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

Yin, Zhicong; Wang, Huijun

doi:https://doi.org/10.5194/acp-16-14843-2016

Articles | Volume 16, issue 23

https://doi.org/10.5194/acp-16-14843-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/acp-16-14843-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 23

Research article

|

30 Nov 2016

Research article |

| 30 Nov 2016

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

Zhicong Yin and Huijun Wang

Data sets

China ground observation data sets CMA http://data.cma.cn/

CPC Soil Moisture data sets CPC http://www.esrl.noaa.gov/psd/data/gridded/data.cpcsoil.html

CPC Daily Antarctic Oscillation Index CPC http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/aao/aao_index.html

Hadley Centre Sea Ice and Sea Surface Temperature data sets HadISST http://www.metoffice.gov.uk/hadobs/hadisst/

NCEP/NCAR Reanalysis data sets NCEP/NCAR http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

NOAA Extended Reconstructed Sea Surface Temperature (SST) V4 data sets NOAA http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v4.html

Short summary

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.