A Study on the Method for Estimating the 30 m-Resolution Daily Temperature Extreme Value Using PRISM and GEV Method

Abstract

This study estimates and evaluates the extreme value of 30 m-resolution daily maximum and minimum temperatures over South Korea, using inverse distance weighting (IDW), parameter-elevation regression on independent slopes model (PRISM) and generalized extreme value (GEV) method. The three experiments are designed and performed to find the optimal estimation strategy to obtain extreme value. First experiment (EXP1) applies GEV firstly to automated surface observing system (ASOS) to estimate extreme value and then applies IDW to produce high-resolution extreme values. Second experiment (EXP2) is same as EXP1, but using PRISM to make the high-resolution extreme value instead of IDW. Third experiment (EXP3) firstly applies PRISM to ASOS to produce the high-resolution temperature field, and then applies GEV method to make high resolution extreme value data. By comparing these 3 experiments with extreme values obtained from observation data, we find that EXP3 shows the best performance to estimate extreme values of maximum and minimum temperatures, followed by EXP1 and EXP2. It is revealed that EXP1 and EXP2 have a limitation to estimate the extreme value at each grid point correctly because the extreme values of these experiments with 30 mresolution are calculated from only 60 extreme values obtained from ASOS. On the other hand, the extreme value of EXP3 is similar to observation compared to others, since EXP3 produces 30m-resolution daily temperature through PRISM, and then applies GEV to that result at each grid point. This result indicates that the quality of statistically produced high-resolution extreme values which are estimated from observation data is different depending on the combination and procedure order of statistical methods.

Future Changes of Agro-Climate and Heat Extremes over S. Korea at 2 and 3 °C Global Warming Levels with CORDEX-EA Phase 2 Projection

“…Therefore, applying the climate model raw data directly to impact assessing models, which are optimized by empirical relationships based on observation, can lead to inappropriate results. To reduce the climate model bias, many bias correction methods have been developed by utilizing the relationships between climate modeling results and observation [28,[30][31][32][33]. However, applying the bias correction method on future projection can be another source of uncertainty because most of the bias correction methods have problems in "stationary assumption", which assume that the relationship established between the model result and observation keep validating in the future climate [34,35].…”

Reproducibility Evaluation of High-Resolution Downscaled Climate Data Using Automated Weather Station and Precise Temperature Monitoring Data.

“…In this study, we used the Parameter-elevation Regression on Independent Slope Model (PRISM) (Daly et al, 1994(Daly et al, , 2002 to estimate high-resolution gridded information by combining observation data with a statistical downscaling scheme. PRISM has been widely utilized in prior studies conducted in Korea (Hong et al 2007;Shin et al 2008;Lee et al 2016;Kim and Kim 2019). However, in an area with a complex topography and fewer observation stations, the estimated data using PRISM were found to be underestimated compared to the observed data (Eum and Kim 2015).…”