Grants and Contributions

The project will use satellite, model, and ground-based data to characterise the extent and variability of mineral dust aerosol (MDA) in the atmosphere above northern Canada, and identify the sources from which it is emitted at the surface.

The boreal biome constitutes an integral component of the climate system. Our limited quantitative understanding of coupled carbon and water fluxes hampers reliably predicting the boreal biome response to projected increases in disturbance frequency and intensity. This project will enhance the ecological realism of the boreal biome in a terrestrial biosphere model (CLASSIC). After exploring empirical relationships between microwave vegetation optical depth (VOD) and tree hydraulic traits, we will introduce a tree hydraulics scheme into CLASSIC. Next, we will use regional VOD-derived tree hydraulic traits to constrain past, present and future flux simulations with CLASSIC across the boreal biome.

How well can satellite measurements of methane (CH4) reduce uncertainty on Canada's anthropogenic methane emissions?

The ocean currents of the Northwest Atlantic show a substantial variability. Accurate forecast systems need to support a range of applications such as emergency response, search and rescue and, transportation. However, the extent to which forecasts are accurate is limited by the presently available satellite observations. SWOT mission may improve this situation, provided the data can be assimilated adequately. We will use SWOT and other satellite data to provide improved forecasts for the Canadian East Coast, using a numerical computer model. New developments in this project will be directly transferrable to the ECCC Prediction System.

In this project, we will adapt this model to satellite land dryness observations, allowing us to estimate evapotranspiration across Canada and globally, and investigate physical mechanisms inducing drier climate.

This project has been developed as a collaboration between academia, the Canadian Forest Service and Environment and Climate Change Canada.

This Project seeks to develop an Artificial Intelligence (AI) enabled new multimodal microscope, based on coherent nonlinear optics and having the ability to learn from previous measurements to selectively choose subsequent imaging techniques. This will increase measurement capacity and allow for new microscopy techniques which can be used to advance the design of materials such as those found in solar cells and soft materials associated with biological applications. The initial focus will be on the challenging but important field of soft matter systems which have applications ranging from bio-implants to flexible batteries.

The development of sustainability assessment based on techno-economic analysis (TEA) and life cycle analysis (LCA) of pulse-based protein processing techniques has not been well established in Canada. Most of the available data for LCA/TEA
practitioners currently use US and European data inventories, when available, in proxy for Canadian data to complete sustainability assessments. Given the sensitivity of assessment results to data inputs, it is essential to capture local/regional data input and make them available in an organized database to support rigorous and meaningful LCA/TEA output. While Canada spearheads the plant-based diets, it is essential to establish environmental and economic implications and trade-offs of current routes to their production.

This Project will develop new machine learning algorithms for automating aspects of physical chemistry by discovering new pathways for creating materials with desired properties. The goal is to use the framework of reinforcement learning as a means of discovering, controlling, and optimizing the production of new materials. This Project will explore a novel new direction to perform decision making associated with established but highly complex processes used to transform one chemical compound into another. From an algorithmic point of view, this research will tackle the challenge of working with highly nonlinear relationships and generating simulation data that is of high quality.

Current technology relies on radio-frequency communication to and from satellites to ground stations. Optical satellite communications can support much larger bandwidths but require adaptive optical correction systems to correct for atmospheric turbulence and maintain high throughput, but conventional bulk-optics systems can be prohibitively expensive. To mitigate cost and space issues, the Project will develop an on-chip adaptive optics (AO) phase shifting system using the integrated silicon photonic analogue of a deformable mirror. The on-chip “deformable mirror” operates through an array of grating couplers, phase sensors, and phase shifters to correct the wavefront of a free-space optical beam directly in the plane of an integrated photonic chip, leading to numerous advantages, including reduced bulk, reduced cost, and increase volume production.