과제정보
본 논문의 개선을 위해 좋은 의견을 제시해 주신 심사위원께 감사를 드립니다. 본 연구는 기상청 출연사업인 (재)차세대수치예보모델개발사업단의 4차원 고품질기상분석을 위한 최신 자료동화기술 개발(KMA2020-02211)의 지원을 받아 수행되었습니다.
참고문헌
- Andersson, E., 2004: Modelling the temporal evolution of innovation statistics. In: Proceedings of Seminar on Recent Developments in Data Asimilation for Atmosphere and Ocean, 8-12 September 2003. ECMWF.
- Bormann, N., A. Collard, and P. Bauer, 2010: Estimates of spatial and inter-channel observation error characteristics for current sounder radiances for NWP, part I: Methods and application to ATOVS data. Q. J. R. Meteorol. Soc., 136, 1036-1050. https://doi.org/10.1002/qj.616
- Bormann, N., A. Fouilloux, and W. Bell, 2013: Evaluation and assimilation of ATMS data in the ECMWF system. J. Geophys. Res. Atmos., 118, 12970-12980, doi:10.1002/2013JD020325.
- Bormann, N., M. Bonavita, R. Dragani, R. Eresmaa, M. Matricardi, and A. McNally, 2016: Enhancing the impact of IASI observations through an updated ob- servation-error covariance matrix. Q. J. R. Meteorol. Soc., 142, 1767-1780, doi:10.1002/qj.2774.
- Bouttier, F., and P. Coutier, 1999: Data Assimilation Concept and Methods. European Centre for Medium-Range Weather Forecasts, 59 pp.
- Chapnik, B., G. Desroziers, F. Rabier, and O. Talagrand, 2006: Diagnosis and tuning of observational error in a quasi-operational data assimilation setting. Q. J. R. Meteorol. Soc., 132, 543-565. https://doi.org/10.1256/qj.04.102
- Dee, D. P., 2005: Bias and data assimilation. Q. J. R. Meteorol. Soc., 131, 3323-3343. https://doi.org/10.1256/qj.05.137
- Dee, D. P., and A. M. da Silva, 1999: Maximum-likelihood estimation of forecast and observation error covariance parameters. Part I, Methodology. Mon. Weather Rev., 127, 1822-1834. https://doi.org/10.1175/1520-0493(1999)127<1822:MLEOFA>2.0.CO;2
- Desroziers, G., and S. Ivanov, 2001: Diagnosis and adaptive tuning of information error parameters in a variational assimilation. Q. J. R. Meteorol. Soc., 127, 1433-1452. https://doi.org/10.1002/qj.49712757417
- Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation background and analysis- error statistics in observation space. Q. J. R. Meteorol. Soc., 131, 3385-3396. https://doi.org/10.1256/qj.05.108
- Eresmaa, R. 2014: Imager-assisted cloud detection for assimilation of infrared atmospheric sounding interferometer radiances. Q. J. R. Meteorol. Soc., 140, 2342-2352, doi:10.1002/qj.2304.
- Garand, L., S. Heilliette, and M. Buehner, 2007: Interchannel error correlation associated with AIRS radiance observations: Inference and impact in data assimilation. J. Appl. Meteorol. Climatol., 46, 714-725. https://doi.org/10.1175/JAM2496.1
- Haben, S. A., A. S. Lawless, and N. K. Nichols, 2011: Conditioning of incremental variational data assimilation, with application to the Met Office system. Tellus A, Dyn. Meterol. Oceanogr., 63, 782-792, doi:10.1111/j.1600-0870.2011.00527.x.
- Hollingsworth, A., and P. Lonnberg, 1986: The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus, Ser A, Dyn Meterol Oceanogr., 38A, 111-136. https://doi.org/10.1111/j.1600-0870.1986.tb00460.x
- Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) system for global weather forecasting. Asia-Pac. J. Atmos. Sci., 54, 267-292. https://doi.org/10.1007/s13143-018-0028-9
- Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.
- 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.
- Kim, E., C.-H. J. Lyu, K. Anderson, R. V. Leslie, and W. J. Blackwell, 2014: S-NPP ATMS instrument prelaunch and on-orbit performance evaluation. J. Geophys. Res. Atmos., 119, 5653-5670, doi:10.1002/2013JD020483.
- 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.
- Rutherford, I. D., 1972: Data assimilation by statistical interpolation of forecast error fields. J. Atmos. Sci., 29, 809-815. https://doi.org/10.1175/1520-0469(1972)029<0809:DABSIO>2.0.CO;2
- Weston, P. P., W. Bell, and J. R. Eyre, 2014: Accounting for error in the assimilation of high-resolution sounder data. Q. J. R. Meteorol. Soc., 140, 2420-2429, doi:10.1002/qj.2306.