Abstract
A time-lagged ensemble method is used to improve 6–15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model, version 2.0.1. The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs, all at the same forecast valid time. This lagged average forecast (LAF) method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean. Our analyses of the Equitable Threat Score, the Hanssen and Kuipers Score, and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6–15 day forecasts of precipitation frequency above 1 mm d−1 and 5 mm d−1 in many regions of China, and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members. In particular, significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40%–50% in the summer season; these regions include northeastern and central to southern China, and the southeastern Tibetan Plateau.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chen, H., R. Yu, J. Li, X. Xin, Z. Wang, and T. Wu, 2011: The coherent interdecadal changes of East Asia climate in midsummer simulated by BCC AGCM2.0.1. Climate Dyn., doi: 10.1007/s00382-011-1154-6.
Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367–374.
Ding, Y., and G. Hu, 2003: A study on water vapor budget over China during the 1998 severe flood periods. Acta Meteorologica Sinica, 61(2), 129–145. (in Chinese)
Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461–2480.
Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550–560.
Hamill, T. M., and S. J. Colucci, 1998: Evaluation of Eta-RSM ensemble probabilistic precipitation forecasts. Mon. Wea. Rev., 126, 711–724.
Hanssen, A. W., and W. J. A. Kuipers, 1965: On the relationship between the frequency of rain and various meteorological parameters. Mededelingen en verhandelingen, 81, 2–15.
Jie, W., and T. Wu, 2010: Hindcast for the 1998 Summer Heavy Precipitation in the Yangtze and Huaihe River valley using BCC AGCM2.0.1 model. Chinese J. Atmos. Sci., 34(5), 962–978. (in Chinese)
Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, E. Williford, S. Gadgil, and S. Surendran, 2000: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13, 4196–4216.
Liu, Y., 2003: Prediction of monthly-seasonal precipitation using coupled SVD patterns between soil moisture and subsequent precipitation. Geophys. Res. Lett., 30, 1827, doi: 10.1029/2003GL017709.
Lu, C., H. Yuan, B. E. Schwartz, and S. G. Benjamin, 2007: Short-range numerical weather prediction using time-lagged ensembles. Wea. Forecasting, 22, 580–595.
Mullen, S. L., and R. Buizza, 2002: The impact of horizontal resolution and ensemble size on probabilistic forecasts of precipitation by the ECMWF ensemble prediction system. Wea. Forecasting, 17, 173–191.
Peel, S., and L. J. Wilson, 2008: A diagnostic verification of the precipitation forecasts produced by the Canadian ensemble prediction system. Wea. Forecasting, 23, 596–616.
Qin, J., and H. M. van den Dool, 1996: Simple extensions of an NWP model. Mon. Wea. Rev., 124, 277–287.
Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570–575.
Schmeits, J. M., and K. J. Kok, 2010: A comparison between raw ensemble output, (modified) Bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts. Mon. Wea. Rev., 138, 4199–4211.
Sivillo, J. K., J. E. Ahlquist, and Z. Toth, 1997: An ensemble forecasting primer. Wea. Forecasting, 12, 809–818.
Stensrud, D. J., and N. Yussouf, 2007: Reliable probabilistic quantitative precipitation forecasts from a short-range ensemble forecasting system. Wea. Forecasting, 22, 3–17.
van den Dool, H., and L. Rukhovets, 1994: On the weights for an ensemble-averaged 6–10 day forecast. Wea. Forecasting, 9, 457–465.
Vitart, F., and F. Molteni, 2009: Dynamical extended-range prediction of early monsoon rainfall over India. Mon. Wea. Rev., 137, 1480–1492.
Walser, A., D. Luthi, and C. Schar, 2004: Predictability of precipitation in a cloud-resolving model. Mon. Wea. Rev., 132, 560–577.
Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.
Wu, T., and Coauthors, 2008: A modified dynamic framework for atmospheric spectral model and its application. J. Atmos. Sci., 65, 2235–2253.
Wu, T., and Coauthors, 2010: The Beijing climate center for atmospheric general circulation model (BCC-AGCM2.0.1): Description and its performance for the present-day climate. Climate. Dyn., 34, 123–147.
Yuan, H., S. L. Mullen, X. Gao, S. Sorooshian, J. Du, and H. H. Juang, 2007: Short-range probabilistic quantitative precipitation forecasts over the southwest United States by the RSM ensemble system. Mon. Wea. Rev., 135, 1685–1698.
Yuan, H., C. Lu, J. A. McGinley, P. J. Schultz, B. D. Jamison, L. Wharton, and C. J. Anderson, 2009: Evaluation of shortrange quantitative precipitation forecasts from a time-lagged multimodel ensemble. Wea. Forecasting, 24, 18–38.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jie, W., Wu, T., Wang, J. et al. Improvement of 6–15 day precipitation forecasts using a time-lagged ensemble method. Adv. Atmos. Sci. 31, 293–304 (2014). https://doi.org/10.1007/s00376-013-3037-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-013-3037-8