Abstract
An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors (MEs) in past intervals. Given the analyses, the ME in each interval (6 h) between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year (2001–2005) GRAPES-GFS (Global Forecast System of the Global and Regional Assimilation and Prediction System) error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.
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References
Bao, M., Y. Q. Ni, and J. F. Chou, 2004: The experiment of monthly mean circulation prediction using the analogydynamical model. Chinese Science Bulletin, 49(12), 1296–1300. (in Chinese)
Berner, J., T. Jung, and T. N. Palmer, 2012: Systematic model error: the impact of increased horizontal resolution versus improved stochastic and deterministic parameterizations. J. Climate, 25, 4946–4961.
Carter, G. M., J. P. Dallavalle, and H. R. Glahn, 1989: Statistical forecasts based on the national meteorological center’s numerical weather prediction system. Wea. Forecasting, 4, 401–412.
Chou, G. F., 1974: A problem of using past data in numerical weather forecasting. Scientia Sinica, 17(6), 814–825. (in Chinese)
Da, C. J., 2011: One scheme which maybe improve the forecasting ability of the global (regional) assimilation and prediction system. Ph. D. dissertation, School of Atmospheric Sciences, Lanzhou University, 100 pp. (in Chinese)
Danforth, C. M., E. Kalnay, and T. Miyoshi, 2007: Estimating and correcting global weather model error. Mon. Wea. Rev., 135, 281–299.
Glahn, H. R., and D. A. Lowry, 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 1203–1211.
Jung, T., 2005: Systematic errors of the atmospheric circulation in the ECMWF forecasting system. Quart. J. Roy. Meteor. Soc., 131, 1045–1073.
Jung, T., and A. M. Tompkins, 2003: Systematic errors in the ECMWF forecasting system. Technical Memorandum, No. 442, ECMWF, Shinfield Park, Reading RG 29AX, U. K., 72 pp.
Murphy, A. H., 1988: Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Wea. Rev., 116, 2417–2424.
Ren, H. L., and J. F. Chou, 2007: Strategy and methodology of dynamical analogue prediction. Science in China D: Earth Sciences, 50(10), 1589–1599.
Xue, H. L., X. S. Shen, and J. F. Chou, 2013: A forecast error correction method in numerical weather prediction using the recent multiple-time evolution data. Adv. Atmos. Sci., 30(5), 1249–1259, doi: 10.1007/s00376-013-2274-1.
Xue, H. L., X. S. Shen, and J. F. Chou, 2015: An online model correction method based on an inverse problem: Part I—Model error estimation by iteration. Adv. Atmos. Sci., 32(10), 1329–1340, doi: 10.1007/s00376-015-4261-1.
Zheng, F., J. Zhu, R.-H. Zhang, and G.-Q. Zhou, 2006: Ensemble hindcasts of SST anomalies in the tropical Pacific using an intermediate coupled model. Geophys. Res. Lett., 33, L19604, doi: 10.1029/2006GL026994.
Zheng, F., J. Zhu, H. Wang, and R.-H. Zhang, 2009: Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles. Adv. Atmos. Sci., 26(2), 359–372, doi: 10.1007/s00376-009-0359-7.
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Xue, H., Shen, X. & Chou, J. An online model correction method based on an inverse problem: Part II—systematic model error correction. Adv. Atmos. Sci. 32, 1493–1503 (2015). https://doi.org/10.1007/s00376-015-4262-0
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DOI: https://doi.org/10.1007/s00376-015-4262-0