Abstract
To enhance the forecast accuracy of PM2.5 concentration in China, we developed a multi-model ensemble forecast system for next 72 h based on CUACE, BREMPS, RAEMS, and an improved CMAQ model. For each site and forecast time, mean ensemble, weighted ensemble, MLR ensemble, ANN ensemble, and a final best ensemble based on real-time evaluation were established. Compared with single models, mean and weighted ensembles only slightly reduced the biases between forecasted and observed PM2.5 concentrations in China during 2015–2016. But MLR and ANN ensembles largely improved the forecast results in most sites of China with NMB values reduced to ±5% and ±10%, respectively. Compared with MLR and ANN ensembles, best ensemble showed stronger comprehensive forecast capability in major pollution regions, with similar NMB and RMSE values but higher R values of 0.6–0.9. In the heavy haze process occurred in south of Jing-Jin-Ji region from February 25 to March 4, 2018, the NMB and R values between best ensemble forecasted and observed PM2.5 concentrations in 3 representative cities varied from –4% to –26% and from 0.49 to 0.77, respectively. These indicate that as the final output of multi-model ensemble system, best ensemble performs better than single models and can provide a strong objective reference to forecaster.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, K., Dickinson, R., Liang, S.: Clear sky visibility has decreased over land globally from 1973 to 2007. Science 323(5920), 1468–1470 (2009). https://doi.org/10.1126/science.1167549
Lelieveld, J., Evans, J., Fnais, M., et al.: The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525(7569), 367–371 (2015)
Intergovernmental Panel on Climate Change (IPCC), Climate Change, The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge (2017)
Savage, N., Agnew, P., Davis, L., et al.: Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation. Geosci. Model Dev. 6(2), 353–372 (2013). https://doi.org/10.5194/gmd-6-353-2013
Lee, P., McQueen, J., Stajner, I., et al.: NAQFC developmental forecast guidance for fine particulate matter (PM2.5). Weather Forecast. 32(1), 343–360 (2017). https://doi.org/10.1175/waf-d-15-0163.1
Zhao, X., Xu, J., Zhang, Z., et al.: Beijing regional environmental meteorology prediction system and its performance test of PM2.5 concentration. Q. J. Appl. Meteorol. 27(2), 160–172 (2016). https://doi.org/10.11898/1001-7313. (In Chinese)
Zhou, G., Xu, J., Xie, Y., et al.: Numerical air quality forecasting over eastern China: an operational application of WRF-Chem. Atmos. Environ. 153, 94–108 (2017). https://doi.org/10.1016/j.atmosenv.2017.01.020
Deng, T., Wu, D., Deng, X., et al.: Numerical forecast system of air quality photochemical smog over pearl river delta region. Environ. Sci. Technol. 36(4), 62–68 (2014). (In Chinese)
Huang, S., Tang, X., Xu, W., et al.: Application of ensemble forecast and linear regression method in improving PM10 forecast over Beijing area. Acta Sci. Circum. 35(1), 56–64 (2015). (In Chinese)
Liu, D., Li, L.: Application study of comprehensive forecasting model based on entropy weighting method on trend of PM2.5 concentration in Guangzhou, China. Int. J. Environ. Res. Public Health 12, 7085–7099 (2015). https://doi.org/10.3390/ijerph120607085
Zhang, W., Wang, Z., An, J., et al.: Update the ensemble air quality modeling system with BP model during Beijing olympics. Climatic Environ. Res. 15(5), 595–601 (2010). (In Chinese)
Zhang, H., Zhang, T., Li, T., et al.: Forecast of air quality pollutants’ concentrations based on BP neural network multi-model ensemble method. China Environ. Sci. 38(4), 1242–1256 (2018). (In Chinese)
Acknowledgments
This work is supported by “National Key Research Program of China” (Grant No. 2016YFC0203301), “Research Program of Key Technologies in Meteorological Forecasting Business of China Meteorological Administration” (Grant No. YBGJXM2018-7A), and “Youth Fund of National Meteorological Centre” (Grant No. Q201808).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, T. et al. (2019). Multi-model Ensemble Forecast System for Surface-Layer PM2.5 Concentration in China. In: Sun, S., Fu, M., Xu, L. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2018. Lecture Notes in Electrical Engineering, vol 550. Springer, Singapore. https://doi.org/10.1007/978-981-13-7123-3_54
Download citation
DOI: https://doi.org/10.1007/978-981-13-7123-3_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7122-6
Online ISBN: 978-981-13-7123-3
eBook Packages: EngineeringEngineering (R0)