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VDRAS and 0-6 Hour NWP - Recent activities Juanzhen Sun RAL/NESL, NCAR VDRAS and its recent applications Retrospective studies of 0-6h NWP with radar DA.

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Presentation on theme: "VDRAS and 0-6 Hour NWP - Recent activities Juanzhen Sun RAL/NESL, NCAR VDRAS and its recent applications Retrospective studies of 0-6h NWP with radar DA."— Presentation transcript:

1 VDRAS and 0-6 Hour NWP - Recent activities Juanzhen Sun RAL/NESL, NCAR VDRAS and its recent applications Retrospective studies of 0-6h NWP with radar DA 4DVAR development

2 VDRAS is an advanced data assimilation system for high- resolution (1-3 km) and rapid updated (6-18 min) analysis Produce Low-level wind, temperature, and humidity analysis VDRAS assimilates mesoscale model data, surface data, and radar radial velocity and reflectivity data from single or multiple radars The core is a 4-dimensional data assimilation scheme based on a warm-rain cloud-scale model It has been installed at nearly 20 sites for nowcasting applications since 1998 and currently running over 10 domains in and outside of U.S. Overview of VDRAS

3 VDRAS analysis flow chart Radar Preprocessing& QC Surface obs. Vr & Ref (x,y,elev) Mesoscale model output (netcdf) Background analysis VAD analysis 4DVar Radar data assimilation Cloud model & adjoint Minimization of cost function Updated analysis U, v, w, T, Qv, Qc, Qr Last cycle Analysis/forecast

4 Summary of recent VDRAS activities Continuing collaboration with BMB - Analysis of convective cases of 2008 and Understanding of convective initiation in Beijing - Development of forecast index Implementation for CWB of Taiwan - Study of terrain-induced convection - Support of ANC for nowcasting in Taiwan Wind energy applications - Evaluation of VDRAS performance for 80m wind analysis - Development of techniques for 0-2 hour wind forecast Others: 10 instances of VDRAS are running in and outside of U.S

5 An example of VDRAS over Taiwan

6 VDRAS for wind energy in Northern Colorado 0135 UTC xx X location of wind farm 0208 UTC xx 0240 UTC xx 0314 UTC xx VDRAS wind and temperature 08 June 2010

7 VDRAS for wind energy in Northern Colorado X location of wind farm xx 2237 UTC x x 2311 UTC x x 2344 UTC x x 0016 UTC x x 0053 UTC x x 0130 UTC x x 0207 UTC x x 0244 UTC x x 0320 UTC x x 0353 UTC VDRAS wind vector and speed June 2010

8 Verification of VDRAS wind against Turbine wind July, 2010, Northern Colorado AUG, 2010, Texas Questions raised for the phase shift on Aug, 2010 case Discrepancy between radar and turbine observations Issue of inadequate vertical resolution in radar obs.? Reliability of turbine wind? VDRAS wind Turbine wind VDRAS wind Turbine wind

9 Wind nowcasting based on VDRAS Feature extrapolation - Convergence line - Temperature gradient - Simple and efficient Direct integration of VDRAS model - Use a 2-D advection wind - May be more accurate than feature extrapolation - More computation

10 Summary of 0-6 h NWP research IHOP retrospective study through NCARs STEP program - Emphasize radar data assimilation and connection between model and nowcasting - Techniques include nudging, 3DVAR, 4DVAR, EnKF - Sensitivity of initial conditions vs. physics WRF 3DVAR operational pre-testing (collaboration with BMB) Further development of advanced techniques, 4DVAR & EnKF Evaluation of 0-6 h NWP with radar DA over Front Range - Strategies for improved 0-6 h NWP for nowcasting purposes - Evaluate pros and cons of different techniques - Running systems of Nudging, 3DVAR, DDFI, EnKF over the same domain and the same period

11 IHOP retrospective study Lesson 1: 0-12 hour forecasts highly sensitive to initial conditions OBS CTRL RadarWSM6 Forecast skill over one-week June 2002 Physics experiments Initial condition experiments

12 IHOP retrospective study Lesson 2: Short-term forecast sensitivity depends on storm type Easy to forecast storm OBS WRF fcst NAM Hard to forecast storm OBSWRF 3-h fcst No radarWith radar WRF fcst GFS

13 IHOP retrospective study Lesson 3: radar data impact depends on storm type Positive impact of radarNegative impact of radar With radar 15 June Preliminary findings: Radar data has less impact on equilibrium and elevated convection Radar data assimilation provides triggers for surface-based convection Challenge: optimal fit to convective-scale while maintaining large-scale balance forecast skill over one-week June 2002 With radar WSM6 microphysics 13 June

14 FRONT - future STEP testbed Pawnee CHILL S-Pol 73 km 42 km 48 km 67 km S-Pol: N of Hwy 52 between I-25 and Hwy 85; near Firestone. Operational ~Summer, 2012 after DYNAMO deployment Testbed for - software development - data assimilation - instrument/model intercomparison/validation - QPE/QPF and nowcasting

15 Evaluation of radar data assimilation systems EnKF Mesoscale and storm-scale data assimilation and prediction RTFDDA latent heat nudging of radar reflectivity WRF 3DVAR radar data assimilation NOAA/ESRL HRRR radar reflectivity initialization June 2009 Front-Range Convection Retrospective Studies 15 Mesoscale data assimilation on CONUS domainStorm-scale DA on Front Range 15 km 3 km

16 No radar DAWith radar DA Frequency of updraft helicity over a 6hr ensemble forecast WRF/DART EnKF storm-scale data assimilation June over Front Range region

17 Development of WRFDA-4DVAR for Radar 1. Radar reflectivity assimilation - Assimilating retrieved rainwater from RF; - The error of retrieved rainwater is specified by error of RF. 2. New control variables and background error covariance - Cloud water (qc), rain water (qr); - Recursive filter is used to model horizontal correlation ; - Vertical correlation is considered by EOFs; 3. Microphysics scheme - Linear/adjoint of a Kessler warm-rain scheme; - Incorporated into WRF tangent/adjoint model.

18 WRF-4DVAR: Impact of reflectivity FCST Time (hour) 3DVAR 4DVAR-RV 4DVAR-RV+RF FCST Time (hour) 3DVAR 4DVAR-RV 4DVAR-RV+RF Reflectivity improves the forecast skill. Threshold: 5 mm/hr ThompsonWSM6

19 Impact of RV outside rain region FCST Time (hour) 3DVAR RF-RV FCST Time (hour) 3DVAR RV outside rain region improves forecast skill with Thompson microphysics. Threshold: 5 mm/hr RF-RVall RF-RV ThompsonWSM6

20 Hourly rainfall at 01Z 13 June BGObs 4D-R2-T15 3D-R2 4D-R2-T15 Thompson 4D-R2-T15-RVall Thompson WSM6

21 Hourly rainfall at 06Z 13 June Obs 3D-R24D-R2-T15 BG4D-R2-T15-RVall 4D-R2-T15 Thompson WSM6

22 Summary VDRAS analysis is an valuable addition to the existing precipitation nowcasting systems Recent applications to wind energy prediction showed promises Active research is being pursued to improve 0-6 hour NWP for nowcsting applications A joint workshop with MWG is being planned on NWP for nowcasting

23 The analysis 4D-RF-RVall3D-RF-RV Surface wind (vector), surface temperature (contour) Precipitable water (shaded)

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