Enabling Data Intensive Research in Physical Oceanography

Physical oceanography is a data intensive science with increasingly large amounts of data generated from observations and numerical models. With high resolution and detailed datasets comes the expectation of improved understanding of our oceans and climate. However, it is a challenge for the ocean and climate community to benefit from this newfound richness of data. Traditional software tools and approaches do not scale well enough to deal with the volume of data produced. Individual researchers, both established and early in their careers, may lack the technical skills to be able to work with such immense datasets. My research is about discovering new methods to enable these researchers to work with big data in physical oceanography.

Across many scientific disciplines, there has been a surge of interest in data science, big data, and data driven discovery as a novel paradigm in science building on more established methods of observation and experiments, theory, and computational simulation. This new approach has generated frameworks extensively used in fields such as bioinformatics, high-energy particle physics, and astronomy. I am exploring how such a data intensive approach could benefit physical oceanography.

Numerical ocean models produce extreme amounts of computational output. Global ocean circulation models at tenth degree resolution are currently being used and there are plans over the next few years to develop models at thirtieth-degree resolution. Such simulations are typically run for several decades of model time generating a significant amount of data. There is an ongoing demand from the ocean modelling community for new methods to be able to analyze efficiently such output. By leveraging computational frameworks from other scientific disciplines, I am developing the new software frameworks that meets that demand.

A historical challenge in oceanography has been a limited amount of observational data. The ocean is effectively opaque to electro-magnetic radiation and in-situ measurements usually require very expensive ship based observations. Recently, there has been a significant increase in data richness as new initiatives such as cabled ocean observatories and autonomous sensor platforms are deployed. Programs such Ocean Networks Canada and the Ocean Observatory Initiative in the U.S. are beginning to produce unprecedented amounts of oceanographic data at very high resolutions. I am pursuing new techniques and technologies to perform data driven discovery with large, varied oceanographic datasets.

My research program seeks to enable physical oceanographers to efficiently gain value from their large datasets obtained from both numerical models and observations. I am proposing to demonstrate the efficacy of these new data intensive methods with the goal of minimizing the time required in training graduate students and reducing the effort required by other researchers to make novel discoveries from oceanographic datasets. Importantly, the development of these new methods and software tools is not done in isolation. In practice, my research is necessarily collaborative which maximizes its overall impact.

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Visiting Research School of Earth Science at ANU

I am on sabbatical for 2017 and spending the year at Australia National University visiting with the Climate & Fluid Physics group with in the Research School of Earth Sciences.

I am primarily doing work on analyzing large data sets from global ocean models: COSIMA Cookbook


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Falling sphere in a two-layer fluid

Congratulations to Lauren White for winning a silver medal and a physics prize that the 2016 Eastern NL Science & Technology Fair.  Lauren project, “Les effets de la masse sur la turbulence dans un fluide de deus couches” was based on an experiment performed in our  stratified fluid dynamics lab.


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Looking for PhD students of iceberg research

New research project

Iceberg drift forecasting is an important applied research for the east coast of Canada.  Ocean currents, winds, and waves contribute to the complicated dynamics of iceberg motion. Iceberg drift is presently forecasted using physics-based models that predict iceberg accelerations based on the forces acting on the iceberg. But even for current state-of-the-art iceberg drift models, it is a challenge to predict the detailed path of an iceberg 12 to 48 hours into the future due to uncertainties in the estimates of the driving forces.

Under a collaborative research grant with an industrial partner, it is anticipated there will be funding available to support two PhD graduate students as of September 1, 2016 to study iceberg drift forecasting with Dr. James Munroe as the principal investigator. This project seeks to improve current iceberg drift prediction models by characterizing the uncertainty using ensemble modeling validated against historical data analysis of iceberg tracks. Ensemble models will incorporate the variations between different ocean models to give a range of uncertainties associated with iceberg drift predictions. The primary research topics to be addressed are:

  1.  Quantifying uncertainty in iceberg forecast models
  2. Improving iceberg drift prediction with ensemble modelling

Looking for students

Subject to approval, funding is currently being secured above the standard PhD funding levels in the Department of Physics and Physical Oceanography. As well, PhD students on this four-year project will have the opportunity to work with the industrial partner, have access to high performance computing resources, and be supported for conference travel.

Dr. Munroe’s broader research expertise is in stratified fluid dynamics, applied mathematics, physical oceanography, and data science.  He also runs an experimental stratified fluids lab that students that would be available to students for their research needs.

Dr. Munroe is seeking two PhD graduate students with a strong computational backgrounds and previous degrees in mathematics, physics, engineering, physical oceanography, and/or computer science.  Ideal candidates would work well in a team oriented research environment and have strong communication skills.  Domain knowledge in physical oceanography and high performance computing may be obtained during the degree for students with previous experience in other technical areas.

Contact Dr. James Munroe (jmunroe@mun.ca) for more information.

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Predicting the Wake Behind a Large AUV Hydrofoil

As part of the REALM project, I’ve submitted an article to Methods in Oceanography on the flow behind a large wing attached to an autonomous underwater vehicle. We predicted that the turbulent wake behind the wing should not affect the hydrodynamics of the aft control planes. This was validated in later field experiments.

Plots of velocity magnitude for tank experiment (top) and numerical simulation (bottom). Experiments are for a flow speed of 0.75 m/s with no angle of attack. Velocity magnitude color bar is in m/s.

Plots of velocity magnitude for tank experiment (top) and numerical simulation (bottom). Experiments are for a flow speed of 0.75 m/s with no angle of attack. Velocity magnitude color bar is in m/s.

Abstract: Measurements of the wake behind an experimental 3.5 m wide hydrofoil fabri- cated to accommodate a large acoustic receiving array on an autonomous under- water vehicle are reported. Results from laboratory experiments using particle image velocimetry of a full scale prototype in a large flume tank are compared to 2D numerical simulations. A parameter space of four flow speeds and five angles of attack are examined. An empirical model to predict the downstream velocity in the wake is developed based on the experimental results and the numerical simulations.


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Paper on turbulent generation of internal waves submitted

Just submitted a paper on Internal wave energy radiated from a turbulent mixed layer (Munroe and Sutherland, 2014) to Physics of Fluids for review.  


We examine mixed-layer deepening and the generation of internal waves in stratified fluid resulting from turbulence that develops in response to an applied surface stress. In laboratory experiments the stress is applied over the breadth of a finite-length tank by a moving roughened conveyor belt. The turbulence in the shear layer is characterized using particle image velocimetry to measure the kinetic energy density. The internal waves are measured using synthetic schlieren to determine their amplitudes, frequencies, and energy density. We also perform fully nonlinear numerical simulations restricted to two dimensions but in a horizontally periodic domain. These clearly demonstrate that internal waves are generated by transient eddies at the integral length scale of turbulence and which translate with the background shear along the base of the mixed layer. In both experiments and simulations we find that the energy density of the generated waves is 1-3% of the turbulent kinetic energy density of the turbulent layer.

Reprint: Internal wave energy radiated from a turbulent mixed layer

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Going to a conference

Our research group will be presenting at the American Physical Society Division of Fluid Dynamics meeting in Pittsburgh this November.


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Connected to a MySQL database behind a firewall

I am sitting at home wanting to connect my Mac OS X laptop to a MySQL database which is running on a server only accessible on campus. I downloaded Sequel Pro 1.0.1 and installed it locally. I set up a SSH based connection within SequelPro through a gateway machine located on the campus network. to connect to the database. Used GRANT ALL db.* TO ‘user’@’gateway.machine’ to allow full privileges when connecting remotely.

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Colormaps in OpenCV

If you have a gray scale image in opencv and want to display it with a false color colormap try:

Works with OpenCV 2.4

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Automating downloading of data from the web

The National Climate Data and Information Archive (Environment Canada) has a web based resource called Climate Data Online.  The task was to grab download the data for a range of dates and store the data locally.

In particular these scripts feature

  • Automating a webpage retrieval using urlib2
  • Using datetime and related modules to loop through a sequence of dates
  • Parsing csv files
  • Creating and populating a single table SQLite database


  • Extracting data from a SQLite database
  • Creating a simple matplotlib plot

This script is responsible for download the data, parsing it, and storing into a SQLite database:

This script shows an example of how to query the data from the database and make a simple plot:

Here’s the final result:


For reference, this took about an hour to design, debug, and execute.

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