Parallel daily temperature observations at site pairs over a 5-year period at 88 locations across Canada were used to derive and validate adjustments required during homogenization process. The data was first 'aligned' for compatible observing times at 12 locations (other locations do not have this problem). Then the homogenization adjustments were obtained using three procedures (Seasonal Bias, Monthly Interpolation and Quantile Matching) and two approaches (using parallel and neighbours observations). The root mean squared error (RMSE) between the daily temperatures of site 1 and site 2, and the percentage of days within 0.5 ∘ C (PD05) between site 1 and site 2 were used to assess the uncertainty in the mean and extreme values, respectively. The instruments were not necessarily collocated as the distance between the two observing sites varied from 0 to 30 km. The results confirm that it is necessary to apply adjustments for known issues first, such as a different observing time. They also show that when a shift between site 1 and site 2 (defined by the annual mean of the daily temperature differences) is small [<0.25 standard deviation (SD)], the adjustments do not reduce the error between site 1 and site 2. When the shift size is between 0.25 and 0.5 SD, the adjustments derived from parallel observations help to reduce the uncertainty. When the shift is large (>0.5 SD), both approaches reduce the error, although the adjustments derived from parallel observations provide better results as compared to those computed from neighbour observations. The results also indicate that Quantile Matching adjustments can provide a better estimate of the adjustments than the other methods evaluated to indices of extreme temperature computed from the adjusted daily values; however, highly correlated neighbours are needed when the adjustments are based on neighbours observations.

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This study presents the development of a new dataset of homogenized temperature for use in trend analysis and monitoring climate change in Canada. This dataset contains daily data for 780 locations across the country: 508 locations with an active station (current observations) and long record (starting prior to 1990); 53 locations with an active station and short record (starting after 1990); and 219 locations with no current observations (station closed) but with more than 30 years of data. Daily observations from nearby sites were often merged into a single record to create a long time series. This new dataset includes observations taken at Reference Climate Stations and from the Canada Aviation Weather Services, which are used to extend past climate observations into recent times. First, the data were quality controlled. The daily minimum temperature was adjusted for the change in observing time at principal stations in 1961. Parallel daily data were used to detect non-climatic shifts when the observations from nearby sites were merged. Series of annual and seasonal mean temperatures were tested for homogeneity. Daily temperatures were adjusted using a quantile-matching procedure if needed. Two main causes of data inhomogeneity affecting the trends over the 1948-2018 and 1900-2018 periods were identified. First, the change in observing time in 1961 introduced a cold bias in the annual means of the daily minimum temperatures after 1961. Second, merging observations from airport stations with older records has often created an artificial decreasing shift in the unadjusted data because of the better exposure of the instruments at airport stations. This new homogenized dataset shows a slightly stronger warming than the unadjusted data: the trend in the annual mean temperature for Canada has changed from 1.69°to 1.74°C for 1948-2018, and the trend for southern Canada has changed from 1.32°to 1.62°C for 1900-2018 because of all the adjustments applied to daily temperature in this study. RÉSUMÉ [Traduit par la rédaction] Cette étude décrit la création d'un nouvel ensemble de données de température homogénéisées destiné à l'analyse des tendances et à la surveillance des changements climatiques au Canada. Cet ensemble contient les données quotidiennes de 780 sites canadiens: 508 stations actives (rapportent actuellement) possédant un long historique de relevés (commençant avant 1990); 53 stations actives possédant un court historique de relevés (commençant après 1990); et 219 sites sans observations actuelles (stations fermées), mais comptant plus de 30 ans de données. Dans plusieurs cas, nous avons fusionné les observations quotidiennes de sites voisins afin de créer une longue série temporelle unique. Ce nouvel ensemble de données comprend des observations prises aux stations climatologiques de référence ou par les Services météorologiques à l'aviation du Canada, et sert à prolonger les observations climatologiques passées jusqu'à des périodes récentes. Tout d'abord, les données ont fait l'objet d'un contrôle de qual...

Abstract. The measurement of precipitation in the Environment and Climate Change Canada (ECCC) surface network is a crucial component for climate and weather monitoring, flood and water resource forecasting, numerical weather prediction, and many other applications that impact the health and safety of Canadians. Through the late 1990s and early 2000s, the ECCC surface network modernization resulted in a shift from manual to automated precipitation measurements. Although many advantages to automation are realized, such as enhanced capabilities for monitoring in remote locations and a higher frequency of observations at lower cost, the increased reliance on automated precipitation gauges has also resulted in additional challenges, especially with data quality and homogenization. The automated weighing precipitation gauges used in the ECCC operational network have an increased propensity for wind-induced undercatch of solid precipitation. One outcome of the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) was the development of transfer functions for the adjustment of high-frequency solid precipitation measurements made with gauge/wind shield configurations used in the ECCC surface network. Using the SPICE universal transfer function (UTF), hourly precipitation measurements from 397 ECCC automated climate stations were retroactively adjusted for wind undercatch. The data format, quality control, and adjustment procedures are described here. The hourly adjusted data set (2001–2019; version v2019UTF) is available via the ECCC data catalogue at https://doi.org/10.18164/6b90d130-4e73-422a-9374-07a2437d7e52 (ECCC, 2021). A basic spatial impact assessment shows that the highest relative total precipitation adjustments occur in the Arctic, where solid precipitation has an overall higher annual occurrence ratio. The highest adjustments for solid precipitation are shared by the Arctic, Southern Prairies, and the coastal Maritimes, where stations tend to be more exposed and snowfall events occur at higher wind speeds.

Abstract. The measurement of precipitation in the Environment and Climate Change Canada (ECCC) surface network is a crucial component for climate and weather monitoring, flood and water resource forecasting, numerical weather prediction and many other applications that impact the health and safety of Canadians. Through the late 1990s and early 2000s, ECCC surface network modernization resulted in a shift from manual to automated precipitation measurements. Although many advantages to automation are realized, such as enhanced capabilities for monitoring in remote locations and higher frequency of observations at lower cost, the increased reliance on automated precipitation gauges has also resulted in additional challenges, especially with data quality and homogenization. The automated weighing precipitation gauges used in the ECCC operational network have an increased propensity for wind-induced undercatch of solid precipitation. One outcome of the WMO Solid Precipitation Inter-Comparison Experiment (SPICE) was the development of transfer functions for the adjustment of high frequency solid precipitation measurements made with gauge/wind shield configurations used in the ECCC surface network. Using the SPICE Universal Transfer Function (UTF), hourly precipitation measurements from 397 ECCC automated climate stations were retroactively adjusted for wind undercatch. The data format, quality control and adjustment procedures are described here. The hourly adjusted data set (2001–2019, version v2019UTF) is available via the ECCC data catalogue: https://doi.org/10.18164/6b90d130-4e73-422a-9374-07a2437d7e52 (ECCC, 2021). A basic spatial impact assessment shows that the highest relative total precipitation adjustments occur in the Arctic where solid precipitation has an overall higher annual occurrence ratio. The highest adjustments for solid precipitation are shared by the Arctic, southern Prairies and the coastal Maritimes, where stations tend to be more exposed and snowfall events occur at higher wind speeds.