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

Page 2 Andrew Lorenc WOAP 2006 © Crown copyright 2006 Data Assimilation – Summary (2005)  Growing field:  0 increase in Met Office R&D effort ( )  7%/year researchers (WMO DA Symposia, )  100%/year computer power (Met Office, )  115%/year operational data volume (Met Office, )  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

Page 3 Andrew Lorenc WOAP 2006 © Crown copyright 2006 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

Page 4 Andrew Lorenc WOAP 2006 © Crown copyright 2006 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

Page 5 Andrew Lorenc WOAP 2006 © Crown copyright 2006 Steady improvement in forecasts

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Page 9 Andrew Lorenc WOAP 2006 © Crown copyright 2006 S.Hem. Z500 T+24 rms v analyses 4D-Var 3DVar+ATOVS ATOVS Model+Cov Radiances+Cov NOAA16+AMSU-B FGAT+Cov 2nd ATOVS New stats 12hr 4D-Var Higher res.

Page 10 Andrew Lorenc WOAP 2006 © Crown copyright 2006 N.Hem. Z500 T+24 rms v analyses 4D-Var 3DVar+ATOVS ATOVS Model+Cov Radiances+Cov FGAT+Cov NOAA16+AMSU-B 12hr 4D-Var New stats 2nd ATOVS Higher res.

Page 11 Andrew Lorenc WOAP 2006 © Crown copyright 2006 Relative scores dates of 4D-Var implementation 4D-Var implementation

Page 12 Andrew Lorenc WOAP 2006 © Crown copyright 2006 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.

Page 13 Andrew Lorenc WOAP 2006 © Crown copyright 2006 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”

Page 14 Andrew Lorenc WOAP 2006 © Crown copyright 2006 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

Page 15 Andrew Lorenc WOAP 2006 © Crown copyright 2006 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.

Page 16 Andrew Lorenc WOAP 2006 © Crown copyright 2006 Recommendations from WOAP DA Report:  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 WOAP: fostering the development of data assimilation techniques for components of the earth system which are not part of operational systems.

Page 17 Andrew Lorenc WOAP 2006 © Crown copyright 2006 Recommendations from WOAP DA Report:  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? WGNE: fostering the use of data assimilation to benefit climate research.

Page 18 Andrew Lorenc WOAP 2006 © Crown copyright 2006 Recommendations from WOAP DA Report:  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. 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.