Advances in Atmospheric Sciences

, Volume 30, Issue 1, pp 15–28

A regional ensemble forecast system for stratiform precipitation events in the Northern China Region. Part II: Seasonal evaluation for summer 2010

  • Jiangshan Zhu (朱江山)
  • Fanyou Kong (孔凡铀)
  • Hengchi Lei (雷恒池)
Article

DOI: 10.1007/s00376-012-1043-x

Cite this article as:
Zhu, J., Kong, F. & Lei, H. Adv. Atmos. Sci. (2013) 30: 15. doi:10.1007/s00376-012-1043-x

Abstract

In this study, the Institute of Atmospheric Physics, Chinese Academy of Sciences — regional ensemble forecast system (IAP-REFS) described in Part I was further validated through a 65-day experiment using the summer season of 2010. The verification results show that IAP-REFS is skillful for quantitative precipitation forecasts (QPF) and probabilistic QPF, but it has a systematic bias in forecasting near-surface variables. Applying a 7-day running mean bias correction to the forecasts of near-surface variables remarkably improved the reliability of the forecasts. In this study, the perturbation extraction and inflation method (proposed with the single case study in Part I) was further applied to the full season with different inflation factors. This method increased the ensemble spread and improved the accuracy of forecasts of precipitation and near-surface variables. The seasonal mean profiles of the IAP-REFS ensemble indicate good spread among ensemble members and some model biases at certain vertical levels.

Key words

short-range ensemble forecast rain enhancement operation probabilistic forecast 

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jiangshan Zhu (朱江山)
    • 1
  • Fanyou Kong (孔凡铀)
    • 2
  • Hengchi Lei (雷恒池)
    • 1
  1. 1.Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Center for Analysis and Prediction of StormsUniversity of OklahomaNormanUSA

Personalised recommendations