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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 23
Atmos. Chem. Phys., 16, 14843-14852, 2016
https://doi.org/10.5194/acp-16-14843-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 16, 14843-14852, 2016
https://doi.org/10.5194/acp-16-14843-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 Nov 2016

Research article | 30 Nov 2016

Seasonal prediction of winter haze days in the north central North China Plain

Zhicong Yin1,2 and Huijun Wang1,2,3 Zhicong Yin and Huijun Wang
  • 1Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, China
  • 2Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 3Climate Change Research Center, Chinese Academy of Sciences, Beijing, China

Abstract. Recently, the winter (December–February) haze pollution over the north central North China Plain (NCP) has become severe. By treating the year-to-year increment as the predictand, two new statistical schemes were established using the multiple linear regression (MLR) and the generalized additive model (GAM). By analyzing the associated increment of atmospheric circulation, seven leading predictors were selected to predict the upcoming winter haze days over the NCP (WHDNCP). After cross validation, the root mean square error and explained variance of the MLR (GAM) prediction model was 3.39 (3.38) and 53% (54%), respectively. For the final predicted WHDNCP, both of these models could capture the interannual and interdecadal trends and the extremums successfully. Independent prediction tests for 2014 and 2015 also confirmed the good predictive skill of the new schemes. The predicted bias of the MLR (GAM) prediction model in 2014 and 2015 was 0.09 (−0.07) and −3.33 (−1.01), respectively. Compared to the MLR model, the GAM model had a higher predictive skill in reproducing the rapid and continuous increase of WHDNCP after 2010.

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Recently, the winter haze pollution over the north central North China Plain has become severe. By treating the year-to-year increment as the predictand, two new statistical schemes were established using the multiple linear regression and the generalized additive model approaches. After cross validation, both of these models could capture the interannual and interdecadal trends and the extremums successfully. Independent tests for 2014 and 2015 also confirmed the good predictive skill.
Recently, the winter haze pollution over the north central North China Plain has become severe....
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