Acknowledge: NCAR/NESL/MMM/DAS, NCAR/RAL/JNT/DAT, AFWA, USWRP, NSF-OPP, NASA, AirDat, PSU,

Hans Huang: WRFDA – Jan 2012 1

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WRFDA 2012 Overview and

Recent Adjoint-based Observation Impact Studies

Hans Huang National Center for Atmospheric Research

(NCAR is sponsored by the National Science Foundation)

Acknowledge: NCAR/NESL/MMM/DAS, NCAR/RAL/JNT/DAT,AFWA, USWRP, NSF-OPP, NASA, AirDat, PSU,KMA, CWB, CAA, BMB, EUMETSAT


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WRFDA Overview• Goal: Community WRF DA system for

• regional/global, • research/operations, and • deterministic/probabilistic

applications.• Techniques:

• 3D-Var• 4D-Var (regional)• Ensemble DA, • Hybrid Variational/Ensemble DA.

• Model: WRF (ARW, NMM, Global)• Observations: Conv. + Sat. + Radar (+Bogus)• Support:

• NCAR/NESL/MMM/DAS (Data Assimilation Section, also supporting WRF/DART)• NCAR/RAL/JNT/DAT (Data Assimilation Team, also supporting GSI)


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Google WRFDA:

www.mmm.ucar.edu/wrf/users/wrfda


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Recent Tutorials at NCAR.

1. WRFDA Overview

2. Observation Pre-processing

3. WRFDA System

4. WRFDA Set-up, Run

5. WRFDA Background Error Estimations

6. Radar Data

7. Satellite Data

8. WRF 4D-Var

9. WRF Hybrid Data Assimilation System

10. WRFDA Tools and Verification

11. Observation Sensitivity

Practice1. obsproc

2. wrfda (3D-Var)

3. Single-ob tests

4. Gen_be

5. Radar

6. Radiance

7. 4D-Var

8. Hybrid

9. Advanced (optional)

The next: July 2012


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WRFDA tutorials21-22 July, 2008. NCAR.

2-4 Feb, 2009. NCAR.

18 April, 2009. South Korea.

20-22 July, 2009. NCAR.

15-31 Oct, 2009. Nanjing, China.

1-3 Feb, 2010. NCAR.

10 April, 2010. Seoul, South Korea.

3-5 August 2010. NCAR.

16 April. Busan, South Korea

20-22 July 2011. NCAR

10-20 October. Bangkok, Thailand.

WRFDA online tutorial and user guide

http://www.mmm.ucar.edu/wrf/users/wrfda


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New features, v3.3

• WRFPLUS3 – WRF TL/AD based on WRF3.3• 4D-VAR redesigned/upgraded• RTM interface updated:

– RTTOV (v10.0)– CRTM (v2.0.2)

• NOAA-19 AMSUA and MHS added.• New background error option (cv6).

– Add additional variables, e.g. unbalanced velocity, to balance computations;

– Fully multivariate including relative humidity

• Capability to generate forecast sensitivity to observations (FSO), compatible with WRF3.3


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A recent adjoint-based observation impact study

(or FSO)

Thomas Auligné, Xin Zhang, Hongli Wang, Anwei Lai, Xiaoyan Zhang, Hui-Chuan Lin, Qingnong Xiao, Yansong Bao, Zhiquan Liu,

Xiang-Yu Huang


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Observation

(y) WRF-VAR

Data

Assimilation

WRF-ARW

Forecast

Model

Forecast

(xf)

Derive

Forecast

Accuracy

Background

(xb)

Analysis

(xa)

Adjoint of

WRF-ARW

Observation

Sensitivity

(F/ y)

Background

Sensitivity

(F/ xb)

Analysis

Sensitivity

(F/ xa

)

Observation Impact

<y-H(xb

)> (F/ y)

Adjoint of

WRF-VAR

Obs Error

Sensitivity

(F/ ob

)

FSO - Forecast Sensitivity to Observations

Gradient

of F

(F/ xf)

Define

Forecast

AccuracyForecast

Accuracy

(F)

Bias Correction

Sensitivity

(F/ k

)

Figure adapted from Liang Xu (NRL)


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From Langland and Baker (2004)

ForecastError

xt is the true state, estimated by the analysis at the time of the forecastxf is the forecast from analysis xaxg is the forecast from first-guess at the time of the analysis xa

Time


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More details (for WRFDA implementation):

Reference state: Namelist ADJ_REF is defined as1: xt = Own (WRFVar) analysis2: xt = NCEP (global GSI) analysis3: xt = Observations

Forecast Aspect: depends on reference state1 and 2: Total Dry Energy3: WRFDA Observation Cost Function: Jo

Geo. projection: Script option for box (default = whole domain)ADJ_ISTART, ADJ_IEND, ADJ_JSTART, ADJ_END,ADJ_KSTART, ADJ_KEND

Forecast Accuracy Norm: e = (xf-xt)T C (xf-xt)


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Limitations

• Approximation of “truth”

• Dependence of norm

• Linear assumptions– Adjoint of the forecast model– Adjoint of the analysis (assimilation)

• …


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ASR (180km) ResultsJanuary 2007


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Design of experiments• Cycling Data assimilation • Start Time:2007081506• Final Time:2007091512 • Boundary Conditions : FNL analysis• The initial condition for 2007081506 is the FNLdata,

The WRF model 6h forecasting is the cycling data assimilation background.

ADJ_REF =1, Its own WRFDA analysis

ADJ_MEASURE =1


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Impact of observations on 24h forecast

(conventional observations)

-50000

-45000

-40000

-35000

-30000

-25000

-20000

-15000

-10000

-5000

0

Soun

d

Syn

op

Pil

ot

Sat

em

Geo

AMV

Pol

arAM

V

AIR

EP

GPS

ZTD

GPS

RF

MET

AR

Shi

ps

SSM

I_RV

SSM

I_TB

SSM

T1

SSM

T2

QSC

AT

Pro

file

r

Buo

y

Bog

us

Pse

udo

Rad

ar

AIR

SR

Son

de_s

fc

MTG

IRS

TAM

DAR


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Impact of observations on 24h forecast (satellite observations)

- 14000

- 12000

- 10000

- 8000

- 6000

- 4000

- 2000

0noaa- 15-

amsuanoaa- 16-

amsuanoaa- 18-

amsuanoaa- 15-

amsub noaa- 16-

amsubnoaa- 17-

amsub


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T8 (45km) ResultsOne month:2007081506-2007091512

• WRFV3.2.1• MAP_PROJ=Mercator• WE =140• SN =94• DX =45km• DY =45km• E_VERT =57• MP_PHYSICS=WSM 5-class scheme• CU_PHYSICS= Grell 3D ensemble scheme

WRFV3 Version 3.2.1

WRFDA Version 3.2.1

WRFPLUS2.0

AWFA T8

Target

region

• WRFPLUS2.0

• ADJ_ISTART=20 ADJ_IEND=60

• ADJ_JSTART=20 ADJ_JEND=60

• ADJ_KSTART=1 ADJ_KEND=27


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Impact of observations on 6h forecast

(grouped as observed variables)


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Impact of observations on 6h forecasts (grouped according to observing systems)


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hPa hPa Km

Vertical Impact distribution, unit: J/Kg

SOUND GeoAMV GPSRO


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Time series of impact of observations on 6h forecasts

X axis :time. 4 times per day during 30days ;

Y axis : Impact on the forecasting (Unit:J/kg)

SOUND

SYNOP

GeoAMV


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Monitoring the observation impact over Taiwan CWB OP23 domain

Xin Zhang and Xiang-Yu Huang


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• In terms of the observational type

– The largest error decrease is due to GeoAMV followed by SOUND, SYNOP and GPSREF

– The impact from SATEM is marginal and neutral.

– On 0000UTC and 1200UTC, the SOUND is the most important observation to decrease the forecast error, followed by GeoAMV, SYNOP, GPSREF/AIREP

– on 0600UTC and 1800UTC, the GeoAMV is the most important observation to decrease the forecast error, followed by SYNOP/GPSREF, AIREP


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• In terms of the time series of impact of observational type

– On 0000 and 1200UTC, the SOUND improve the forecasts in general.

– On 0600UTC and 1800UTC, the SOUND almost degrades the 1/3 of the forecasts. Why ? Are the few SOUND obs. bad quality or something else is wrong ?

– For other observation types, at 0600UTC and 1800UTC, there are many degraded cases due to observations. Need further investigation.


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• In terms of impact per observation for each type

– The GPSREF is the most efficient observation to reduce the 24h forecast error per observation, followed by SYNOP, SHIPS and SOUND. It is consistent with the result from AFWA domains ( personal communication with Jason T. Martinelli of AFWA)


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SOUND observations around 500hPa (206 records)


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500hPa SOUND impact


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SYNOP observations


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SYNOP impact


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• In terms of the geographical distribution of observation impact

– Gives us an insight of the detail information about the observation impact for each observation.

– Please note: On Taiwan, there are lots SYNOP observations. Due to the plotting technique, the blue dots are overwritten by the red dots.


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Preliminary results• SOUND is the most important data to improve the forecast

among the conventional data.

• GPSREF is the most efficient data to improve the forecast and is the most important observation at 0600 and 1800UTC.

• The impact of SATEM is marginal and not stable.

• For OP23 system, at 0600UTC and 1800UTC, the observation impacts from all observational types are not stable. (Why?)

• In two days (2010120500 and 2010121812), almost all the observations show the negative impact. Preliminary investigation shows that, in the first case (2010120500), the linear approximation of WRFPLUS is not valid, the nonlinear forecasts show the positive impact of observation, but the WRFPLUS derived the negative impact.


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Future research topics• Identify observations with continuous

negative impact.

• Tune WRFDA based on observation impact results.

• Improve the assimilation strategy of surface observation assimilation.

• Investigate the differences of verifying forecasts to EC analysis, NCEP GFS analysis, WRFDA analysis and Observations.


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Future research topics

• For limited–area forecast models, the forecast errors not only come from initial condition (IC), but also come from lateral boundary condition (LBC). Need to include the initial forecast error gradient with respect to LBC in FSO.

• Current WRF tangent linear adjoint models (WRFPLUS) only include 3 simplified physics packages. Need to study the accuracy of the linear approximation in FSO applications. We also plan to improve the physics packages in WRFPLUS.


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Summary1. WRFDA 2012 Overview

• Community System

• Techniques: 3D-Var, 4D-Var, EnKF, Hybrid

• New features in v3.3

2. A recent adjoint-based observation impact study• The concept and limitations

• The WRFDA implementation

• Applications

• The next step

Acknowledge: NCAR/NESL/MMM/DAS, NCAR/RAL/JNT/DAT, AFWA, USWRP, NSF-OPP, NASA, AirDat, PSU,

Documents

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