Adjusted Daily Rainfall and Snowfall Data for Canada

ABSTRACT This article documents how Environment and Climate Change Canada’s Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset was developed. The adjustments include (i) conversion of ruler measurements of snowfall to its water equivalent using a previously developed snow water equivalent (SWE) ratio map for Canada; (ii) corrections for gauge-related issues including undercatch and evaporation caused by wind effects and gauge-specific wetting loss, as well as for trace precipitation amounts, using previously developed procedures for Canada. Various data flags (e.g., accumulation flags) were also treated. This dataset contains all Canadian stations reporting daily rainfall and snowfall for which we have metadata to implement the adjustments. The length of the data record varies from one station to another, starting as early as 1840. The results show that the original unadjusted total precipitation data in Environment and Climate Change Canada’s digital archive underestimate the total precipitation in northeastern Canada by more than 25% and by about 10–15% in most of southern Canada. Such large underestimates make the original data unsuitable for water availability and/or balance studies or for numerical model validation, among many other applications. The use of the assumed 10:1 SWE ratio for the archived total precipitation data is the primary cause of the underestimate, which is most severe in northeastern Canada. The trace correction adds 5–20% to precipitation values in northern Canada but less than 5% in southern Canada. The gauge-related corrections do not show an organized spatial pattern but add 5–10% to the precipitation at 312 stations. Long runs (≥3 months) of miscoded missing values were also identified and corrected. The latest version of the AdjDlyRS dataset is available from the Canadian Open Data Portal; currently it is version 2016, which contains 3346 stations and covers the period from station inception to February 2016. This dataset is suitable for producing gridded precipitation datasets, as well as other applications.

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