Andrea Scott
Associate Professor, Associate Chair
Location: EC4 2037
Phone: 519-888-4567 x32811
Status: Active

Biography

Andrea Scott is an Associate Professor in the Systems Design Engineering department at the University of Waterloo.
Her research focuses on data to improve model predictions. One of Professor Scott’s projects involve the assimilation of remote sensing data to improve knowledge of the state of sea ice in the Arctic. More specifically, she conducts research on the assimilation of sea ice thickness and/or sea ice temperatures from visual/infrared sensors, and uses Synthetic Aperture Radar (SAR) data to estimate sea ice concentration. Other projects of Professor Scott’s include the use of data assimilation to estimate parameters in turbulence models and the development of scale aware/scale adaptive parameterizations.
Before working as a professor at UWaterloo, Professor Scott was a postdoctoral researcher at Environment Canada, where she was part of a team working on the assimilation of data to improve forecasts of sea ice conditions.

Research Interests

  • Assimilation of remote sensing data to improve our knowledge of the state of sea ice in the Arctic, The assimilation of sea ice thickness and/or sea ice temperature from visual/infrared sensors., The information content of passive microwave and visual infrared sensors as it pertains to sea ice concentration and thickness, The assimilation of SAR (Synthetic Aperture Radar) data, Using data assimilation to estimate parameters in turbulence models, The development of scale aware/scale adaptive parameterizations, Water

Education

  • 2008, Doctorate Mechanical Engineering, University of Waterloo, Canada
  • 2001, Master of Applied Science Mechanical Engineering, McMaster University, Canada
  • 1999, Bachelor of Applied Science Mechanical Engineering, University of Waterloo, Canada

Teaching*

  • SYDE 102 - Seminar
    • Taught in 2021, 2022
  • SYDE 113 - Elementary Engineering Mathematics
    • Taught in 2017, 2018, 2020, 2021
  • SYDE 302 - Seminar
    • Taught in 2021
  • SYDE 351 - Systems Models 1
    • Taught in 2017, 2018, 2020
  • SYDE 621 - Mathematics of Computation
    • Taught in 2020
  • SYDE 730 - Selected Topics in Societal-Environmental Systems
    • Taught in 2018

* Only courses taught in the past 5 years are displayed.

Selected/Recent Publications

  • J42 A.P. Grace*, M. Stastna, K.G. Lamb and KA Scott (2022) Gravity currents in the cabbelingregime, Physics of Fluids, FV10205.
  • J41 D Sola* and KA Scott (2022) Efficient Shallow Network for River Ice Segmentation, Remote Sensing, 14(10, 2378, doi:10.3390/rs14102378.
  • J40 N Asadi*, P Lamontage, M King , R Martin and KA Scott (2022) Probabilistic griddedseasonal sea ice presence forecasting using sequence to sequence learning, accepted for publication in The Cryosphere, tc-2021-282.
  • J39 M Tamber*, KA Scott (2022) Accounting for label error when training a convolutionalneural network to estimate sea ice concentration using operational ice charts, IEEE Journal ofSelected Topics in Applied Earth Observations and Remote Sensing,15, p. 1502-1514.
  • J38 AP Grace*, Stastna, M., Lamb, K.G. and KA Scott (2022) Numerical simulations of thethree-dimensionalization of a shear ow in radiatively forced cold water below the density maxi-mum, Physical Review Fluids, 7, 023501.
  • J37 N Saberi*, KA Scott, and C.R. Duguay (2022) Incorporating aleatoric uncertainties of lakeice mapping in SAR imagery using CNN, Remote Sensing, 14(3), doi:10.3390/rs14030644.
  • J36 A Grace*, M Stastna, KG. Lamb and KA Scott (2021) Asymmetries in gravity currentsattributed to the non-linear equation of state, Journal of Fluid Mechanics, 915 (A18).
  • J35 AS Nagi*, D Kumar, D Sola* and KA Scott (2021) E ective sea ice oe segmentationusing end-to-end RES-NET-CRF with dual loss. Remote Sensing, 13(13): doi.org/10.3390/rs13.
  • J34 K Radhakrishnan*, KA Scott and DA Clausi (2021) Sea Ice Concentration Estimation:Using Passive Microwave and SAR Data with a Fully Convolutional Network, IEEE Journal ofSelected Topics in Applied Earth Observations and Remote Sensing, 14, p.5339-5351.doi.10.1109/JSTARS.2021.3076109.
  • J33 A Atoufi*, KA Scott and M Waite (2021) Kinetic energy cascade in stably stratified open- channel flows, Journal of Fluid Mechanics, 925 (A25).J32 P Lee, K Radharishnan*, DA Clausi and KA Scott (2021) Beluga whale detection in theCumberland Sound Bay using convolutional neural networks, Canadian Journal of Remote Sens-ing, 42(2), 10.1080/07038992.202.
  • J31 KA Scott, L. Xu and H. Kheyrollah-Pour (2020) Retrieval of ice/water observations fromSAR imagery for use in lake ice data assimilation, Journal of Great Lakes Research, 46(6), 1521-1532.
  • J30 A Atoufi *, KA Scott and M Waite (2020) Characteristics of quasistationary near-wall turbulence subjected to strongly-stable strati cation, Physical Review Fluids, 5(6), 064603, 25 pages, single column.
  • J29 N Saberi*, R Kelly, J Pan, M Durand, J Goh and KA Scott (2020) The use of a Monte CarloMarkov Chain method for snow depth retrievals: A case study based on airborne microwave ob-servations and emission modeling experiments of tundra snow, IEEE Transactions on Geoscience and Remote Sensing, 14 pages, double column.
  • J28 N Asadi*, KA Scott, AS Komarov, M Buehner and DA Clausi (2020) Evaluation of aneural network for detection of ice and water in SAR imagery and the role of uncertainty infor-mation, IEEE Transactions on Geoscience and Remote Sensing, 59(1), 247-259.
  • J27 KA Scott (2020) Extended categorical triple collocation for evaluating sea ice/open wa-ter datasets, IEEE Geoscience and Remote Sensing Letters,10.1109/LGRS.2020.299033218(6),doi:10.1109/LGRS.2020.2990332, 5p double column.
  • J26 A Atoufi *, KA Scott, and M Waite (2019) Wall turbulence response to surface coolingand formation of strongly stable strati ed boundary layer, Physics of Fluids, 31, 085114, 17 pages, double column.
  • J25 KA Scott (2019) Assessment of categorical triple collocation for sea ice/open water ob-servations: Application to the Gulf of Saint Lawrence, IEEE Transactions on Geoscience andRemote Sensing, 57(12), pages 9659-9673.
  • J24, S Marshall*, KA Scott and R Scharien (2019) Passive microwave microwave melt onsetretrieval with a variable threshold: Assessment in the Canadian Arctic Archipelago, Remote Sensing, 11(11), doi:10.3390/rs11111304, 17 pages, single column.
  • J23 Y Xu*, and KA Scott (2019) Impact of intermediate ice concentration training data onsea ice concentration estimates from a convolutional neural networks, International Journal ofRemote Sensing, 40(15), doi:10.1080/01431161.2019.1582113, 13 pages single column.
  • J22 J Liu*, KA Scott and P Figeuth (2018) Detection of marginal ice zone in SAR Imageryusing curvelet based features: A case study on the Canadian east coast, Journal of Applied Remote sensing, 13(1), doi:10.1117/1.JRS.13.014505, 17 pages single column.
  • J21 C Cooke* and KA Scott (2018) Estimating sea ice concentration: Training convolutionalneural networks with passive microwave data, IEEE Transactions on Geoscience and Remote Sensing, 57(7), pages 4735-4747.
  • J20 N Asadi*, KA Scott and DA Clausi (2019) Data fusion and data assimilation of ice thicknessobservations using a regularization framework, Tellus A, doi:10.1080/16000870.2018.1564487, 20 pages double column.
  • J19 KA Scott, C Chen*, PG Myers (2018) Assimilation of Argo temperature and salinity profile using a bias-aware EnOI scheme for the Labrador Sea, Journal of Atmospheric and OceanicTechnology, 35(9), pages 1819-1834.
  • J18 G Stonebridge*, KA Scott and M Buehner (2018) The impact of assuming a diagonal ob-servation error covariance matrix on sea ice analyses: Experiments using a toy model, Tellus A,70(1), doi:10.1080/16000870.2018.1445379, 13 pages double column.
  • J17 H Kheyrollah Pour, CR Duguay, KA Scott and K-K Kang (2017) Improvement of lakeice thickness retrieval from MODIS satellite data using a thermodynamic model, IEEE Transac-tions on Geoscience and Remote Sensing, 55(10), pages 5956-5965.
  • J16 L Wang*, KA Scott and D Clausi (2017) Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network, Remote Sensing: Special issue onUnderstanding Remote Sensing Imagery, 9(5), 408; doi:10.3390/rs9050408, 20 pages single column.
  • J15 L Pogson, T Geldsetzer, M Buehner, T Carrieres, M Ross and KA Scott (2016) Collect-ing empirically-derived SAR characteristic values over one year of sea ice environments for use in data assimilation, Monthly Weather Review, 145(1), pages 323-334.
  • J14 A Mozafiari*, KA Scott, N. Azad, S Chenouri (2016) A hierarchical selective ensemblerandomized neural network hybridized with heuristic feature selection for estimation of sea-icethickness, Applied Intelligence, 46: 16. doi:10.1007/s10489-016-0815-x. 18 pages single column.
  • J13 J Liu*, KA Scott, A Gawish, and P Fieguth (2016) Automatic detection of the ice edge inSAR imagery using curvelet transform and active contour, Remote Sensing, 8, 480, doi:10.3390/rs8060480, 16 pages single column.
  • J12 L Wang*, KA Scott, L Xu and D Clausi (2016) Sea ice concentration estimation duringmelt from dual-pol SAR scenes using deep convolutional neural networks: A case study, IEEETransactions on Geoscience and Remote Sensing, 54, pages 4524-4533.
  • J11 L Wang*, KA Scott, and D Clausi (2016) Improved sea ice concentration estimation throughfusing classi ed SAR imagery and AMSR-E data, Canadian Journal of Remote Sensing, 42(1),doi:10.1080/07038992.2016.1152547, 20 pages single column.
  • J10 A Moza ari*, KA Scott, NL Azad, S Chenouri (2016) A modular ridge extreme learningmachine with differential evolutionary distributor applied to the estimation of sea ice thickness,Soft Computing, doi:10.1007/s00500-016-2074-5, 25 pages single column.
  • J9 KA Scott, Z Ashouri*, M Buehner, L Pogson and T Carrieres (2015) Assimilation of iceand water observations from SAR imagery to improve estimates of sea ice concentration, TellusA, 67, doi/10.3402/tellusa.v67.27218, 17 pages double column.
  • J8 W Tan*, KA Scott and E LeDrew (2014) Enhanced Arctic sea ice concentration estimationmerging MODIS sea ice surface temperature and SSM/I sea ice concentration, Atmosphere-Ocean, 52(2), 115-124.
  • J7 KA Scott, A Wong and E Li* (2014) Sea ice surface temperature estimation using MODISand AMSR-E data within a guided variational model along the Labrador Coast, IEEE Journal ofSelected Topics in Applied Earth Observations and Remote Sensing, 7(9), 3685-3694.
  • J6 KA Scott, M Buehner and T Carrieres (2014) An assessment of sea ice thickness from AMSR-E and MODIS data for operational data assimilation, IEEE Transactions on Geoscience and Remote Sensing, 52(5), 2726-2738.
  • J5 KA Scott, M Buehner, A Caya and T Carrieres (2013) A preliminary evaluation of theimpact of assimilating AVHRR data on sea ice concentration analyses, Remote Sensing of Envi-ronment, 128, p 212-223.
  • J4 KA Scott, M Buehner, A Caya and T Carrieres (2012) Direct assimilation of AMSR-E bright-ness temperatures to estimate sea ice concentration, Monthly Weather Review, 140, p 997-1113.
  • J3 KA Scott and FS Lien (2010) A derivation of the NS-alpha model and preliminary appli-cation to plane channel ow, Journal of Turbulence, 11, N31, p 1-26,.
  • J2 KA Scott and FS Lien (2010) Application of the NS-alpha model to a recirculatingFlow Turbulence and Combustion, 84, p 167-192,.
  • J1 S Ziada, KA Scott, and D Arthurs, (2007) Acoustic excitation by ow in T-Junctions, Journalof Pressure Vessel Technology, 129, p 14-20.

Graduate Studies