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Multi-model Ensemble Forecast System for Surface-Layer PM2.5 Concentration in China

  • Tianhang Zhang
  • Hengde ZhangEmail author
  • Bihui Zhang
  • Xiaoqin Rao
  • Linchang An
  • Mengyao Lv
  • Ran Xu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

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.

Keywords

Multi-model ensemble Forecast PM2.5 concentrations 

Notes

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).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Tianhang Zhang
    • 1
  • Hengde Zhang
    • 1
    Email author
  • Bihui Zhang
    • 1
  • Xiaoqin Rao
    • 1
  • Linchang An
    • 1
  • Mengyao Lv
    • 1
  • Ran Xu
    • 1
  1. 1.Nation Meteorological Center of China Meteorological AdministrationBeijingChina

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