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Forecast sensitivity to Observation Carla Cardinali

Forecast sensitivity to Observation Carla Cardinali. Outline Part 2: Forecast Sensitivity. Forecast Sensitivity to Observation Sensitivity gradient A-TReC campaigns Comparing Observation Analysis Influence and Observation Forecast Impact Results and Conclusion. the sensitivity respect

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Forecast sensitivity to Observation Carla Cardinali

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  1. Forecast sensitivity to ObservationCarla Cardinali

  2. Outline Part 2: Forecast Sensitivity • Forecast Sensitivity to Observation • Sensitivity gradient • A-TReC campaigns • Comparing Observation Analysis Influence and Observation Forecast Impact • Results and Conclusion

  3. the sensitivity respect the initial condition xa Analysis sensitivity with respect the observation J is measures the forecast error: its gradient respect the observation vector y gives the forecast error sensitivity respect observations used in the initial condition for model forecast Forecast Sensitivity to Observation

  4. Define Forecast Sensitivity

  5. Change of Variable

  6. za z Linear system to solve Computation

  7. Tangent Linear Model ResolutionT95 L60 Forecast Sensitivity to Observations

  8. 500 hPa Temperature Sensitivity Gradients 100*TE at t=0 100*KE at t=0 TE at t=T1 KE at t=T1 T1 Sensitive area Verification area Sensitivity Gradients

  9. Fc Sensitivity to Aircraft Temperature 500 hPa

  10. Fc Sensitivity to Surface Pressure

  11. An 5 Dec 18 UTC Targeting = VerificationRegion Lat(30,50)-Lon(-85,-60) 42h AtreC 13% MSLP Relative Fc Improvement 9% Total Energy Observation Campaign 5 Dec 18 UTC---Verification 7 Dec 12 UTC TargetIN/NOTargetIN % AMDAR 2.5 SONDE 5.5

  12. Observations Contribution to Forecast Total Contribution Mean Contribution

  13. Forecast and Analysis Sensitivity to Targeted Observations

  14. 200-300 hPa Targeted Aircraft Temperature Forecast Error

  15. Observation Influence in Analysis Aircraft Observation U-Comp 200-300 hPa Background Influence = 1-Observation Influence Forecast Impact

  16. TEMP Observation Temperature 850-1000 hPa Observation Influence in Analysis Background Influence = 1-Observation Influence Forecast Impact

  17. Total Forecast Error 5 Dec 2003 TargetIN/NOTargetIN 8%

  18. TargetingLat(30,60)-Lon(-70,-15) Verification Lat(45,65)-Lon(-15,+10) 54h AtreC -71% MSLP Relative Fc Degradation -7% Total Energy Observation Campaign 8 Dec 18 UTC---Verification 11 Dec 00 UTC TargetIN/NOTargetIN % AMDAR 2.6 SONDE 0.9

  19. 200-300 hPa Targeted Aircraft U Forecast Error

  20. Total Forecast Error 8 Dec 2003 TargetIN/NOTargetIN 3.5%

  21. Conclusions • Forecast sensitivity to observations has been computed for the campaigns showing an impact (ATreC-Cntr)/Cntr ≥ ± 10% • 13 cases out of 38: 9 positive and4 negative • Two campaigns have been shown • 5 Dec at 18 UTC - Targeted observations improved the forecast of a cyclone moving along the east coast of North America for which severe weather impact was forecast • 8 Dec at 18 UTC – Targeted observations deployed to clarify the models uncertainties for the remnants of the east cost storm, degraded the forecast over Northern Europe – UK

  22. Forecast sensitivity to observations • Baker and Daley, 2000. Observation and background adjoint sensitivity in the adaptive observation-targeting problem. QJRMS 126, 1431-1454 • Langland and Baker, 2004. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56A, 189-201 • Gelaro, Buizza, Palmer and Klinker, 1998. Sensitivity analysis of forecast errors and the construction of optimal perturbations using singular vectors. JAS 55, 1012-1037 • Influence Matrix • Cardinali, Pezzulli and Andersson, 2004. Influence-matrix diagnostic of data assimilation system. QJRMS 130, 2767-2786 • Pre-processing • Järvinen. Observations and diagnostic tools for data assimilation. ECMWF lecture note References

  23. time Sensitive area Verification area Singular vectors brief definition • Singular vectors was one of technique used in AtreC-TOST campaign to find sensitivity areas where releasing additional observations • Singular vectors (SVs) define perturbations with fastest growth during a finite time interval (optimization time interval). They are defined by: • The model characteristics: TL95L60, dry, with simplified physics • The norm used to measure growth: localized total energy • The optimization time interval: 42-54 hours • Diagnostic Singular vectors have been computed to investigate the observation impact in the forecast

  24. Linear combination of 10 Diagnostic SVs valid at observation time AtreC observation time forecast step T1 localized total energy maximum in verification area eigenvalues decomposition forecast error step T1 proj. fc error onto SVs Back to the observation time T1 Sensitive area Verification area

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