• Title/Summary/Keyword: Spatial downscaling method

Search Result 3, Processing Time 0.037 seconds

Implementation of Spatial Downscaling Method Based on Gradient and Inverse Distance Squared (GIDS) for High-Resolution Numerical Weather Prediction Data (고해상도 수치예측자료 생산을 위한 경도-역거리 제곱법(GIDS) 기반의 공간 규모 상세화 기법 활용)

  • Yang, Ah-Ryeon;Oh, Su-Bin;Kim, Joowan;Lee, Seung-Woo;Kim, Chun-Ji;Park, Soohyun
    • Atmosphere
    • /
    • v.31 no.2
    • /
    • pp.185-198
    • /
    • 2021
  • 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.

Spatial Downscaling Method for Use of GCM Data in A Mountainous Area (산악지역에 GCM 자료를 이용하기 위한 공간 축소방법 개발)

  • Kim, Soojun;Kang, Na Rae;Kim, Yon Soo;Lee, Jong So;Kim, Hung Soo
    • Journal of Wetlands Research
    • /
    • v.15 no.1
    • /
    • pp.115-125
    • /
    • 2013
  • This study established a methodology for the application of downscaling technique in a mountainous area having large spatial variations of rainfall and tried to estimate the change of rainfall characteristics in the future under climate change using the established method. The Namhan river basin, which is in the mountainous area of the Korean peninsula, has been chosen as the study area. Artificial Neural Network - Simple Kriging with varying local means (ANN-SKlm) has been built by combining artificial neural network, which is one of the general downscaling techniques, and SKlm technique, which can reflect the geomorphologic characteristics like elevation of the study area. The evaluation of SKlm technique was done by using the monthly rainfalls at six weather stations which KMA(Korea Meteorological Administration) is managing in the basin. The ANN-SKlm technique was compared with the Thiessen technique and ordinary kriging(OK) technique. According to the evaluation result of each technique the SKlm technique showed the best result.

Spatial Downscaling of Grid Precipitation Using Support Vector Machine Regression (SVM 회귀 모형을 활용한 격자 강우량 상세화 기법)

  • Moon, Heewon;Baik, Jongjin;Hwang, Sukhwan;Choi, Minha
    • Journal of Korea Water Resources Association
    • /
    • v.47 no.11
    • /
    • pp.1095-1105
    • /
    • 2014
  • A spatial downscaling method using the Support Vector Machine (SVM) Regression for 25 km Tropical Rainfall Measuring Mission (TRMM) Monthly precipitation is proposed. The nonlinear relationship among hydrometeorological variables and precipitation was effectively depicted by the SVM for predicting downscaled grid precipitation. The accuracy of spatially downscaled precipitation was estimated by comparing with rain gauge data from sixty-four stations and found to be improved than the original TRMM data in overall. Especially the positive bias of the original TRMM data was effectively removed after the downscaling procedure. The spatial distributions of 25 km and 1 km grid precipitation were generally similar, while the local spatial trend was better detected by 1 km grid precipitation. The downscaled grid data derived from the proposed method can be applied in hydrological modelling for higher accuracy and further be studied for developing optimized downscaling method incorporation other regression methods.