1. Strategies of using radar/conventional data for improving QPF at cloud-resolving scale by the ensemble Kalman filter Kao-Shen Chung 1, Weiguang Chang 1, Seung-Jong Baek 2 and Luc Fillion 2 Collaborators: Isztar Zawadzki 1, M.K Yau 1 1.Dept of Atmospheric and Oceanic Sciences, McGill University 2.Meteorological Research Division, Environment Canada Kao-Shen Chung( 鍾高陞 ) National Central University May 6th, 2014

2. Outline 1.Introduction of the Canadian High Resolution Ensemble Kalman Filter (HREnKF) system 2. Examine the impact of position error with radar data assimilation 3. Strategies of improving QPF at convective scale a) regional assimilation system b) Adaptive Radar observation 4. Summary and future works

3. 1. High Resolution Ensemble Kalman Filter System ( HREnKF ) Initial guess Ensembl e member s Add random perturbations Data assimilatio n Observations Perturbed observation s GEM-LAM forecast for all the members Add random perturbation s (model error) Analysis step Forecast step GEnKF (2005 operational system) LAM 1-km ( 300x300) HREnKF for radar data assimilation

4. Features of the system Sequential processing of batches of observations Houtekamer and Mitchell 2001

5. Sub-ensemble 1 Sub-ensemble 2 Sub-ensemble 3 Ensemble members (80) Sub-ensemble 4 K1 Gain matrix K1 K2 Gain matrix K2 K3 Gain matrix K3 K4 Gain matrix K4 Partitioning the ensemble (to deal with the underestimation of the error structure)

6. Control variables: U, V, W, T, HU (specific humidity) Observations are perturbed according to its variance (no correlation). Simplified random perturbations to consider the model errors Localization: 10-km in horizontal; 2 * ln( Pressure levels ) in vertical 80 members Some features of the current set up: For the HREnKF For the GEM_LAM model at 1-km resolution Cycling hydrometeor variables Microphysical scheme: double moment scheme (Milbrandt and Yau, 2005) Fixed lateral boundary conditions  Ensemble lateral boundary conditions for all ensemble members

7. Summer casesFeatures June 12, 2011Scattered and localized convection. (up to 90-min) June 23, 2011Wide spread stratiform system (up to 90-min) June 29, 2011Squall line system QPF Improvement only up to 1-h Analysis 30-min 90-min60-min

8. Position error of precipitation Poor background fields Is it important? (impact of assimilating radar data) Is there any way to improve it?

9. In the low elevation angle: Directly update U and V Indirect update W through flow-dependent background error cross-covariance Increment: Error reduction: 2. Examine the impact of position error with radar data assimilation Experiment designed:

12. Global EnKF system (GEnKF) Regional EnKF system (REnKF) High resolution EnKF (HREnKF) 傳統氣象資料 衛星資料 Obtain ensemble members 傳統氣象資料 衛星資料 氣象雷達資料 3. Strategies of improving QPF at convective scale a) conventional observations + regional assimilation system

13. Impact of Regional EnKF with conventional observations Vertical velocity W Global EnKF (ensemble mean) Regional EnKF (ensemble mean) REnKF_15km 1-day cycling 0000 1200 0000 0600 6-h short-term forecast

14. Control run (from DF) Precipitation (mean of REnKF) Improvement of the background field

15. Control run: No radar assimilation, from ensemble mean of the REnKF 0000 HREnKF: cycling for 60-min and launch the short-term forecast 0000 1830 UTC 0230 Radar radial wind (assimilating every 5-min) 0100 short-term ensemble forecasts 1.5 hr 2.5 h model integration Cycling assimilation procedure Experiment of the HREnKF with radial wind assimilation

16. Impact of using the ensemble set from REnKF system From random perturbations From REnKF Converge toward observations underestimate Covered the rms of P f

17. Verification Analysis 30-min 90-min 60-min

18. Adaptive radar observations How to optimize using radar observations?

20. 4. Summary and future works At cloud-resolving scale, if there is any position error of precipitation, it is important to correct it before assimilate radar observations. Initial ensemble set from the REnKF is better than random perturbations 1. Capture mesoscale circulation better 2. Ensemble spread is able to cover forecast errors  assimilate more radar observations The verification of the radial component shows that the improvement of short-term forecast is up to 1.5-hr. (Both bias and root-mean-square errors) How to use radar observation properly? Adaptive observation strategy is able to improve the effectiveness of assimilating radar data

21. Data assimilation (bridge) observations Numerical model About future work

22. The solid line represents the theoretical limit of predictability, the dashed line indicates NWP models, and the dotted line represents nowcasting methods (Austin et al., 1987).Austin et al., 1987 Berenguer et al. 2012 Forecast skill ( nowcasting versus NWP ) precipitation 0 - 6 hr QPF Resolution of NWP Extra Observations Final goal

23. Reflectivity 0000 UTC Simulated reflectivity 0000 UTC Construct regional and mesoscale analysis fields Simulated reflectivity observationsPoor background field Good background field Large scale forcingData assimilation How many cycling of regional EnKF & conventional data? Optimal assimilation window ( 6-h or 3-h ) ?

24. a. Assimilate both radial wind and reflectivity observations Extra Observations (other than conventional data)

25. (Feng et al. 2009) (Humidity) (Precipitation) b. Refractivity Apply to a EnKF system

26. C. Dual-Polarization observations: ( Putnam et al 2013 )

27. Microphysics versus Dual-Polarization parameters ( Putnam et al 2013 )

28. d. Cloud radars

29. Use more complicated observation operator Identify error structure in a) observations Consider: proper geometry, accurate propagation Include: the sampling volume, signal and its processing Fabry and Kilambi (2011)

30. Identify error structure in B) numerical model

31. Error correlation of TT profile V.S. Vertical correlation of TT tendency ( Ensemble Forecasts) (stochastic perturbation of SCM) Single column model (SCM)  Represent the error structure Microphysics

32. 移動雷達車移動雷達車 JWD雨滴譜儀JWD雨滴譜儀2DVD雨滴譜儀2DVD雨滴譜儀 剖風儀剖風儀 MRR 微波雨量 雷達 MRR 雨量計雨量計 Verify QPF (quantitative precipitation forecast) over Taiwan region Identify error structure in B) numerical model Global Precipitation Mission (GPM)

33. 報告完畢 歡迎指教 謝謝！

35. 3. Impact of assimilating radial wind component Is it able to propagate information to other control variables? Temperature Humidity V-wind Obs Z

36. McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) Variational Echo Tracking technique ( Laroche and Zawadzki 1995 ) : Estimate the motion field of precipitation and a modified semi-Lagrangian backward scheme for advection. (capable of stretching and rotation)