CWB NWP Overview
Chin‐Tzu Fong
Meteorological Information Center
Central Weather Bureau
Outline
1. Brief history for NWP at CWB
2. Recent advances
• Global forecast system
• WRF‐based regional forecast system
3. Outlook and conclusion
Brief History for CWB/NWP (I)
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Background
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A demand for CWB to improve heavy rainfall forecasts.
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Taiwan suffered severe floods in 1981, causing more than NT$ 10 billion dollars loss and many people to die.
A trend of the NWP technology popularly used by other national meteorological services.
Introduction of NWP
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CWB started the NWP development in 1983.
Taiwanese‐American experts organized to assist CWB
A strategy proposed for the whole process
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Take progress step by step to ensure CWB’s staff to get trained, aiming to
Cultivate in‐house professionals, in the end to
Establish in‐house capability for independent maintenance and development
Since 1983, CWB had been through four phases of NWP related development plan. The fifth‐phase development plan from 2010 to 2015 is undergoing.
Brief History for CWB/NWP (II)
2010‐2015
Sustain the advance of numerical prediction technology, particularly improving the skill for high‐impact weathers. Goals including:
•Higher resolution: GFS~20km, Reg.~3‐2km
•More advanced DA system to enhance the use of satellite and radar observations.
•High resolution ensemble prediction system
1995‐2009
(Establishing in‐house capability) CWB specialists were getting skilled in NWP technology and gradually dominated the development work. CWB extended the application of numerical prediction technology from weather to short‐term climate forecast.
1983‐1994
(starting from scratch)
Overseas experts dominated the implementation of NWP systems, but trained CWB staff to create in‐house professionals.
2011：CWB WRF ensemble prediction system
operational, 20 members at 00/12Z run.
2011：CWB spectral GFS upgraded to T319L40.
2008：CWB WRF operational.
2007：CWB spectral GFS upgraded to T239L30.
2001：CWB non-hydrostatic regional model
operational with 45/15/5km domains.
1994: The 2nd generation global spectral and regional models
operational at CWB with resolution of T79L18 and
60/20km nested domains.
1989: The 1st generation global/regional models operational
at CWB with resolution of 275km/90km.
Supercomputer Evolution
1987
1994
2000
2006
• Averagely, CWB upgrades the supercomputer every 6‐7 years, able to gain 15‐20 times more computing power than the old one.
New Generation Supercomputer

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Three‐year installment, providing 6%, 6% and 88% capacity from 2012 to 2014 respectively.
The new supercomputer will be a Peta‐Scale machine, near 100 times faster than the old one.
Fujitsu HPC
Recent Advances of NWP
•
• Global Forecast System
WRF‐Based Regional Forecast System
Global Forecast System - DA

Data assimilation method
Before • OI
2003 • Only GTS convectional obs. assimilated
• NCEP/SSI (Spectral Statistical Interpolation)
2003 • Besides GTS, radiance added but very limited
2010

• NCEP/GSI (Grid Statistical Interpolation)
• GPS/RO and more radiance assimilated
Observations

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GTS – through the channel of JWA and NOAA/GSD
Radiance – direct from NCEP FTP Site starting 2003
Radiance Assimilation at CWB/GFS
According to assessment of data contribution to forecast improvement by ECMWF, Satellite observations of AMUSA, IASI and AIRS are the top priority for radiance assimilation at CWB/GFS.
Only NOAA15 AMUSA
before 2010
500hPa Anomaly Correlation
20N‐80N
Currently, all AMUSA
assimilated by GSI
IASI added in 2013
AIRS added in 2014
80%
60%
40%
20%
CWB/GFS radiance use relative to NCEP/GFS
20S‐80S
6hr gain in
forecast
Global Forecast System ‐ Model

Resolution
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T319L40 (~42km) for 8‐day forecast.
T119L30 (~110km) for 45‐day forecast at 00/12Z, participating in a NCEP’s activity for dynamical model MJO forecast.
Physics
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Surface layer : similarity
Gravity wave drag (Palmer et al. 1986)
Shallow convection scheme (Li and Young 1993)
Year
Parameterization
Old Version Updated version
2005
Land surface model
Bucket method
Two‐layer land model
(Pan and Mahrt 1987)
2007
Grid scale precip.
Diagnostic method
Cloud microphysics scheme
(Zhao and Carr 1997)
2008
Radiation
Radiation package by Hashvardhan et al. 1987
Unified two‐stream scheme with k‐
correlated method
(Fu and Liou 1992;1993 and Fu et al. 1997)
2009
Cumulus
Relaxed A‐S scheme
NCEP/Simplified A‐S scheme
(Pan and Wu 1994)
2009
Boundary layer
TKE‐ scheme
NCEP/First‐order non‐local scheme
(Troen and Mahrt 1986)
Impact of CUP/PBL Update on CWB/GFS
500hPa Anomaly Correlation
OLD CUP/PBL
Too strong sub‐tropical high due to warm bias over tropics Dash line : New CUP/PBL
Solid line : Old Version
6hr gain in
summer forecast
2008年颱風路徑預報平均誤差
Typhoon track forecasts
in 2008
New CUP/PBL
400
Improvement on sub‐tropical high prediction
Dash line : Analysis
Solid line : 5‐day forecast
預報誤差(公里)
350
300
250
Track forecast
improved as well
OLD
200
NEW
150
100
50
0
12
24
36
48
預報時間(小時)
60
72
Ongoing Update for Physical Parameterization
Year
Parameterization
New version ongoing
2013
Land surface model
NCEP/Noah land model
2014
Cumulus/ Shallow convection
NCEP/New Simplified A‐S scheme
5‐day forecast RMSE for temp. at lower layer
Oper. land model
Noah land model
Improve cold bias over land
Dash line : Noah
Solid line : Oper
Temperature
RMSE reduced
Forecast Performance Progress for CWB/GFS
Day 5 Anomaly Correlation over 20N‐80N for 500hPa H
(1999‐2012)
1999年至2012年期間，CWBGFS在20‐80N內，第5日預報500hPa高度場之距平相關(AC,縱軸)。黑
線帶實心圓點為月平均變化，紅色粗實線為12個月移動平均(running mean)。
Continuous forecast improvement over the years due to:
• Better forecast model
• Better initial state (better data assimilation system, more observations)
WRF‐Based Forecast System
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ARW‐WRF, NCAR community model system, was implemented at CWB in 2004 and went through comprehensive evaluations.
WRF operational in 2008 as a new generation regional forecast system. Current status of CWB/WRF system as follows:
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45/15/5km three nested domains with 45 layers in the vertical
WRF 3DVar using partial cycling similar to NCEP Lateral boundary condition from NCEP GFS output as primary option
84 hrs forecasts at 00/06/12/18Z
WRF‐based high‐resolution ensemble system operational in 2011
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Same domain design as deterministic model
20 members predictions at 00/12Z using multi‐physics suites
Support 368 townships weather forecast
Provide better typhoon QPF over Taiwan
Improvement for CWB/WRF

Although WRF started to be operational at CWB in 2008, the performance was not so satisfied particularly for typhoon tracks.
An example of poor WRF typhoon track forecasts for Morakot of 2009
Typhoon Morakot devastated Taiwan in August 2009, killing near 700 people.
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Effort has been taken to improve WRF forecasts over the years, including
 Initial field improved by
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use of 12‐hour partial cycling similar to NCEP.
outer loops adopted in DA.
implementation of typhoon vortex relocation, crucial for typhoon track forecast.
Model physics improved by
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use of K‐F cumulus parameterization and reducing its associated warm bias, greatly beneficial for typhoon forecast.
use of GWD parameterization, significantly improving the wintertime forecast.
CWB has a close cooperation with NCAR’s scientists on WRF improvement.
Improvement of CWB/WRF Track Forecasts
26
8 043
1
2
0
4 7 84 9 5 16
2
2 3 4 39 3 8
0 5
17922
1089 194 6
0
7 88
4
0219 7 43 6
05
98 1
83
7
4
4 2 68 7
79 8
4 47
9 51
66 9
2
5
6 32 7
403 0 1 75 502 6 6 28 36 57 36
55
3
1
3
1
1
Tracks by 2009 operational version
450
400
447
24
hr
420
395
350
300
250
100
314
259
227
200
150
305
301
48hr
205
183
178
24hr
138
2008
118
72hr
2009
43
51
2
1
4 3
2
1
Tracks by 2011 operational version
TC track forecast errors of WRF
(2008‐2012)
72hr
2
2010
104
2011
121
2012
In 2010, WRF used:
• partial cycle/outer‐loop
• relocation
• K‐F scheme
In 2011, WRF used
• modified K‐F scheme
Overall Performance Progress for CWB/WRF
Yearly averaged score
72hr forecast RMSE for 500hPa H 72hr forecast RMSE for 850hPa T •
•
Modified K‐F scheme in 2011
Gravity wave drag in 2012
Typhoon QPF by CWB/WRF EPS
To improve typhoon QPF, an application system established to facilitate the use of rainfall forecast data from WRF ensemble prediction system.
Typhoon QPF over Taiwan
‐ Climatology Method
• Due to mountainous terrain, rainfall distribution and amount is closely linked to the relative location of a typhoon to Taiwan.
• A climatology method for typhoon QPF has been widely used at CWB based on a statistical relationship between the historical rainfall data and typhoon tracks.
Each panel represents a composite rainfall estimate for a typhoon located at the corresponding panel center based on a statistical climatology method .
(葉天降 2009)
Typhoon QPF over Taiwan
•
‐ Climatology Method
Pro and con for the climatology method
• Pro: reasonable QPF for normal typhoons
• Con: QPF tends to be significantly underestimated for cases of extreme rainfall, particularly associated with an interaction with the southwest or northeast monsoon flow like Morakot (2009) and Magi (2010).
Typhoon Morakot (2009) embedded in a large‐scale convection zone
Taiwan
Typhoon Megi (2010) interacting with a cold front
Taiwan
>1200mm in a day
>600mm in half a day
Ensemble Typhoon QPF (ETQPF)
•
Based on the concept of the climatology method, a dynamical ensemble method for typhoon QPF was established by using real‐time predicted rainfall and typhoon tracks from WRF ensemble system instead of historical database.
• Pro: extreme typhoon rainfall QPF can be detectable if predicted by models
• Con: QPF accuracy depends on model capability in rainfall prediction
Distribution of the predicted
typhoon locations from EPS
Given a specified track scenario, a composite rainfall map can be made with predicted rainfall of points within a certain circle around the track.
3‐hour accumulated rainfall predictions from members in a specific circle
A user friendly system established to easily produce a composite QPF under different track scenarios
22
Outlook for Future NWP Development
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Model
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Data assimilation
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GFS ‐ go to 25km first, aiming to reach 15km
Global ensemble system for week 2 forecast
WRF ‐ 15/3km nested
A rapid‐cycling WRF‐based model with a very high resolution (~1km) over Taiwan area for very short‐range QPF
Ensemble‐3D/4D variation hybrid analysis technology
Enhance the use of local observations particularly from the radar network to improve short‐range QPF
Physical parameterization
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Better simulation for hydrological cycle, particularly over Taiwan’s complex topography
Challenge for QPF over Complex Terrain
Due to the difficulty in properly handling the physical interaction with terrain, accurate QPF over Taiwan is very challenging . Currently, in 5km resolution domain of WRF, clear deficiency in QPF is found. More detail diagnostics is needed to look for effective improvement.
OBS
Deterministic model
Ensemble mean
accumulated rainfall of 12‐24hour forecast by WRF (a monthly averaged result for May in 2012)
WRF預報第12~24小時累積雨量
Conclusion
NWP at CWB has made continuous progress over the years. The new Fujitsu supercomputer offers CWB an unprecedented opportunity for NWP development. With more advanced NWP technology and higher resolution models implemented in the near future, CWB is looking forward to providing the nation more accurate weather information, particularly for typhoon forecast, week 2 forecast and overall QPF.
Top500/氣象排名
No14/No2
Top500/氣象排名
No91/No14
Top500/氣象排名
No293/No18
Top500/氣象排名
No45/No5
CRAY
YMP-8i
CDC
Cyber 205
Computing power
1/3704
Computing power
1/303
2012-2014
FUJITSU
FX10/PFX10
2006
2000
1994
1987
2012-2014
IBM
P5-575
FUJITSU
VPP5000
Computing power
1/15
Computing power
~100
Computing power
1
GFS : 40KM
WRF-based : 45/15/5KM
WRF EPS : 40 members/day
GFS : 275KM
RFS : 90KM
●
GFS : 165KM
RFS : 65/20KM
●
GFS : 110KM
NFS : 45/15/5KM
●
●
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
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Global : <20KM
Regional : 1~2KM (finest)
Ensemble : >100 members
per days, more freq. and
members
Radar and satellite
observations well
assimilated by more
advanced technology
Thank you for your attention.
Impact of the Partial Cycle
full‐cycle and 12‐hour partial‐cycle WRF analysis minus NCEP analysis
Full cycle
Partial cycle
(composite results from 78 cases for 700 hPa temperature)
•
•
Full cycling accumulated systematic warm bias over data sparse area, the North‐Western Pacific Ocean and the Indian Ocean.
Partial cycling suppressed the bias growth significantly.
Impact of Vortex Relocation
• First‐guest vortex separated from whole fields • The vortex relocated at the observed position before DA.
• Overall track forecasts improved due to correct track tendency in the beginning phase.
w/o vortex relocation
with vortex relocation
Hsiao et al. 2010 (Mon. Wea. Rev.)
Use of K‐F Cumulus Parameterization
• The Grell‐Devenyi scheme used originally was unable to well maintain the typhoon intensity, usually resulting in poor typhoon track forecasts. • The K‐F scheme can effectively improve the typhoon intensity and track forecasts. However it appears a significant warm bias over tropic.
• A new trigger function in K‐F was adopted to reduce this bias, benefiting typhoon forecasts again due to more correct subtropical high.
EC analysis
K-F
Original KF
G-D
Modified KF
Warm bias
Impact of GWD Parameterization
GWD (gravity wave drag) was added in 2012.
Blue ‐ forecast
Red ‐ analysis
Shading ‐ diff.
72hr forecast against analysis
(lighter shading, less bias)
500hPa H
500hPa T
RMSE of 72‐hr forecast for December 2008
w/o GWD
w/o GWD
with GWD
with GWD
with GWD
w/o GWD
目前無法顯示此圖像。
Application System for ETQPF