1. Radar Data Assimilation for Explicit Forecasting of Storms Juanzhen Sun National Center for Atmospheric Research

2. 2 Outline Introduction: background and motivation Methodologies for storm-scale DA 4D-Var radar data assimilation at NCAR Case studies and results Issues and opportunities

3. 3 Cloud-scale modeling since 1960’s Used as a research tool to study dynamics of moist convection Initialized by artificial thermal bubbles superimposed on a single sounding Rarely compared with observations From Weisman and Klemp (1984)

4. Yes, it was time because we had NEXRAD network Increasing computer power Advanced DA techniques Experience in cloud-scale modeling Lilly’s motivating publication (1990) -- NWP of thunderstorms - has its time come? “ Because of the inherent difficulty of predicting Initial storm development, our main focus will probably be on predicting the evolution of existing storms and development of new ones from outflow Interaction.” “ We are not sure what will happen if we start a model with these incomplete data and fill in the rest of the volume with mean-flow condition, but it is not likely to be inspiring.”

5. Operational NWP: poor short-term QPF skill Current operational NWP can not beat extrapolation-based radar nowcast technique for the first few forecast hours. One of the main reasons is that NWP is not initialized by high- resolution observations, such as radar. 0.1 mm hourly precipitation skill scores for Nowcast and NWP averaged over a 21 day period From Lin et al. (2005)

6. Example of model spin-up from BAMEX 6h forecast (July 6 2003)12h forecast Radar observation at 0600 UTC at 1200 UTC Graphic source: http://www.joss.ucar.edu Without high-resolution initialization: A model can takes a number of hours to spin up. Convections with weak synoptic-scale forcing can be missed.

7. 7 Comparing radar DA with conventional DA Conventional DARadar DA Obs. resolution ~ a few 100 km -- much poorer than model resolutions Obs. resolution ~ a few km -- equivalent to model resolutions Every variable (except for w) is observed Only radial velocity and reflectivity are observed Optimal Interpolation Retrieval of the unobserved fields Balance relations Temporal terms essential observation model grid

8. 8 Methodologies for storm- scale DA

9. 9 Two general methodologies  Sequential initialization - Model dynamical, thermodynamical, and microphysical fields are derived separately using different methods - Is usually simple and efficient - Initial conditions may lack consistency  Simultaneous initialization - Model initial fields are obtained simultaneously - Is usually computational demanding - Initial fields satisfy the constraining numerical model

10. 10 An examples of sequential initialization Large-scale background and radial velocity u,v,w 3DVar constrained by simple balance equations Step 1 Reflectivity and cloud information T, q r,q c,q v Cloud analysis Step 2

11. 11 An examples of simultaneous initialization V1 V3 V2 4DVar constrained by a NWP model Large-scale background, radar radial velocity, and reflectivity Input u,v,w,T, q r,q c,q v t2t2 t3t3 t1t1 The analysis variables are balanced through the Numerical model

12. 12 Sequential initialization Techniques:  Successive correction + cloud analysis LAPS (FSL)  3DVar + cloud analysis ARPS (CAPS)  3D wind retrieval + thermodymical retrieval + microphysical specification (Weygandt et al. 2002)  3D wind retrieval + latent heat nudging (Xu et al. 2004)

13. 13 Simultaneous initialization techniques  3D-Var WRF (NCAR)  4D-Var VDRAS (NCAR), MM5 4DVar (FSU)…  EnKF (Snyder and Zhang 2004, Dowell et al. 2004)

14. 14 4D-Var radar data assimilation at NCAR

15. 15 VDRAS and WRF-4DVar VDRAS has been developed since early 1990’s - Specifically designed for radar data assimilation - WRF output and mesonet data are also used but as first guess and background for 4DVar radar DA - Control variables are model prognostic variables - Warm-rain cloud model with no terrain - Frequent update (18 min.) - Used in real time since 1997 WRF-4DVar was developed recently - Extended from WRF-3DVar; same control varialbes as WRF 3DVar; stream function, geopotential height, unbalanced temperature, etc.. - Adjoint of microphysics is still under development

16. 16 Data Ingest Rawinsondes Mesoscale model data Mesonet Doppler radars Data Preprocessing Quality control Interpolation Background analysis First Guess Display (CIDD) Plots and images Animations Diagnostics and statistics 4DVAR Assimilation Cloud-scale model Adjoint model Cost function Weighting specification Minimization Flow chart showing major processes of VDRAS

17. 17 Cost Function v r - (u,v,w) Relation: Z-q r Relation Background term Observation term Penalty term

18. 18 What is an adjoint model? Forecast model: The adjoint operator is the transpose of the tangent linear model operator. Integration of the adjoint model from the time step k to 0 gives the gradient of J with respect to x 0 Adjoint model: Tangent linear model : Model state at time 0 Model state at time k

19. 19 Continuous 4DVar analysis cycles KVNX KDDC KICT KTLX 0 min time 12 min18 min Forecast 30 min42 min54 min Cold start Mesoscale analysis as first guess Forecast as first guess; Mesoscale analysis Forecast as first guess; Mesoscale analysis 4DVar Output of u,v,w,div,qv,T’

20. 20 RUC first-pass Barnes analysis with a radius of influence of 200km VAD second-pass Barnes analysis with a radius of influence of 50 km Surface data Barnes analysis Combine surface and upper-air analyses via vertical least-squares fitting Mesoscale background Procedures of the mesoscale background analysis

21. 21 4D-Var cycles ° Last iteration TIME (Min) Atmospheric State 510 15 2025 First Iteration Cycle 1 Cycle 2 Forecast cycle 30

22. 22 Radar data preprocessing in VDRAS &WRF-VAR Real-time data ingest 1km PPI in MDV format VDRAS Preprocessing module Ground clutter, Sea clutter, and AP removal Noise removal Filtering and super-obbing Velocity dealiasing VDRAS and WRF-VAR Specifying observation error Data filling

23. 23 Case Studies and Results

24. 24 Cpol Kurnel l rms(u dual – u vdras ) = 1.4 m/s rms(v dual – v vdras ) = 0.8 m/s November 3 rd, VDRAS-Dual Doppler comparison During Sydney 2000 Olympics ¼ of analysis domain VDRAS low-level analysis Apply VDRAS to the low-level (below 5 km) Focus on low-level convergence and gust front Has been run in real time for a number of years in several locations

25. 25 DateMean vector difference Mean vector 9/18/20002.1 m/s6.2 m/s 10/3/20003.5 m/s9.4 m/s 10/8/20002.6 m/s5.0 m/s 11/03/00 2.2 m/s 5.0 m/s Verification of VDRAS winds using aircraft data (AMDARs) Sydney 2000

26. 26 High-resolution data assimilation reveals how cold pools trigger storms 0611 2046 UTC - 0612 1250 UTC from IHOP Pert. Temp. (color) Shear vector (black arrow) Wind vector at 0.1875km (brown arrow) Contour (35 dBZ reflectivity) 4DVar analysis with radar data assimilation via VDRAS

27. 27 Initialization and forecasting of an IHOP squall line Occurred in IHOP domain, on June 12-13, 2002 ~ 12 hour life time: 20:00 – 8:00 UTC Formed near a triple point of a dry line and a stationary outflow boundary

28. 28 Model and DA set-up Observation Domain size: 480kmx440km Resolution: 4km 4 NEXRAD radars ~30 METAR surface stations Cold start first guess: radiosonde + VAD + surface obs. 50 min assimilation period which includes three 10 min 4DVar cycles 015400 UTC

29. 29 5-hour forecast of IHOP June 12 squall line Frame interval: 20 min. White contour: observation

30. Evolution of cold pool t = 0 t = 1.5 hr t = 3 hr -8 o c -2 o c The initial cold pool of -8 o c played a key role in the development of the storm.

31. 31 Forecast verification Model Persistence Extrapolation Rainwater correlation

32. 32 WRF 4DVar radar DA experiments Initial time: 0000 UTC 13 June 2002 (Selection of this initial time because more conventional data are available) GTS data included: SOUND, PILOT,Profiler, SYNOP, METAR, and GPSPW. 4DVAR time window: 0  15m, 3DVAR time window: -15m  15m, but the Radar data only at time=0. Verification: hourly rainfall from NCEP Stage_IV data

33. 33 061300Z, 3/4VAR Exp. Initial time 061300Z 061306Z 061312Z 4DVAR time window 3DVAR time window 051015m 00 4DVAR 3DVAR

34. 34 Radar data distribution

35. 35 Increments of temperature Increments of water vapor mixing ratio GFS analysis3DVAR analysis4DVAR analysis

36. 36 Hourly precipitation ending at 0200 UTC 13 June GFS 3DVAR OBS 4DVAR

37. 37 Hourly precipitation ending at 0400 UTC 13 June GFS 3DVAR OBS 4DVAR

38. 38 Hourly precipitation at 0600 UTC 13 June GFS 3DVAR OBS 4DVAR

39. 39 Hourly precipitation ending at 1000 UTC 13 June OBS GFS 3DVAR 4DVAR

40. 40 Threat scores with Radar data 4DVAR only Green dashed-line is the assimilation of Radar radial velocity only Blue dot-line is the assimilation of Radar radial velocity and GTS observation data

41. 41 Issues and Opportunities Further improvement of data assimilation techniques New observations - Radar refractivity, polarimetric obs., CASA, TAMDAR… Accuracy of large-scale analysis Model error/physical parameterization Computation/limited area implementation

42. 42 Sensitivity with respect to first guess Humidity first guess: background Humidity first guess: Background + saturation within convection

43. 43 Issues and Opportunities Further improvement of data assimilation techniques New observations - Radar refractivity, polarimetric obs., CASA, TAMDAR… Accuracy of large-scale analysis Model error/physical parameterization Computation/limited area implementation

44. 44 Impact of TAMDAR data Relative humidity without TAMDARRelative humidity with TAMDAR 1-hour q r forecast without TAMDAR 1-hour q r forecast with TAMDAR White contour: Observed reflectivity Greater than 30 dBZ

45. 45 Issues and Opportunities Further improvement of data assimilation techniques New observations - Radar refractivity, polarimetric obs., CASA, TAMDAR… Accuracy of large-scale analysis Model error/physical parameterization Computation/limited area implementation

46. 46 Sensitivity of the simulation with respect to environmental condition

47. 47 Issues and Opportunities Further improvement of data assimilation techniques New observations - Radar refractivity, polarimetric obs., CASA, TAMDAR… Accuracy of large-scale analysis Model error/physical parameterization Computation/limited area implementation

48. 48 Microphysical parameter retrieval Change of the parameter with respect to iteration number Cycle 1Cycle 2Cycle 3 5 m/s - Value in control simulation Terminal VelocityEvaporation rate Iteration First Guess Adjusting model microphysical parameters along with initial condition by fitting the model to radar observations

49. 49 Issues and Opportunities Further improvement of data assimilation techniques New observations - Radar refractivity, polarimetric obs., CASA, TAMDAR… Accuracy of large-scale analysis Model error/physical parameterization Computation/limited area implementation

50. 50 References Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint: Part I. model development and simulated data experi­ments. J. Atmos. Sci., 54, 1642-1661. Sun, J., and N. A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint: Part II. Retrieval experiments of an observed Florida con­vective storm, J. Atmos. Sci., 55, 835-852. Sun, J., and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data, Wea. Forecasting, 16, 117-132. Crook, N., A., and J. Sun, 2002: Assimilating radar, surface and profiler data for the Sydney 2000 forecast demonstration project. J. Atmos. Oceanic Technol., 19, 888-898. Sun, J., 2005: Convective-scale assimilation of radar data: progress and challenges. Q. J. R. Meteorol. Soc., 131, 3439-3463 Sun, J. and Y. Zhang, 2008 : Assimilation of multipule WSR_88D Radar observations and prediction of a squall line observed during IHOP. Mon. Wea. Rev., 136, 2364-2388. Sun, J., E. Lim, and Y. Guo, 2008: Assimilation and forecasting experiments using radar observations and the 4DVAR technique for two IHOP cases, 5 th European Conference on Radar in Meteorology and Hydrology., Helsinki, Finland, 30 June – 4 July, 2008.