1. Slide 1 International Typhoon Workshop Tokyo 2009 Slide 1 Impact of increased satellite data density in sensitive areas Carla Cardinali, Peter Bauer, Roberto Buizza, Jean-Noël Thépaut ECMWF Florian Harnisch and Martin Weissmann DLR Many thanks to Fernando Prates

2. Slide 2 International Typhoon Workshop Tokyo 2009 Slide 2 Background Thinning of data is applied to: － reduce data volume － avoid the introduction of spatial observation error correlation that is currently not accounted for in data assimilation algorithm Thinning is performed statically on a fixed latitude/longitude grid. Objective Evaluate impact of selective satellite observational data thinning on medium-range NWP aiming at denser data in sensitive areas and less dense data in other areas  trade-off between data impact and data volume. Approach Experiments with global data thinning: － Change global latitude/longitude thinning grid. Experiments with data thinning in selected regions: － Increased density in sensitive areas and reduced density elsewhere using a Singular Vector based measure to identify areas from which forecast errors are growing fast (ECMWF 2007 QJ papers). － Sensitive areas are computed for Southern Hemisphere Study contents

3. Slide 3 International Typhoon Workshop Tokyo 2009 Slide 3 More dense satellite data coverage on SV-areas in the Southern Hemisphere More dense data coverage in SV-areas in Typhoon areas Typhoon Track Forecast impact assessment Forecast sensitivity to evaluate the 24-hour forecast impact Outline

4. Slide 4 International Typhoon Workshop Tokyo 2009 Slide 4 Southern Hemisphere Experiments Selective data thinning Thinn_Cntrl: ~ is 1.25 o proxy for Thinn_1.25 Thinn_SV: ~ is 1.25 o and 0.625 o in SV areas. Thinn_RD: ~ is 1.25 o and 0.625 o in randomly distributed areas. Thinn_CSV: ~ is 1.25 o and 0.625 o in Climatological SV areas. Thinn_0.625 Additional information All experiments are run at T511L91 (12-hour 4D-Var) for 01/12/2008- 28/02/2009. All experiments are verified with T799L91 operational model analyses (without first 7 days (spin-up) i.e. 83 cases). All SV/RD/CSV areas occupy same fraction (15%) of the Southern hemisphere. The SV-based climatology derived from the mean 2007 SV-areas.

5. Slide 5 International Typhoon Workshop Tokyo 2009 Slide 5 Data coverage: Single case 01/01/2009 00 UTC data density AMSU-A channel 9: Singular Vectors: Randomly distributed circular areas: 2007 Singular Vector climatology:

6. Slide 6 International Typhoon Workshop Tokyo 2009 Slide 6 Data coverage: Average 01-07/01/2009 00 and 12 UTC data density AMSU-A channel 9: Singular Vectors: Randomly distributed circular areas: 2007 Singular Vector climatology:

7. Slide 7 International Typhoon Workshop Tokyo 2009 Slide 7 Selective data thinning: DFS Decrease of DFS relative to the Thin_0.625 experiment Global Southern Hemisphere

8. Slide 8 International Typhoon Workshop Tokyo 2009 Slide 8 Selective data thinning: Forecast impact SV-CNTRL Southern H. Normalized RMSE 95% confidence 83 cases 1000 hPa 500 hPa 200 hPa 0 1 2 3 4 5 6 7 8 Forecast Day

9. Slide 9 International Typhoon Workshop Tokyo 2009 Slide 9 Selective data thinning: Forecast impact SV-RD Southern H. Normalized RMSE 95% confidence 83 cases 1000 hPa 500 hPa 200 hPa 0 1 2 3 4 5 6 7 8 Forecast Day

10. Slide 10 International Typhoon Workshop Tokyo 2009 Slide 10 Selective data thinning: Forecast impact SV - CSV Southern H. Normalized RMSE 95% confidence 83 cases 1000 hPa 500 hPa 200 hPa 0 1 2 3 4 5 6 7 8 Forecast Day

11. Slide 11 International Typhoon Workshop Tokyo 2009 Slide 11 Sinlaku: Track forecast between 00 UTC 09 - 19 Sept. last forecast verification time 12 UTC 20 Sept. (classified as extra-tropical in best track data from 00 UTC 21 Sept) Hagupit: track forecast between 00 UTC 20 - 24 Sept. last forecast verification time 00 UTC 25 Sept. (dispersing over land, tropical depression from 00 UTC 25 Sept) Jangmi: track forecast between 00 UTC 25 - 29 Sept. last forecast verification time 12 UTC 30 Sept. (classified as extra-tropical in best track data from 00 UTC 01 Oct) Typhoons TPARK campaign Summer 2008

12. Slide 12 International Typhoon Workshop Tokyo 2009 Slide 12 Targeting Typhoon season with extra-satellite data Selective data thinning experiments Cntrl: 1.25 o Global SV-Sat: 1.25 o Global and 0.625 o in SV areas. Drop: 1.25 o Global +Targeted Dropsondes SV-Sat-Drop: Targeted Dropsondes+ SV areas 0.625 o Additional information All experiments are run at T799TL95/159/255 L91 (12-hour 4D-Var) 06-30 September 2008 Verification and SV-target region 10-50N, 110-180E 20 Leading T95L62 SV SVs area cover 20% of the target region

13. Slide 13 International Typhoon Workshop Tokyo 2009 Slide 13 Targeting Typhoon season with extra-satellite data: SV-areas

14. Slide 14 International Typhoon Workshop Tokyo 2009 Slide 14 09 + 10 11 Sept SV-Sat + Drop cntrl Sinlaku 09-19 September: mean track error km Drop cntr cntrl SV -Sat

15. Slide 15 International Typhoon Workshop Tokyo 2009 Slide 15 intensification 1 2 3

16. Slide 16 International Typhoon Workshop Tokyo 2009 Slide 16 CNTRL very accurate track forecast : difficult to improve Hagupit 20-24 September

17. Slide 17 International Typhoon Workshop Tokyo 2009 Slide 17 cntrl SV-Sat + Drop SV -Sat cntrl Drop cntrl Hagupit 20-24 September

18. Slide 18 International Typhoon Workshop Tokyo 2009 Slide 18 Difficult to determine TC position over land Jangmi 25-27 September

19. Slide 19 International Typhoon Workshop Tokyo 2009 Slide 19 cntrl SV-Sat+Drop Drop cntrl SV-Sat cntrl Jangmi 25-27 September

20. Slide 20 International Typhoon Workshop Tokyo 2009 Slide 20 Forecast sensitivity to observation The tool provides information on the observation type, subtype, variable and level responsible for the forecast error variation Forecast error KTKT Observation departure

21. Slide 21 International Typhoon Workshop Tokyo 2009 Slide 21 Forecast Sensitivity to Obs: SV-Sat+Drop

22. Slide 22 International Typhoon Workshop Tokyo 2009 Slide 22 Forecast Sensitivity to Obs: SV-Sat+Drop Forecast error and Verifying analysis

23. Slide 23 International Typhoon Workshop Tokyo 2009 Slide 23 Conclusions Selective data thinning Forecast scores are best for experiment with increased data density in SV-based areas that are updated for each analysis. 40% loss of DFS by increasing the data density over SV areas instead than globally. Targeting Typhoon with extra satellite data Limited statistical sample Extra-satellite data gave a more consistent impact due to homogeneous coverage and data diversity (moist, temperature, cloud, precipitation and surface wind) Forecast Sensitivity To Observation (FSO) The forecast value per Observation shows that dropsondes are more beneficial that extra-radiances Strong impact per dropsonde produces more extreme beneficial/detrimental impact Computation of forecast error by using observation instead of analysis field is likely to shows larger dropsonde impact on typhoon.

24. Slide 24 International Typhoon Workshop Tokyo 2009 Slide 24 Forecast sensitivity to observation: Equations and Solution Analysis solution Analysis sensitivity to observation and background J is a measure of the forecast error: energy norm Forecast error sensitivity to the analysis The tool providesinformation on the observation type, subtype, variable and level responsible for the forecast error variation Rabier F, et al. 1996. Solve the linear system: Compute the δJ

25. Slide 25 International Typhoon Workshop Tokyo 2009 Slide 25 Global data thinning: Forecast impact Thin_2.5-Thin_1.2 500hPa Normalized RMSE 95% confidence 55 cases 0 1 2 3 4 5 6 7 8 Forecast Day North H South H

26. Slide 26 International Typhoon Workshop Tokyo 2009 Slide 26 Global data thinning: Forecast impact Thin_1.2-Thin_0.6 500hPa Normalized RMSE 95% confidence 83 cases North H South H 0 1 2 3 4 5 6 7 8 Forecast Day