Gabrielle AllenCenter for Computation & TechnologyDept Computer ScienceLouisiana State Universitygallen@cct.lsu.edu

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

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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