VDRAS - Radar Data Assimilation and Explicit Forecasting of Convections Juanzhen Sun National Center for Atmospheric Research

2 Outline Introduction: background and motivation Methodologies for storm-scale DA VDRAS - a 4D-Var based radar data assimilation system Case studies and results Issues and opportunities Summary

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)

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 China national radar network -- CINRAD The Chinese Meteorological Administration is developing a network of 126 CINRAD radars: 66 radars are S band (red dots) 60 radars are C band (blue squares) WSR-98D

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)

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

8 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

9 Methodologies for storm- scale DA

10 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

11 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

12 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

13 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)

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

15 VDRAS - a 4D-Var based radar data assimilation system

16 History of VDRAS/VLAS 1987: First study of 4DVar for 3-D wind retrieval (Wolfberg 1987) 1991: First version of VDRAS developed and successfully applied to simulated radar data (Sun et al 1991) 1994: Applied to real single-Doppler observations (Sun and Crook 1994) 1997: Extended to a full troposphere cloud model (Sun and Crook 1997,1998) 1998: Implemented in real-time at Sterling, NWS (Sun and Crook 2001) 2000: Installed at Sydney, Australia for the Olympics (Crook and Sun, 2002)

17 History of VDRAS/VLAS Cont… : Field Demonstrations every summer for the FAA convective weather program 2001: Applied to simulated lidar data for convective boundary layer (Lin et al. 2001) 2000-now: Research on forecasting gust front and storm evolution (Crook and Sun 2004, Warner et al. 2002, Sun 2005, ……) 2003-now: VLAS and VDRAS applications for homeland security projects : Real-time demonstration using multiple WSR-88Ds for high- resolution analysis and QPF

18 Data Ingest Rawinsondes Profilers 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

19 The Numerical Model Anelastic approximation Adams-Bashforth time differencing Arakawa C-grid spatial differencing Liquid water potential temperature is used as the thermodynamical variable. Cloud water and temperature are diagnosed. Bulk warm-rain parameterization Constant diffusion coefficients No surface fluxes

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

21 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

Time (min) First guess: Mesoscale analysis VAD analysis Mesoscale analysis Barnes mesonet analysis First guess: Cycle 1 analysis VAD analysis Mesoscale analysis First guess: Cycle 2 analysis VAD analysis Mesoscale analysis First guess: Mesoscale analysis VAD analysis Mesoscale analysis Barnes mesonet analysis V1V2V5V4V3 4DVAR Cycle 14DVAR Cycle 2 Continuous 4DVar analysis cycles

23 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

24 4D-Var cycles ° Last iteration TIME (Min) Atmospheric State First Iteration Cycle 1 Cycle 2 Forecast cycle 30

25 Case Studies and Results

26 Low-level wind 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

27 November 3 rd tornadic hailstorm event, left-moving supercell, clockwise rotating tornado. gust front sea breeze Sydney 2000 Tornadic hailstorm

28 Cpol Kurnell 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 ¼ of analysis domain

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

30 Fort Worth NWS office real time VDRAS Severe storm of May 4-5, 2006 Convergence White contour: 30 dBZ reflectivity Perturbation temperature VDRAS analyses every 20 min

31 Initialization and forecasting of a supercell storm Occurred near Bird City, Kansas, on June 29, 2000 ~ 5 hour life time: 22:00 – 3:00 UTC Formed ahead of an advancing surface boundary Produced large hail and a F1 tornado VDRAS assimilates data from one radar (KGLD)

32 Storm evolution (40 dBZ contour)

33 Vertical profile of radial wind RMS error RMS error (m/s) Height (km)

34 Comparison of forecast with observation (40 dBZ contours every 20 min for two hours) ObservationForecast

35 Storm Track for three experiments (40 dBZ contours every 20 min for two hours) Control No evaporative cooling in both analysis and forecast Evaporative cooling in analysis but not in forecast

36 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

37 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 UTC

38 5-hour forecast of IHOP June 12 squall line Frame interval: 20 min. White contour: observation 5-hour forecast

39 Detailed look of the analysis wind Radial velocity and wind at Z = 0.25 km Reflectivity and wind at z=3.25 km Radar

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.

41 Forecast verification Model Persistence Extrapolation Rainwater correlation

42 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

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

44 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

45 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

46 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

47 Sensitivity of the simulation with respect to environmental condition

48 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

49 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

50 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

51 Summary Radar data assimilation is one of the critical aspects for improvement of QPF. VDRAS was developed and applied to study the high- resolution analysis and initialization using radar observations. Case studies and real time implementations have demonstrated that the 4DVar-based technique has potentials in improving short range QPF Improving DA techniques, adding new high-resolution observations, dealing with scale interaction and model errors, computational efficiency are among a series of future challenges.

Column maximum reflectivity (dBZ) WRF 3DVar radar data assimilation Of the IHOP June squall line With Radar DA Observation 1-hour forecast6-hour forecast No radar DA WRF 3DVar assimilates Radial velocity and Reflectivity simultaneously. A warm rain process is used to balance the hydrometeors and Thermodynamics. Courtesy of Q. Xiao

QPF verification Red: No radar Gray: one radar Purple: 11 radars Courtesy of Xiao

Use of VDRAS Vertical Velocities in Thunderstorm Nowcasting 60 min extrapolation Contours of Vertical velocity 0.1 m/s 0.3 m/s 0.5 m/s

Use of VDRAS Vertical Velocities in Thunderstorm Nowcasting Verification

56 4D-Var vs. EnKF Both are constrained by a numerical model (dynamical assimilation). 4D-Var finds an analysis trajectory using several time levels of observations (variational), while EnKF produces an analysis at a single time level using observations at that time level (sequential). EnKF is more dependent on background covariance, while 4D-Var relies more on observations. 4D-Var has a longer history in atmospheric data assimilation than EnKF and have had more real-time and operational implementations.