Radar Data Assimilation Using VDRAS and WRF-VAR

Juanzhen SunNCAR, Boulder, Colorado

Oct 17, 2011

AcknowledgmentHongli WangQingnong XiaoYing ZhangZhuming Ying

Outline

• Historical Background• Key findings with VDRAS• Experiences with WRF-VAR - Relative impact of VR and RF• Thoughts on the future

Oct 17, 2011

Historical Background (VDRAS)

• Early works focused on proof of concept - Single Doppler retrieval for the boundary layer (Sun et al 1991, 1994) - Initializing cloud-scale model (Sun and Crook 1997, 1998)• Real-time applications of VDRAS - Real-time nowcasting for NWS, ATEC, DOD,…(Sun & Crook 2001) - Demonstrations in two summer Olympics (Crook and Sun 2004, Sun

et al 2010)• VDRAS as a tool for - Understanding convective dynamics and developing conceptual

models for nowcasting - Providing predictor fields for Automated nowcasting systems - Initialization of mesoscale models (Liou et al 2011)

Historical Background (WRF-VAR)

• WRF 3DVAR radar data assimilation - Convective rainfall (Xiao et al 2005, Xiao and Sun 2007)

- Tropical cyclone (Xiao et al 2007, Pu et al 2009) - Statistical evaluation over consecutive periods (Xiao et al 2008, Sun et al 2011) - Recent improvement on reflectivity assimilation > Cloud analysis > Assimilate rainwater instead of reflectivity > Use of saturation water vapor as data in the cost function

• WRF 4DVAR radar data assimilation - Adjoint of a warm rain microphysics - Control variables of microphysics - Being tested with convective cases

VDRAS wind analysis for a study of terrain-inducedconvection in southern Taiwan 03 UTC - 10 UTC

What is the adequate resolution to resolvesome small-scale features?

VDRAS continuous analyses of divergence and windFrame interval: 15 min

3KM 1KM

Inserting VDRAS analysis into WRF inner domain

• Interpolated fields of VDRAS to WRF inner domain– U-wind at the 1st level– Without (left) and with (right) blending of VDRAS & WRF near boundaries

RTFDDA_d02

VDRAS

19 UTC 15 June 2002

Observation (061302) No VDRAS

With VDRAS

2-h WRF forecasts valid at 061302

Observation(061305)

No VDRAS

With VDRAS

5-h WRF forecasts valid at 061302

OBS

WRF only

VDRAS only

VDRAS+WRF

0

0.15

0.3

0.45

6 10 14 18Thresholds (mm)

ET

S

OBS_EC

WRF

OBS_EC+WRF

ETS score for accumulated 2-hr rainfallOBS_EC: Using VDRAS alone.WRF : Using WRF alone.OBS_EC+WRF: Combining VDRAS and WRF.

Lessons learned by running VDRAS

• 10-15 min 4DVAR window seems to be optimal for analyzing the convective-scale dynamical and thermodynamical structures • Continuous 4DVAR cycling reveals dynamically consistent evolution of convective features• The rapid updating and use of derived rainwater avoids displacement error of storms, a common problem in microphysical initialization• Radial velocity plays a more important role than reflectivity• VDRAS analysis can be used to initialize mesoscale models• A two-step procedure (non-radar data are used to provide an improved storm environment before radar DA) enables a closer fit to radar observations• 1 km resolution resolves much more convective details

Study of a supercell storm using a 4DVAR system VDRASSun (2004)

Observation Forecast

Color contour: qr

w

w

qv

qv

Radial velocity only

Reflectivity only

Observation

Z only

vr only

vr and z

• Without radial velocity, the rain falls out quickly.• Radial velocity assimilation results in slantwise updraft and moisture, but not the reflectivity assimilation

Rainwater correlation

Recent WRF 3DVAR Experiments

• IHOP June 10 – June 16 one week continuous run

- Active convective period - 3 hourly update cycle - 3 km horizontal resolution - Assimilate 25 radars - Cloud analysis option

• Beijing 4 cases evaluation

- 3 hourly update cycle - 3 km horizontal resolution - Assimilate 6 radars - Assimilate in-cloud saturated water vapor

Beijing

IHOP Results

• NORD: Control with no radar DA • RV: Assimilate radial velocity• RF: Assimilate reflectivity• RVRF: Assimilate both

One-week FSS skill (5mm)

RFRV

6-h Forecasts after four 3DVAR cycles

Beijing Results

• NORD: Control with no radar DA• RV: Assimilate radial velocity• RF: Assimilate reflectivity• RVRF: Assimilate both

FSS skill for four 2009 summer cases

FSS skill for July 22, 2009

RV RF

FSS skill for July 23, 2009

2-hour forecasts initialized at 09 UTC on July 22, 2009

QPE CON

CRWCRV

OBS No Radar

RV RF

WRF 4DVAR Radar Data Assimilation4-hour forecasts from a case study (13 June 2002)

OBS 3DVAR

4D_RV 4D_RF

ETS of 0-6 hour forecast

4D_RF

4D_RV 1 mm

5 mm

3DVAR

Thoughts on the future

• Technical improvement of DA systems

- Observation error statistics – based on information content - Background error statistics – evolving with system improvement - Rapid update cycle less than 1 hour for 3DVAR - Choice of control variables - Add terrain effect (VDRAS)

• Polarimetric radar data assimilation

- Observation operator - Use estimated microphysics - Quantify the impact on forecast

Thoughts on the future…Continued

• Further study of Radar DA impact on convective forecasting

- QPF: dependence on convection type, diurnal cycle, scale, etc… - Wind, temperature, humidity

• Improving accuracy of storm environment

- Operational models that are used as background do not have required accuracy for convective initiation forecast, especially in the low-level - Radar clear-air returns do not have adequate coverage - Make better use of other observations, e.g., surface obs.

Diurnal variation of Radar DA impact

00Z

12Z

• Radar DA has longerpositive impact for late Evening initializations

• The positive impactonly lasted 4 hours formorning initializations

• It seems to indicate that the radar DA worksmore effectively for growing storms thandissipation storms

HRRR verification using radiosondes

Slides providedby Mei Xu