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VDRAS and 0-6 Hour NWP - Recent activities

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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 Overview of VDRAS 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.

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

4 Summary of recent VDRAS activities
Continuing collaboration with BMB - Analysis of convective cases of 2008 and 2009 - 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
VDRAS wind and temperature 08 June 2010 X location of wind farm 0314 UTC x 0208 UTC x 0135 UTC x 0240 UTC x

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

8 Verification of VDRAS wind against Turbine wind
10-11 July, 2010, Northern Colorado 04-05 AUG, 2010, Texas Turbine wind VDRAS wind Turbine wind VDRAS wind 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?

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 NCAR’s 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 Forecast skill over one-week 10-16 June 2002 OBS CTRL Physics experiments Initial condition experiments Radar WSM6

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

13 Lesson 3: radar data impact depends on storm type
IHOP retrospective study Lesson 3: radar data impact depends on storm type forecast skill over one-week 10-16 June 2002 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 With radar WSM6 microphysics Positive impact of radar Negative impact of radar 15 June 13 June With radar With radar

14 FRONT - future STEP testbed
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 Pawnee CHILL 42 km 67 km 73 km S-Pol 48 km

15 June 2009 Front-Range Convection Retrospective Studies
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 Mesoscale data assimilation on CONUS domain Storm-scale DA on Front Range 15 km 3 km 15 15

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

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
WSM6 Thompson Threshold: 5 mm/hr Threshold: 5 mm/hr 4DVAR-RV+RF 4DVAR-RV+RF 4DVAR-RV 4DVAR-RV 3DVAR 3DVAR FCST Time (hour) FCST Time (hour) Reflectivity improves the forecast skill.

19 Impact of RV outside rain region
WSM6 Threshold: 5 mm/hr Thompson Threshold: 5 mm/hr RF-RVall RF-RV RF-RV RF-RVall 3DVAR 3DVAR FCST Time (hour) FCST Time (hour) RV outside rain region improves forecast skill with Thompson microphysics.

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

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

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 Surface wind (vector), surface temperature (contour)
Precipitable water (shaded) 3D-RF-RV 4D-RF-RVall

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