領域雲解像アンサンブル解析予報システムの開発と検証

Goal 2: Development of
a regional cloud-resolving ensemble
analysis and forecast systems
(㡿
㡿ᇦ㞼ゎീ䜰䞁䝃䞁䝤䝹䝅䝇䝔䝮䛾
㛤Ⓨ䛸᳨ド)
Meteorological Research Institute,
Japan Agency for Marine-Earth Science and Technology,
Japan Meteorological Agency,
Tohoku University,
Kobe University,
Disaster Prevention Research Institute, Kyoto University, etc.
Research subject ṟᾉSuper high performance mesoscale NWP
䠄㉸㧗⢭ᗘ䝯䝋䝇䜿䞊䝹Ẽ㇟ண 䛾ᐇド䠅
1) Development of cloud resolving 4DDA systems
䠄㡿ᇦ㞼ゎീ4ḟඖྠ໬ᢏ⾡䛾㛤Ⓨ䠅
Ὁfeasibility of dynamical prediction of local heavy rainfall
in very sort range forecast
MRI, JAMSTEC, DPRI/Kyoto Univ., NIED, ISM
2) Development and validation of a cloud resolving
ensemble NWP system
䠄㡿ᇦ㞼ゎീ䜰䞁䝃䞁䝤䝹 ゎᯒணሗ䝅䝇䝔䝮䛾㛤Ⓨ䛸᳨ド䠅
Ὁquantitative prediction of the probability of local heavy fall with a
lead time to disaster prevention
JAMSTEC, MRI, JMA, Tohoku Univ., DPRI/Kyoto Univ.
3) High performance atmospheric model
䠄㧗⢭ᗘ㡿ᇦ኱Ẽ䝰䝕䝹䛾㛤Ⓨ䛸䛭䜜䜢⏝䛔䛯ᇶ♏◊✲䠅
ȷEvaluation of model’s uncertainty through super high resolution
numerical experiments using LES and/or BIN models
JAMSTEC, MRI, Tokyo Univ., Nagoya Univ., DPRI/Kyoto Univ., etc.
Bin
LES
Goal 2: Development of a regional cloud-resolving
ensemble analysis and forecast systems
䠄 㡿ᇦ㞼ゎീ䜰䞁䝃䞁䝤䝹ゎᯒணሗ䝅䝇䝔䝮䛾㛤Ⓨ䛸᳨
᳨ド䠅
Goal2: Development of a regional cloud-resolving ensemble analysis
and forecast systems
z rainfalls less than half-day earlier ,
Conduct probability forecast of torrential
while specifying the occurrence time, location and intensity using the cloud
resolving ensemble forecasts.
➨2┠ᶆ䠖㡿ᇦ㞼ゎീ䜰䞁䝃䞁䝤䝹ゎᯒணሗ䝅䝇䝔䝮䛾㛤Ⓨ䛸
᳨ド
㞼ゎീ䜰䞁䝃䞁䝤䝹ணሗ䛻䜘䜚䚸
㞟୰㇦㞵䛾༙᪥௨ୖ๓䛾ண 䜢䚸
z
㻻㻮㻿
᫬㛫䞉ሙᡤ䞉ᙉᗘ䜢≉ᐃ䛧䛶
☜⋡ⓗ䛻⾜䛖䚹
㻝㻤㻶㻿㼀
18᫬ CNTL
Ẽ㇟◊✲ᡤ䞉ᾏὒ◊✲㛤Ⓨᶵᵓ䞉ᮾ໭኱Ꮫ䞉
ி㒔኱Ꮫ䞉⚄ᡞ኱Ꮫ䞉ᩘ್ணሗㄢ
18᫬ m01
18᫬ m02
18᫬ m03
18᫬ p01
18᫬ p02
18᫬ p03
᱁Ꮚ㛫㝸㻞䟜䛾㝆Ỉศᕸ㻔୍㒊㻕
Goal 2: Development of a regional cloud-resolving
ensemble analysis and forecast systems
䠄 㡿ᇦ㞼ゎീ䜰䞁䝃䞁䝤䝹ゎᯒணሗ䝅䝇䝔䝮䛾㛤Ⓨ䛸᳨
᳨ド䠅
䞉Forecasts with probability are desired because it is difficult to
predict severe events (ண 䛜ᅔ㞴䛺㢧ⴭ⌧㇟䛾☜⋡ணሗ).
䞉Ensemble prediction is also expected to reduce the miss rate
of their forecasts because they provide many scenarios of
severe phenomena䠄ぢ㏨䛧䛾ῶᑡ䜈䛾ᮇᚅ䠅.
䞉The ensemble forecast systems are under development using the
K-computer, and then applied to several phenomena such as
heavy rainfalls䠄ி䛷䜰䞁䝃䞁䝤䝹ணሗ䝅䝇䝔䝮㛤Ⓨ䚸㇦㞵䛻㐺⏝䠅.
䞉The outputs of ensemble forecasts have been used as input
data of flood and landslide predictions in this project 䠄䜰䞁䝃䞁
䝤䝹ணሗ䛾ฟຊ䜢ὥỈ䝰䝕䝹䜔ᅵ◁⅏ᐖ䝰䝕䝹➼䜈䛾㐺⏝䠅.
Goal 2: Development of a regional cloud-resolving
ensemble analysis and forecast systems
䠄 㡿ᇦ㞼ゎീ䜰䞁䝃䞁䝤䝹ゎᯒணሗ䝅䝇䝔䝮䛾㛤Ⓨ䛸᳨ド䠅
䞉The ensemble forecast systems are under development using the
K-computer, and then applied to several phenomena such as
heavy rainfalls䠄ி䛷䜰䞁䝃䞁䝤䝹ணሗ䝅䝇䝔䝮㛤Ⓨ䚸㇦㞵䛻㐺⏝䠅.
䞉The outputs of ensemble forecasts have been used as input data
of flood and landslide predictions in this project䠄䜰䞁䝃䞁䝤䝹ணሗ
䛾ฟຊ䜢ὥỈ䝰䝕䝹䜔ᅵ◁⅏ᐖ䝰䝕䝹➼䜈䛾㐺⏝䠅.
.
Results of the ensemble forecast systems and the
applications using the outputs of ensemble forecasts, which
will be not present in the following talks of this session, are
shown briefly 䠄We䠾➼䛷⤂௓䛧䛶䛔䜛ᡂᯝ䜔䚸᫬㛫䛾㒔ྜ➼䛷
௒ᅇ䛾◊✲఍䛷Ⓨ⾲䛷䛝䛺䛔ᡂᯝ䜢⤂௓䛧䜎䛩䠅.
Goal 2: Development of a regional cloud-resolving
ensemble analysis and forecast systems
䠄 㡿ᇦ㞼ゎീ䜰䞁䝃䞁䝤䝹ゎᯒணሗ䝅䝇䝔䝮䛾㛤Ⓨ䛸᳨ド䠅
Leading products
䞉Northern Kyushu heavy rainfall in July 2012
䠄2012ᖺ䛾஑ᕞ໭㒊㇦㞵䛾෌⌧ᐇ㦂䠅
䞉Simulation of Sea breeze
䠄ᾏ㢼౵ධ䛾䝅䝭䝳䝺䞊䝅䝵䞁䠅
䞉1000 member’s ensemble forecasts
䠄1000䝯䞁䝞䞊䛾䝕䞊䝍ྠ໬ᐇ㦂䠅
䞉Data assimilation and storm surge experiments
䠄䝃䜲䜽䝻䞁䝘䝹䜼䝇䛾㧗₻䛾䜰䞁䝃䞁䝤䝹ணሗ䠅
Northern Kyushu heavy rainfalls in July 2012
‫ق‬2012ফ峘ృପਨ৖ൻං峘ગਠৰୡ‫ك‬
3-h accumulated rainfall (OBS)
Surface weather map on 1800 UTC 11 July 2012.
Rainfall totals reached as much
as 800 mm over 5 days.
(Kunii, 2013)
Forecast results (FT=18)
OBS
POP > 50mm/3h
MSM (JNoVA)
(
)
MSM (LETKF)
Maximum
These
information
would contribute
to decisionmaking process.
(Kunii, 2013)
Simulation of Sea breeze
ᾏ㢼౵ධ䛾䝭䝳䝺䞊䝅䝵䞁䠅
(ᾏ
15 km
LETKF nested system
2 km
400 m
100 m
Downscale forecasts
s
Domain of CFD
25km*25km*700m
2500*2500*70 points
10 km (dx=10 m)
(Chen et al, 2013)
Building resolving LES
(䝡
䝡䝹䜢ゎീ䛩䜛LES䠅
Chen et al. (2015a; 2015b; Mon. Wea. Rev.)10
Building resolving LES
(䝡
䝡䝹䜢ゎീ䛩䜛LES䠅
Top: U wind over downtown; Bottom: Temperature near station
Chen et al. (2015a; 2015b; Mon. Wea. Rev.)
1000-member ensemble forecast
㻔㻝㻜㻜㻜ɩɻɘόȃȪɻȽɻɞɳҸ๡㻕
• The EnKF has an advantage that a flow-dependent background error
covariance can be estimated explicitly in the process.
• The finite ensemble size introduces a sampling error into the
background error covariance, leading to degradation of the accuracy
of the analysis fields.
Maps of the horizontal distribution of the error correlation of
the horizontal wind at the 500-hPa level from the center location.
(Kunii, 2014)
Ensemble Kalman filter data
assimilation and storm surge
experiments of tropical cyclone Nargis
˄ȽȬȷɵɻɒɳȶɁȃɏόɇ਼ॆǽ儈▞Ҹ⑜˅
Nargis was a severe
storm which formed in
Bay of Bengal in April
2008 and made landfall
in the Irrawaddy delta,
resulting in massive
damage and loss of life
in Myanmar.
(Duc, 2014)
Ensemble Kalman filter data
assimilation and storm surge
experiments of tropical cyclone Nargis
˄ȽȬȷɵɻɒɳȶɁȃɏόɇ਼ॆǽ儈▞Ҹ⑜˅
(Duc, 2014)
The control experiment resulted in a track forecast close to the
observed one. This forecast outperformed GSM downscaling
especially in the landfall location and time䠄ୖ㝣᫬้䞉఩⨨䛸䜒䛻䚸
䛭䜜䜎䛷䛾඲⌫ゎᯒ䜢ึᮇ್䛸䛩䜛ணሗ䜢኱䛝䛟ᨵၿ䠅㻚
Ensemble Kalman filter data assimilation and
storm surge experiments of tropical cyclone Nargis
˄ȽȬȷɵɻɒɳȶɁȃɏόɇ਼ॆǽ儈▞Ҹ⑜˅
(Duc, 2014)
Forecasts of the water levels at Irrawaddy and Yangon have significantly
improved the results in the previous studies.䠄Ỉ఩ୖ᪼䛾䝸䝇䜽䛾⾲⌧䠅
㡿ᇦ኱Ẽᾏὒ⤖ྜࣔࢹࣝࢆ⏝࠸ࡓ
࢔ࣥࢧࣥࣈ࣐ࣝ࢝ࣝࣥࣇ࢕ࣝࢱࡢᵓ⠏
(Kunii et al. 2015)
SSTࡢ㡿ᇦᖹᆒ್㸦㟷㸸1ḟඖᾏὒ⾲ᒙ䝰䝕䝹䜢⤖ྜ䛧䛯SST㸪⥳㸸ほ 䛥䜜䛯SST㸧࡜࢔ࣥࢧࣥࣈࣝࢫࣉࣞࢵࢻ㸦㉥㸧ࡢ᫬㛫᥎⛣㸬(a) Rb=0.6,
(b) Rb=0.25࡜ࡋࡓᐇ㦂㸬
ΰྜᒙࣔࢹࣝࡢ㖄┤ΰྜ࡟㛵ࡍࡿࣃ࣓࣮ࣛࢱ㸦critical bulk Richardson number Rb㸧ࡢ
ឤᗘࢆㄪᰝࡋࡓ㸬Rbࡢࢆ0.25࡜ࡋ㖄┤ΰྜࢆᙅࡵࡓ࡜ࡇࢁ㸪1ḟඖᾏὒ⾲ᒙ䝰䝕䝹䜢
⤖ྜ䛧䛯SSTࡣᐇ㦂㛤ጞᚋࡢࢫࣆࣥ࢔ࢵࣉᮇ㛫࡟࠾࠸࡚ṇࣂ࢖࢔ࢫࡀぢࡽࢀࡿࡶࡢࡢ
㸪ࡑࢀ௨㝆ࡣOBS࡜Ⰻࡃ୍⮴ࡋ࡚࠾ࡾ㸪㈇ࣂ࢖࢔ࢫࡶゎᾘࡉࢀ࡚࠸ࡿ㸦ᅗ1b㸧㸬
㡿ᇦ኱Ẽᾏὒ⤖ྜࣔࢹࣝࢆ⏝࠸ࡓ
࢔ࣥࢧࣥࣈ࣐ࣝ࢝ࣝࣥࣇ࢕ࣝࢱࡢᵓ⠏
(Kunii et al. 2015)
2014ᖺ8᭶10᪥00UTC࡟࠾ࡅࡿSSTࡢศᕸ㸬(a)Ẽ㇟ᗇ඲⌫᪥ูᾏ㠃Ỉ ゎ
ᯒ್㸪(b) 1ḟඖᾏὒ⾲ᒙ䝰䝕䝹䜢⤖ྜ䛧䛯SST㸪(c)ほ 䛥䜜䛯SST 㸬
1ḟඖᾏὒ⾲ᒙ䝰䝕䝹䜢⤖ྜ䛧䛯SSTࡣྎ㢼11ྕ࡟ࡼࡿᾏ㠃Ỉ ࡢపୗࢆ෌⌧ࡋ࡚
࠾ࡾ㸪Ẽ㇟ᗇ඲⌫᪥ูᾏ㠃Ỉ ゎᯒ್࡜ẚ㍑ࡍࡿ࡜ほ ࡉࢀࡓSST㸦OISST㸧࡟
ࡼࡾ㏆࠸ศᕸࢆ♧ࡋ࡚࠸ࡿ㸬
Results of the ensemble forecast systems and
applications using outputs of ensemble forecasts
• 㡿ᇦ㞼ゎീ䜰䞁䝃䞁䝤䝹ゎᯒணሗ䝅䝇䝔䝮 䛾㛤Ⓨ䛸᳨ド
℩ྂ ᘯ㻔Ẽ㇟◊✲ᡤ㻛ᾏὒ◊✲㛤Ⓨᶵᵓ㻕
• 㻺㻴㻹㻙㻸㻱㼀㻷㻲䜢⏝䛔䛯䜂䜎䜟䜚㻤ྕ䛾䝷䝢䝑䝗䝇䜻䝱䞁ྠ໬ᐇ㦂
ᅧ஭ ຾㻔Ẽ㇟◊✲ᡤ㻕
• 㻵㼙㼜㼞㼛㼢㼕㼚㼓㻌㼘㼛㼏㼍㼘㻌㼣㼑㼍㼠㼔㼑㼞㻌㼒㼛㼞㼑㼏㼍㼟㼠㻌㼛㼢㼑㼞㻌㼏㼛㼙㼜㼘㼑㼤㻌㼟㼡㼞㼒㼍㼏㼑㼟㻌㼡㼟㼕㼚㼓㻌㼍㻌㼎㼡㼕㼘㼐㼕㼚㼓㻙㼞㼑㼟㼛㼘㼢㼕㼚㼓㻌㼡㼞㼎㼍㼚㻙
㼟㼏㼍㼘㼑㻌㼜㼞㼑㼐㼕㼏㼠㼕㼛㼚㻌㼟㼥㼟㼠㼑㼙
㝞 ᱇⯆㻔୰ᅜ୰ᒣ኱Ꮫ㻕
• ᚑ᮶ᆺほ 䛾䜏䛻䜘䜛᪥ᮏᇦ䜢ᑐ㇟䛸䛧䛯㛗ᮇ㡿ᇦ෌ゎᯒ䝅䝇䝔䝮ᵓ⠏䛻ྥ䛡䛯◊✲
⚟஭ ┿㻔ᮾ໭኱Ꮫ㻛Ẽ㇟◊✲ᡤ㻕
• ி䝁䞁䝢䝳䞊䝍䜢⏝䛔䛯ᗈᇦ䞉㉸㧗ゎീᗘᩘ್ணሗ
኱Ἠ ఏ㻔ᾏὒ◊✲㛤Ⓨᶵᵓ㻛Ẽ㇟◊✲ᡤ㻕
• ᒣᓅᆅᇦᅵ◁⅏ᐖண 䛾䛯䜑䛾㻴㼥㼐㼞㼛㻙㻰㼑㼎㼞㼕㼟㻞㻰㻌䛸㻟㻰䛾㛤Ⓨ䞉ᨵⰋ
ᒣᩜ ᗤு䠄ி㒔኱Ꮫ⥲ྜ⏕ᏑᏛ㤋䠅
• ி䝁䞁䝢䝳䞊䝍䜢⏝䛔䛯㧗㏿㻛㧗ゎീὥỈỏ℃䝅䝭䝳䝺䞊䝅䝵䞁
ᑠᯘ ೺୍㑻㻔⚄ᡞ኱Ꮫ㻕
• 㻱㼚㼟㼑㼙㼎㼘㼑㻌㼞㼍㼕㼚㼒㼍㼘㼘㻌㼍㼚㼐㻌㼒㼘㼛㼛㼐㻌㼒㼛㼞㼑㼏㼍㼟㼠㼟㻌㼒㼛㼞㻌㼟㼑㼢㼑㼞㼑㻌㼠㼥㼜㼔㼛㼛㼚㻌㼑㼢㼑㼚㼠㻌㼡㼟㼕㼚㼓㻌㼔㼕㼓㼔㻙㼞㼑㼟㼛㼘㼡㼠㼕㼛㼚㻌
㻺㼡㼙㼑㼞㼕㼏㼍㼘㻌㼃㼑㼍㼠㼔㼑㼞㻌㻼㼞㼑㼐㼕㼏㼠㼕㼛㼚㻌㼙㼛㼐㼑㼘㻌㼎㼍㼟㼑㼐㻌㼛㼚㻌㻺㻴㻹㻙㻸㻱㼀㻷㻲㻌㼟㼥㼟㼠㼑㼙
䝴䞉䝽䞁䝅䜽㻔㡑ᅜᛅ༡኱Ꮫ㻕
Results of the ensemble forecast systems and
applications using outputs of ensemble forecasts
•
•
•
•
•
•
•
•
Development and validation of a cloud resolving ensemble analysis and prediction
system
Hiromu Seko (MRI/JAMSTEC)
Data assimilation of rapid-scan atmospheric motion vectors derived from Himawari-8
with the NHM-LETKF
Masaru Kunii (MRI)
Improving local weather forecast over complex surfaces using a building-resolving
urban-scale prediction system
Guixing Chen (Sun Yat-sen Univ.)
A study for constructing a long-term regional reanalysis system over Japan
assimilating only conventional observations
Shin Fukui (Tohoku Univ./MRI)
An Ultra-high Resolution Numerical Weather Prediction with a Large Domain using
the K Computer
Tsutao Oizumi (JAMSTEC/MRI)
Development and Improvement of Hydro-Debris 2D & 3D for prediction of sediment
hazard in mountain zone
Yosuke Yamashiki (Kyoto Univ.)
High-speed/high-resolution flood inundation simulation using K supercomputer
Ken-ichiro Kobayashi (Kobe Univ.)
Ensemble rainfall and flood forecasts for severe typhoon event using high-resolution
Numerical Weather Prediction model based on NHM-LETKF system
Yu Wansik (Chungnam National Univ..)