Grants and Contributions

This project will focus on analysis of data from the CSA-supported OSIRIS instrument. We will analyze the retrieved aerosol and investigate the systematic impacts in order to understand the limitations of the OSIRIS measurements of Hunga Tonga aerosol. This research responds directly to CSA objectives by applying satellite data analysis of this record breaking volcanic eruption to improve understanding of extreme events and climate change. This work will transfer knowledge to government to improve climate prediction and adaptation. This project will train young researchers and contribute to new understanding of satellite remote sensing and the study of climate from space.

Overshooting deep convection is an extreme weather condition that is affected by but also feeds back to climate change. Its complex nature makes it difficult to represent it in global climate models and to observe it with satellites. We propose to integrate satellite measurements with high-resolution numerical modeling to improve the characterization of the cloud, humidity, temperature (including that inside clouds) and radiation fields associated with overshooting convection. The research will help improve Canada’s capacity for climate and weather prediction and strengthen its leadership role in Satellite Earth Observation.

High-latitude surface water dynamics are complex, fast-moving, and driven by snow, rainfall, ice, and permafrost. Modeling efforts to understand the mechanisms, and to predict variability due to natural processes and climate change, often lack basic hydraulic measurements, or the spatial and temporal coverage, or the information is poor. In particular, glacial lake dynamics are poorly constrained, yet critically important as they can induce positive feedbacks to the glacier (via rapid melting). The proposed research will use ICESat-2 laser altimetry data to investigate how glacial lake elevations and discharge vary as the lakes grow or shrink in area.

The Mackenzie River Basin (MRB) in Canada’s North is one of the most important river systems in North America with competing needs of humans, ecosystems and industry. Climate change has altered temperature and precipitation patterns in the region, and is expected to intensify in the coming decades. Scientists rely on computer models of the water cycle to make predictions that can help society to adapt to this change. This proposal uses satellite measurements of the atmosphere and land surface to help improve these models, to make more accurate predictions of water availability for society and industry in the MRB.

At high Canadian latitudes, warming is increasing which is having significant impacts on permafrost stability, disturbance regimes, and vegetation. Despite advancements in carbon observation efforts they remain sparse. This proposal will validate a new modelling framework (CAN-TG) focused on northern Canada ecosystems to predict carbon accumulation and flux. The model is remote sensing focused and will be calibrated and validated using observations from CSA supported missions. The project will advance CSA priorities by providing estimates of carbon flux for national reporting, increase use of data acquired with CSA support and increase the number of scientists with PhDs in Canada.

When internal loads from surface winds and ocean current reach critical threshold in shear, compression or tension, sea ice floes slide relative to another along fracture planes where large amounts of heat, moiture and salt are exchanged between the ocean and atmosphere. We propose to use the Synthetic Aperture Radar (SAR) images from the Canadian RADARSAT Constellation Mission to create a new high-resolution (1 km) sea ice deformation dataset with full Arctic coverage and higher temporal resolution when compared with existing RADARSAT-1-derived products. This new data set will be used to develop a new granular-physics sea ice model.

By combining C-band and L-band synthetic aperture radar (SAR) imagery and developing a machine learning algorithm, the project aims to enhance the accuracy of sea ice information.

SMOS and SMAP missions have shown the unprecedented capability of L-Band radiometry to monitor central variables related to the energy, water and carbon cycles. However, despite the unique capacity of L-Band radiometry, no missions are planned to continue L-band observations. A team of twelve scientists, are proposing a new L-band radiometry mission (FRESCH). They propose to improve approaches using L-Band radiometry in Canadian northern regions using drone and ground-based L-Band radiometers. The work will be crucial to the preparison of the FRESCH proposal and will ensure that the Canadian community will be well placed to participate into the scientic studies of FRESCH.

The locations of zooplankton-food for Right Whales can be linked to ocean fronts which can therefore be a predictor for their habitat. The reserachers propose to detect these fronts from sea surface temperature patterns, which can be obtained from satellite observations. Their focus is the Gulf of St. Lawrence, which is recognized as a whale feeding area. Using probability distribution functions of frontal activity and whale sightings, they will develop probability maps for Right Whale aggregation patterns. They will also correlate these results with satellite-derived indices of dominant currents, like the Gaspé Current, which provides the zooplankton-food for the whales.

Following a first-place success in the European Space Agency’s AutoICE Challenge, the Vision and Image Processing Lab (VIP Lab) is continuing to advance state-of-the-art machine learning algorithms towards the automation of operational sea ice mapping using remotely sensed and ancillary data.