Overview
The Met1km dataset at daily temporal resolution covers the time period from 1901 to 2100. The historical part described here spans 1901-2017. Met1km is generated based on four coarser gridded meteorological datasets for the historical period: CRU JRA, PNWNAmet, NRCANmet, and the Princeton dataset which were downscaled to 1 km resolution using the re-baselining method based on the WorldClim2 dataset as spatial templates. The dataset was specifically developed for modelling and mapping permafrost at high spatial resolutions in Canada.
Provider's contact information
This dataset was created by Natural Resources Canada and Agriculture and Agri-Food Canada Please contact the authors for data: yu.zhang@canada.ca
Licensing
Unknown.
Published by Yu Zhang, Canada Centre for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, yu.zhang@canada.ca
Variable name and units:
Total precipitation (mm)
Spatial coverage and resolution:
Spatial coverage: The entire Canadian landmass.
Spatial resolution: 1 km
Temporal coverage and resolution:
The historical period covers 1901-2017. However the dataset expands until 2100 based on climate model projections.
Information about observations (number, homogeneity)
For the historical period Met1km was built from four datasets:
- CRU JRA for the period from 1901 to 1947 and the year 2017
- Princeton dataset for the period 1948 to 2016
- NRCANmet (=ANUSPLIN) for air temperature and precipitation from 1950 to 2013 (replacing the daily air temperature and precipitation from the Princeton dataset)
- PNWNAmet for air temperature, precipitation, and wind speed from 1945 to 2013 for western Canada
Methodology
Four observation datasets were downscaled to a resolution of 30 arc seconds latitude/longitude (about 1-km) based on the WorldClim2 dataset (Fick & Hijmans, 20217) using the re-baselining method proposed by Way and Bonnaventure (2015). The WorldClim2 dataset was selected since it has fine spatial resolution and includes the climate variables required for the objective of creating a dataset for permafrost studies (except downward longwave radiation). The re-baselining is a method to fill missing observations using gridded regional climate anomalies. The method is based on the commonly observed phenomenon that climate anomalies at the regional scale typically co-vary, whereas long-term averages at different sites can be different due to local topography and other site conditions. The method can easily integrate datasets of various spatial resolutions without affecting their temporal variations and trends. (Yang et al., 2020)
Information about the technical and scientific quality
According to the authors the accuracy of Met1km is similar to or better than the four coarser gridded datasets on which it is based. Errors in long-term averages and average seasonal patterns are reported to be small and occur mainly in day-to-day fluctuations. This error can be reduced significantly when averaging over 5 to 10 days.
Limitations and strengths for application in North Canada
The dataset was specifically developed for modelling and mapping permafrost and is available at a particularly high spatial resolution.
References to documents describing the methodology or/and the dataset
Zhang, Y., B. Qian, and G. Hong, 2020: A Long-Term, 1 km Resolution Daily Meteorological Dataset for Modeling and Mapping Permafrost in Canada. Atmosphere. 11(12),1363, https://doi.org/10.3390/atmos11121363
Link to download the data and format of data:
The data can be obtained from the authors.
Publications including dataset evaluation or comparison with other data in northern Canada
Zhang, Y., B. Qian, and G. Hong, 2020: A Long-Term, 1 km Resolution Daily Meteorological Dataset for Modeling and Mapping Permafrost in Canada. Atmosphere. 11(12),1363, https://doi.org/10.3390/atmos11121363