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Abstract

A method was developed to estimate a synthetic precipitation record for ungauged sites using irregular coarse observations. The proposed synthetic precipitation data were produced with ultra-high hourly resolution on a regular 1Ã?Â?1 km grid. The proposed method was used to analyze selected real-time observational data collected in South Korea from 2010 to the end of 2014. The observed precipitation data were measured using the Automatic Weather System and Automated Synoptic Observing System. The principal objective of the proposed method was to estimate the additional effects of orography on precipitation introduced by ultra-high-resolution (1Ã?Â?1 km) topography provided by a digital elevation model. The Global Forecast System analysis of the National Centers for Environmental Prediction was used for the upper atmospheric conditions, necessary for estimating the orographic effects. Precipitation data from 48 of the more than 600 observation sites used in the study, which matched the grid points of the synthetic data, were not included in the synthetic data estimation. Instead, these data were used to evaluate the proposed method by direct comparison with the real observations at these sites. A bias score was investigated by comparison of the synthetic precipitation data with the observations. In this comparison, the number of Hit, False, Miss, and Correct results for 2010-2014 was 74738, 25778, 7544 and 367981, respectively. In the hit cases, the bias score was 1.22 and the correlation coefficient was 0.74. The means of the differences between the synthetic data and the observations were 0.3, -3.9, -14.4 and -34.9 mm h-1 and the root mean square errors (RMSEs) were 2.7, 8.3, 19.3, and 39.6 mm h-1 for the categories of 0.5-10.0, 10.0-30.0, 30.0-50.0 and 50.0-100.0 mm h-1, respectively. In addition, in each range, the 60% difference between the synthetic precipitation data and the observation data was -1.5 to +1.5, -5.0 to +5.0, -17.0 to +17.0 and -33.0 to +33.0 mm h-1, respectively. Overall, the correlation coefficient of the synthetic precipitation data was >0.7 for 43 of the 48 test stations and the RMSE was <4 mm h-1 at 31 stations. The results are significant at all evaluation stations at the 0.05 significance level.

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