Canadian Gridded Homogenized Monthly Precipitation (CanGridP mly)

The first version of this gridded dataset (on a 10-km EASE grid) of monthly total precipitation amounts was produced using a kriging-based gridding scheme to grid the Canadian homogenized monthly precipitation dataset CanHoPmlyV1 (Wang et al. 2023; available at https://open.canada.ca/data/dataset/1dd0c28e-2266-42e2-8985-2f47659e9d02). More specifically, ordinary kriging was used to grid the 1961-1990 climate normal values and the relative anomalies, separately; the resulting gridded datasets were used to produce this gridded dataset of monthly total precipitation amounts (Wang et al. 2023, Abbasnezhadi and Wang, 2024). As detailed in Wang et al. (2023), CanHomPmlyV1 is based on the quality-controlled version 2020 of the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset (Wang et al. 2017, available at https://open.canada.ca/data/en/dataset/d8616c52-a812-44ad-8754-7bcc0d8de305), and on daily total precipitation data from automated gauges (including Belfort, Fisher & Porter, Nipher, Geonor, and Pluvio), with some records from neighbouring stations being joined to form long-term data series. Version 1 of ANUSPLIN surfaces of the adjusted monthly precipitation (MacDonald et al. 2021) was used to infill temporal data gaps in the 425 data series. A comprehensive semi-automatic data homogenization procedure was used to homogenize the data series. The aforementioned ANUSPLIN data and the Twentieth Century Reanalysis 20CRv3 ensemble-mean series of monthly precipitation (Slivinski et al., 2019) were used as reference in the homogeneity tests (Wang et al., 2023). The homogenized dataset CanHoPmlyV1, and its gridded version CanGridP mlyV1, which was called CanKrig mlyPv1 in Wang et al. (2023), provide more realistic estimates of precipitation trends (Wang et al. 2023). Although the latter is much better than the pre-existing CanGRD precipitation relative anomalies data, both gridded datasets contain biases due to changes in data availability over time and space (i.e., inhomogeneous sampling), which are non-negligible in the early period. Such biases are being assessed and corrected to produce a sampling bias-corrected gridded dataset CanGridP mlyV2 (upcoming).


References:

Abbasnezhadi, K. and X. L. Wang, 2024: Comparison of gridding methods for precipitation over Canada and assessment of station/data density effects on gridding results. Atmos.-Ocean, 62:4, 320-346, https://doi.org/10.1080/07055900.2024.2394829.

Wang, X.L, Y. Feng, V. Y. S. Cheng, H. Xu, 2023: Observed precipitation trends inferred from Canada’s homogenized monthly precipitation dataset, J. Clim., 36, 7957-7971. DOI: 10.1175/JCLI-D-23-0193.1. 

Wang, X. L., H. Xu, B. Qian, Y. Feng, E. Mekis, 2017: The adjusted daily rainfall and snowfall data for Canada. Atmos.-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163.

MacDonald, H., D. W. McKenney, X. L. Wang, J. Pedlar, P. Papadopol, K. Lawrence, M. F. Hutchinson, 2021: Spatial Models of adjusted precipitation for Canada at varying time scales. J. Appl. Meteor. And Climatol., 60, 291-304. DOI: 10.1175/JAMC-D-20-0041.1.

Slivinski, L. and coauthors, 2019: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteor. Soc., 2876-2908, https://doi.org/10.1002/qj.3598.

Organization: Environment and Climate Change Canada | Environnement et Changement climatique Canada