• Title/Summary/Keyword: Atmospheric grid

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Estimate and Analysis of Planetary Boundary Layer Height (PBLH) using a Mobile Lidar Vehicle system (이동형 차량탑재 라이다 시스템을 활용한 경계층고도 산출 및 분석)

  • Nam, Hyoung-Gu;Choi, Won;Kim, Yoo-Jun;Shim, Jae-Kwan;Choi, Byoung-Choel;Kim, Byung-Gon
    • Korean Journal of Remote Sensing
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    • v.32 no.3
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    • pp.307-321
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    • 2016
  • Planetary Boundary Layer Height (PBLH) is a major input parameter for weather forecasting and atmosphere diffusion models. In order to estimate the sub-grid scale variability of PBLH, we need to monitor PBLH data with high spatio-temporal resolution. Accordingly, we introduce a LIdar observation VEhicle (LIVE), and analyze PBLH derived from the lidar loaded in LIVE. PBLH estimated from LIVE shows high correlations with those estimated from both WRF model ($R^2=0.68$) and radiosonde ($R^2=0.72$). However, PBLH from lidar tend to be overestimated in comparison with those from both WRF and radiosonde because lidar appears to detect height of Residual Layer (RL) as PBLH which is overall below near the overlap height (< 300 m). PBLH from lidar with 10 min time resolution shows typical diurnal variation since it grows up after sunrise and reaches the maximum after 2 hours of sun culmination. The average growth rate of PBLH during the analysis period (2014/06/26 ~ 30) is 1.79 (-2.9 ~ 5.7) m $min^{-1}$. In addition, the lidar signal measured from moving LIVE shows that there is very low noise in comparison with that from the stationary observation. The PBLH from LIVE is 1065 m, similar to the value (1150 m) derived from the radiosonde launched at Sokcho. This study suggests that LIVE can observe continuous and reliable PBLH with high resolution in both stationary and mobile systems.

Global Ocean Data Assimilation and Prediction System in KMA: Description and Assessment (기상청 전지구 해양자료동화시스템(GODAPS): 개요 및 검증)

  • Chang, Pil-Hun;Hwang, Seung-On;Choo, Sung-Ho;Lee, Johan;Lee, Sang-Min;Boo, Kyung-On
    • Atmosphere
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    • v.31 no.2
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    • pp.229-240
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    • 2021
  • The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.