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On-Demand Lake Circulation Modeling

On-Demand Lake Circulation Modeling. Paul Hanson Tim Kratz Tim Meinke Luke Winslow Center for Limnology, Trout Lake Station University of Wisconsin. Chin Wu Nobuaki Kimura Environmental Fluid Mechanics Laboratory University of Wisconsin. Kenneth Chiu Yinfei Pan

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On-Demand Lake Circulation Modeling

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  1. On-Demand Lake Circulation Modeling Paul HansonTim KratzTim MeinkeLuke Winslow Center for Limnology, Trout Lake StationUniversity of Wisconsin Chin WuNobuaki Kimura Environmental Fluid Mechanics LaboratoryUniversity of Wisconsin Kenneth ChiuYinfei Pan Grid Computing Research LaboratorySUNY-Binghamton

  2. On-Demand Circulation Modeling • Hindcast: • Enter a date/time range. • Met data extracted from CFL database. • Model launched. • Results displayed.

  3. Goals • Develop a reusable solution for high-quality lake circulation modeling with validation. • Provide historical results on demand for analysis. • Support data assimilation, coupled models, etc. • Support pluggable models. • Support out-sourcing most ICT deployment to remote site. • Lake sites do not have the expertise, so they just provide the data.

  4. Data Sources • Model inputs • Meterological data • Water temperature etc. • Model outputs (validation or assimilation) • Acoustic Doppler Current Profiler (ADCP) • Provides 3D flow vectors of a vertical column of water. • One vector for each depth. • Measured waves • Subsurface, via high-frequence temperature fluctuations. • Surface

  5. Challenges • Cyberinfrastructure is a social/institutional problem as much as a technical problem. • E.g., technically, interoperability is a solved problem. • Not a human-computer-interaction problem, but a human-institution-computer interaction challenge. (“Institution” in the broad sense.) • Two-way interaction, institutions can themselves be changed. • Iterative • Develop rapid prototype, get feedback, repeat • Collaborative • Avoid over-engineering, be pragmatic.

  6. Instrumented buoys and locations Map of Lake Trout Buoy Dissolve Oxygen sensor RUSS buoy @34m deep ADCP Thermistor chain ADCP buoy @15mdeep

  7. Dataflow extension FLOW (Data retrieve and conversion) ADCP (Observed Velocity) Pre-processing CIRC (Circulation module) ANT (Animation of temp. & Velocity) Post-processing COMP (Comparison between observation and prediction)

  8. Trout Lake Serial2Ethernet IP addr. + port # Data- logger ADCP Unit Ethernet Radio Ethernet Radio Workflows in WB-CAST Logger Data Trout Lake Station ADCP Binary ADCP Binary acquir Logger Data ADCP computer ADCP Meta Oracle ADCP Binary Madison CFL Madison EFM SUN Logger Data Model results ADCP Binary

  9. Pre- & Post-processing of the circulation model Linux computer CFL Logger- net data Model Compute Velocity W-temp, etc… DB- Badger Extract data Matlab-1 Conversion input files Matlab-2 Creating plot files Data base Output Binary FLOW CIRC ANT COMP ADCP Binary extract_ adcp ADCP

  10. The way to drive the 3D circulation model Initial conditions bathymetry, water temprature, velocity surface elevation) Input data Forcing in time serial (MED) wind, Heat flux, …etc. • Characteristics • - Non-hydrostatic pressure • Bottom partial cell • Finite volume method 3D Circulation Model OUTPUT Temperature profile Velocity profile

  11. Remote Database Server (Oracle) Firewall Request Dir. Firewall Local Database Server (Mysql) Application Server Data acquisition Web Browser (Firefox, IE, Netscape …) Check Requests Find requests (Do Modeling …) Modeling & animation Finished (Update Database) Internet Web Server (tomcat)

  12. Future Work • Gather feedback! • Package as a toolkit? Service? Opal? • Parallelization, increased resolution • Job scheduling, Pragma integration? • Data assimilation using MPC? • Coupled models • Biological, chemical • Fluid-surface interactions • Real-time wave reconstruction from captured video

  13. Credits • Chin Wu, Nobuaki Kimura (EFM-UWI) • Modeling, output components • Paul Hanson, Luke Winslow (CFL-UWI) • Data extraction, processing • Tim Kratz, Tim Meinke (TLS-UWI) • Equipment, deployment, sensor network • Yinfei Pan, Kenneth Chiu (SUNY-Binghamton) • ADCP acquisition/management, job launching, monitoring, integration, web development

  14. Acknowledgements • We’d like to thank the generous support of Moore Foundation, the NSF LTER program, and NSF awards DBI-0446298, IIS-0513687, CNS 0454298, OCI-0330568.

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