Research Scientist - Environment and Climate Change Canada
My primary position is as a Research Scientist with the Climate Research Division of Environment and Climate Change Canada. I am a part of the Climate Data and Analysis Section (CDAS) and am located in Victoria, BC at the Canadian Centre for Climate Modelling and Analysis (CCCma). I continue to be affiliated with Prof. William Hsieh's Climate Prediction Group at UBC.
My research collaborations with UBC deal mainly with the development and application of machine learning and statistical models to climate and weather analysis and prediction tasks, including:
estimation of hydroclimatological extremes; climate downscaling algorithms; climate model post-processing and bias correction; synoptic map-pattern classification and weather typing; assessing predictive uncertainty; and climate impacts on environmental systems
I'm one of the Editors-In-Chief of Atmosphere-Ocean and am on the editorial advisory/editorial boards of Stochastic Environmental Research and Risk Assessment and Computers & Geosciences. I'm a past member of the AMS Committee on Artificial Intelligence Applications to Environmental Science.
https://cran.r-project.org/package=qrnn - Quantile regression neural network
https://cran.r-project.org/package=MBC - Multivariate climate model bias correction
https://cran.r-project.org/package=ClimDown - Gridded climate downscaling
https://cran.r-project.org/package=monmlp - Monotone multi-layer perceptron
https://cran.r-project.org/package=CaDENCE - Conditional density estimation network (CDEN)
https://cran.r-project.org/package=GEVcdn - Generalized extreme value CDEN
96. Shrestha, R.R., A.J. Cannon, M.S. Schnorbus, and H. Alford, Climatic controls on future hydrologic changes in a subarctic river basin in Canada. Journal of Hydrometeorology.
95. Snauffer, A., W.W. Hsieh, and A.J. Cannon, Machine learning estimates of snow water equivalent using gridded products, snow modeling and land covariates. Water Resources Research.
90. Cannon, A.J. and S. Innocenti, 2019. Projected intensification of sub-daily and daily rainfall extremes in convection-permitting climate model simulations over North America: Implications for future Intensity-Duration-Frequency curves. Natural Hazards and Earth System Sciences, 19:421-440. doi:10.5194/nhess-19-421-2019
86. Tam, B., K. Szeto, B. Bonsal, G. Flato, A.J. Cannon, and R. Rong, in press. CMIP5 projections of droughts in Canada based on the Standardized Precipitation Evapotranspiration Index. Canadian Water Resources Journal. doi:10.1080/07011784.2018.1537812
85. Mahony, C.R. and A.J. Cannon, 2018. Wetter summers can intensify departures from natural variability in a warming climate. Nature Communications, 9:783. doi:10.1038/s41467-018-03132-z
84. Cannon, A.J., 2018. Multivariate quantile mapping bias correction: An N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2):31-49. doi:10.1007/s00382-017-3580-6
83. Cannon, A.J., 2018. Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes. Stochastic Environmental Research and Risk Assessment, 32(11):3207-3225. doi:10.1007/s00477-018-1573-6
82. Ouali, D. and A.J. Cannon, 2018. Estimation of rainfall Intensity-Duration-Frequency curves at ungauged locations using quantile regression methods. Stochastic Environmental Research and Risk Assessment, 32(10):2821-2836. doi:10.1007/s00477-018-1564-7
81. Neilsen, D., M. Bakker, T. Van der Gulik, S. Smith, A.J. Cannon, I. Losso, A. Warwick Sears, 2018. Landscape based agricultural water demand modeling - a tool for water management decision making in British Columbia, Canada. Frontiers in Environmental Science, 6:74. doi:10.3389/fenvs.2018.00074
80. Wang, H-., J. Chen, A.J. Cannon, Xu, C-., and H. Chen, 2018. Transferability of climate simulation uncertainty to hydrological climate change impacts. Hydrology and Earth System Sciences, 22:3739-3759. doi:10.5194/hess-22-3739-2018
79. Snauffer, A., W.W. Hsieh, A.J. Cannon, and M.A. Schnorbus, 2018. Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models. The Cryosphere, 12(3):891-905. doi:10.5194/tc-12-891-2018
78. Hiebert, J., A.J. Cannon, T. Murdock, S. Sobie, and A. Werner, 2018. ClimDown: Climate Downscaling in R. The Journal of Open Source Software, 3(22):360. doi:10.21105/joss.00360
77. Li, G., X. Zhang, A.J. Cannon, T.Q. Murdock, S. Sobie, F.W. Zwiers, K. Anderson, and B. Qian, 2018. Indices of Canada's future climate for general and agricultural adaptation applications. Climatic Change, 148(1-2):249-263. doi:10.1007/s10584-018-2199-x
76. Stiff, H. W., K. D. Hyatt, M. M. Stockwell, and A. J. Cannon. 2018. Downscaled GCM Trends in Projected Air and Water Temperature to 2100 Due To Climate Variation in Six Sockeye Watersheds. Can. Tech. Rep. Fish. Aquat. Sci. 3259: vi + 83 p.
72. Zhang, X., F.W. Zwiers, G. Li, H. Wan, and A.J. Cannon, 2017. Complexity in estimating past and future extreme short-duration rainfall. Nature Geoscience, 10:255-259. doi:10.1038/NGEO2911
71. Mahony, C., A.J. Cannon, T. Wang, and S. Aitken, 2017. A closer look at novel climates: new method and insights at continental to landscape scales. Global Change Biology, 23:3934-3955. doi:10.1111/gcb.13645
70. Eum, H.I., A.J. Cannon, and T.Q. Murdock, 2017. Intercomparison of multiple statistical downscaling methods: Application of multi-criteria decision making to a model selection procedure. Stochastic Environmental Research and Risk Assessment, 31(3):683–703. doi:10.1007/s00477-016-1312-9
69. Eum, H.I. and A.J. Cannon, 2017. Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble. International Journal of Climatology, 37(8):3381-3397. doi:10.1002/joc.4924
68. Peng, H., A.R. Lima, A. Teakles, J. Jin, A.J. Cannon, and W.W. Hsieh, 2017. Forecasting hourly air quality concentration in Canada using updatable machine learning methods. Air Quality, Atmosphere and Health, 10(2):195-211. doi:10.1007/s11869-016-0414-3
67. Neilsen, D., S. Smith, G. Bourgeois, B. Qian, A.J. Cannon, G. Neilsen, and I. Losso, 2017. Modelling changing suitability for tree fruits in complex terrain. Acta Horticulturae (ISHS), 1160:207-214. doi:10.17660/ActaHortic.2017.1160.30