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Assimilation of 3D Radar Data and Derived Objects on the Convective Scale with an Ensemble-based Data Assimilation System 7 th International Symposium on Data Assimilation 24th of January 2019 RIKEN Center for Computational Science, Kobe, Japan

  1. Assimilation of 3D Radar Data and Derived Objects on the Convective Scale with an Ensemble-based Data Assimilation System 7 th International Symposium on Data Assimilation 24th of January 2019 RIKEN Center for Computational Science, Kobe, Japan Christian A. Welzbacher E. Bauernschubert, U. Blahak, R. Feger, A. de Lozar, R. Potthast, C. Schraff, K. Stephan, M. Werner

  2. Observations from Radar Network ➢ Systems: 17 Doppler C-Band ➢ Observables: Reflectivity, radial wind, polarimetric moments ➢ Temporal resolution: Volume scan + terrain-following precipitation-scan every 5 minutes ➢ Spatial resolution: 1° azimuthal angular 10 elevations (between 0.5° and 25°) 1 km radial (up to 180 km) Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 2

  3. Overview: Assimilation of Radar Data Object generation: Nowcasting Object ID Radar data direct Assimilation of Object generation: (radar-) objects Thresholding of composites indirect III. Features I. Direct assimilation of II. Texture 3D radar data Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 3

  4. DA-System: KENDA KENDA # : 4D-LETKF † (conventional data + 3D radar data / derived objects ) [+ LHN * ] x(t): state vector in model space Forward operator (e.g. EMVORADO [Zeng2016] ) H (x,t): model equivalent in observation space 1 hour 1 hour K : Kalman gain for ensemble mean # [Schraff2016], † [Hunt2007], * [Stephan2008] Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 4

  5. - Full 3D radar data similar [Bick2016] - Height & elevation dependent I. Direct observation error by statistics assimilation of [Desroziers2005] 3D radar data - Trigger for missing convection (warm bubbles, LHN) Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 5 5

  6. Assimilated Observations Observables: Reflectivity and radial wind (Volume scan only) Temporal resolution: Every 60 minutes (analysis time) Spatial resolution: 10 km (superobbing) Trigger: Warm bubbles if missing cells Superobbing grid Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 6

  7. Object generation: Threshold on radar reflectivity Thresholding of composites composites II. Texture Assimilation of (radar-) objects indirect Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 7

  8. Object generation: Threshold on radar reflectivity Thresholding of composites composites II. Texture Poster p3-16 by Shigenori Otsuka Assimilation of et al.: promising results using this (radar-) objects approach applied to precipitation indirect with SPEEDY-LETKF Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 8

  9. Texture Assimilation - Idea Normalized counts (observation) Normalized counts (model equivalent) = fulfills condition (e.g. reflectivity > threshold) → Observation: normalized number of in box = p( ) Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 9

  10. Texture Assimilation - Idea Normalized counts (observation) Normalized counts (model equivalent) = fulfills condition (e.g. reflectivity > threshold) → Observation: normalized number of in box = p( ) Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 10

  11. - Adaptive thresholding Object generation: - 3D Cells with attributes Nowcasting Object ID - Tracks and forecasts available Assimilation of (radar-) objects III. Features indirect - Spatial distribution of features - Number of features in area - Mean distance of features in area Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 11

  12. Feature Assimilation - Idea Counts (observation) Counts (model equivalent) / = identified objects (with collective attributes) Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 12

  13. Feature Assimilation - Idea Counts (observation) Counts (model equivalent) / = identified objects (with collective attributes) Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 13

  14. Results I. Direct vs. II. Texture assimilation of 3D radar data Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 14 14

  15. DA-cycle: FG- and ANA-RMSE RMSE: RH T WIND : first guess : analysis • 14 days with p hourly cycle • 40 members + deterministic • verified obs. same in all experiments 3D Radar Data + bubbles Texture (T=30 dBZ, b= 7x7 gp) No Radar Data Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 15

  16. Forecast Verification (6h, Reflectivity) 3D Radar Data + warm bubbles FSS-Threshold: 20 dbZ Texture (T=30 dBZ, b= 7x7 gp) No Radar Data FSS 27.05.-10.06.2016 FSS-Threshold: 30 dbZ FSS-Scale: 21x21 gp 6h forecasts, hourly 10-18 UTC, deterministic run Fraction Skill Score [Roberts & Lean, 2008] → Similar results for precipitation verification Lead time [h] Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 16

  17. Forecast Verification (24h, Surface/Temp) 3D Radar Data + warm bubbles better No Radar Data better ▪ Wind direction ▪ Wind speed ▪ Relative humidity ▪ Temperature Improvement in RMSE up to 5 % Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 17

  18. Conclusions 1. Direct assimilation of 3D Radar Data (Reflectivity and Radial Wind) + Positive in FG (RH, wind) * + Positive in reflectivity/precipitation (not shown) up to 0.2 in FSS over 6h * + Consistent improvement in upper air and surface verification * + Neutral/positive results in DA-Cycle, reflectivity, upper air and surface verification, worse in precipitation verification (compared to operational system, not shown) * compared to no radar data 2. Texture 3. Features (future) + Neutral (RH, T), positive (Wind) in FG + No matching, reducing double penalty + Almost equal skill in FSS for same threshold ? Vertical localization, proper metric ? Vertical localization, proper metric - Loss of information - Loss of information (smaller threshold) - Double counting of observations - Double counting of observations Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 18

  19. ご清聴ありがとうございました Thank you for your attention Gracias por tu atención Obrigado pela sua atenção Bedankt voor uw aandacht Takk for din oppmerksomhet Grazie per l'attenzione Kiitos huomiostasi 谢谢你的关注 Merci de votre attention مكمامتهلب اركش Спасибо за внимание Danke für Ihre Aufmerksamkeit Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 19

  20. Literature & Contact [Bick2015]: doi 10.1002/qj.2751 [Desroziers2005]: doi 10.1256/qj.05.108 [Hunt2007]: doi j.physd.2006.11.008 [Roberts & Lean, 2008]: doi 10.1175/2007MWR2123.1 [Schraff2016]: doi 10.1002/qj.2748 Dr. Christian A. Welzbacher Data Assimilation Unit (FE12) [Stephan2008]: doi 10.1002/qj.269 Deutscher Wetterdienst [Zeng2016]: doi 10.1002/qj.2904 E-Mail: christian.welzbacher@dwd.de Christian A. Welzbacher - Radar Data Assimilation – ISDA 2019 20

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