Toshihiro Nemoto
1
Masaru Kitsuregawa
2
1
Earth Observation Data Integration and Fusion Initiative, The Univ. of Tokyo
2
Director General of National Institute of Informatics(NII)
Professor, Institute of Industrial Science, The Univ. of Tokyo
President of Information Processing Society (IPSJ )
IEEE Fellow ACM Fellow
DIAS
•
Data Integration and Analysis System
–
For providing access to global and regional sensing data
–
information storage infrastructure for public benefit applications
and the deepening of scientific knowledge in the areas of
•
Climate
•
Water cycle
•
…
–
for application in
•
Fisheries
•
Agriculture
•
Biodiversity
•
…
Global Earth Observation System of Systems
（
GEOSS
）
Disk Arrays Storage Layer File System Layer
Data Management Layer
•PB scale logical file ••Storage management Power management •Database management system
App. Layer
User Apps.
Data Integration & Information Fusion Platform
User Apps. User Apps. User Apps. User Apps.
•Data Mingrator •Data Navigator •Meta Data Manger •Visualizer(w display wall)
•Discovery Work Flow Assist •Data Quality Manager
Common Utility Layer
•Data Transformer •Data Crawler •ETL WCRP CMIP3 MetBroker Simulations Satellite Imagery
NOAA Antenna
（
Antenna installed at Roppongi 1980, Operation started in
1981, Stationary service in 1983
）
Late Prof. M. Takagi
Hand made Receiving Station
（
bit synchronizer,
Mainframe Machine
(FACOM M160/170)
Mass Storage
STK 9310 (Powder Horn)
DIAS Today
disk + tape > 20PB
DIAS/GRENE System Structure
Servers (Cluster) •8 nodes •CPU 16 cores/node •Memory 48GB/node Disk Array •~1.4PB Servers (Cluster) •8 nodes •CPU 16or12cores/node •Memory 48GB/node Disk Array •~0.7PB Server •CPU 64 cores •Memory 1024GB Servers (Cluster) •64 nodes •CPU 16 cores/node •Memory 48GB/node Server •CPU 32 cores •Memory 512GB Disk Array •~5.2PB Tape Library •~6.2PB Server •CPU 80 cores •Memory 2048GB Servers (Cluster) •60 nodes •CPU 20 cores/node •Memory 64GB/node •with HPC coprocessor Disk Array •~11.6PBKitami Institute of Technology Hokkaido University
Institute of Industrial Science, The university of Tokyo
National Institute of Informatics Chiba Annex
~2006
2007
2008
2009
2010
2011
Server-Storage Coupled System
サーバ
ディスクアレイ
1ギガビットイーサネット FCスイッチ FCスイッチ 1.67GHz 8core 128GB 1.67GHz 8core 128GB5GHz 32core
512GB
2012
テープライブラリ2.26GHz 64core
1024GB
16core 48GB 16core 48GB 16core 48GB 16core 48GB・・・
16core 48GB 16core 48GB 64ノード 10ギガビットイーサネット ~160TB ~0.6PB ~0.8PB ~3PB ~0.8PB ~2PB ~2PB19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 CMIP5 GCM20 ERA Interim JP10 K-1 FieldServer CMIP3 JRA-25 GPV DIAS Satellite CEOP Model CEOP Satellite AMSR-E MODIS Mongolia MODIS AIT MODIS NASA MODIS UT MTSAT GMS
Solutions in DIAS
4Vs for Bigdata
Volume
Variety
Velocity
Veracity
Velocity
Centralized Data System General Circulation Model
DHM
Data
Assimilation
Improved
Initial
Condition
Regional/Meso ModelSocio-Economic Data
Improved
Prediction
Global Data to Local Information
Data
Assimilation
Satellite data
In-situ data
Improved prediction
Flood Peak Reduction
0 500 1000 1500 2000 2500 3000 7/8.1z 7/9.1z 7/10.1z 7/11.1z 7/12.1z 2002 discharge [m 3 /s] Optimized rules Outflow eq. 0 Outflow eq. inflow
Proactive
Upper Tone River Basin
Fujiwara Sonohara Naramata Yamba Yagisawa Aimata Shimagawa0 100 200 300 400 500 600 700 800 900 1000 7/8.1z 7/9.1z 7/10.1z 7/11.1z 7/12.1z 2002 discharge [m 3 /s] 600 605 610 615 620 625 630 635 640 645 650 waterlev el [m] Sim outflow Sim inflow Sim water level Obs water level
Flood reduction
by
Proactive
Control of Dam
Discharge with GPV 13~18
0 500 1000 1500 2000 2500 3000 7/8.1z 7/9.1z 7/10.1z 7/11.1z 7/12.1z 2002 discharge [m 3 /s] Optimized rules Outflow eq. 0 Outflow eq. inflow0 100 200 300 400 500 600 700 800 900 1000 7/8.1z 7/9.1z 7/10.1z 7/11.1z 7/12.1z 2002 d is c h a rg e [ m 3 /s ] 470 480 490 500 510 520 530 540 550 560 570 w a te rl e v e l [m ] Sim outflow Sim inflow Sim water level Obs water level
Flood peak
reduction
Fujiwara dam Sonohara dam
Iwamoto gauge
Peak created due to
water
release from dams
Water is stored until
max capacity is reached
Water level increase due to storage
Optimized releaseObserved
Ensemble Prediction
legend
T12, 2011, Aimata T12, 2011, Maebashi
T15, 2011, Murakami T15, 2011, Maebashi
Prediction System WEB-DHM River Runoff Soil Moisture Pre-processing Radar, GPV Error Estimation Statistical Analysis Pre-processing Model input data
Rainfall Pattern 1
Flood Pattern 1 Real Time Data
Management System
Data Integration and Analysis System (DIAS)
Temp. Radar Runoff Water Level
(C-band)
NWP Output (GPV) wind
Meteorological Data Radar, NWP River, Dam
rain sunshine Dam: WL, inflow, release
Rainfall Pattern 2 Rainfall Pattern N
Flood Pattern 2 Flood Pattern N
Ensemble Rainfall Prediction
Ensemble Flood Prediction
Dam Operation Simulator
(Human Operation)
Data Integration and Analysis System
a legacy for Japan's contributions to GEOSS
accelerating data
archiving
, including data
Data Integration and Analysis System
a legacy for Japan's contributions to GEOSS
accelerating data
archiving
, including data
From late April to late May, there are two warm anomalies developing around the Tibetan Plateau.
One is just above the Tibetan Plateau developing upward.
The other is over the southern slope of the Tibetan Plateau developing downward from the tropopause.
The former can be explained by the land surface heating of the Tibetan Plateau, but what causes the upper-level warming over the southern slope of the Tibetan Plateau?
3D Powered Visualizer
zonal
asymmetric
anomaly of θ
= θ at given
grid
–
global mean
of θ at given
latitude
Climatology by NCEP/NCAR reanalysis dataデモ
Adiabatic warming is found to be associated with upper-anticyclone derived from tropical convective heating. In other words, Matsuno-Gill type atmospheric response to convective heating causes the upper-level warming around the Tibetan Plateau.
