How I Ended Up in Geophysical Fluid Dynamics

How I Ended Up in Geophysical Fluid Dynamics

Lately, I’ve been disclosing the various biases I bring to practicing and enabling Data Science. Motivated by my decision to (finally) self-curate an online, multimedia portfolio, I felt such biases to be material in providing the context that frames this effort. Elsewhere, I’ve shared my inherently scientific bias. In this post, I want to provide additional details. These details I’ve been able to extract verbatim from a blog post I wrote for Bright Computing in January 2015; once I’d settled on geophysics (see below), I aspired to be a seismologist … but, as you’ll soon find out, things didn’t pan out quite the way I’d expected:

I always wanted to be a seismologist.

Scratch that: I always wanted to be an astronaut. How could I help it? I grew up in suburban London (UK, not Ontario) watching James Burke cover the Apollo missions. (Guess I’m also revealing my age here!)

Although I never gave my childhood dream of becoming an astronaut more than a fleeting consideration, I did pursue a career in science.

As my high-school education drew to a close, I had my choices narrowed down to being an astronomer, geophysicist or a nuclear physicist. In grade 12 at Laurier Collegiate in Scarboro (Ontario, not UK … or elsewhere), I took an optional physics course that introduced me to astronomy and nuclear physics. And although I was taken by both subjects, and influenced by wonderful teachers, I dismissed both of these as areas of focus in university. As I recall, I had concerns that I wouldn’t be employable if I had a degree in astronomy, and I wasn’t ready to confront the ethical/moral/etc. dilemmas I expected would accompany a choice of nuclear physics. Go figure!

And so it was to geophysics I was drawn, again influenced significantly by courses in physical geography taught by a wonderful teacher at this same high school. My desire to be a seismologist persisted throughout my undergraduate degree at Montreal’s McGill Universitywhere I ultimately graduated with a B.Sc. in solid Earth geophysics. Armed with my McGill degree, I was in a position to make seismology a point of focus.

But I didn’t. Instead, at Toronto’s York University, I applied Geophysical Fluid Dynamics (GFD) to Earth’s deep interior – mostly Earth’s fluid outer core. Nothing superficial here (literally), as the core only begins some 3,000 km below where we stand on the surface!

Full disclosure: In graduate school, the emphasis was GFD. However, seismology crept in from time to time. For example, I made use of results from deep-Earth seismology in estimating the viscosity of Earth’s fluid outer core. Since this is such a deeply remote region of our planet, geophysicists need to content themselves with observations accessible via seismic and other methods.

From making use of Apache Spark to improve the performance of seismic processing (search for “Reverse-Time Seismic Migration” or “RTM” in my Portfolio), to the analysis of ‘seismic data’ extracted from Twitter (search for “Twitter”in my Portfolio), seismology has taken center stage in a number of my projects as a practitioner of Data Science. However, so has the geophysical fluid dynamics of Earth’s mantle and outer core. Clearly, you can have your geeky cake and eat it too!

Multi-Touch Computational Steering

About 1:35 into

Jeff Han impressively demonstrates a lava-lamp application on a multi-touch user interface.

Having spent considerable time in the past pondering the fluid dynamics (e.g., convection) of the Earth’s atmosphere and deep interior (i.e., mantle and core), Han’s demonstration immediately triggered a scientific use case: Is it possible to computationally steer scientific simulations via multi-touch user interfaces?

A quick search via Google returns almost 20,000 hits … In other words, I’m likely not the first to make this connection 😦

In my copious spare time, I plan to investigate further …

Also of note is how this connection was made: A friend sent me a link to an article on Apple’s anticipated tablet product. Since so much of the anticipation of the Apple offering relates to the user interface, it’s not surprising that reference was made to Jeff Han’s TED talk (the video above). Cool.

If you have any thoughts to share on multi-touch computational steering, please feel free to chime in.

One more thought … I would imagine that the gaming industry would be quite interested in such a capability – if it isn’t already!

Digital Terrain Mapping via LIDAR

From the purely scientific (ozone-column mapping, imaging hydrometeors in clouds) to commercial (on-board detection of clear air turbulence, CAT), my exposure to LIDAR applications has been primarily atmospheric.

Of course, other applications of LIDAR technology exist, and one of these is Digital Terrain Mapping (DTM).

Terra Remote Sensing Inc. is a leader in LIDAR-based DTM. Particularly impressive is their ability to perform surface DTM in areas of dense vegetation. As I learned at a very recent meeting of the Ontario Association of Remote Sensing (OARS), Terra has already found a number of very practical applications for LIDAR-based DTM.

Some additional applications that come to mind are:

  • DTM of urban canopies for atmospheric experiments – Terra has already mapped buildings for various purposes. The same approach could be used to better ground (sorry 😉 atmospheric experiments. For example, the boundary-layer modeling that was conducted for Joint Urban 2003 (JU03) employed a digitization of Oklahoma City. A LIDAR-based DTM would’ve made this an even-more realistic effort.
  • Monitoring the progress of Global Change in the Arctic – In addition to LIDAR-based DTM, Terra is also having some success characterizing surfaces based on LIDAR intensity measurements. Because open water and a glacier would be expected to have different DTM and intensity characteristics, Terra should also be able to monitor Global Change as nunataks are progressively transformed into traditional islands (land isolated and surrounded by open water). With the Arctic as a bellwether for Global Change, it’s not surprising that the nunatak-to-island transformation is getting attention.

Although my additional examples are (once again) atmospheric in nature, as Terra is demonstrating, there are numerous applications for LIDAR-based technologies.

Genetic Aesthetics: Generative Software Meets Genetic Algorithms

I’m still reading Cloninger’s book, and just read a section on Generative Software (GS) – software used by contemporary designers to “… automate an increasingly large portion of the creative process.” As implied by the name, GS can produce a tremendous amount of output. It’s then up to the designer to be creatively stimulated as they sift through the GS output.

As I was reading Cloninger’s description, I couldn’t help but make my own connections with Genetic Algorithms (GAs). I’ve seen GAs applied in the physical sciences. For example, GAs can be used to generate models to fit data. The scientist provides an ancestor (a starting model), and then variations are derived through genetic processes such as mutation. Only the models with appropriate levels of fitness survive subsequent generations. Ultimately, what results is the best (i.e., most fit) model that explains the data according to the GA process.

In an analogous way, this is also what happens with the output from GS. Of course, in the GS case, it is the designer her/himself who determines what survives according to their own criteria.

The GS-GA connection is even stronger than my own association may cause you to believe.

In interviewing Joshua Davis for his book, Cloninger states:

At one point, you talked about creating software that would parse through the output of your generative software and select the iterations you were most likely to choose.

Davis responds:

That’s something [programmer] Branden Hall and I worked on called Genetic Aesthetic. It uses a neural network and genetic algorithms to create a “hot or not” situation. It says, “Rate this composition I generated on a scale from 1 to 10.” If I give it a 1, it says, “This isn’t beautiful. I should look at what kind of numbers were generated in this iteration and record those as unfavorable.” You have to train the software. Because the process is based on variables and numbers, over a very short period of time it’s able to learn what numbers are unsatisfactory and what numbers are satisfactory to that individual human critic. It changes per individual.

That certainly makes the GS-GA connection explicit and poetic, Genetic Aesthetic – I like that!

I’ve never worked with GAs. However, I did lead a project at KelResearch where our objective was to classify hydrometeors (i.e., raindrops, snowflakes, etc.). The hydrometeors were observed in situ by a sensor deployed on the wing of an airplane. Data was collected as the plane flew through winter storms. (Many of these campaigns were spearheaded by Prof. R. E. Stewart.) What we attempted to do was automate the classification of the hydrometeors on the basis of their shape. More specifically, we attempted to estimate the fractal dimension of each observed hydrometeor in the hopes of providing at automated classification scheme. Although this was feasible in principle, the resolution offered by the sensor made this impractical. Nonetheless, it was a interesting opportunity for me to personally explore the natural Genetic Aesthetics afforded by Canadian winter storms!

Fall AGU Meeting 13,000 Strong!

Unofficially, the 2006 Fall Meeting of the American Geophysical Union was attended by some 13,000 people.

That’s a lot of attedees!

More than many IT events!

And not bad for an organization that caters largely to physical scientists.

With focus groups like Earth and Space Science Informatics on the rise, I can’t see this number decreasing!

Of course, the fact that the Fall Meeting has branded itself with San Francisco doesn’t hurt 🙂

Annotating at the AGU

Perhaps two years ago, it was a challenge to find appropriate sessions at the American Geophysical Union Fall Meeting for submissions that addressed the intersection between geophysics and knowledge representation.

A year ago, there were quite a few to choose from.

This year, I was almost overwhelmed by choice.

I ended up selecting the “Earth and Space Science Cyberinfrastructure: Application and Theory of Knowledge Representation” session in the “Earth and Space Science Informatics” section. The work I intend to present, co-authored with Jerusha Lederman and Keith Aldridge also of York University, is described via an abstract elsewhere. I’ll need to prepare well as I’m presenting in good company and have only 15 minutes!

The makings for a productive and stimulating meeting are clearly present.

And for a Canadian in December, it’s pretty difficult not to enjoy the Bay Area!

Alternate Mechanism for Earth’s Magnetic Field

About 3,000 km beneath our feet lies Earth’s third ocean. Quite unlike the water-based first and air-based second, this third ocean is an iron-based alloy. Because this liquid-state alloy (aka. Earth’s fluid/liquid outer core) is an electrical conductor, currents can exist. Owing to a well-known reciprocity between electrical currents and magnetic fields, this third ocean factors significantly in an observable known to any of us surface dwellers who have ever wielded a compass.

Although there’s no question that Earth possesses a magnetic field, there are a number of open scientific questions about this pervasive, natural phenomenon. A number of these questions are directed at the sustainability of this magnetic field over geologically significant timescales. Well evidenced in the geologic record over a few billion years, Earth’s magnetic field requires a self-sustaining mechanism (aka. a geodynamo) to account for its very existence and peculiarities (such as reversals).

The starting point for scientific investigators is that this electrically conducting region is under rotation. (Taken from the appropriate scientific perspective, a perspective that takes into account planetary scales and fluid dynamical properties, it turns out this region is rotating rapidly.) The same Earth’s rotation that causes deflection of trade winds in the atmosphere (via the Coriolis effect) also figures significantly in the energetics of Earth’s magnetic field. Rotational effects alone, however, cannot account for the existence, longevity and peculiarities of Earth’s magnetic field over the visible geologic past.

This conundrum has forced the scientists who study this phenomenon to speculate on mechanisms for Earth’s magnetic field. Many of the suggested mechanisms call from some degree of additional stirring. This additional stirring causes deviations from the otherwise steady state of solid body rotation. Simply put, these deviations cause motion in the electrically conducting fluid that in turn result in magnetic fields.

Since the late 1970s, the favored mechanism for additional stirring has been based on buoyancy. In the case of Earth’s third ocean, buoyancy is thought to result from solidification. More specifically, as Earth’s centremost region (know as the inner core) grows by iron crystallization, the residual light element(s) in the alloy is/are released buoyantly. This combined effect of chemistry and fluid dynamics is thought to result in compositional convection.

Compositional convection, however, can be challenged on a number of fronts. Rather than pursue that here, my present purpose is to relate another mechanism for Earth’s magnetic field. As with the previous mechanism, deviations from an otherwise steady state of solid body rotation are key in this case as well. Rotation enforces cylindrical symmetry. This enforcement even applies when the body that’s under rotation (Earth in this case) has an almost spherical symmetry to it. Almost is definitely the operative word here, as Earth isn’t perfectly spherically symmetric. In fact, Earth is an oblate spheroid that bulges at the equator and is depressed at the poles. This combination results in an opposition of symmetries, cylindrical (owing to Earth’s rotation) versus spheroidal (owing to the boundary that contains Earth’s third ocean). As has been demonstrated experimentally and theoretically, the introduction of deviations in the presence of such symmetry oppositions can cause significant instabilities. In Earth’s case, periodic deviations might originate from precession and/or tides.

Experimental, theoretical, numerical and observational studies of such instabilities have been one of Keith Aldridge‘s research themes for over a decade at Toronto’s York University. In addition to ongoing experimental studies with graduate student Ross Baker, Keith has been collaborating with post-doc David McMillan and I on supportive observational evidence. Because the instabilities we’re all interested in are periodic, we’ve been looking for indirect evidence in historical records of Earth’s magnetic field. Deep-ocean sediments, extracted as drill cores, are proving useful in our attempts to analyze relative variations in Earth’s magnetic field intensity over the past 70,000 years. In short, our analysis of variations in paleointensity allows us to further support fluid-flow instabilities as a viable mechanism for Earth’s magnetic field.

A scientific account of this investigation has recently been accepted for publication in an appropriate journal, Physics of the Earth and Planetary Interiors. A preprint is currently available online.