Estimation of Fine-Scale Daily Temperature with 30 m-Resolution Using PRISM

Abstract

This study estimates and evaluates the daily January temperature from 2003 to 2012 with 30 m-resolution over South Korea, using a modified Parameter-elevation Regression on Independent Slopes Model (K-PRISM). Several factors in K-PRISM are also adjusted to 30 m grid spacing and daily time scales. The performance of K-PRISM is validated in terms of bias, root mean square error (RMSE), and correlation coefficient (Corr), and is then compared with that of inverse distance weighting (IDW) and hypsometric methods (HYPS). In estimating the temperature over Jeju island, K-PRISM has the lowest bias (−0.85) and RMSE (1.22), and the highest Corr (0.79) among the three methods. It captures the daily variation of observation, but tends to underestimate due to a high-discrepancy in mean altitudes between the observation stations and grid points of the 30 m topography. The temperature over South Korea derived from K-PRISM represents a detailed spatial pattern of the observed temperature, but generally tends to underestimate with a mean bias of −0.45. In bias terms, the estimation ability of K-PRISM differs between grid points, implying that care should be taken when dealing with poor skill area. The study results demonstrate that K-PRISM can reasonably estimate 30 m-resolution temperature over South Korea, and reflect topographically diverse signals with detailed structure features.

Improvement of daily precipitation estimations using PRISM with inverse-distance weighting

“…The topographic facets and coastal proximities calculated from this map were used as the PRISM input data. Sixteen directions (N, NNE, NE, NEE, E, SEE, SE, SSE, S, SSW, SW, SWW, W, NWW, NW, and NNW) and a flat for the topographic facets are utilized as used in Ahn et al (2014), so topographic facets may have 17 directions. Coastal proximity refers to the degree that the same oceanic climate affects the data.…”

“…The 30-km influence of radius utilized in this study is the one used by K-PRISM, which is optimized in consideration of South Korea's geography and climate, and by many prior studies (e.g., Hong et al 2007;Shin et al 2008;Kim et al, 2011). However, with fewer than five observation stations within the radius of influence, the radius of influence is increased by 5 km up to 50 km (Hong et al 2007;Ahn et al 2014). The individual weights are calculated at the observation stations within the radius of influence.…”

“…Among the regression coefficients used in the PRISM grid point estimation, the slope ofβ 1 À Á may be abnormally large or small if the altitudes and distances of the observation stations from some of the target grid points within the radius of influence are not variably distributed and this may result in unrealistic estimations (Shin et al 2008). Ahn et al (2014) calculated the daily temperature lapse rate and standard deviation averaged over 40 years from 1973 to 2012, while also limiting the upper and lower limitations of the slope of the regression equation by using the result obtained by calculating a 10-day moving average to remove small variabilities. They also limited the maximum and minimum values of the slope using the reduction rate of the daily mean precipitation according to the altitudes averaged over 40 years by applying the method used in Ahn et al (2014).…”

“…Ahn et al (2014) calculated the daily temperature lapse rate and standard deviation averaged over 40 years from 1973 to 2012, while also limiting the upper and lower limitations of the slope of the regression equation by using the result obtained by calculating a 10-day moving average to remove small variabilities. They also limited the maximum and minimum values of the slope using the reduction rate of the daily mean precipitation according to the altitudes averaged over 40 years by applying the method used in Ahn et al (2014). We set the maximum and minimum values of the slope to + 0.5 σ and − 0.5 σ, respectively (Fig.…”

“…A hypsometric technique (Ahrens 2003) and a parameter-elevation regression on independent slopes model (PRISM) (Daly et al 1994(Daly et al , 2002Daly 2006) are two of the typical objective analysis methods that consider geographical features. In particular, since PRISM has a relatively low dependence on the adjacent meteorological data compared to other methods, it may be usefully employed to estimate high-resolution grid data from observational data that are distributed spatially inhomogeneously and coarsely (Ahn et al 2014).…”