hans huang: wrfda – jan 2012 1 da wrfda 2012 overview and recent adjoint-based observation impact...
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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
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