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Paul Joe, Stella Melo, William R. Burrows, Barbara Casati, Robert W. Crawford, Armin Deghan, Gabrielle Gascon, Zen Mariani, Jason Milbrandt, and Kevin Strawbridge


The goal of the Canadian Arctic Weather Science (CAWS) project is to conduct research into the future operational monitoring and forecasting programs of Environment and Climate Change Canada in the Arctic where increased economic and recreational activities are expected with enhanced transportation and search and rescue requirements. Due to cost, remoteness and vast geographical coverage, the future monitoring concept includes a combination of space-based observations, sparse in situ surface measurements, and advanced reference sites. A prototype reference site has been established at Iqaluit, Nunavut (63°45'N, 68°33'W), that includes a Ka-band radar, water vapor lidars (both in-house and commercial versions), multiple Doppler lidars, ceilometers, radiation flux, and precipitation sensors. The scope of the project includes understanding of the polar processes, evaluating new technologies, validation of satellite products, validation of numerical weather prediction systems, development of warning products, and communication of their risk to a variety of users. This contribution will provide an overview of the CAWS project to show some preliminary results and to encourage collaborations.

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Paul Joe, Stella Melo, William R. Burrows, Barbara Casati, Robert W. Crawford, Armin Deghan, Gabrielle Gascon, Zen Mariani, Jason Milbrandt, and Kevin Strawbridge
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John Hanesiak, Ronald Stewart, Peter Taylor, Kent Moore, David Barber, Gordon McBean, Walter Strapp, Mengistu Wolde, Ron Goodson, Edward Hudson, David Hudak, John Scott, George Liu, Justin Gilligan, Sumita Biswas, Danielle Desjardins, Robyn Dyck, Shannon Fargey, Robert Field, Gabrielle Gascon, Mark Gordon, Heather Greene, Carling Hay, William Henson, Klaus Hochheim, Alex Laplante, Rebekah Martin, Marna Albarran Melzer, and Shunli Zhang

The Storm Studies in the Arctic (STAR) network (2007–2010) conducted a major meteorological field project from 10 October–30 November 2007 and in February 2008, focused on southern Baffin Island, Nunavut, Canada—a region that experiences intense autumn and winter storms. The STAR research program is concerned with the documentation, better understanding, and improved prediction of meteorological and related hazards in the Arctic, including their modification by local topography and land–sea ice–ocean transitions, and their effect on local communities. To optimize the applicability of STAR network science, we are also communicating with the user community (northern communities and government sectors). STAR has obtained a variety of surface-based and unique research aircraft field measurements, high-resolution modeling products, and remote sensing measurements (including Cloudsat) as part of its science strategy and has the first arctic Cloudsat validation dataset. In total, 14 research flights were flown between 5 and 30 November 2007, with eight coinciding with Cloudsat passes. The aircraft was outfitted with many instruments that measure cloud microphysical parameters and three unique Doppler-polarized airborne radars operating in Ka, X and W bands. The project area, instrumentation platforms, real-time forecasts, storm cases, and results thus far are discussed in this article. A number of synoptic and mesoscale features were sampled—such as fronts, upslope/terrain-enhanced precipitation, convective precipitation, and boundary layer clouds/precipitation—as well as targeted Cloudsat missions. One significant and unique event included a research flight into an intense high-latitude storm leftover from Hurricane Noel—an intense tropical and extratropical disturbance that caused many fatalities in the tropics and extensive damage on the eastern North American seaboard. These synoptic and mesoscale features and high-latitude storms will be studied in detail over the next several years. It is anticipated that scientific progress in better understanding the nature of these arctic storms and their hazards will lead to improved conceptual models and improved prediction of such events.

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