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