Advances in Atmospheric Sciences

, Volume 31, Issue 1, pp 230–240

A methodological study on using weather research and forecasting (WRF) model outputs to drive a one-dimensional cloud model

Article

DOI: 10.1007/s00376-013-2257-2

Cite this article as:
Jin, L., Kong, F., Lei, H. et al. Adv. Atmos. Sci. (2014) 31: 230. doi:10.1007/s00376-013-2257-2

Abstract

A new method for driving a One-Dimensional Stratiform Cold (1DSC) cloud model with Weather Research and Forecasting (WRF) model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4–5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor profiles extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities (including downdraft) derived from the WRF model, the new WRF-driven 1DSC modeling system (WRF-1DSC) was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to reproduce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional shortrange forecasting system. This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.

Key words

cloud-seeding model Weather Research and Forecasting (WRF) model rain enhancement 

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ling Jin
    • 1
  • Fanyou Kong
    • 2
  • Hengchi Lei
    • 1
  • Zhaoxia Hu
    • 1
  1. 1.Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Center for Analysis and Prediction of StormsUniversity of OklahomaNormanUSA