Grants and Contributions

The objective of this research is to develop an atmospheric drag model that incorporates the effects of solar activity, atmospheric density, satellite altitude and geomagnetic activity. Atmospheric drag effects cause large and unpredictable uncertainties in orbit determination/prediction, particularly during periods of highly active space weather. By analyzing and modeling the atmospheric drag forces acting on the CASSIOPE satellite, the researchers seek to understand the temporal and spatial variability of these forces and their effects on satellite orbit, altitude decay and lifetime. These findings will contribute to a better understanding of the dynamics of the Earth's atmosphere and its impact on space-based assets.

The proposal will further develop a novel machine learning model of geomagnetic disturbances which will aid stakeholders and forecasters in making operational decisions to limit the impact of space weather.

By utilizing new ground-based observations, ionospheric modelling, employing various in-situ measurements and machine learning techniques, it will enhance the understanding of the magnetosphere-ionosphere system and pave the way for practical applications in space weather monitoring and prediction.

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.