development of convective-scale data assimilation techniques for 0-12h high impact weather...
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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)