andrew lorenc head of data assimilation & ensembles numerical weather prediction met office, uk

Andrew Lorenc WOAP 2006 © Crown copyright 2006

Andrew LorencHead of Data Assimilation & Ensembles

Numerical Weather PredictionMet Office, UK

Data Assimilation – WGNE etc.WOAP August 2006


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


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


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 predictabilitydata assimilation for numerical weather and climate predictions, and estimation of derived climatological quantities

exchange of information through publications, workshops and meetings


Steady improvement in forecasts


S.Hem. Z500 T+24 rms v analyses

4D-Var 3DVar+ATOVS

ATOVSModel+Cov

Radiances+Cov

NOAA16+AMSU-BFGAT+Cov

2nd ATOVSNew stats

12hr 4D-VarHigher res.


N.Hem. Z500 T+24 rms v analyses

4D-Var 3DVar+ATOVS

ATOVSModel+Cov

Radiances+Cov FGAT+Cov

NOAA16+AMSU-B

12hr 4D-VarNew stats

2nd ATOVS

Higher res.


RMS errors with mean intra-annual variability removed

-30%

-20%

-10%

0%

10%

20%

30%

40%

Oct

-03

Nov

-03

Dec

-03

Jan-

04

Feb

-04

Mar

-04

Apr

-04

May

-04

Jun-

04

Jul-0

4

Aug

-04

Sep

-04

Oct

-04

Nov

-04

Dec

-04

Jan-

05

Feb

-05

Mar

-05

Apr

-05

May

-05

Jun-

05

Jul-0

5

Aug

-05

Sep

-05

trend UK ECMWF USAFrance Germany Japan Canada

Relative scores 2003-5 + dates of 4D-Var implementation4D

-Var im

plementation


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.


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”


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


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.


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.



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.



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.

andrew lorenc head of data assimilation & ensembles numerical weather prediction met office, uk

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