Automated Quality Control Procedure for the "Water Equivalent of Snow on the Ground" Measurement

Thomas W. Schmidlin Department of Geography, Water Resources Research Institute, Kent State University, Kent, Ohio

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Daniel S. Wilks Department of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca, New York

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Megan McKay Department of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca, New York

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Richard P. Cember Department of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca, New York

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Abstract

Snow water equivalent (SWE) has been measured daily by the United States National Weather Service since 1952, whenever snow depth is 2 in. (5 cm) or greater. These data are used to develop design snow loads for buildings, for hydrological forecasting, and as an indicator of climate change. To date they have not been subjected comprehensively to quality control. An automated quality control procedure for these data is developed here, which checks daily SWE values for common data entry errors, values beyond reasonable limits, and consistency with daily precipitation and estimated melt. Potential effects of drifting in high winds and of the intrinsic microscale variability of SWE are also considered. An SWE measurement is declared suspicious if a sufficient discrepancy is found with respect to the expected SWE. Data values flagged as potential errors are checked manually. Results of applying the procedure to available SWE data from the northeastern UnitedStates are also summarized.

Abstract

Snow water equivalent (SWE) has been measured daily by the United States National Weather Service since 1952, whenever snow depth is 2 in. (5 cm) or greater. These data are used to develop design snow loads for buildings, for hydrological forecasting, and as an indicator of climate change. To date they have not been subjected comprehensively to quality control. An automated quality control procedure for these data is developed here, which checks daily SWE values for common data entry errors, values beyond reasonable limits, and consistency with daily precipitation and estimated melt. Potential effects of drifting in high winds and of the intrinsic microscale variability of SWE are also considered. An SWE measurement is declared suspicious if a sufficient discrepancy is found with respect to the expected SWE. Data values flagged as potential errors are checked manually. Results of applying the procedure to available SWE data from the northeastern UnitedStates are also summarized.

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