Urgent Computing for Hurricane Forecasts

Transcription

1 Urgent Computing for Hurricane Forecasts Gabrielle Allen Center for Computation & Technology Dept Computer Science Louisiana State University Representing the SURA Coastal Ocean Observing and Prediction Program (SCOOP) NSF DynaCode CNS

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3 SURA Coastal Ocean Observing and Prediction (SCOOP) Integrating data from regional observing systems for realtime coastal forecasts in SE Coastal modelers working closely with computer scientists to couple models, provide data solutions, deploy ensembles of models on the Grid, assemble realtime results with GIS technologies. Three scenarios: event-driven ensemble prediction, retrospective analysis, 24/7 forecasts

4 Urgent Coastal Scenarios Hurricane forecasts Emergency preparedness Oilspill behaviour Sea rescue Military operations Hypoxia Dead Zone Algae blooms 4

5 Katrina Image Katrina: 29th August 2005 Image: MODIS Rapid Response Gallery 5

6 Rita Image Rita: 24th September 2005 Image: MODIS Rapid Response Gallery 6

7 Wilma Image Wilma: 24th October 2005 Image: MODIS Rapid Response Gallery 7

8 Statistics Hurricane KATRINA Date : Aug, 2005 Category 3 landfall (Peak Winds : 145 mph) on 29th Aug, 6:10 am CDT, near Buras LA Voluntary evacuation New Orleans : 37 hours before landfall. Mandatory evacuation : 19 hours before landfall Human Casualties : 1836 approx Property Damage : 120 Billion, New Orleans population reduced by 50% from before Katrina Storm Size (width across) at landfall miles Radius of Hurricane Force winds at landfall miles Coastal Storm surge Feet Third most powerful Hurricane to hit U.S Coast, Most Expensive Hurricane RITA Date : Sep, 2005 Category 3 landfall (Peak Winds : 120 mph ) on 24th Sep, 2:40 am CDT, Texas Louisiana Border Mandatory evacuation Galveston : 19 hours before landfall Property Damage : 35 Billion 10% population displaced from Houston and Galveston Storm Size (width across) at landfall miles Radius of Hurricane Force winds at landfall - 85 miles Coastal Storm surge Feet --- 8

9 Hurricane tracks 9

10 Hurricane Tracks 10

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12 Model-Model-Data Coupling 12

13 Varied Data Sources Simulation data (forecast, nowcast, hindcast) 2D, 3D Sensor data (time series) Remote imaging Aerial photography LIDAR Weather satellites GIS GOES - 12 MM5 Temp. MM5 Wind LIDAR AERIAL LANDSAT MODIS ADCIRC 13

14 SCOOP Prototype Lab GoMOOS BIO External feeds GFDL COAMPS NAM others SCOOP net ELCIRC winds WW3 VIMS All data for archive UAH UNC winds ADCIRC TAMU In situ data MM5 LSU SWAN WAM Translated products All data for archive winds UF ANA GFDL CH3D Windgen UM 14

15 SCOOP Ensemble Modeling Wind Forcing NCEP MM5 NCAR or Regional Archives or Synthetic Wind Ensembles Ensemble wind fields from varied and distributed sources Select region and time range Transform and transport data Wave and/or Surge Models ADCirc ElCirc WAM SWAN Ensemble of models run across distributed resources Result Dissemination Archive Verification Visualization Analysis, storage, cataloging, visualization of output 15

16 Current Models Variety of meshes and included properties. Models are suited for different conditions: e.g. shallow water, deep ocean. Basic variables are water level and current, and wave height, frequency and direction. All require forcing (wind, tides), boundary conditions, initial conditions ( hotstart or spin-up ) Currently porting, benchmarking, optimizing, etc. 16

17 Current Workflow 17

18 5 SCOOP Surge Dr. Brian Blanton -- UNC

19 4SCOOP Surge Dr. Brian Blanton -- UNC

20 3SCOOP Surge Dr. Brian Blanton -- UNC

21 2SCOOP Surge Dr. Brian Blanton -- UNC

22 1SCOOP Surge SCOOP ADCIRC Dr. Brian Blanton -- UNC

23 Current Workflow [ over 5 days before landfall ] Experts/blogs monitoring Atlantic conditions flag potential activity [ around 5 days before landfall ] National Hurricane Center (NHC) start releasing advisories (every 6 hours). Advisories describe current storm and predicted track. [ for each advisory ] An ensemble description is created 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 23

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25 Current Workflow [ For each advisory] Analytic wind fields are created, some of which use data from other products. [As each wind input arrives] Wind inputs have different resolutions, regions. Appropriate models are deployed (limited use of priority information). Resulting data pushed to archives. Analysis of results, dissemination to web sites, e.g. MOM, MOF, MEOW and compare with observations [ 48 hours before landfall ] Evacuation decisions 25

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27 Integrating with Google Earth ELCIRC CH3D 27

28 Extending Workflow Prioritization & urgency, threat level for application and resources, notification DDDAS Dynamic scheduling, contracts, resource negotiations. Real time nested and coupled models Ensembles tailored from past results Data assimilation from realtime data Much more. Dealing with multiple simultaneous events 28

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30 Louisiana Optical Network (LONI) State initiative ($50M) to support research: 40 Gbps optical network Connects 7 sites Grid resources (100TF) at sites LIGO/CAMD New possibilities: Dynamical provisioning and scheduling of network bandwidth Network dependent scenarios EnLIGHTened Computing (NSF) 30

31 Assume Resource Needs 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 } (2005: 400 advisories/year 575; 2006: 255) 31

32 Data Flow 32

33 Prototyping Customized Ensembles Level 0 Atlantic/GOM/Caribbean Status Nothing happening SCOOP Response 24/7 runs 1 2 NHC messages, distant event NHC 3-day COD in ROI ANA winds generated, reduced ensembles run Full ensembles run 3 NHC>=watch in ROI Appropriate regional ensembles run 33

34 Comments Science will change as domain scientists adjust to availability of resources and DDDAS approach Complex workflows: from nested/coupled models, lack of data standards etc. Can already do urgent computing but real policies are hard (e.g. changing queues, need to know decision maker, documentation) Reliability and flexibility is a major challenge Scheduling: cope with data arriving semiregularly, need deadline-based 34 schedule

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