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http://dx.doi.org/10.14191/Atmos.2019.29.1.021

Analysis of Forecast Performance by Altered Conventional Observation Set  

Han, Hyun-Jun (Korea Institute of Atmospheric Prediction Systems)
Kwon, In-Hyuk (Korea Institute of Atmospheric Prediction Systems)
Kang, Jeon-Ho (Korea Institute of Atmospheric Prediction Systems)
Chun, Hyoung-Wook (Korea Institute of Atmospheric Prediction Systems)
Lee, Sihye (Korea Institute of Atmospheric Prediction Systems)
Lim, Sujeong (Korea Institute of Atmospheric Prediction Systems)
Kim, Taehun (Korea Institute of Atmospheric Prediction Systems)
Publication Information
Atmosphere / v.29, no.1, 2019 , pp. 21-39 More about this Journal
Abstract
The conventional observations of the Korea Meteorological Administration (KMA) and National Centers for Environmental Prediction (NCEP) are compared in the numerical weather forecast system at the Korea Institute of Atmospheric Prediction Systems (KIAPS). The weather forecasting system used in this study is consists of Korea Integrated Model (KIM) as a global numerical weather prediction model, three-dimensional variational method as a data assimilation system, and KIAPS Package for Observation Processing (KPOP) as an observation pre-processing system. As a result, the forecast performance of NCEP observation was better while the number of observation is similar to the KMA observation. In addition, the sensitivity of forecast performance was investigated for each SONDE, SURFACE and AIRCRAFT observations. The differences in AIRCRAFT observation were not sensitive to forecast, but the use of NCEP SONDE and SURFACE observations have shown better forecast performance. It is found that the NCEP observations have more wind observations of the SONDE in the upper atmosphere and more surface pressure observations of the SURFACE in the ocean. The results suggest that evenly distributed observations can lead to improved forecast performance.
Keywords
Conventional observation; Korean integrated model; observation impact; forecast performance;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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1 Kwon, I.-H., and Coauthors, 2018: Development of an operational hybrid data assimilation system at KIAPS. Asia-Pac. J. Atmos. Sci., 54, 319-335, doi:10.1007/s13143-018-0029-8.   DOI
2 Lee, E.-H., I.-J. Choi, K.-B. Kim, J.-H. Kang, J. Lee, E. Lee, and K.-H. Seol, 2017: Verification of the Global Numerical Weather Prediction Using SYNOP Surface Observation Data. Atmosphere, 27, 235-249, doi:10.14191/Atmos.2017.27.2.235(in Korean with English abstract).   DOI
3 Lee, S., J.-H. Kim, J.-H. Kang, and H.-W. Chun, 2013: Development of pre-processing and bias correction modules for AMSU-A satellite data in the KIAPS observation processing system. Atmosphere, 23, 453-470, doi:10.14191/Atmos.2013.23.4.453 (in Korean with English abstract).   DOI
4 Lee, S., S. Kim, H.-W. Chun, J.-H. Kim, and J.-H. Kang, 2014: Pre-processing and Bias Correction for AMSU-A Radiance Data Based on Statistical Methods. Atmosphere, 24, 491-502, doi:10.14191/Atmos.2014.24.4.491 (in Korean with English abstract).   DOI
5 Saarinen, S., 2004: ODB User Guide. ECMWF Reading UK, 289 pp [available at https://www.ecmwf.int/sites/default/files/elibrary/2004/12074-part-i-observation-processing.pdf].
6 Schulze, G. C., 2007: Atmospheric observations and numerical weather prediction. S. Afr. J. Sci., 103, 318-323.
7 Sienkiewicz, J. M., 1990: An example of the importance of ship observations. Wea. Forecasting, 5, 683-687.   DOI
8 Song, H.-J., and I.-H. Kwon, 2015: Spectral transformation using a cubed-sphere grid for a three-dimenstional Variational data assimilation system. Mon. Wea. Rev., 143, 2581-2599, doi:10.1175/MWR-D-14-00089.1.   DOI
9 Arnold, C. P. Jr., and C. H. Dey, 1986: Observing-Systems Simulation Experiments: Past, Present, and Future. Bull. Amer. Meteor. Soc., 67, 687-695.   DOI
10 Atlas, R., 1997: Atmospheric observations and experiments to assess their usefulness in data assimilation. J. Meteorol. Soc. Jpn., 75, 111-130.   DOI
11 Berrisford, P., and Coauthors, 2011: The ERA-Interim archive Version 2.0. ECMWF ERA Report Series-1, 23 pp.
12 Bouttier, F., and G. Kelly, 2001: Observing-system experiments in the ECMWF 4D-Var data assimilation system. Q. J. Roy. Meteor. Soc., 127, 1469-1488.   DOI
13 Choi, S.-J., and S.-Y. Hong, 2016: A global non-hydrostatic dynamical core using the spectral element method on a cubed-sphere grid. Asia-Pac. J. Atmos. Sci., 52, 291-307, doi:10.1007/s13143-016-0005-0.   DOI
14 Choi, S.-J., F. X. Giraldo, J. Kim, and S. Shin, 2014: Verification of a non-hydrostatic dynamical core using the horizontal spectral element method and vertical finite difference method: 2-D aspects. Geosci. Model Dev., 7, 2717-2731, doi:10.5194/gmd-7-2717-2014.   DOI
15 Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) system for global weather forecasting. Asia-Pac. J. Atmos. Sci., 54, 267-292, doi:10.1007/s13143-018-0028-9.   DOI
16 Jo, Y., S. Lim, I.-H. Kwon, and H.-J. Han, 2018: Investigation of analysis effects of ASCAT data assimilation within KIAPS-LETKF system. Atmosphere, 28, 263-272, doi:10.14191/Atmos.2018.28.3.263 (in Korean with English abstract).   DOI
17 Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112-126.   DOI
18 Hwang, S.-O., and S.-Y. Hong, 2012: The impact of observation system on medium-range weather forecasting in a global forecast system. Asia-Pac. J. Atmos. Sci., 48, 159-170, doi:10.1007/s13143-012-0016-4.   DOI
19 Jo, Y., J.-S. Kang, and H. Kwon, 2015: Optimization of the vertical localization scale for GPS-RO data assimilation within KIAPS-LETKF system. Atmosphere, 25, 529-541, doi:10.14191/Atmos.2015.25.3.529 (in Korean with English abstract).   DOI
20 Kang, J.-H., and Coauthors, 2018: Development of an observation processing package for data assimilation in KIAPS. Asia-Pac. J. Atmos. Sci., 54, 303-318, doi:10.1007/s13143-018-0030-2.   DOI
21 Kelly, G., J.-N. Thepaut, R. Buizza, and C. Cardinali, 2007: The value of observations. I: Data denial experiments for the Atlantic and the Pacific. Q. J. R. Meteorol. Soc., 133, 1803-1815.   DOI
22 Keyser, D., cited 2018: PREPBUFR processing at NCEP [Available online at http://www.emc.ncep.noaa.gov/mmb/data_processing/prepbufr.doc/document.htm].
23 Kim, K.-B., E.-H. Lee, and K.-H. Seol, 2017: Sensitivity of typhoon simulation to physics parameterizations in the global model. Atmosphere, 27, 17-28, doi:10.14191/Atmos.2017.27.1.017 (in Korean with English abstract).   DOI
24 Koo, M.-S., S. Baek, K.-H. Seol, and K. Cho, 2017: Advances in Land Modeling of KIAPS Based on The Noah Land Surface Model. Asia-Pac. J. Atmos. Sci., 53, 361-373, doi:10.1007/s13143-017-0043-2.   DOI