Abstract
Precipitation extremes, such as the record-breaking Meiyu characterized by frequent occurrences of rainstorms that resulted in severe flooding over the Yangtze—Huai River valley (YHRV) in June–July 2020, are always attracting considerable interest, highlighting the importance of improving the forecast accuracy at the medium-to-long range. To elevate the skill in forecasting heavy precipitation events (HPEs) with both long and short durations, the Key Influential Systems Based Analog Model (KISAM) was further improved and brought into operational application in 2020. Verification and comparison of this newly adapted analog model and ensemble mean forecasts from the ECMWF at lead times of up to 15 days were carried out for the identified 16 HPEs over the YHRV in June–July 2020. The results demonstrate that KISAM is advantageous over ECMWF ensemble mean for forecasts of heavy precipitation ⩾ 25 mm day−1 at the medium-to-long (6–15-day) lead times, based on the traditional dichotomous metrics. At short lead times, ECMWF ensemble mean outperforms KISAM due largely to the low false alarm rates (FARs) benefited from an underestimation of the frequency of heavy precipitation. However, at the medium-to-long forecast range, the large fraction of misses induced by the high degree of underforecasting overwhelms the fairly good FARs in the ECMWF ensemble mean, which partly explains its inferiority to KISAM in terms of the threat score. Further assessment on forecasts of the latitudinal location of accumulated heavy precipitation indicates that smaller displacement errors also account for a part of the better performance of KISAM at lead times of 8–12 days.
This is a preview of subscription content, log in via an institution to check access.
References
Ben Daoud, A., E. Sauquet, G. Bontron, et al., 2016: Daily quantitative precipitation forecasts based on the analogue method: Improvements and application to a French large river basin. Atmos. Res., 169, 147–159, doi: https://doi.org/10.1016/j.atmosres.2015.09.015.
Bougeault, P., Z. Toth, C. Bishop, et al., 2010: The THORPEX Interactive Grand Global Ensemble. Bull. Amer. Meteor. Soc., 91, 1059–1072, doi: https://doi.org/10.1175/2010bams2853.1.
Chen, T., F. H. Zhang, C. Yu, et al., 2020: Synoptic analysis of extreme Meiyu precipitation over Yangtze River Basin during June-July 2020. Meteor. Mon., 46, 1415–1426, doi: https://doi.org/10.7519/j.issn.1000-0526.2020.11.003. (in Chinese)
Chen, Y., and P. M. Zhai, 2013: Persistent extreme precipitation events in China during 1951–2010. Clim. Res., 57, 143–155, doi: https://doi.org/10.3354/cr01171.
Ding, Y. H., Z. Y. Wang, and Y. Sun, 2008: Inter-decadal variation of the summer precipitation in East China and its association with decreasing Asian summer monsoon. Part I: Observed evidences. Int. J. Climatol., 28, 1139–1161, doi: https://doi.org/10.1002/joc.1615.
Duan, W. L., B. He, D. Nover, et al., 2016: Floods and associated socioeconomic damages in China over the last century. Nat. Hazards, 82, 401–413, doi: https://doi.org/10.1007/s11069-016-2207-2.
Gibergans-Báguena, J., and M. C. Llasat, 2007: Improvement of the analog forecasting method by using local thermodynamic data. Application to autumn precipitation in Catalonia. Atmos. Res., 86, 173–193, doi: https://doi.org/10.1016/j.atmosres.2007.04.002.
Huang, L., and Y. L. Luo, 2017: Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season. J. Geophys. Res. Atmos., 122, 8494–8516, doi: https://doi.org/10.1002/2017jd026512.
Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–471, doi: https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2.
Krishnamurti, T. N., A. D. Sagadevan, A. Chakraborty, et al., 2009: Improving multimodel weather forecast of monsoon rain over China using FSU superensemble. Adv. Atmos. Sci., 26, 813–839, doi: https://doi.org/10.1007/s00376-009-8162-z.
Liu, B. Q., Y. H. Yan, C. W. Zhu, et al., 2020: Record-breaking Meiyu rainfall around the Yangtze River in 2020 regulated by the subseasonal phase transition of the North Atlantic Oscillation. Geophys. Res. Lett., 47, e2020GL090342, doi: https://doi.org/10.1029/2020g1090342.
Liu, L., C. Gao, Q. Zhu, et al., 2019: Evaluation of TIGGE daily accumulated precipitation forecasts over the Qu River basin, China. J. Meteor. Res., 33, 747–764, doi: https://doi.org/10.1007/s13351-019-8096-z.
Liu, Y. Y., and Y. H. Ding, 2020: Characteristics and possible causes for the extreme Meiyu in 2020. Meteor. Mon., 46, 1393–1404, doi: https://doi.org/10.7519/j.issn.1000-0526.2020.11.001. (in Chinese)
Lorenz, E. N., 1969: Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci., 26, 636–646, doi: https://doi.org/10.1175/1520-0469(1969)26<636:aparbn>2.0.co;2.
Luo, Y. L., J. S. Sun, Y. Li, et al., 2020: Science and prediction of heavy rainfall over China: Research progress since the reform and opening-up of new China. J. Meteor. Res., 34, 427–459, doi: https://doi.org/10.1007/s13351-020-0006-x.
Matulla, C., X. Zhang, X. L. Wang, et al., 2008: Influence of similarity measures on the performance of the analog method for downscaling daily precipitation. Climate Dyn., 30, 133–144, doi: https://doi.org/10.1007/s00382-007-0277-2.
Mehrotra, R., J. P. Evans, A. Sharma, et al., 2014: Evaluation of downscaled daily rainfall hindcasts over Sydney, Australia using statistical and dynamical downscaling approaches. Hydrol. Res., 45, 226–249, doi: https://doi.org/10.2166/nh.2013.094.
Niu, R. Y., C. H. Liu, W. Y. Liu, et al., 2018: Characteristics of temporal and spatial distribution of regional rainstorm processes to the east of 95°E in China during 1981–2015. Acta Meteor. Sinica, 76, 182–195, doi: https://doi.org/10.11676/qxxb2017.092. (in Chinese)
Ralph, F. M., E. Sukovich, D. Reynolds, et al., 2010: Assessment of extreme quantitative precipitation forecasts and development of regional extreme event thresholds using data from HMT-2006 and COOP observers. J. Hydrometeor., 11, 1286–1304, doi: https://doi.org/10.1175/2010jhm1232.1.
Ran, Q. H., W. Fu, Y. Liu, et al., 2018: Evaluation of quantitative precipitation predictions by ECMWF, CMA, and UKMO for flood forecasting: Application to two basins in China. Nat. Hazards Rev., 19, 05018003, doi: https://doi.org/10.1061((acce)nh.1527-6996.0000282.
Roebber, P. J., 2009: Visualizing multiple measures of forecast quality. Wea. Forecasting, 24, 601–608, doi: https://doi.org/10.1175/2008waf2222159.1.
Scheuerer, M., and T. M. Hamill, 2015: Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Mon. Wea. Rev., 143, 4578–4596, doi: https://doi.org/10.1175/mwr-d-15-006L1.
Sharma, S., R. Siddique, N. Balderas, et al., 2017: Eastern U.S. verification of ensemble precipitation forecasts. Wea. Forecasting, 32, 117–139, doi: https://doi.org/10.1175/waf-d-16-0094.1.
Su, X., H. L. Yuan, Y. J. Zhu, et al., 2014: Evaluation of TIGGE ensemble predictions of Northern Hemisphere summer precipitation during 2008–2012. J. Geophys. Res. Atmos., 119, 7292–7310, doi: https://doi.org/10.1002/2014jd021733.
Sukovich, E. M., F. M. Ralph, F. E. Barthold, et al., 2014: Extreme quantitative precipitation forecast performance at the Weather Prediction Center from 2001 to 2011. Wea. Forecasting, 29, 894–911, doi: https://doi.org/10.1175/waf-d-13-00061.1.
Xie, Z. Q., Y. Du, Y. Zeng, et al., 2018: Classification of yearly extreme precipitation events and associated flood risk in the Yangtze-Huaihe River Valley. Sci. China Earth Sci., 61, 1341–1356, doi: https://doi.org/10.1007/s11430-017-9212-8.
Yang, X.-S., and S. Deb, 2009: Cuckoo search via Lévy flights. Proc. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, India, 9–11 December, IEEE, Coimbatore, India, 210–214, doi: https://doi.org/10.1109/NABIC.2009.5393690.
Yang, X.-S., and S. Deb, 2014: Cuckoo search: recent advances and applications. Neural Comput. Appl., 24, 169–174, doi: https://doi.org/10.1007/s00521-013-1367-1.
Zhai, P. M., Y. Q. Ni, and Y. Chen, 2013: Mechanism and forecasting method of persistent extreme weather events: Review and prospect. Adv. Earth Sci., 28, 1177–1188. (in Chinese)
Zhang, F. H., T. Chen, F. Zhang, et al., 2020: Extreme features of severe precipitation in Meiyu period over the middle and lower reaches of Yangtze River Basin in June-July 2020. Meteor. Mon., 46, 1405–1414, doi: https://doi.org/10.7519/j.issn.1000-0526.2020.11.002. (in Chinese)
Zhang, R. H., 2015: Changes in East Asian summer monsoon and summer rainfall over eastern China during recent decades. Sci. Bull., 60, 1222–1224, doi: https://doi.org/10.1007/s11434-015-0824-x.
Zhou, B. Q., and P. M. Zhai, 2016: A new forecast model based on the analog method for persistent extreme precipitation. Wea. Forecasting, 31, 1325–1341, doi: https://doi.org/10.1175/waf-d-15-0174.1.
Zhou, B. Q., P. M. Zhai, and R. Y. Niu, 2018: Comparative assessment of two objective forecast models for cases of persistent extreme precipitation events in the Yangtze-Huai River valley in summer 2016. Wea. Forecasting, 33, 221–238, doi: https://doi.org/10.1175/waf-d-17-0039.1.
Zhou, T. J., D. Y. Gong, J. Li, et al., 2009: Detecting and understanding the multi-decadal variability of the East Asian Summer Monsoon—Recent progress and state of affairs. Meteor. Z., 18, 455–467, doi: https://doi.org/10.1127/0941-2948/2009/0396.
Zorita, E., and H. Von Storch, 1999: The analog method as a simple statistical downscaling technique: Comparison with more complicated methods. J. Climate, 12, 2474–2489, doi: https://doi.org/10.1175/1520-0442(1999)012<2474:tamaas>2.0.co;2.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Key Research and Development Program of China (2018YFC1507700), National Natural Science Foundation of China (41905082), and Basic Research to Operation Fund of Chinese Academy of Meteorological Sciences (2019Y009).
Rights and permissions
About this article
Cite this article
Zhou, B., Zhai, P. & Niu, R. Application of an Improved Analog-Based Heavy Precipitation Forecast Model to the Yangtze—Huai River Valley and Its Performance in June–July 2020. J Meteorol Res 35, 987–997 (2021). https://doi.org/10.1007/s13351-021-1059-1
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13351-021-1059-1