Etienne Leclerc

Office: DTB A527
Office email: etiennel AT uvic DOT ca
CV
Resumé
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I am a PhD student in applied mathematics, under the supervision of Prof. Boualem Khouider. My work concerns tropical convection modeling, i.e. using fluid dynamics to predict the lifecycles of clouds near the equator. Tropical convection models are an important puzzle piece in mankind's quest to accurately forecast the weather and climate - not only for such tropical phenomena as monsoons and hurricanes, but indeed for weather and climate over the entire globe (consider the devastating impacts of atmosperic river-induced floods throughout Vancouver Island, for example).

A plain-language summary of my topic is provided in the following section. For the experts: I am developing a scale-aware mass-flux convection model which marries the stochastic multicloud model of Khouider and others, with a prognostic relaxation of the quasi-equilibrium assumption. Early work on this topic was published in JAMES in 2019 (see publications below).

My academic interests encompass mathematics, statistics, computer science (particularly machine learning and statistical learning theory), and atmospheric physics. There is a special place in my heart for fields of math whose proofs begin with such statements as "let ε>0," or "let U be open."

My extracurricular interests currently range from chess to kickboxing, poetry to salsa dancing. Here is one of the salsa routines I have choreographed and performed; and here is another.


Summary of research

I am developing a cloud model which handles so-called "grey-scale" resolutions. As an analogy, consider that one person laughing will sound qualitatively different from an entire crowd laughing. But what will three, or four, or even ten, people laughing sound like - will that sound bear more in common with the laughter of an individual, or of a group? What number of people demarcates this qualitative transition? How many grains of sand does it take to make a pile of sand?

A similar story holds for cloud models. On the one hand, operating at very high resolutions, cloud-resolving models distinguish each individual cloud clearly like a single laugh. On the other hand, so-called "quasi-equilibrium mass-flux models" operate at very coarse resolutions and distinguish only ensembles of clouds operating in tandem like a crowd of people laughing. There is a "sour-spot" at intermediate resolutions where neither the high- nor low-resolution models correctly capture the cloudy behavior. The model I am developing seeks to address this issue.


Education


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