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http://dx.doi.org/10.14191/Atmos.2021.31.2.185

Implementation of Spatial Downscaling Method Based on Gradient and Inverse Distance Squared (GIDS) for High-Resolution Numerical Weather Prediction Data  

Yang, Ah-Ryeon (BomIn Science Consulting)
Oh, Su-Bin (BomIn Science Consulting)
Kim, Joowan (Department of Atmospheric Science, Kongju National University)
Lee, Seung-Woo (Numerical Data Application Division, Numerical Modeling Center, Korea Meteorological Administration)
Kim, Chun-Ji (BomIn Science Consulting)
Park, Soohyun (BomIn Science Consulting)
Publication Information
Atmosphere / v.31, no.2, 2021 , pp. 185-198 More about this Journal
Abstract
In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.
Keywords
Spatial downscaling method; gradient and inverse distance squared; geostatistical downscaling method; limited-area ensemble prediction system;
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