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Radar Data Assimilation for Explicit Forecasting of Storms. Juanzhen Sun National Center for Atmospheric Research. Outline. Introduction: background and motivation Methodologies for storm-scale DA 4D-Var radar data assimilation at NCAR Case studies and results Issues and opportunities. - PowerPoint PPT Presentation
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Radar Data Assimilation for Explicit Forecasting of Storms
Juanzhen Sun
National Center for Atmospheric Research
2
Outline
• Introduction: background and motivation
• Methodologies for storm-scale DA
• 4D-Var radar data assimilation at NCAR
• Case studies and results
• Issues and opportunities
3
Cloud-scale modeling since 1960’s
• Used as a research tool to study dynamics of moist convection
• Initialized by artificial thermal bubbles superimposed on a single sounding
• Rarely compared with observations From Weisman and Klemp (1984)
Yes, it was time because we had• NEXRAD network• Increasing computer power• Advanced DA techniques• Experience in cloud-scale modeling
Lilly’s motivating publication (1990)-- NWP of thunderstorms - has its time come?
“ Because of the inherent difficulty of predicting Initial storm development, our main focus will probably be on predicting the evolution of existingstorms and development of new ones from outflow Interaction.”
“ We are not sure what will happen if we start a model with these incomplete data and fill in the rest of the volume with mean-flow condition, but it is not likely to be inspiring.”
Operational NWP: poor short-term QPF skill
• Current operational NWP can not beat extrapolation-based radar nowcast technique for the first few forecast hours.
• One of the main reasons is that NWP is not initialized by high-resolution observations, such as radar.
0.1 mm hourly precipitation skill scores for Nowcast and NWP averaged over a 21 day period
From Lin et al. (2005)
Example of model spin-up from BAMEX 6h forecast (July 6 2003) 12h forecast
Radar observation at 0600 UTC at 1200 UTC
Graphic source:http://www.joss.ucar.edu
Without high-resolution
initialization:
• A model can takes a number of hours to spin up.
• Convections with weak synoptic-scale forcing can be missed.
7
Comparing radar DA with conventional DA Conventional DA Radar DA
Obs. resolution ~ a few 100 km -- much poorer than model resolutions
Obs. resolution ~ a few km -- equivalent to model resolutions
Every variable (except for w) is observed
Only radial velocity and reflectivity are observed
Optimal Interpolation Retrieval of the unobserved fields
Balance relations Temporal terms essential
observation
model grid
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Methodologies for storm-scale DA
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Two general methodologies
Sequential initialization
- Model dynamical, thermodynamical, and microphysical fields are derived separately using different methods - Is usually simple and efficient - Initial conditions may lack consistency Simultaneous initialization
- Model initial fields are obtained simultaneously - Is usually computational demanding - Initial fields satisfy the constraining numerical model
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An examples of sequential initialization
Large-scale backgroundand radial velocity
u,v,w
3DVar constrained by simple balance equations
Step 1
Reflectivity and cloud information
T, qr,qc ,qv
Cloud analysis
Step 2
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An examples of simultaneous initialization
V1 V3V2
4DVar constrained by a NWP model
Large-scale background,radar radial velocity, andreflectivity
Input
u,v,w,T, qr,qc ,qv
u,v,w,T, qr,qc ,qv
u,v,w,T, qr,qc ,qv
t2 t3t1
The analysis variables arebalanced through the Numerical model
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Sequential initialization Techniques:
Successive correction + cloud analysis LAPS (FSL)
3DVar + cloud analysis ARPS (CAPS)
3D wind retrieval + thermodymical retrieval + microphysical specification (Weygandt et al. 2002) 3D wind retrieval + latent heat nudging (Xu et al. 2004)
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Simultaneous initialization techniques
3D-Var WRF (NCAR)
4D-Var VDRAS (NCAR), MM5 4DVar (FSU)…
EnKF (Snyder and Zhang 2004, Dowell et al. 2004)
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4D-Var radar data assimilation at NCAR
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VDRAS and WRF-4DVar
• VDRAS has been developed since early 1990’s - Specifically designed for radar data assimilation - WRF output and mesonet data are also used but as first guess and background for 4DVar radar DA - Control variables are model prognostic variables - Warm-rain cloud model with no terrain - Frequent update (18 min.) - Used in real time since 1997
• WRF-4DVar was developed recently - Extended from WRF-3DVar; same control varialbes as WRF 3DVar; stream function, geopotential height, unbalanced temperature, etc.. - Adjoint of microphysics is still under development
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Data Ingest• Rawinsondes • Mesoscale model data• Mesonet• Doppler radars
Data Preprocessing • Quality control• Interpolation• Background analysis• First Guess
Display (CIDD) • Plots and images• Animations• Diagnostics and statistics
4DVAR Assimilation• Cloud-scale model• Adjoint model• Cost function• Weighting specification• Minimization
Flow chart showing major processes of VDRAS
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Cost Function
J =(x0 −xb)
T B−1(x0 −xb) + [ηv(F (vr ) −vro)2 +
σ ,t∑ ηz(F (Z) −Z0 )2 ] + J p
v
r=
x−xr
ru+
y−yr
rv+
z−zr
r(w−VT )
Z =43.1+17.5log10(ρqr )
vr - (u,v,w) Relation:
Z-qr Relation
Background termObservation term
Penalty term
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What is an adjoint model?
Forecast model: xk=G(x0 )
δxk =G'(δx0 ) δx0′G⏐ →⏐ δxk
∇x0
J =G'T∇xkJ ∇x0
J ′G T← ⏐⏐ ⏐ ∇xk
J
• The adjoint operator is the transpose of the tangent linearmodel operator.
• Integration of the adjoint model from the time step k to 0 gives the gradient of J with respect to x0
Adjoint model:
Tangent linear model :
x0 Model state at time 0
xk
xk Model state at time k
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Continuous 4DVar analysis cycles
KVNX
KDDC KICT KTLX
0 min
time12 min 18 min
Forecast Forecast
30 min 42 min 54 min
Cold startMesoscale analysisas first guess
Forecast as first guess;Mesoscale analysis
Forecast as first guess;Mesoscale analysis
4DVar 4DVar 4DVar
Output of u,v,w,div,qv,T’
Output of u,v,w,div,qv,T’
Output of u,v,w,div,qv,T’
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RUC first-pass Barnes analysis with a radius of influence of 200km
VAD second-pass Barnes analysis with a radius of influence of 50 km
Surface data Barnes analysis
Combine surface and upper-air analyses via vertical least-squares fitting
Mesoscale background
Procedures of the mesoscale background analysis
21
4D-Var cycles
°
••
•Last iteration
TIME (Min)
Atmospheric State
5 10 15 20 25
First Iteration
Cycle 1 Cycle 2
• ••
Forecast cycle
30
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Radar data preprocessing in VDRAS &WRF-VAR
Real-time data ingest
1km PPI inMDV format
VDRAS Preprocessing
module
Ground clutter, Sea clutter, and AP removal
Noise removal
Filtering and super-obbing
Velocity dealiasing
VDRAS and WRF-VAR
Specifying observationerror
Data filling
23
Case Studies and Results
24
Cpol
Kurnell
rms(udual – uvdras) = 1.4 m/s
rms(vdual – vvdras) = 0.8 m/s
November 3rd, VDRAS-Dual Doppler comparison During Sydney 2000 Olympics
¼ of analysis domain
VDRAS low-level analysis• Apply VDRAS to the low-level
(below 5 km)• Focus on low-level
convergence and gust front• Has been run in real time for
a number of years in several locations
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Date Mean vector difference
Mean vector
9/18/2000 2.1 m/s 6.2 m/s
10/3/2000 3.5 m/s 9.4 m/s
10/8/2000 2.6 m/s 5.0 m/s
11/03/00 2.2 m/s 5.0 m/s
Verification of VDRAS winds using aircraft data
(AMDARs)
Sydney 2000
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High-resolution data assimilation reveals how cold pools trigger storms
0611 2046 UTC - 0612 1250 UTC from IHOP
Pert. Temp. (color)Shear vector (black arrow)Wind vector at 0.1875km (brown arrow)Contour (35 dBZ reflectivity)
4DVar analysis with radar data assimilationvia VDRAS
QuickTime™ and aBMP decompressor
are needed to see this picture.
27
Initialization and forecasting of an IHOP squall line
• Occurred in IHOP domain, on June 12-13, 2002• ~ 12 hour life time: 20:00 – 8:00 UTC• Formed near a triple point of a dry line and a stationary outflow boundary
28
Model and DA set-up
Observation
• Domain size: 480kmx440km Resolution: 4km
• 4 NEXRAD radars
• ~30 METAR surface stations
• Cold start first guess: radiosonde + VAD + surface obs.
• 50 min assimilation period which includes three 10 min 4DVar cycles
015400 UTC
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5-hour forecast of IHOP June 12 squall line Frame interval: 20 min. White contour: observation
QuickTime™ and aBMP decompressor
are needed to see this picture.
Evolution of cold poolt = 0
t = 1.5 hr
t = 3 hr
-8oc -2ocThe initial cold pool of -8oc played a keyrole in the development of the storm.
31
Forecast verification
Model
Persistence
Extrapolation
Rainwater correlation
32
WRF 4DVar radar DA experiments
• Initial time: 0000 UTC 13 June 2002
(Selection of this initial time because more conventional data are available)
• GTS data included: SOUND, PILOT,Profiler, SYNOP, METAR, and GPSPW.• 4DVAR time window: 0 15m,
3DVAR time window: -15m 15m, but the Radar data only at time=0.•Verification: hourly rainfall from NCEP Stage_IV data
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061300Z, 3/4VAR Exp. Initial time
061300Z 061306Z 061312Z
4DVAR time window
3DVAR time window
05 10 15m
004DVAR
3DVAR
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Radar data distribution
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Increments of temperature
Increments of water vapor mixing ratio
GFS analysis 3DVAR analysis 4DVAR analysis
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Hourly precipitation ending at 0200 UTC 13 June
GFS
3DVAR
OBS
4DVAR
37
Hourly precipitation ending at 0400 UTC 13 June
GFS
3DVAR
OBS
4DVAR
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Hourly precipitation at 0600 UTC 13 June
GFS
3DVAR
OBS
4DVAR
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Hourly precipitation ending at 1000 UTC 13 June
OBS GFS
3DVAR 4DVAR
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Threat scores with Radar data 4DVAR only
Green dashed-line is the assimilation of Radar radial velocity only
Blue dot-line is the assimilation of Radar radial velocity and GTS observation data
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Issues and Opportunities
• Further improvement of data assimilation techniques
• New observations
- Radar refractivity, polarimetric obs.,
CASA, TAMDAR…• Accuracy of large-scale analysis• Model error/physical parameterization• Computation/limited area implementation
42
Sensitivity with respect to first guess
Humidity first guess: background
Humidity first guess:Background + saturation within convection
43
Issues and Opportunities
• Further improvement of data assimilation techniques
• New observations
- Radar refractivity, polarimetric obs.,
CASA, TAMDAR…• Accuracy of large-scale analysis• Model error/physical parameterization• Computation/limited area implementation
44
Impact of TAMDAR data
Relative humidity without TAMDAR Relative humidity with TAMDAR
1-hour qr forecast without TAMDAR 1-hour qr forecast with TAMDAR
White contour:Observed reflectivity
Greater than30 dBZ
45
Issues and Opportunities
• Further improvement of data assimilation techniques
• New observations
- Radar refractivity, polarimetric obs.,
CASA, TAMDAR…• Accuracy of large-scale analysis• Model error/physical parameterization• Computation/limited area implementation
46
Sensitivity of the simulation with respect to environmental condition
47
Issues and Opportunities
• Further improvement of data assimilation techniques • New observations
- Radar refractivity, polarimetric obs.,
CASA, TAMDAR…• Accuracy of large-scale analysis• Model error/physical parameterization• Computation/limited area implementation
48
Microphysical parameter retrieval
Change of the parameter with respect to iteration number
Cycle 1 Cycle 2 Cycle 3
5 m/s - Value incontrol simulation
Terminal Velocity
Evaporation rate
Iteration Iteration
FirstGuess
Adjusting model microphysical parameters along with initialcondition by fitting the model to radar observations
49
Issues and Opportunities
• Further improvement of data assimilation techniques • New observations
- Radar refractivity, polarimetric obs.,
CASA, TAMDAR…• Accuracy of large-scale analysis• Model error/physical parameterization• Computation/limited area implementation
50
ReferencesSun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from
Doppler radar observations using a cloud model and its adjoint: Part I. model development and simulated data experi ments. J. Atmos. Sci., 54, 1642-1661.
Sun, J., and N. A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint: Part II. Retrieval experiments of an observed Florida con vective storm, J. Atmos. Sci., 55, 835-852.
Sun, J., and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data, Wea. Forecasting, 16, 117-132.
Crook, N., A., and J. Sun, 2002: Assimilating radar, surface and profiler data for the Sydney 2000 forecast demonstration project. J. Atmos. Oceanic Technol., 19, 888-898.
Sun, J., 2005: Convective-scale assimilation of radar data: progress and challenges. Q. J. R. Meteorol. Soc., 131, 3439-3463
Sun, J. and Y. Zhang, 2008: Assimilation of multipule WSR_88D Radar observations and prediction of a squall line observed during IHOP. Mon. Wea. Rev., 136, 2364-2388.
Sun, J., E. Lim, and Y. Guo, 2008: Assimilation and forecasting experiments using radar observations and the 4DVAR technique for two IHOP cases, 5th European Conference on Radar in Meteorology and Hydrology., Helsinki, Finland, 30 June – 4 July, 2008.