CMOS BULLETIN - Bias Correcting Surface Snow Water Equivalent Estimates using Machine Learning
Check our this new article in the Canadian Meteorological and Oceanographic Society bulleting by C1W collaborator Dr. Fraser King. Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. This article discusses a snow-melt bias correction method developed by Dr. Fraser King and other C1W collaborators including Dr. Andre Erler and Dr. Steve Frey.
This new method relies on machine learning methods to correct some of the bias in snow water equivalent (SWE) estimates from the SNOw Data Assimilation System (SNODAS), and evaluates the accuracy of this bias correction for the Southern Ontario region.
Click here to read the article.
a) Relative bias in SNODAS SWE estimates when compared to in situ estimates from ECCC; and b) 7 year timeseries of SWE on ground estimates from SNODAS and ECCC.
Timeseries comparisons of monthly area-normalized discharge from SNODAS and the bias corrected SWE melt estimates at three river gauges in Ontario.
“Snowmelt-derived flooding has become increasingly problematic across much of Canada in recent decades as global temperatures continue to rise.”