi-model ensemble research in TIGGE should help.

13. ECMWF/GEO Workshop on Atmospheric Reanalysis(June’06) • reported by Adrian • “how to determine and convey to users information on uncertainty and problems is paramount” • “many users want measures of expected accuracy or uncertainty”

14. DA can estimate errors that are being modelled(1) variances • OI gave analysis error variance for resolved random errors only • VAR can approximate this (via Hessian) • Deterministic ensemble methods (EnSRF, ETKF ...) use same eqns as OI • Stochastic ensemble methods (EnKF) rely on modelling of error distn – perturbed obs • All these methods underestimate total error – ad hoc “inflation” to fit (o-b)2 statistics is needed

15. DA can estimate errors that are being modelled(2) biases • Observation & model bias correction methods are being developed – could in principle estimate errors in determined bias • Above methods are often described as dealing with model error. In fact they are assuming that a different model (stochastic, with a few unknown parameters) is perfect. • Few methods consider “unknown unknowns”:- multi-model ensembles, “shadowing”. • Obtaining reliable total error estimates from a single DA system will be difficult, requiring modelling of all significant error sources in DA, model & obs.

16. Recommendations from WOAP DA Report: WOAP: fostering the development of data assimilation techniques for components of the earth system which are not part of operational systems. • Collate a list of groups with capability and interest to develop DA methods for fields of interest to WCRP but not currently part of established systems • Encourage them to make their results system (near-real-time analyses, seasonal climatologies, or extended re-analyses) available to the established centres, as part of a loosely coupled system. • Encourage the established centres to support these new developments: make available necessary output, validate and test, support bids

17. Recommendations from WOAP DA Report: WGNE: fostering the use of data assimilation to benefit climate research. • Using DA in model development. Comparing analyses with research obs globally and mesoscale. Climate models validated in assimilation mode. • Persuading operational centres to develop and maintain their DA systems in a way that they can be used for climate research such as re-analyses. (USA) • Promoting coupled land-atmosphere assimilation. • Focus attention on atmospheric model developments needed to help coupled modelling. How to improve models to better fit fluxes deduced from coupled ocean models?

18. Recommendations from WOAP DA Report: GSOP. Operational centres are focussing only on analyses for Seasonal-Interannual forecasting. Not yet a comparable sustained reanalysis activity addressing Dec-Cen and ACC prediction problems, (only in research). Nor adequate support of the general community. • GSOP should concentrate initially on all aspects of ocean re-analysis but should, in parallel, begin to approach the coupled problem involving ocean, atmosphere and sea ice.