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http://dx.doi.org/10.5322/JESI.2018.27.11.1141

Study on the Impact of Various Observations Data Assimilation on the Meteorological Predictions over Eastern Part of the Korean Peninsula  

Kim, Ji-Seon (Department of Earth Science, Pusan National University)
Lee, Soon-Hwan (Department of Earth Science Education, Pusan National University)
Sohn, Keon-Tae (Department of Statistics, Pusan National University)
Publication Information
Journal of Environmental Science International / v.27, no.11, 2018 , pp. 1141-1154 More about this Journal
Abstract
Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.
Keywords
Data assimilation; Meteorological prediction; Numerical model; WRF;
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1 Mishra, S. K., Srinivasan, J., 2010, Sensitivity of the simulated precipitation to changes in convective relaxation time scale, Ann. Geophys., 28,1827-1846.   DOI
2 Moreno, T., Querol, X., Alastuey, A., Reche, C., Cusack, M., Amato, F., Pandolfi, M., Pey, J., Richard, A., Prevot, A. S. H., Furger, M., Gibbons, W., 2011, Variations in time and space of trace metal aerosol concentrations in urban areas and their surroundings, Atmos. Chem. Phys., 11, 9415-9430.   DOI
3 Racherla, P. N., Adams, P. J., 2006, Sensitivity of global tropospheric ozone and fine particulate matter concentrations to climate change, J. Geophys. Res., 111, D24103.   DOI
4 Skamarock, W. C., Klemp, J. B., 2008, A Time-split nonhydrostatic atmospheric model for weather research and forecasting applications, J. Comput. Phys., 227, 3465-3485.   DOI
5 Souri, A. H., Choi, Y. S., Li, X. S., Kotsakis, A., Jiang, X., 2016, A 15-year climatology of wind pattern impacts on surface ozone in Houston, Texas, Atmos. Res., 174-175, 124-134.   DOI
6 Stauffer, D. R., Seaman, N. L., 1994, Multiscale four -dimensional data assimilation, J. Appl. Meteor., 33, 416-434.   DOI
7 Thishan Dharshana, K. G., Coowanitwong, N., 2008, Ambient $PM_{10}$ and respiratory illnesses in Colombo City, Sri Lanka, J. Environ. Sci. Health, 43, 1064-1070.   DOI
8 Zhang, S. Q., Zupanski, M., Hou, A. Y., Lin, X., Cheung, S. H., 2013, Assimilation of precipitation-affected radiances in a cloud-resolving WRF ensemble data assimilation system. Mon. Wea. Rev., 141, 754-772.   DOI
9 Zheng, Y., Alapaty, K., Herwehe, J. A., Genio, A. D. D., Niyogi, D., 2016, Improving high-resolution weather forecasts using the weather research and forecasting (WRF) model with an updated kain-fritsch scheme. Mon. Wea. Rev., 144, 833-860.   DOI
10 Bao, J. W., Michelson, S. A., Persson, P. O. G., Djalalova, I. V., Wilczak, J. M., 2008, Observed and WRF-Simulated low-level winds in a high-ozone episode during the Central California Ozone Study, J. Appl. Meteor. Climatol., 47, 2372-2394.   DOI
11 Case, J. L., Crosson, W. L., Kumar, S. V., Lapenta, W. M., Peters-Lidard, C. D., 2008, Impacts of high-resolution land surface initialization on regional sensible weather forecasts from the WRF Model, J. Hydrometeor., 9, 1249-1266.   DOI
12 Hong, S. Y., Y. Noh, J. Dudhia, 2006, A New vertical diffusion package with explicit treatment of entrainment processes, Mon. Wea. Rev., 134, 2318-2341.   DOI
13 Ide, K., Courtier, P., Ghil, M., Lorenc, A. C., 1997, Unified notation for data assimilation: Operational, sequential, and variational, J. Meteor. Soc. Japan, 75, 181-189.   DOI
14 Jeon, W., Choi, Y., Percell, P., Souri, A. H., Song, C. K., Kim, S. T., Kim, J., 2016, Computationally efficient air quality forecasting tool: implementation of STOPS v1.5 model into CMAQ v5.0.2 for a prediction of Asian dust. Geosci. Model Dev., 9, 3671-3684.   DOI
15 Li, X., Choi, Y., Czader, B., Roy, A., Kim, H., Lefer, B., Pan, S., 2016, The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign, Atmos. Chem. Phys., 16, 3127-3144.   DOI
16 Mathiesen, P., Collier, C., Kleissl, J., 2013, A High-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting, Sol. Energy, 92, 47-61.   DOI