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Report from Japan Meteorological Agency by Tomoaki OSE (Meteorological Research Institute / JMA). - PowerPoint PPT Presentation
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Report from Japan Meteorological Agencyby Tomoaki OSE (Meteorological Research Institute / JMA)
• JMA finally started the operational seasonal prediction using the output of a couple model of the JMA/MRI-CGCM3 in Feb, 2010 instead of a two-way system. The skill for NINO3.4 SST ACC is comparable to the top models shown in Jin et al (2008). High predictability for East Asia climate through the Indian Ocean SST anomaly is expected.
•
• A 4D-Var reanalysis project (JRA-55) is started at JMA, targeting the period of 1958-2012 with TL319L60 and 0.1hPa_top. Dry bias over the Amazon basin is improved as well as cold bias in the lower stratosphere, found in JRA-25 for 1979-2004.
•
• Monitor and verification webpage of TIGGE data (http://tparc.mri-jma.go.jp/TIGGE/), including TIGGE MJO forecast (http://tparc.mri-jma.go.jp/TIGGE/tigge_MJO.html), is operated and updated every day.
• A new high-resolution MRI-AGCM with TL959L64 (20km) improves East Asia precipitation and tropical cyclones distribution.
Kiyotoshi TakahashiClimate Prediction Division/Japan Meteorological Agency
Performance of JMA atmosphere-ocean coupled model
1. Introduction
JMA-CGCMA:T42L21
O:2.5x(0.5-2.0)M6(LAF)Aug.1999
El-Niño prediction model
JMA-CGCM02A:T42L40
O:2.5x(0.5-2.0)M6(LAF)Jul.2003
JMA/MRI-CGCMA:TL95L40
O:1.0x(0.3-1.0)M12(LAF)Feb.2008
Seasonal forecast model
AGCMT63L40M31
Mar.2003
AGCMTL95L40M31
Mar.2006
JMA/MRI-CGCMA:TL95L40
O:1.0x(0.3-1.0)M51(9m,LAF)
Feb.2010
Flow of models for seasonal forecast and El-Niño outlook
Current system
SST
JMA/MRI-CGCMA:TL95L40
O:1.0x(0.3-1.0)M30(5m,LAF)
Feb.2009
AGCMTL95L40M51
Sep.2007
Jin E. K., James L. Kinter III, B. Wang, C.-K. Park, I.-S. Kang, B. P. Kirtman, J.-S. Kug, A. Kumar, J.-J. Luo, J. Schemm, J. Shukla and T. Yamagata, 2008: Current status of ENSO prediction skill in coupled ocean–atmosphere models. Clim. Dyn., 31, 647–666.
NINO.3.4 SST ACC: dependency on lead time (quote from Fig. 8 of Jin et al. 2008)
Initial: February (1980-2001)
0 1 2 3 4 5
1.0
0.8
0.6
0.4
0.2
0.0 0 1 2 3 4 5
Lead time (month)
1.0
0.8
0.6
0.4
0.2
0.0
(JMA/MRI-CGCM) Lead time (month)
Initial: August (1980-2001)
NINO.3.4 region: 120W-170W, 5S- 5N
Impact of IOBW on world climate (JJA)
Impact of above-normal IOBW on world climate in boreal summer
When above-normal IOBW SST persists through post-El Niño summer, impacts like the right panel are expected.
Expected atmospheric responses to warmer anomaly in IOBW SST.
( Kelvin wave )Ref. Xie .et al(2009)
Northern Indian Ocean Tropical Western Pacific
Comparison of monthly SST ACC SST prediction by new EPS systemPrescribed SST for current 2-tier system
Anomalycorrelation
3. Comparison of performance SST between current and New systems
improvement
SST ACC for Dec.(4 month lead), Initial: the last day of July
improvement
Lead time (month) Lead time (month)
SST ACC (1084-2005)SST ACC (1084-2005)
North Western Pacific monsoon are
Indian monsoon area
(average for 4 initials)
☺☹
3. Comparison of performance precipitation ACC: dependency on lead time
Precip. ACC 3 month mean (1984-2005)
Precip. ACC 3 month mean (1984-2005)
Lead timeLead time
Ensemble method for New EPS system• 9 members per 1 initial, 13 months forecast• Execution by every 5 day→ LAF . LAF for a month• 6 initials→51members are used for one-month forecast
-25day
0day1 initial date9 members
51 members from 6 initial dates
4. Summary
Yuhei Takaya (JMA/CPD)ytakaya@met.kishou.go.jp
South China Flood in June 2010
Precipitation in June 2010 (ratio to normals)
Zhejiang 174 %
Fuzhou 157 %
Based on CLIMAT reports
June 2010 forecasts (init:May) precipitation anomaly
Beijing ECMWF Exeter Melbourne Melbourne
Montreal Moscow Seoul Tokyo Tokyo
Toulouse Washington
For details, please refer to the WMO L C websitehttp://www.wmolc.org/
Observation; OLR anomaly(original data are provided by NOAA)
(mm/day)
JRA-55the Japanese 55-year reanalysis project
- status and plan -
Ayataka Ebita, Yukinari Ota, Shinya Kobayashi*, Masami Moriya, Ryouji Kumabe, Kiyotoshi Takahashi and Kazutoshi Onogi
Japan Meteorological Agency
Specifications of data assimilation system
JRA-25 (1979~2004) JRA-55 (1958~2012)
Resolution T106L40(top layer at 0.4 hPa)
TL319L60(top layer at 0.1 hPa)
Time integration Eularian Semi-Lagrangian
Long-wave radiation
Line absorptionStatistical band model
Water vapor continuume-type
Line absorptionTable lookup + K-distribution
Water vapor continuume-type + p-type
Assimilation scheme 3D-Var 4D-Var
(with T106 inner model)
B matrix ConstantDifferent B matrices for
pre-satellite and satellite eras
Bias correction(radiosonde)
Radiation bias only(Andrae et al., 2004)
RAOBCORE v1.4(Haimberger, 2007, J. Climate)
Bias correction(radiances) Offline Variational Bias Correction
Soil wetness in the root layer(Oct 1990 ~ Sep 1991)
JRA-25 JRA-55 JRA-55 minus JRA-25
(Saturation ratio [0~1]) (Saturation ratio [0~1]) (Saturation ratio)
6-hour precipitation and surface pressure increment
(Oct 1990 ~ Sep 1991)
Difference from GPCP precipitation (mm/day)
Surface pressure increment (hPa/day)
The dry bias over the Amazon basin is likely due to an artificial anticyclonic anomaly caused by bias of surface pressure observations and the consequent lack of precipitation.
Difference from GPCP precipitation (mm/day)
Surface pressure increment (hPa/day)
JRA-25 JRA-55 Exp
Summary
• JRA-55 improves upon JRA-25 in many respects,
– a longer reanalysis period, extending back in 1958,– much better forecast performance than JRA-25,– significantly reduced cold bias in the lower stratosphere, and– reduced dry bias over the Amazon basin.
• Quality of analysis changes inevitably due to changes in observing systems, but there is a good prospect that a reasonably homogeneous analysis will be produced in the northern hemisphere troposphere.
– Quality of analysis is reasonablely high over the regions that radiosondes cover even if no satellite data is available.
– On the other hand, it is anticipated that analyses of the pre-satellite era would degrade seriously in the southern hemisphere troposphere.
Summary (cont.)
• There is a considerable possibility that quality of analysis in the pre-satellite era will improve by tuning the background error.
– An experiment with a global constant scaling factor showed a small but positive result in the southern hemisphere.
• The JRA-55 production is planned to start in Jun 2010 and expected to complete by early 2013.
Use of TIGGE data
THORPEX Interactive Grand Global Ensemble
The Observing System Research and Predictability Experiment
under WWRP
To improve the accuracy of 1-day to 2 week high-impact weather forecasts
Ensemble forecast data from 10 global NWP centers
TIGGE: THORPEX Interactive Grand Global Ensemble
http://tparc.mri-jma.go.jp/TIGGE/
TIGGE: THORPEX Interactive Grand Global Ensemble
http://tparc.mri-jma.go.jp/TIGGE/tigge_MJO.html
High-resolution models
Previous
JRACMAP
Seasonal Change of East Asia Precip
Lines : Height Thickness ( 200hPa-500hPa )Shade : PrecipitationArrows : 850hPa Wind
Asian Summer Monsoon ( JJA )
TRMM
NEW
20km
GPCP
Variability of Tropical Precipitation (JJA) 20kmEOF1 EOF2
Previous
NEW
Present climate simulation of TC formation &tracking distribution by 20km Atmos. Model (25 years)
後期モデル
前期モデル
観測
Considerably improving
Overestimating Improving a littleImproving
Improving Improving
: Observation: Earlier model: Updated model
Tendency of formation at more eastern locations
Western North Pacific
Observation(1979-2003) 20km Updated Model (1979-2003)
20km Earlier Model(1979-2003)Blue : January - MarchGreen : April- JuneRed : July-SeptemberOrange : October - December
※TC detection is adjusted so that total global number of formations is equal to that of observed number
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