A. Doerenbecher 1, M. Leutbecher 2, D. S. Richardson 3 & collaboration with G. N. Petersen 4

Transcription

1 slide 1 Comparison of observation targeting predictions during the (North) Atlantic TReC 2003 A. Doerenbecher 1, M. Leutbecher 2, D. S. Richardson 3 & collaboration with G. N. Petersen 4 1 Météo-France, Toulouse, Fr 2 ECMWF, Reading, UK 3 Met Office, Exeter, UK 4 Reading University, UK

2 slide 2 Plan & Motivations Motivations: Diagnose the behaviour of the sensitive areas computed during the TReC Do they agree together? Did the various techniques correctly predicted the sensitive areas? Plan: Few reminders about the real-time targeting; Study of the mutual agreement of the sensitive areas; Study of the impact of lead-time, observation coverage on the quantitative techniques

3 slide 3 Key ingredients for real-time targeting Definition of a real-time targeting case: Forecast to improve: verification domain + range Targeting time (deduce optimisation time) Lead-time (between now and targeting time) Current techniques involve complex tools Description of the future routine observation coverage (for HSV and ETKF techniques) Adjoint model (for singular vector based techniques) Ensemble of forecasts and associated transform (for ETKF) Assimilation operator (for quantifying techniques) All NA-TReC techniques generated sensitive areas

4 slide 4 Targeting with sensitive areas in a single scheme sensitive area targeting time adjoint model transform algorithm verification optimisation time forecast l ea im dt e Additional observations initial time flight plan Montreal 6-10th December st THORPEX International Science Symposium

5 slide 5 Targeting techniques involved in the NA-TReC 2003 Method 0 applies to the ETKF, ran at NCEP and Met Office Météo-France ran a TE-d95 configuration of singular vectors

6 slide 6 SAP matrix targeting date TReC case & subcase priority method opt-time origin lead-time verif-time verif-area

7 slide 7 Computation of the overlap ratio Evaluation of the agreement between sensitive areas Selection of the size (a) of sensitive areas : 8, 4, 2 or 1x10 6 km 2 ; Define the sensitive areas as a 1/0 fields (S j or S k ); Compute the overlap ratio (O jk ) by pair of sensitive areas aiming at the same case; Results: Importance of the a priori choice of the fixed sizes: bigger size, higher overlap rate! Need to define a significance on the overlap rate High similarity between TESV techniques, ETKF often left apart

8 slide 8 Examples of overlap TReC case 24, on 2 nd Dec 2003 at 18Z. ECMWF Hessian SVs and Met Office ETKF: small overlap ECMWF total energy SVs, Dry-T42 and Moist-T95: high overlap

9 slide 9 Sensitive areas overlap ratios: few results Focusing on the size 4x10 6 km 2

10 slide 10 Summary maps with Hessian singular vectors Apply to quantitative targeting methods, e.g.: Hessian SVs Summary map is a composite product each point value means an expected variance reduction each point represents the effect of a single probe-test network Configurations of the single probe-test networks: configuration 1 (test): 9 sondes with t/u/v on 3 levels on a 5 grid networks are spread on a 5 grid 300 hpa configuration 2 (real): 25 sondes with t/u/v on 3 levels on a 2.5 grid 500 hpa networks are spread on a 5 grid 850 hpa other configurations: increase vertical resolution to 11 levels Results on 2 cases: TReC 24 & 26.2

11 slide 11 Examples of summary maps Increase of the overlap ratios: resolution of the probe-test network has a spread effect. Summary map of original ETKF and with ECMWF HSV on TReC 24

12 slide 12 Influence of the lead time Reduce the lead time from 66 to 06 hours on TReC 26.2 Influence noticed on the similarity index between HSV sets (<75%) Not so strong an impact on the overall SAP (e.g. SAP with Hessians)

13 slide 13 Effect of the lead-time on the ETKF Influence of the lead-time on the ETKF perturbation spectrum? On the TReC 26.2, the lead-time has a stronger effect! collaboration with G.N. Petersen

14 slide 14 Influence of the data coverage (on HSVs) To predict a reasonable future routine observation coverage remains a challenge: it implies strong hypotheses The observation coverage used in the Hessian metric: All cloud sensitive observations are filtered out All TReC supplementary observations are filtered out Compare the coverage of the reference (prognostic) or real (diagnostic) routine observations (without TReC data) Results on TReC cases 24 & 26.2 High similarity index: about 90% (and more) Noticeable impact of TReC observations on the SAP shape: (still good overlap ratio):

15 slide 15 Observation coverage and real-time Hessian SVs Charts on the TReC 24 Reference versus real (nontrec+amdar) Real (nontrec+amdar) versus real (nontrec)

16 slide 16 few CONCLUSIONS Validation of the targeting techniques The quantitative estimates (from ETKF and HSV based) strongly differ. However we still don t know which one works better. These techniques (especially Hessian based approach) need extensive validation with a lot of cases; TReC is just the beginning About targeting techniques in general! Non quantitative techniques remain attractive because of their cost. Optimal targeting techniques depend on the system they are built with. Data assimilation system Nature of the chased weather phenomena (synoptic or meso-scale, depending on an IC problem) Quantitative methods should allow advanced targeting. An integrated approach could enable automation and fine user dependant tuning. In the future Find out which technique really improves the forecasts Start thinking about smaller scale targeting applications

17 slide 17 Constraints on real-time singular vector computation