Radar Data Assimilation for Explicit Forecasting of Storms

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

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. 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. Lilly’s motivating publication (1990)-- NWP of thunderstorms - has its time come? Yes, it was time because we had • NEXRAD network • Increasing computer power • Advanced DA techniques • Experience in cloud-scale modeling “ 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 0.1 mm hourly precipitation skill scores for Nowcast and NWP averaged over a 21 day period • 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. From Lin et al. (2005)

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

7. Comparing radar DA with conventional DA Conventional DA Radar DA observation model grid

8. Methodologies for storm- scale DA

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

11. An examples of simultaneous initialization Input Large-scale background, radar radial velocity, and reflectivity V1 V2 V3 4DVar constrained by a NWP model u,v,w,T, qr,qc ,qv u,v,w,T, qr,qc ,qv u,v,w,T, qr,qc ,qv The analysis variables are balanced through the Numerical model t1 t2 t3

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

14. 4D-Var radar data assimilation at NCAR

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

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

18. What is an adjoint model? Model state at time 0 Model state at time k Forecast model: Tangent linear model : Adjoint 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 x0

19. 0 min 12 min 18 min 30 min 42 min 54 min time Forecast 4DVar Forecast 4DVar 4DVar Cold start Mesoscale analysis as first guess Forecast as first guess; Mesoscale analysis Forecast as first guess; Mesoscale analysis Output of u,v,w,div,qv,T’ Output of u,v,w,div,qv,T’ Output of u,v,w,div,qv,T’ Continuous 4DVar analysis cycles KVNX KDDC KICT KTLX

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

21. Forecast cycle • Cycle 2 Cycle 1 • • • Last iteration • • First Iteration ° 4D-Var cycles Atmospheric State 5 10 20 25 30 15 TIME (Min)

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

23. Case Studies and Results

24. November 3rd, VDRAS-Dual Doppler comparison During Sydney 2000 Olympics Cpol Kurnell 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 ¼ of analysis domain rms(udual – uvdras) = 1.4 m/s rms(vdual – vvdras) = 0.8 m/s

25. Sydney 2000 Verification of VDRAS winds using aircraft data (AMDARs)

26. High-resolution data assimilation reveals how cold pools trigger storms0611 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. 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. Model and DA set-up • 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 Observation

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

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

31. Forecast verification Rainwater correlation Extrapolation Model Persistence

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. 061300Z, 3/4VAR Exp. Initial time 061300Z 061306Z 061312Z 3DVAR time window 4DVAR time window 05 10 15m 4DVAR 00 3DVAR

34. Radar data distribution

35. Increments of temperature Increments of water vapor mixing ratio GFS analysis 3DVAR analysis 4DVAR analysis

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

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

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

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

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

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

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. Sensitivity of the simulation with respect to environmental condition

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

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. 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, 5th European Conference on Radar in Meteorology and Hydrology., Helsinki, Finland, 30 June – 4 July, 2008.