Abstract
Hybrid data assimilation combines a conventional 3-D or 4-D variational system with background error covariance (BEC) generated from ensemble forecast systems. In order to achieve better BEC, three perturbation schemes, namely, the random combination of multiple physical paramterization schemes (referred to as MP), the MP plus stochastical perturbation on physical process tendencies (MP-SPPT), and the unified perturbation of stochastic physics with bias correction (UPSB, proposed by the authors of this paper in a previous work), were first used in a regional ensemble model, i.e., the Global and Regional Assimilation and Prediction System-Regional Ensemble Prediction System (GRAPES-REPS), and the BECs thus obtained were compared for 7-day ensemble forecasts. The results show that UPSB, which is in fact an MP-SPPT but with the systematic model bias removed, has a better consistency, i.e., the ratio between root-mean-square error (RMSE) and ensemble spread is much closer to 1, especially at low model levels, compared to the other two schemes. Moreover, the BEC derived from UPSB captured more reasonable distributions of forecast errors.
Second, performance of a hybrid data assimilation system (the GRAPES-MESO hybrid En-3DVar) was evaluated by using the BECs from the three perturbation schemes for 7-day hybrid data assimilation forecasts, and thus disclosing the effect of the model bias correction (assuming that the random stocastical features are in general offset in the three perturbation schemes) on the hybrid system forecasts. A covariance weight of 0.8 was prescribed, and this value was determined through sensitivity experiments. The forecast results from the hybrid data assimilation system show that UPSB reduced the false correlation between distant points. The quality of analysis fields of the UPSB scheme shows visible improvement, i.e., the analysis fields produced by UPSB have much smaller RMSEs than those of the other two schemes, at all vertical model levels. The quality of the hybrid data assimilation forecast fields was also improved by this scheme. Furthermore, the improvement was much greater in the early stage of the assimilation cycle than in the late stage. Generally, the quality of the hybrid data assimilation of GRAPES-MESO hybrid En-3DVar could be efficiently improved by the model bias correction in the UPSB scheme.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Berner, J., G. J. Shutts, M. Leutbecher, et al., 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603–626, doi: https://doi.org/10.1175/2008JAS2677.1.
Betts, A. K., 1986: A new convective adjustment scheme. Part I: Observational and theoretical basis. Quart. J. Roy. Meteor. Soc., 112, 667–691, doi: https://doi.org/10.1002/qj.49711247307.
Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420–436, doi: https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2.
Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 1013–1043, doi: https://doi.org/10.1256/qj.04.15.
Buehner, M., P. L. Houtekamer, C. Charette, et al., 2010a: Inter-comparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: Description and single-observation experiments. Mon. Wea. Rev., 138, 1550–1566, doi: https://doi.org/10.1175/2009MWR3157.1.
Buehner, M., P. L. Houtekamer, C. Charette, et al., 2010b: Inter-comparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations. Mon. Wea. Rev., 138, 1567–1586, doi: https://doi.org/10.1175/2009MWR3158.1.
Buizza, R., and T. N. Palmer, 1995: The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci., 9, 1434–1456, doi: https://doi.org/10.1175/1520-0469(1995)052<1434:TSVSOT>2.0.CO;2.
Charron, M., G. Pellerin, L. Spacek, et al., 2009: Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev., 138, 1877–1901, doi: https://doi.org/10.1175/2009MWR3187.1.
Chen, J., J. Z. Wang, J. Du, et al., 2019: Forecast bias correction through model integration: A dynamical wholesale approach. Quart. J. Roy. Meteor. Soc., doi: https://doi.org/10.1002/qj.3730.
Chen, L. L., J. Chen, J. S. Xue, et al., 2015: Development and testing of the GRAPES regional ensemble-3DSAR hybrid data assimilation system. J. Meteor. Res., 29, 981–996, doi: https://doi.org/10.1007/s13351-015-5021-y.
Cui, B., Z. Toth, Y. J. Zhu, et al., 2006: The trade-off in bias correction between using the latest analysis/modeling system with a short, vs. an older system with a long archive. Proceedings of the First THORPEX International Science Symposium, World Meteorological Organization, Montreal, Canada, 281–284.
Du, J., 2007: Uncertainty and Ensemble Forecast. NOAA/NWS Science and Technology Infusion Lecture Series, Nation Weather Service, 42 pp.
Gneiting, T., A. E. Raftery, A. H. Westveld III, et al., 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 1098–1118, doi: https://doi.org/10.1175/MWR2904.1.
Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis schme. Mon. Wea. Rev., 128, 2905–2919, doi: https://doi.org/10.1175/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2.
Hamill, T. M., J. S. Whitaker, M. Fiorino, et al., 2011: Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., 139, 668–688, doi: https://doi.org/10.1175/2010MWR3456.1.
Hollingsworth, A., 1980: An experiment in Monte Carlo forecasting procedure. Proceedings of ECMWF Workshop on Stochastic Dynamic Forecasting, ECMWF.
Hong, S.-Y., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339, doi: https://doi.org/10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2.
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341, doi: https://doi.org/10.1175/MWR3199.1.
Kain, J. S., and J. M. Fritsch., 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802, doi: https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.
Kain, J. S., and J. M. Fritsch., 1993: Convective parameterization for mesoscale models: The Kain-Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, K. A. Emanuel, and D. J. Raymond, Eds., American Meteorological Society, Boston, MA, 165–170.
Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181, doi: https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.
Liu, C. S., and Q. N. Xiao, 2013: An ensemble-based four-dimensional variational data assimilation scheme. Part III: Antarctic applications with Advanced Research WRF using real data. Mon. Wea. Rev., 141, 2721–2739, doi: https://doi.org/10.1175/MWR-D-12-00130.1.
Liu, C. S., Q. N. Xiao, and B. Wang, 2009: An ensemble-based four-dimensional variational data assimilation scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW). Mon. Wea. Rev., 137, 1687–1704, doi: https://doi.org/10.1175/2008MWR2699.1.
Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP—A comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183–3203, doi: https://doi.org/10.1256/qj.02.132.
Ma, X. L., J. S. Xue, and W. S. Lu, 2008: Preliminary study on ensemble transform Kalman filter-based initial perturbation scheme in GRAPES global ensemble prediction. Acta. Meteor. Sinica, 66, 526–536, doi: https://doi.org/10.3321/j.issn:0577-6619.2008.04.006. (in Chinese)
Ma, X.-L., X. Lu, Y.-M. Yu, et al., 2014: Progress on hybrid ensemble-variational data assimilation in numerical weather prediction. J. Trop. Meteor., 30, 1118–1195, doi: https://doi.org/10.3969/j.issn.1004-4965.2014.06.020. (in Chinese)
Molteni, F., R. Buizza, T. N. Palmer, et al., 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73–119, doi: https://doi.org/10.1002/qj.49712252905.
Monache, L. D., T. Nipen, X. X. Deng, et al., 2006: Ozone ensemble forecasts: 2. A Kalman filter predictor bias correction. J. Geophys. Res. Atmos., 111, D05308, doi: https://doi.org/10.1029/2005JD006311.
Mullen, S. L., and D. P. Baumhefner, 1994: Monte Carlo simulations of explosive cyclogenesis. Mon. Wea. Rev., 122, 1548–1567, doi: https://doi.org/10.1175/1520-0493(1994)122<1548:MC-SOEC>2.0.CO;2.
Pan, H.-L., and W.-S. Wu, 1995: Implementing a Mass Flux Convective Parameterization Package for the NMC Medium-Range Forecast Model. NMC Office Note 409, NMC Office, Washington DC, 1–40.
Raftery, A. E., T. Gneiting, F. Balabdaoui, et al., 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155–1174, doi: https://doi.org/10.1175/MWR2906.1.
Ren, H. L., and J. F. Chou, 2005: Analogue correction method of errors by combining both statistical and dynamical methods together. Acta. Meteor. Sinica, 63, 988–993, doi: https://doi.org/10.3321/j.issn:0577-6619.2005.06.015. (in Chinese)
Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079–3102, doi: https://doi.org/10.1256/qj.04.106.
Tennant, W. J., G. J. Shutts, A. Arribas, et al., 2010: Using a stochastic kinetic energy backscatter scheme to improve MO-GREPS probabilistic forecast skill. Mon. Wea. Rev., 139, 1190–1206, doi: https://doi.org/10.1175/2010MWR3430.1.
Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317–2330, doi: https://doi.org/10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.
Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297–3319, doi: https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2.
Toth, Z., O. Talagrand, G. Candille, et al., 2003: Probability and ensemble forecasts. Forecast Verification: A Practitioner’s Guide in Atmospheric Science, I. T. Jolliffe, and D. B. Stephenson, Eds., Wiley, New York, 137–163.
Wang, H. X., and X. F. Zhi, 2015: Statistical downscaling of precipitation forecast based on TIGGE multimodel ensemble. J. Meteor. Sci., 35, 430–437, doi: https://doi.org/10.3969/2014jms.0058. (in Chinese)
Wang, J. Z., J. Chen, J. Du, et al., 2018: Sensitivity of ensemble forecast verification to model bias. Mon. Wea. Rev., 146, 781–796, doi: https://doi.org/10.1175/MWR-D-17-0223.1.
Wang, X. G., 2011: Application of the WRF hybrid ETKF-3DVAR data assimilation system for hurricane track forecasts. Wea. Forecasting, 26, 868–884, doi: https://doi.org/10.1175/WAF-D-10-05058.1.
Wang, X. G., and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 1140–1158, doi: https://doi.org/10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2.
Wang, X. G., D. M. Barker, C. Snyder, et al., 2008a: A hybrid ETKF-3DSAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116–5131, doi: https://doi.org/10.1175/2008MWR2444.1.
Wang, X. G., D. M. Barker, C. Snyder, et al., 2008b: A hybrid ETKF-3DSAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132–5147, doi: https://doi.org/10.1175/2008MWR2445.1
Wang, X. G., D. Parrish, D. Kleist, et al., 2013: GSI 3DVAR-based ensemble-variational hybrid data assimilation for NCEP global forecast system: Single-resolution experiments. Mon. Wea. Rev., 141, 4098–4117, doi: https://doi.org/10.1175/MWR-D-12-00141.1.
Wei, M. Z., Z. Toth, R. Wobus, et al., 2006: Ensemble transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus A: Dyn. Meteor. Oceanogr., 58, 28–44, doi: https://doi.org/10.1111/j.1600-0870.2006.00159.x.
Wilby, R. L., and T. M. L. Wigley, 1997: Downscaling general circulation model output: A review of methods and limitations. Prog. Phys. Geogr.: Earth Environ., 21, 530–548, doi: https://doi.org/10.1177/030913339702100403.
Xia, Y., J. Chen, Y. Liu, et al., 2018: A tentative experiment of GRAPES En-3DSAR hybrid data assimilation method over the Tibet Plateau. Trans. Atmos. Sci., 41, 239–247, doi: https://doi.org/10.13878/j.cnki.dqkxxb.20160119001. (in Chinese)
Xia, Y., J. Chen, J. Du, et al., 2019: A unified scheme of stochastic physics and bias correction in an ensemble model to reduce both random and systematic errors. Wea. Forecasting, 34, 1675–1691, doi: https://doi.org/10.1175/WAF-D-19-0032.1.
Yuan, Y., X. L. Li, J. Chen, et al., 2016: Stochastic parameterization toward model uncertainty for the GRAPES mesoscale ensemble prediction system. Meteor. Mon., 42, 1161–1175, doi: https://doi.org/10.7519/j.issn.1000-0526.2016.10.001. (in Chinese)
Zhang, F. Q., Y. H. Weng, J. A. Sippel, et al., 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an Ensemble Kalman Filter. Mon. Wea. Rev., 137, 2105–2125, doi: https://doi.org/10.1175/2009MWR2645.1.
Zhang, M. Y., L. F. Zhang, B. Zhang, et al., 2015: Flow-dependent characteristics of background error covariance in hybrid variational-ensemble data assimilation. J. Meteor. Sci., 35, 728–736, doi: https://doi.org/10.3969/2015jms.0069. (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Natural Science Foundation of China (41605082 and 91437113) and Jiangsu Province Postgraduate Research and Innovation Program (KYCX17_0869).
Rights and permissions
About this article
Cite this article
Xia, Y., Chen, J., Zhi, X. et al. Impact of Model Bias Correction on a Hybrid Data Assimilation System. J Meteorol Res 34, 400–412 (2020). https://doi.org/10.1007/s13351-020-9088-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13351-020-9088-8