Abstract
Assessing the capability of sub-seasonal rainfall forecast of dynamic model and proposing correction method is quite an important topic in current climate research field. From the perspective of rainfall amount, rainy days and rainfall-belt evolution, the sub-seasonal forecast ability of the European Centre for Medium-Range Weather Forecasts (ECMWF) model for the main rainy-season rainfall in eastern China is evaluated. Evaluation results show that the forecast biases increase gradually with the increase of forecast lead time, characterized by the predicted rainfall amount being obviously higher and the rainy days being much longer than observation. In order to reduce the forecast biases of sub-seasonal rainfall forecast of ECMWF model, the rainy day-based correction (RDC) method is proposed in this study. Cross validation results indicate that RDC method can modify the number of rainy days forecast of ECMWF model with the SCC of rainy days increasing by 12.96% ~ 18.62%, and the RMSE decreasing by 56.49% ~ 63.78%. The problem of maximum continuous rainy days being too long in the model forecast can be also improved. Meanwhile, the spatial correlation coefficient (SCC) of rainfall amount forecast of the ECMWF model with the observation weakly increases by 0.61% ~ 1.56% and the root mean square error (RMSE) decreases by 3.50% ~ 7.60% after the RDC treatment. Therefore, RDC method presents a good performance on improving the sub-seasonal forecast of rainy days, and maximum continuous rainy days, which can be further applied in other models’ sub-seasonal forecast error correction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The ECMWF model forecast data can be achieved from European Centre for Medium-Range Weather Forecasts (http://apps.ecmwf.int/datasets/data/s2s). The observation data are provided by the National Meteorological Information Center of China (http://data.cma.cn).
References
Buizza R, Bidlot JR, Wedi N et al (2006) The ECMWF variable resolution ensemble prediction system (VarEPS). Meteorol Sect ECMWF Newsl 108:14–20. https://doi.org/10.21957/st10ye392d
Fan K, Liu Y, Chen H (2012) Improving the prediction of the east Asian summer monsoon: new approaches. Weather Forecast 27:1017–1030. https://doi.org/10.1175/waf-d-11-00092.1
Feng G, Zhao J, Zhi R et al (2013) Recent progress on the objective and quantifiable forecast of summer precipitation based on dynamical statistical method. J Appl Meteorol Sci 24:656–665
Gong Z, Hutin C, Feng G (2016) Methods for improving the prediction skill of summer precipitation over east Asia-west Pacific. Weather Forecast 31:1381–1392. https://doi.org/10.1175/waf-d-16-0007.1
Ha PT, Tan PV, Roderick VDL, Andreas HF (2022) The performance of ECMWF subseasonal forecasts to predict the rainy season. Weather Forecast 37:113–124. https://doi.org/10.1175/WAF-D-21-0144.1
He Z, Hsu P, Liu X, Wu T, Gao Y (2019) Factors limiting the forecast skill of the boreal summer intraseasonal oscillation in a subseasonal-to-seasonal model. Adv Atmos Sci 36:104–118
Hsu PC, Yang Y (2016) Contribution of atmospheric internal processes to the interannual variability of the South Asian summer monsoon. Int J Climatol 36:2917–2930. https://doi.org/10.1002/joc.4528
Hsu P, Li T, You L, Gao J, Ren H (2015) A spatial–temporal projection model for 10–30 day rainfall forecast in South China. Clim Dyn 44:1227–1244. https://doi.org/10.1007/s00382-014-2215-4
Huang J, Yi Y, Wang S (1993) An analogue-dynamical long-range numerical weather prediction system. Q J R Meteorol Soc 119:547–565
Jung T (2010) Systematic errors of the atmospheric circulation in the ECMWF forecasting system. Q J R Meteorol Soc 131:1045–1073. https://doi.org/10.1256/qj.04.93
Kang IS, Lee JY, Park CK (2004) Potential predictability of summer mean precipitation in a dynamical seasonal prediction system with systematic error correction. J Clim 17:834–844
Lang X, Wang H (2010) Improving extraseasonal summer rainfall prediction by merging information from GCMs and observations. Weather Forecast 25:1263–1274. https://doi.org/10.1175/2010waf2222342.1
Li W, Hsu P, He J, Zhu Z, Zhang W (2016) Extended-range forecast of spring rainfall in southern China based on the Madden–Julian oscillation. Meteorol Atmos Phys 128:331–345. https://doi.org/10.1007/s00703-015-0418-9
Li W, Chen J, Li L et al (2019a) Evaluation and bias correction of S2S precipitation for hydrological extremes. J Hydrometeorol 20:1887–1906. https://doi.org/10.1175/JHM-D-19-0042.1
Li X, Fan K, Yu E (2019b) Hindcast of extreme rainfall with high-resolution WRF: model ability and effect of physical schemes. Theoret Appl Climatol 139:639–658. https://doi.org/10.1007/s00704-019-02945-2
Liang P, Lin H (2017) Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system. Clim Dyn 50:1007–1022. https://doi.org/10.1007/s00382-017-3658-1
Liang P, Lin H, Ding Y (2018) Dominant modes of subseasonal variability of East Asian summertime surface air temperature and their predictions. J Clim 31:2729–27343. https://doi.org/10.1175/JCLI-D-17-0368.1
Lin H, Gagnon N, Baurgard S et al (2016) GEPS-based monthly prediction at the Canadian meteorological centre. Mon Weather Rev 144:4867–4883. https://doi.org/10.1175/MWR-D-16-0138.1
Lin H, Mo R, Vitart F, Stan C (2018) Eastern Canada flooding 2017 and its subseasonal predictions. Atmos Ocean 57:195–207. https://doi.org/10.1080/07055900.2018.1547679
Liu Y, Fan K (2013) A new statistical downscaling model for autumn precipitation in China. Int J Climatol 33:1321–1336. https://doi.org/10.1002/joc.3514
Liu X, Wu T, Yang S et al (2015) Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m). Adv Atmos Sci 32:1156–1172. https://doi.org/10.1007/s00376-015-4194-8
Liu L, Wang X, Feng G et al (2020) Variation of main rainy-season precipitation in eastern China and relevance to regional warming. Int J Climatol 41:1767–1783. https://doi.org/10.1002/joc.6929
Miyakoda K, Sirutis J (1990) Subgrid scale physics in 1-month forecasts. Part II: systematic error and blocking forecasts. Mon Weather Rev 118:1065–1081
Olaniyan E, Adefisan EA, Oni F et al (2018) Evaluation of the ECMWF sub-seasonal to seasonal precipitation forecasts during the peak of west Africa monsoon in Nigeria. Front Environ Sci 6:1–15. https://doi.org/10.3389/fenvs.2018.00004
Olaniyan E, Adefisan EA, Balogun AA, Lawal KA (2019) The influence of global climate drivers on monsoon onset variability in Nigeria using S2S models. Model Earth Syst Environ 5:1405–1428. https://doi.org/10.1007/s40808-019-00606-x
Phakula S, Landman WA, Engelbrecht CJ, Makgoale T (2020) Forecast skill of minimum and maximum temperatures on subseasonal-to-seasonal timescales over South Africa. Earth Space Sci 7:1–11. https://doi.org/10.1029/2019ea000697
Rozante JR, Moreira DS, Godoy RCM, Fernandes AA (2014) Multi-model ensemble: technique and validation. Geosci Model Dev 7:2333–2343. https://doi.org/10.5194/gmd-7-2333-2014
Tian XR, Zhao FJ, Shu LF, Miao QL, Wang MY (2014) Changes of climate and fire dynamic in China vegetation zone during 1961–2010. J Appl Ecol 25:3279–3286
Vigaud N, Robertson AW, Tippett MK, Acharya N (2017) Subseasonal predictability of boreal summer monsoon rainfall from ensemble forecasts. Front Environ Sci 5:1–19. https://doi.org/10.3389/fenvs.2017.00067
Wang QJ, Schepen A, Robertson DE (2012) Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging. J Clim 25:5524–5537. https://doi.org/10.1175/jcli-d-11-00386.1
Wang J, Yang J, Ren H et al (2021) Dynamical and machine learning hybrid seasonal prediction of summer rainfall in China. J Meteorol Res 35:583–593. https://doi.org/10.1007/s13351-021-0185-0
Wu J, Jin FF (2021) Improving the MJO forecast of S2S operation models by correcting their biases in linear dynamics. Geophys Res Lett 48:1–10. https://doi.org/10.1029/2020gl091930
Yang S, Wu R, Jian M et al (2021) Subseasonal to seasonal prediction of atmospheric circulation and rainfall over Southeast Asia. Climate change in Southeast Asia and surrounding areas. Springer Singapore, Singapore, pp 357–420
Zhou Y, Yang B, Chen H et al (2018) Effects of the Madden–Julian oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database. Clim Dyn 52:6671–6689. https://doi.org/10.1007/s00382-018-4538-z
Zhu C, Nakazawa T, Li J, Chen L (2003) The 30–60 day intraseasonal oscillation over the western North Pacific Ocean and its impacts on summer flooding in China during 1998. Geophys Res Lett. https://doi.org/10.1029/2003gl017817
Acknowledgements
This work was supported by the National Key Research and Development Program of China (2018YFA0606301), the National Natural Science Foundation of China Project (42075057, 42130610, and 42275050). The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Nanjing University of Information Science & Technology.
Funding
This work was supported by the National Key Research and Development Program of China (2018YFA0606301), the National Natural Science Foundation of China Project (42075057, 42275050 and 41875093).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, L., Bai, H., Feng, G. et al. Evaluation and correction of sub-seasonal dynamic model forecast of precipitation in eastern China. Clim Dyn 61, 4643–4659 (2023). https://doi.org/10.1007/s00382-023-06788-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-023-06788-6