[논문]도시건물정보를 반영한 초고해상도 규모상세화 수치자료 산출체계(KMAPP) 구축 및 풍속 평가

김도형; 이승욱; 정형세; 박성화; 김연희

doi:10.14191/atmos.2022.32.3.179

도시건물정보를 반영한 초고해상도 규모상세화 수치자료 산출체계(KMAPP) 구축 및 풍속 평가
Development and Wind Speed Evaluation of Ultra High Resolution KMAPP Using Urban Building Information Data 원문보기

대기 = Atmosphere, v.32 no.3, 2022년, pp.179 - 189

김도형 (국립기상과학원 기상응용연구부) , 이승욱 (국립기상과학원 기상응용연구부) , 정형세 (국립기상과학원 기상응용연구부) , 박성화 (국립기상과학원 기상응용연구부) , 김연희 (기상청 수치모델링센터)

Abstract ▼ AI-Helper

The purpose of this study is to build and evaluate a high-resolution (50 m) KMAPP (Korea Meteorological Administration Post Processing) reflecting building data. KMAPP uses LDAPS (Local Data Assimilation and Prediction System) data to detail ground wind speed through surface roughness and elevation corrections. During the detailing process, we improved the vegetation roughness data to reflect the impact of city buildings. AWS (Automatic Weather Station) data from a total of 48 locations in the metropolitan area including Seoul in 2019 were used as the observation data used for verification. Sensitivity analysis was conducted by dividing the experiment according to the method of improving the vegetation roughness length. KMAPP has been shown to improve the tendency of LDAPS to over simulate surface wind speeds. Compared to LDAPS, Root Mean Square Error (RMSE) is improved by approximately 23% and Mean Bias Error (MBE) by about 47%. However, there is an error in the roughness length around the Han River or the coastline. Accordingly, the surface roughness length was improved in KMAPP and the building information was reflected. In the sensitivity experiment of improved KMAPP, RMSE was further improved to 6% and MBE to 3%. This study shows that high-resolution KMAPP reflecting building information can improve wind speed accuracy in urban areas.

주제어

표/그림 (11)

그림 Fig. 1. The model (a) orography, (b) land-sea mask, (c) topographic peak height, and (d) the silhouette roughness of KMAPP input data using SRTM 30 m resolution data.
그림 Fig. 2. Vegetation roughness length of KMAPP used in the (a) CTL, (b) EXP1, and (c) EXP2 experiment. Note that the EXP3 experiment uses the same vegetation roughness length as in the EXP2 experiment.
표 Table 1. KMAPP model sensitivity experiment design.
그림 Fig. 3. The spatial distribution of wind speed during theentire period of simulation for (a) difference between CTLand EXP1, (b) difference between EXP1 and EXP2.
표 Table 2. RMSE and MBE of wind speed for simulation according to experiments (m s^-1).
그림 Fig. 4. The RMSE of wind speed for LDAPS and KMAPP experiments with (a) Seoul and (b) Incheon and Gyonggi AWS.
그림 Fig. 5. The spatial distribution of wind speed RMSE(KMAPP-LDAPS) by Seoul, Incheon and Gyonggi AWS.
표 Table 3. Case by bad simulation experiment was analyzed by averaged wind speed, RMSE, MBE, Height and Roughness length.
그림 Fig. 6. The surrounding environment of Yeongdeungpo AWS station. (a) Land cover map and (b) KMAPP (50 m) vegetation roughness length.
그림 Fig. 7. The spatial distribution of KMAPP wind speed (a) RMSE and (b) MBE by metropolitan urban meteorological observation network.
그림 Fig. 8. (a) Location of 16 subsites of the observation network over Seolleung, (b) ratios between measured wind speeds at Seolleung and predicted wind speed of KMAPP wind speed (Seolleung observation/KMAPP).

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