Radar Data Assimilation for Explicit Forecasting of Storms

Radar Data Assimilation for Explicit Forecasting of Storms

Juanzhen Sun

National Center for Atmospheric Research

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Outline

• Introduction: background and motivation

• Methodologies for storm-scale DA

• 4D-Var radar data assimilation at NCAR

• Case studies and results

• Issues and opportunities

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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.

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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’



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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

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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

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Case Studies and Results

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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.

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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

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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.

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Forecast verification

Model

Persistence

Extrapolation

Rainwater correlation

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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

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GFS

3DVAR

OBS

4DVAR

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Hourly precipitation at 0600 UTC 13 June

GFS

3DVAR

OBS

4DVAR

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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

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Sensitivity with respect to first guess

Humidity first guess: background

Humidity first guess:Background + saturation within convection

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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

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Sensitivity of the simulation with respect to environmental condition

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• Further improvement of data assimilation techniques • New observations



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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

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• Further improvement of data assimilation techniques • New observations



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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.

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Radar Data Assimilation for Explicit Forecasting of Storms