On demand lake circulation modeling
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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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On demand lake circulation modeling

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


On demand circulation modeling

On-Demand Circulation Modeling

  • Hindcast:

    • Enter a date/time range.

    • Met data extracted from CFL database.

    • Model launched.

    • Results displayed.


Goals

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.


Data sources

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


Challenges

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.


Instrumented buoys and locations

Instrumented buoys and locations

Map of Lake Trout

Buoy

Dissolve

Oxygen

sensor

RUSS buoy

@34m deep

ADCP

Thermistor

chain

ADCP buoy

@15mdeep


Dataflow

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)


On demand lake circulation modeling

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


Pre post processing of the circulation model

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


The way to drive the 3d circulation model

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


On demand lake circulation modeling

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)


Future work

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


Credits

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


Acknowledgements

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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