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http://dx.doi.org/10.5467/JKESS.2021.42.5.504

A Comparative Study of the Atmospheric Boundary Layer Type in the Local Data Assimilation and Prediction System using the Data of Boseong Standard Weather Observatory  

Hwang, Sung Eun (Operational Systems Development Department, National Institute of Meteorological Sciences)
Kim, Byeong-Taek (Operational Systems Development Department, National Institute of Meteorological Sciences)
Lee, Young Tae (Operational Systems Development Department, National Institute of Meteorological Sciences)
Shin, Seung Sook (Operational Systems Development Department, National Institute of Meteorological Sciences)
Kim, Ki Hoon (Operational Systems Development Department, National Institute of Meteorological Sciences)
Publication Information
Journal of the Korean earth science society / v.42, no.5, 2021 , pp. 504-513 More about this Journal
Abstract
Different physical processes, according to the atmospheric boundary layer types, were used in the Local Data Assimilation and Prediction System (LDAPS) of the Unified Model (UM) used by the Korea Meteorological Administration (KMA). Therefore, it is important to verify the atmospheric boundary layer types in the numerical model to improve the accuracy of the models performance. In this study, the atmospheric boundary layer types were verified using observational data. To classify the atmospheric boundary layer types, summer intensive observation data from radiosonde, flux observation instruments, Doppler wind Light Detection and Ranging(LIDAR) and ceilometer were used. A total number of 201 observation data points were analyzed over the course 61 days from June 18 to August 17, 2019. The most frequent types of differences between LDAPS and observed data were type 1 in LDAPS and type 2 in observed(each 53 times). And type 3 difference was observed in LDAPS and type 5 and 6 were observed 24 and 15 times, respectively. It was because of the simulation performance of the Cloud Physics such as that associated with the simulation of decoupled stratocumulus and cumulus cloud. Therefore, to improve the numerical model, cloud physics aspects should be considered in the atmospheric boundary layer type classification.
Keywords
Atmospheric boundary layer type; Observation data; Local Data Assimilation and Prediction System (LDAPS); Cloud physics part;
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1 LOCK. A. P., BROWN, A. R., BUSH, M. R., MARTIN, G. M., SMITH, R. N. B., 2000, A New Boundary Layer Mixing Scheme. Part I: Scheme Description and Single-Column Model Tests. MONTHLY WEATHER REVIEW. 3187-3199.   DOI
2 Basu, S. A., Holtslag, A. M., Caporaso, L., Riccio, A., Steeneveld, G. J., 2014, Observational support for the stability dependence of the bulk Richardson number across the stable boundary layer. Boundary-layer meteorology, 150(3), 515-523.   DOI
3 Essou. G. R., Brissette, F., Lucas-Picher, P., 2017, Impacts of combining reanalyses and weather station data on the accuracy of discharge modelling. Journal of hydrology, 545, 120-131.   DOI
4 Emeis, S., Schafer, K., Munkel, C., 2008, Surface-based remote sensing of the mixing-layer height-a review. Meteorologische Zeitschrift, 17(5), 621.   DOI
5 Harvey. N. J., Hogan, R. J., Dacre, H. F., 2013, Amethod to diagnose boundary-layer type using Doppler lidar. Q. J. R. Meteorol. Soc. 139, 1681-1693.   DOI
6 Hennemuth, B., Lammert, A., 2006, Determination of the atmospheric boundary layer height from radiosonde and lidar backscatter. Boundary-Layer Meteorology, 120(1), 181-200.   DOI
7 Huang. L. X., Isaac, G. A., Sheng, G., 2012, Integrating NWP forecasts and observation data to improve nowcasting accuracy. Weather and forecasting, 27(4), 938-953.   DOI
8 Davies, F., Middleton, D. R., Bozier, K. E., 2007, Urban air pollution modelling and measurements of boundary layer height. Atmospheric environment, 41(19), 4040-4049.   DOI
9 Moeng, C. H., Rotunno, R., 1990, Vertical-velocity skewness in the buoyancy-driven boundary layer. Journal of Atmospheric Sciences, 47(9), 1149-1162.   DOI
10 Lee. Y. H., OH, S. B., Jang, J. H., Lee, G. H., 2017, Local forecast model atmospheric boundary layer type diagnosis using data from Boseong Standard Weather Observatory, Proceeding of the Autumn Meeting of KMS, 345-346.
11 Richardson, H., Basu, S., Holtslag, A. A. M., 2013, Improving stable boundary-layer height estimation using a stability-dependent critical bulk Richardson number. Boundary-layer meteorology, 148(1), 93-109.   DOI
12 Salman. A. G., Kanigoro, B., Heryadi, Y., 2015, Weather forecasting using deep learning techniques. In 2015 international conference on advanced computer science and information systems (ICACSIS). 281-285.
13 Salman. A. G., Heryadi, Y., Abdurahman, E., Suparta, W., 2018, Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting. Procedia Computer Science, 135, 89-98.   DOI