Development of convective-scale data assimilation techniques for 0-12h high impact

weather forecasting

Juanzhen SunNCAR, Boulder, Colorado

Oct 25, 2011

Outline

• Introduction - Unique aspects of convective-scale DA - Overview of techniques• Success and Issues• Future challenges

Oct 25, 2011

This talk is in the context of• Warm-season QPF• Radar observations• NCAR experiences

What makes convective-scale DA different?

• Objective - QPF, high-impact weather nowcast/forecast - Forecast accuracy: county/city scale • Predictability of high-impact weather systems - Rapid error growth - Small-scale with multiple scale interaction• Observations - Limited high-resolution in-situ observations - Remote sensing: high resolution, but limited coverage, limited and indirect variables

Convective-scale DA strategies

• Place storms at right locations - Warm Start: Cloud analysis, latent heating insertion, saturation

adjustment, updraft profiling• Use frequent update - Sub-hourly; 10-15 min window for 4DVAR - Take advantage of high temporal frequency obs. - Forced by predictability limitation• Consider cloud-scale balance - Temporal derivative terms should not be neglected - Different balance from the large-scale • Use different error statistics - Large-scale error statistics is not applicable - Research is still lacking

Overview of techniques• Techniques based on reflectivity or precipitation - DFI, nudging, cloud analysis - Simple and efficient - No or limited multivariant balance• 3D techniques assimilating both RV and RF from radar - 3DVAR - Efficient - Balance is mostly large-scale• 4D techniques assimilating both RV and RF - 4DVAR, EnKF (and its variants) - Computationally expensive - Full model balance, but compromised in practice (limited ensemble

members, limited assimilation window)

Latent Heat Nudging

ingest radar reflectivity observations (converted to QR/QS)

add tendency terms to model variables QR/QS and T based on the model state and observations

result in thermodynamic and microphysical adjustment

Hydrometeor increment per Δt(dQR/dt)obs * Δt if QRmod < QRobs & (dQR/dt)obs >0

ΔQR = g *(QRobs- QRmod) if QRmod > QRobs

0 otherwiseTemperature increment

ΔT = CLS/CPM * ΔQR

where CLS is the latent heat of condensation (or fusion)CPM=CP*(1.+0.8*QV) is the specific heat for moist air

Mei Xu

Impact of radar data LHN case 200906 1206

observation no radar with radar LHN

analysis

1 h forecast

Impact of radar data LHN case 200906 1206

observation no radar with radar LHN

2 h forecast

Impact of radar data LHN case 200906 1206

observation no radar with radar LHN

3 h forecast

Impact of radar data LHN case 200906 1206

observation no radar with radar LHN

Skills for June 11-17, 2009 Front Range Domain

FSS Evaluation

Analysis period

Averaged over 24 forecasts

WRF 3DVAR Radar DA

• Reflectivity data assimilation - Assimilate rainwater - Cloud analysis (optional) - Assimilate saturation water vapor within cloud (optional)• Control variables

- stream function- unbalanced velocity potential- unbalanced temperature- unbalanced surface pressure- pseudo relative humidity

• Cost function

€

J = Jb + Jo + Jvr + Jqr + JqvFor radar DA

Hongli Wang

IHOP one-week runs

• 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

Cycled 3DVAR

Both

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

RV RFOBS No Radar

RV RF

2-hour forecasts

Shuiyong Fan

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 suggeststhat the radar DA worksmore effectively for growing storms thandissipation storms

Dashed lines:Warm start

Solid lines:Cold start

An example of failed forecast

Cold start analysis

3DVAR cycled analysisCold start

Cycled 3DVAR

RF

From Sugimoto et al. (2009)

Radial component Tangential component

Can 3DVAR retrieve the tangential wind?

Truth

Ana

lysi

s

Radars with overlap Corr: 0.724

Single radars Corr: 0.402

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

RF only

RV only

RV and RF

• Without radial velocity, the rain falls out quickly.• Radial velocity assimilation results in slantwise updraft and moisture, but not the reflectivity assimilation• Assimilating both RV and RF consistently outperforms RV or RF only

Rainwater correlation

4DVAR systems: VDRAS and WRF 4DVAR

VDRAS• Developed for a cloud model• Trajectory is modeled by the nonlinear model• Full adjoint of the cloud model is used to calculate the gradient in the minimization• Control variables are model prognostic variables

WRF 4DVAR• Developed for WRF model• Trajectory is modeled by the tangent linear model of WRF with

reduced physics• Adjoint of the reduced tangent linear model• Control variables follow those in WRF 3DVAR

Inserting VDRAS analysis into WRF inner domain

VDRAS 3km

19 UTC 15 June 2002

WRF 9 km

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

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

Observations

Mem 1 control

Mem 1 w/ radar assim

00Z 01Z 02Z

WRF/DART EnKF Convective scale data assimilation

Glen Romine

Future Challenges

• DA for nowcasting application requires different configurations

- Frequent updating - Radar DA crucial for minimizing spinup time - Different background error statistics - Multiple pass for observations with different resolutions - Different DA schemes - Make better use of surface observations - Different physics options?

• Rapid cycling with/without radar DA can have negative impact on convective initiation - Will more frequent updating with radar DA help? - Diurnal variation of radar DA impact - The impact also depends on convection type

Opportunities and Challenges

• Radar DA still a great challenge

- Reflectivity assimilation > Improve the accuracy of the latent heating and relative humidity specification in the simple techniques > Balance with dynamics > Error statistics

- Radial velocity assimilation > Retrieval of the tangential component in 3DVAR > Clear air returns > Balance with thermodynamics and microphysics

Opportunities and Challenges

• Challenges for the 4D techniques

- Computation cost - Large resource required for developing a full 4DVAR - Choice of control variables for the convective scale in 4DVAR - Sample issues and maintenance of ensemble spread for EnKF - Model errors

VDRAS radar data assimilation reveals how cold pools trigger storms

0611 2046 UTC - 0612 1250 UTC

Pert. Temp. (color)Shear vector (black arrow)Wind vector at 0.1875km (brown arrow)Contour (35 dBZ reflectivity)