urgent computing for hurricane forecastsgallen/presentations/... · •oilspill behaviour ......
TRANSCRIPT
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Gabrielle AllenCenter for Computation & TechnologyDept Computer ScienceLouisiana State [email protected]
Representing theSURA Coastal Ocean Observing and PredictionProgram (SCOOP)NSF DynaCode
Urgent Computing forHurricane Forecasts
CNS-0540374
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SURA Coastal Ocean Observingand Prediction (SCOOP)
• Integrating data from regional observing systems forrealtime coastal forecasts in SE
• Coastal modelers working closely with computerscientists to couple models, provide data solutions,deploy ensembles of models on the Grid, assemblerealtime results with GIS technologies.
• Three scenarios: event-driven ensemble prediction,retrospective analysis, 24/7 forecasts
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Urgent Coastal Scenarios
• Hurricane forecasts• Emergency preparedness• Oilspill behaviour• Sea rescue• Military operations• Hypoxia “Dead Zone”• Algae blooms
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Katrina Image
Katrina: 29th August 2005Image: MODIS Rapid Response Gallery
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Rita Image
Rita: 24th September 2005Image: MODIS Rapid Response Gallery
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Wilma Image
Wilma: 24th October 2005Image: MODIS Rapid Response Gallery
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---Third most powerful Hurricane to hit U.SCoast, Most Expensive
Storm Size (width across) at landfall - 410 milesRadius of Hurricane Force winds at landfall - 85milesCoastal Storm surge - 15 -20 Feet
Storm Size (width across) at landfall - 460milesRadius of Hurricane Force winds at landfall- 125 milesCoastal Storm surge - 18 -22 Feet
Property Damage : 35 Billion10% population displaced from Houston andGalveston
Human Casualties : 1836 approxProperty Damage : 120 Billion,New Orleans population reduced by 50%from before Katrina
Mandatory evacuation Galveston : 19 hoursbefore landfall
Voluntary evacuation New Orleans : 37hours before landfall. Mandatoryevacuation : 19 hours before landfall
Category 3 landfall (Peak Winds : 120 mph ) on24th Sep, 2:40 am CDT, Texas LouisianaBorder
Category 3 landfall (Peak Winds : 145 mph)on 29th Aug , 6:10 am CDT, near Buras LA
Date : 18-26 Sep, 2005Date : 23-30 Aug, 2005
Hurricane RITAHurricane KATRINA
Statistics
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Hurricane tracks
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Hurricane Tracks
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Model-Model-Data Coupling
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Varied Data Sources• Simulation data
(forecast, nowcast,hindcast)– 2D, 3D
• Sensor data (timeseries)
• Remote imaging• Aerial photography• LIDAR• Weather satellites• GIS
ADCIRC
MODIS
LANDSAT
AERIAL
LIDAR
MM5 Wind
MM5 Temp.
GOES - 12
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TAMULSU
UM
UF
UAH
UNC
VIMS
GoMOOS BIO
All datafor archive
SCOOP net
All datafor archive
In situ dataMM5
windsADCIRC
win
ds ANAGFDLCH3D
ELC
IRC
Translated products
win
ds
SWANWAM Windgen
WW
3External feeds GFDL COAMPS NAM others
SCOOP Prototype Lab
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SCOOP Ensemble Modeling
RegionalArchives
Ensemble wind fieldsfrom varied and
distributed sources
ADCirc
ElCirc
WAM
Ensemble of modelsrun acrossdistributedresources
Archive
Verification
Visualization
Analysis,storage,
cataloging,visualization of
output
Selectregion andtime range
Transformand
transportdata
Wind Forcing Wave and/or
Surge Models ResultDissemination
Synthetic Wind Ensembles
NCEP
MM5
NCARor
orSWAN
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Current Models• Variety of meshes and
included properties.• Models are suited for
different conditions: e.g.shallow water, deepocean.
• Basic variables arewater level and current,and wave height,frequency and direction.
• All require forcing (wind,tides), boundaryconditions, initialconditions (“hotstart” or“spin-up”)
• Currently porting,benchmarking,optimizing, etc.
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Current Workflow
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3
2
1
5 SCOOP Surge
Dr. Brian Blanton -- UNC
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3
2
1
4SCOOP Surge
Dr. Brian Blanton -- UNC
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3
2
1
3SCOOP Surge
Dr. Brian Blanton -- UNC
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3
2
1
2SCOOP Surge
Dr. Brian Blanton -- UNC
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3
2
1
SCOOP ADCIRC
1SCOOP Surge
Dr. Brian Blanton -- UNC
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Current Workflow• [ over 5 days before landfall ] Experts/blogs
monitoring Atlantic conditions flag potentialactivity
• [ around 5 days before landfall ] NationalHurricane Center (NHC) start releasingadvisories (every 6 hours). Advisories describecurrent storm and predicted track.
• [ for each advisory ] An ensemble description iscreated based on storm properties (e.g.strength, ROI) [and available resources]– Which wind forcing, which models, which regions,
which priorities and urgencies– Currently around 34 members
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Current Workflow• [ For each advisory] “Analytic” wind fields are
created, some of which use data from otherproducts.
• [As each wind input arrives] Wind inputs havedifferent resolutions, regions. Appropriatemodels are deployed (limited use of priorityinformation). Resulting data pushed toarchives.
• Analysis of results, dissemination to websites, e.g. MOM, MOF, MEOW and comparewith observations
• [ 48 hours before landfall ] Evacuationdecisions
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Integrating with Google Earth
ELCIRC
CH3D
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Extending Workflow• Prioritization & urgency,
threat level for applicationand resources, notification
• DDDAS– Dynamic scheduling,
contracts, resourcenegotiations.
– Real time nested and coupledmodels
– Ensembles tailored from pastresults
– Data assimilation from real-time data
– Much more ….• Dealing with multiple
simultaneous events
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Louisiana Optical Network (LONI)State initiative ($50M) to supportresearch:
40 Gbps optical networkConnects 7 sitesGrid resources (100TF) at sitesLIGO/CAMD
New possibilities:
Dynamical provisioningand scheduling ofnetwork bandwidth
Network dependentscenarios
“EnLIGHTenedComputing (NSF)”
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Resource Needs• Assume
– 20 storm events / year– 5 days / storm– 4 advisories / day– 34 wind fields / advisory– 64 SUs for WW3 (wave) forecast– 8 SUs for ADCIRC (surge) forecast
• 1 million SUs / year
400 advisories/year(2005: 575; 2006: 255)}
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Data Flow
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Prototyping CustomizedEnsembles
Appropriate regionalensembles run
NHC>=watch in ROI3
Full ensembles runNHC 3-day COD in ROI2
ANA windsgenerated, reducedensembles run
NHC messages, distantevent
1
24/7 runsNothing happening0
SCOOP ResponseAtlantic/GOM/CaribbeanStatus
Level
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Comments• Science will change as domain scientists
adjust to availability of resources andDDDAS approach
• Complex workflows: from nested/coupledmodels, lack of data standards etc.
• Can already do “urgent computing” but realpolicies are hard (e.g. changing queues,need to know decision maker,documentation)
• Reliability and flexibility is a major challenge• Scheduling: cope with data arriving semi-
regularly, need deadline-based schedule
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The End