Development and Testing of Canada-Wide Interpolated Spatial Models of Daily Minimum–Maximum Temperature and Precipitation for 1961–2003

Abstract

The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of Canada, at a spatial resolution of 300 arc s of latitude and longitude, for the period 1961-2003. Each daily model was optimized automatically by minimizing the generalized cross validation. The fitted trivariate splines incorporated a spatially varying dependence on ground elevation and were able to adapt automatically to the large variation in station density over Canada. Extensive quality control measures were performed on the source data. Error estimates for the fitted surfaces based on withheld data across southern Canada were comparable to, or smaller than, errors obtained by daily interpolation studies elsewhere with denser data networks. Mean absolute errors in daily maximum and minimum temperature averaged over all years were 1.18 and 1.68C, respectively. Daily temperature extremes were also well matched. Daily precipitation is challenging because of short correlation length scales, the preponderance of zeros, and significant error associated with measurement of snow. A two-stage approach was adopted in which precipitation occurrence was estimated and then used in conjunction with a surface of positive precipitation values. Daily precipitation occurrence was correctly predicted 83% of the time. Withheld errors in daily precipitation were small, with mean absolute errors of 2.9 mm, although these were relatively large in percentage terms. However, mean percent absolute errors in seasonal and annual precipitation totals were 14% and 9%, respectively, and seasonal precipitation upper 95th percentiles were attenuated on average by 8%. Precipitation and daily maximum temperatures were most accurately interpolated in the autumn, consistent with the large well-organized synoptic systems that prevail in this season. Daily minimum temperatures were most accurately interpolated in summer. The withheld data tests indicate that the models can be used with confidence across southern Canada in applications that depend on daily temperature and accumulated seasonal and annual precipitation. They should be used with care in applications that depend critically on daily precipitation extremes.

Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas

“…7c; Hutchinson et al 2009). The interpolation process introduces a smoothing of daily precipitation that results in a negative bias for RX1day (underestimation of RX1day values estimated from recorded series) for almost all NRCan grid points next to stations (only two stations in the north had positive biases and these stations were probably not integrated with the NRCan product).…”

“…The NRCan dataset provides daily precipitation and temperature from Environment and climate change Canada stations gridded on a horizontal grid of ~10 km using the thin plate smoothing spline implemented in the ANUSP-LIN climate modelling software (Hutchinson et al 2009). A two-stage approach was applied for interpolating daily precipitation by estimating the spatial domain where precipitation occurred prior to carrying out the interpolation of observed precipitation amounts.…”

Rain-on-snow events over North America based on two Canadian regional climate models

“…The Hopkinson et al (2011) precipitation dataset covers the Canadian landmass south of 60°N at a spatial resolution of 0.1°. This Canadian dataset was developed from daily observations from Environment Canada's climate stations, using a thin plate smoothing spline surface fitting method (Hutchinson et al 2009). The gridded daily SWE used for validation is that from Brown et al (2003), available at 0.3° resolution, for the 1979-1996 period.…”

Rainfall and temperature estimation for a data sparse region

“…Although the idea of interpolating meteorological data is not new, by definition it is seldom attempted for extensive, data sparse regions with complex terrain. Previous studies have interpolated climate records (Grant et al, 2004), daily meteorological data (Hutchinson et al, 2009), or extreme values (Pereira et al, 2010) but these methods are potentially susceptible to patchy or discontinuous data affecting aggregate statistics. Others simulate multi-site rainfall and temperature using resampling (Buishand and Brandsma, Figure 13 Observed and modelled 10-year return period daily rainfall totals for Network A.…”