• 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.

• Atmosphere
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• v.28 no.4
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• pp.369-382
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• 2018

Planetary boundary layer height (PBLH), produced by the Local Data Assimilation and Prediction System (LDAPS), was verified using RawinSonde (RS) data obtained from observation at Daegwallyeong (DGW) and Sokcho (SCW) during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018). The PBLH was calculated using RS data by applying the bulk Richardson number and the parcel method. This calculated PBLH was then compared to the values produced by LDAPS. The PBLH simulations for DGW and SCW were generally underestimation. However, the PBLH was an overestimation from surface to 200 m and 450 m at DGW and SCW, respectively; this result of model's failure to correctly simulate the Surface Boundary Layer (SBL) and the Mixing Layer (ML) as the PBLH. When the accuracy of the PBLH simulation is low, large errors are seen in the mid- and low-level humidity. The highest frequencies of Planetary boundary layer (PBL) types, calculated by the LDAPS at DGW and SCW, were presented as types Ι and II, respectively. Analysis of meteorological factors according to the PBL types indicate that the PBLH of the existing stratocumulus were overestimated when the mid- and low-level humidity errors were large. If the instabilities of the surface and vertical mixing into clouds are considered important factors affecting the estimation of PBLH into model, then mid- and low-level humidity should also be considered important factors influencing PBLH simulation performance.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.793-806
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• 2020

In this study,for YSU (Yonsei University), MYJ(Mellor-Yamada-Janjic), ACM2 (Asymmetric Convective Model), and BouLac (Bougeault-Lacarrere) PBL schemes, numerical experiments were performed for the case period (June 26-30, 2014). The PBLH calculated by using the backscatter signal produced by the mobile vehicle-mounted lidar system (LIVE) and the PBLH calculated by the prediction of each PBL schemes of WRF were compared and analyzed. In general, the experiments using the non-local schemes showed a higher correlation than the local schemes for lidar observation. The standard deviation of the PBLH difference for daylight hours was small in the order of YSU (≈0.39 km), BouLac (≈0.45 km), ACM2 (≈0.47 km), MYJ (≈0.53 km) PBL schemes. In the RMSE comparison for the case period, the YSU PBL scheme was found to have the highest precision. The meteorological lider mounted on the vehicle is expected to provide guidance for the analysis of the planetary boundary layer in a numerical model under various weather conditions.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.171-187
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• 2017

Thisstudy presentsthe method for deriving surface visibility from satellite retrieved AOD. To do thisthe height of aerosol distribution isrequired. This distribution would be in thisstudy represented by the two heights; if there is a discrete atmospheric layer, which is physically separated from the above layer, the upper height of the layer is assumed as Aerosol Layer Height(ALH). In this case there is clear minimum in the Relative Humanity vertical distribution. Otherwise PBLH(Planetary Boundary Layer Height) is used. These heights are obtained from the forecast data of Regional Data Assimilation and Prediction System(RDAPS). The surface visibility is estimated from MODIS AOD and ALH/PBLH, using Koschmieder's Law for ALH and the empirical relations for PBLH. The estimated visibility are evaluated from the visibility measurements of 9 eve-measurement stations and 17 PWD22 stations for the spring of 2015 and 2016. Verification of the estimated visibility shows that there are considerable differencesin statistical verification value depending on stations, years, morning(Terra)/afternoon(Aqua). The better results are shown in the midwest part of korean peninsula for Terra of 2016. The results are summarized as; correlation coefficients of higher than 0.65, for low visibility RMSE of 3.62 km and ME of 2.29 km or less, POD of higher than 0.65 and FAR of 0.5 or less. Verification results were better with increase in the number of low-visibility data.

• Atmosphere
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• v.32 no.2
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• pp.119-133
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• 2022

From 2015 to 2021, high-concentration fine dust episodes with a daily average PM_2.5 concentration of 50 ㎍ m^-3 or higher were selected and classified into 3 types [long range transport (LRT), mixed (MIX) and Local emission and stagnant (LES)] using synoptic chart and backward trajectory analysis. And relationships between the fine particle data (PM_2.5 and PM₁₀ concentration and PM_2.5/PM₁₀ ratio) and meteorological data (PBLH, Ta, WS, U-wind, and Rainfall) were analyzed using hourly observation for the classification episodes on the Korean Peninsula and the Seoul metropolitan area (SMA). In LRT, relatively large particles such as dust are usually included, and in LES, fine particle is abundant. In the Korean peninsula, the rainfall was relatively increased centered on the middle and western coasts in MIX and LES. In the SMA, wind speed was rather strong in LRT and weak in LES. In LRT, rainfall was centered in Seoul, and in MIX and LES, rainfall appeared around Seoul. However, when the dust cases were excluded, the difference between the LRT and other types of air quality was decreased, but the meteorological variables (Ta, RH, Pa, PBLH, etc.) were further strengthened. In the case of the Korean Peninsula, it is difficult to find a clear relationship because regional influences (topographical elevation, cities and coasts, etc.) are complexly included in a rather wide area. In the SMA, it is analyzed that the effects of urbanization such as the urban heat island centered on Seoul coincide with the sea and land winds, resulting in a combination of high concentrations and meteorological phenomena.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.