Efficient data assimilation with OpenDAModule 1:

An introduction

14-03-12

Nils van Velzen, Martin Verlaan, Stef Hummel, Julius Sumihar, Alja Vrieling, Albrecht Weerts, Ghada El Serafy, Herman Gerritsen

Outline

Data assimilation and calibration in one sheet

What is OpenDA

OpenDA architecture

Coupling models into OpenDA

Example of Calibration

Example of Data assimilation

OpenDA and Parallel computing

Summary

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Data assimilation and Model calibration in one sheet

• A generic toolbox for data-assimilation

– (real time) data assimilation

– model calibration

– impact of observations

– reconstruction of sources

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What is OpenDA

• Motivation for a toolbox:

– The algorithms are model/observation independent BUT(!)

– DA methods work on top of models → serious changes to model code.

– Spaghetti code

– Difficult to change algorithm

– Maintenance problems

– Difficult to test algorithms

– Difficult/impossible to reusecode

– Expensive!

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What is OpenDA

• Content:

– Set of interfaces that define interactions between components

– Library of data-assimilation algorithms

– DA philosophy

– Building blocks only need to be implemented once

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What is OpenDA

• Open source (LGPL)

• Why OpenDA?

– More efficient than development for each application

– Shared knowledge between applications

– Development of algorithms with e.g. universities

– Easier to test, which should result in fewer bugs

– Optimized building blocks

– Development template

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OpenDA Website

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• www.openda.org• Downloads

– Users

– Developers

• Documentation

• Wrappers for models

• OpenDA association

OpenDA Main Application

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Single program for

• Editing input files

• Running simulations and calibrations

• All models and observation sources

• GUI and command line

• Sequential and parallel

OpenDA architectureObject Oriented Approach

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Coupling models into OpenDA

Formal form of a model

State of model instance

Object oriented concepts:

Data encapsulation; state cannot be directly changed only through the methods like:

GetState, AxpyState, Compute...

Multiple instances of model

Algorithm has no knowledge on model internals

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dxdt

=M (x(t ) , u(t) , p ,w(t ))

x (t) ,u (t) , p ,w(t)

Coupling models into OpenDA

• A model wrapper is needed for each model:

– Blackbox

– Native:

• Simple, only a few functions

• Full, the (whole) interface

• parallel

– Java

• Simple java models for testing and research

• Direct implementation of java interface

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Coupling models into OpenDA

• Models in Data assimilation context are stochastic!

• Normal dynamic model (codes) are not

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OpenDA InterfaceBlack Box Wrapper

• Uses input and output files of the model

• No source code of the model is needed

• Well suited for calibration

• Sometimes suited for Data Assimilation

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OpenDA InterfaceNative Wrapping

• Implement a specified set of functions (C/Fortran)

• Create a dynamic library

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Example of model calibration SWAN 3rd generation wave model

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Wave breaking and interaction over a bar

0.4

0.3

0.2

0.1

0

0.5m

X

Z0

1:20

10

1:10

20 30m

WAVES

STATIONS01 02 0403 0705 06 08

totS =wind input+ non-linear interactions (quadruplets & triads)+ whitecapping + bottom friction+ depth induced wave breaking

Example of model calibration

SWAN 3rd generation wave model

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Example of model calibrationSWAN 3rd generation wave model

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Example of data assimilationOperational storm surge forecasting

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Example of data assimilationOperational storm surge forecasting

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OpenDA and Parallel Computing

• Model evaluation is often the most time consuming part (90-99%)

• Often natural parallelism in the model timestepping

• Parallel time stepping for all native OpenDA models (OO)

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Summary

• OpenDA: Data assimilation toolbox for Data assimilation/calibration.

• Object oriented design allows easy exchange of building blocks.

• End users do not need to do any programming for experimenting.

• Various ways for adjusting (your) model for use in OpenDA.

• Conceptual support for all kinds of parallel computing.

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