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

• 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

VDRAS analysis flow chart

Radar Preprocessing& QC

Surfaceobs.

Vr & Ref(x,y,elev)

Mesoscale model output

(netcdf)

Background analysis

VADanalysis

4DVar Radar data assimilation

Cloud model &adjoint

Minimizationof cost function

Updated analysisU, v, w, T, Qv, Qc, Qr

Last cycle Analysis/forecast

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

An example of VDRAS over Taiwan

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VDRAS for wind energy in Northern Colorado

0135 UTC

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X location of wind farm

0208 UTC

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0240 UTC

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0314 UTC

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VDRAS wind and temperature08 June 2010

VDRAS for wind energy in Northern Colorado

X location of wind farm

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2237 UTC

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2311 UTC

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2344 UTC

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0016 UTC

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0053 UTC

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0130 UTC

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0207 UTC

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0244 UTC

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0320 UTC

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0353 UTC

VDRAS wind vector and speed10-11 June 2010

Verification of VDRAS wind against Turbine wind

10-11 July, 2010, Northern Colorado 04-05 AUG, 2010, Texas

Questions raised for the phase shift on 04-05 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

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

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

IHOP retrospective studyLesson 1: 0-12 hour forecasts highly sensitive to initial conditions

OBS CTRL

Radar WSM6

Forecast skill over one-week10-16 June 2002

Physics experimentsInitial condition experiments

IHOP retrospective studyLesson 2: Short-term forecast sensitivity depends on storm type

“Easy to forecast” storm

OBS WRF fcst NAM

“Hard to forecast” storm

OBS WRF 3-h fcst WRF 3-h fcst

No radar With radar

WRF fcst GFS

IHOP retrospective study

Lesson 3: radar data impact depends on storm type

Positive impact of radar Negative impact of radar

With 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-week10-16 June 2002

With radar

WSM6 microphysics

13 June

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

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 domain Storm-scale DA on Front Range

15 km 3 km

No radar DA With radar DA

Frequency of updraft helicity over a 6hr ensemble forecast

WRF/DART EnKF storm-scale data assimilation June 12 2009 over Front Range region

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.

WRF-4DVAR: Impact of reflectivity

FCST Time (hour)

3DVAR

4DVAR-RV4DVAR-RV+RF

FCST Time (hour)

3DVAR

4DVAR-RV

4DVAR-RV+RF

• Reflectivity improves the forecast skill.

Threshold: 5 mm/hr Threshold: 5 mm/hrThompsonWSM6

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 Threshold: 5 mm/hr

RF-RVall

RF-RVall

RF-RV

ThompsonWSM6

Hourly rainfall at 01Z 13 June

BGObs

4D-R2-T153D-R2 4D-R2-T15

Thompson

4D-R2-T15-RVall

Thompson

WSM6

WSM6

WSM6

Hourly rainfall at 06Z 13 June

Obs

3D-R2 4D-R2-T15

BG 4D-R2-T15-RVall

4D-R2-T15

Thompson

Thompson

WSM6

WSM6

WSM6

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”

The analysis

4D-RF-RVall3D-RF-RV

Surface wind (vector), surface temperature (contour)Precipitable water (shaded)