Statistical Downscaling of Urban-scale Air Temperatures Using an Analog Model Output Statistics Technique

Abstract

This study was conducted to evaluate the suitability of an analog model output statistics (MOS) downscaling technique for urban-scale meteorology research and compares this MOS-Analog technique with the sliding window technique. We downscaled air temperatures forecasted for the Seoul metropolitan area from 1.5 km resolution (using data from the Unified Model-Local Data Assimilation and Prediction System, UM-LDAPS) to 25 m resolution using the analog MOS technique described in the paper. The support vector machine (SVM) technique was employed for empirical computational modeling, using urban surface parameters calculated using the Climate Analysis Seoul (CAS) workbench and automated weather station (AWS) observational data as training data. The comparison of the downscaled prediction results with the AWS observations for the periods of July/August 2016 and 2017 resulted in a lower root mean square error (RMSE) and higher correlation coefficients (CC) than those obtained for the LDAPS prediction results. The prediction performance was also stable for September, during which precipitation episodes and seasonal fluctuations occurred. The results of this study demonstrate that the proposed technique, which overcomes the limitations of the sliding window technique, is applicable to urban-scale meteorology research and potentially applicable other areas.

Characteristics of LDAPS-Predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

“…KMA developed the local data assimilation and prediction system (LDAPS) based on the unified model (UM) designed by the UK Met Office. To prepare for weather disasters caused by local severe weather events over the entire Korean peninsula, LDAPS employs a high-resolution grid system with 1.5 km horizontal resolution and 70 vertical layers [13]. However, the spatial and temporal resolution of LDAPS is insufficient to resolve small obstacles such as buildings and hilly terrain, which cause external forcing in urban-or smaller-scale flows [14].…”

Heat Exposure Information at Screen Level for an Impact-Based Forecasting and Warning Service for Heat-Wave Disasters

“…Refs. [42,43] proposed a method to parameterize downscaled spatial data, such as buildings in urban areas and land cover, to produce high-resolution meteorological data at an urban scale. Recently, methods such as machine learning and geographically weighted regression analyses have also been employed.…”

Characteristics of LDAPS-predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

“…KMA developed the local data assimilation and prediction system (LDAPS) based on the unified model (UM) designed by the UK Met Office. To prepare for weather disasters caused by local severe weather events over the entire Korean peninsula, LDAPS employs a high-resolution grid system with 1.5 km horizontal resolution and 70 vertical layers [10]. However, the spatial and temporal resolution of LDAPS is insufficient to resolve small obstacles such as buildings and hilly terrain, which cause external forcing in urban-scale or smaller flows [11].…”