@article {23891,
	title = {Application of machine learning techniques for regional bias correction of SWE estimates in Ontario, Canada},
	journal = {Hydrology and Earth System Sciences},
	year = {Submitted},
	abstract = {Snow is a critical contributor to Ontario{\textquoteright}s water-energy budget with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n = 383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 \% (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction however, only the RF method captures the nonlinearity in the bias, and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5{\textdegree} N and East (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs, and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.},
	url = {https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-593/},
	author = {Fraser King and Andre R. Erler and Steven K. Frey and Christopher G. Fletcher}
}
@article {23890,
	title = {Using CloudSat-CPR Retrievals to Estimate Snow Accumulation in the Canadian Arctic},
	journal = {Earth and Space Science},
	volume = {7},
	year = {2020},
	abstract = {Snow is a critical contributor to our global water and energy budget, with profound impacts for water resource availability and flooding in cold regions. The vast size and remote nature of the Arctic present serious logistical and financial challenges to measuring snow over extended time periods. Satellite observations provided by the Cloud Profiling Radar instrument{\textemdash}installed on the National Aeronautics and Space Administration satellite CloudSat{\textemdash}allow the retrieval of snowfall rates in high-latitude regions, which have been used to estimate surface snow accumulation. In this study, a validation of CloudSat-derived terrestrial snow estimates is presented at four Environment and Climate Change Canada weather stations situated in the Arctic for the common period 2007{\textendash}2015. Comparisons of monthly climatological snow accumulation show mean biases of less than 1.5-mm snow water equivalent annually. Monthly time series exhibit correlations above 0.5 and root-mean-square error below 10-mm snow water equivalent at the two highest-latitude stations (Eureka and Resolute Bay) with correlations falling below 0.5 south of 70{\textdegree}N. CloudSat was also found to underestimate annual mean snow accumulation at the majority of sites, suggesting a potential negative bias in CloudSat{\textquoteright}s accumulation estimates, or underestimation related to sampling. These results imply that CloudSat can provide reliable estimates of snow accumulation across similar high-latitude regions above 70{\textdegree}N.},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019EA000776},
	author = {Fraser King and Christopher G. Fletcher}
}
@presentation {23893,
	title = {A comparison of CloudSat snow accumulation, in situ SWE and gridded SWE products in the Canadian Arctic},
	journal = {European Geosciences Union (EGU)},
	year = {2019},
	author = {Fraser King and Christopher G. Fletcher}
}
@mastersthesis {23892,
	title = {Validating CloudSat-CPR retrievals for the estimation of snow accumulation in the Canadian Arctic},
	year = {2019},
	type = {Master{\textquoteright}s Thesis},
	abstract = {Snow is a critical contributor to our global water and energy budget, with profound impacts for water resource availability, snow albedo feedback and flooding in cold regions. The vast size and remote nature of the Arctic present serious logistical and financial challenges to measuring snow over extended time periods. Satellite observations provided by the Cloud Profiling Radar (CPR) instrument-installed on the NASA satellite CloudSat-allow the retrieval of snowfall rates in high latitude regions, which have been used to estimate surface snow accumulation. In this study, a validation of CloudSat-derived terrestrial snow estimates is presented at four Environment and Climate Change Canada (ECCC) weather stations situated in the Arctic for the common period 2007-2015. Comparisons of monthly climatological snow accumulation show mean biases of less than 1.5 mm SWE annually. Monthly time series exhibit correlations above 0.5\&nbsp;and RMSE below 10 mm SWE at the two highest latitude stations (Eureka and Resolute Bay) with correlations falling below 0.5 south of 70 degrees N. CloudSat was also found to underestimate annual mean snow accumulation at the majority of sites, suggesting a potential negative bias in CloudSat{\textquoteright}s snowfall estimates, or underestimation related to sampling. These results imply that CloudSat can provide reliable estimates of snow accumulation across similar high latitude regions above 70 degrees N. Accurate space-based snowfall measurements provide new important observational perspectives of Arctic snow accumulation, which is a critical region for environmental monitoring in an era of global change.},
	url = {https://uwspace.uwaterloo.ca/handle/10012/14736},
	author = {Fraser King}
}
