The implementation of the ice-phase microphysical process into a four-dimensional Variational Doppler Radar Analysis System (VDRAS) and its impact on parameter retrieval and quantitative precipitation nowcasting

Shao Fan Chang, Yu Chieng Liou, Juanzhen Sun, Sheng Lun Tai

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

The microphysical process of a cloud-scale model used by a four-dimensional Variational Doppler Radar Analysis System (VDRAS) is extended from its original warm rain parameterization scheme to a cold rain process containing ice and snow. The development of the adjoint equations for the additional control variables related to ice physics is accomplished by utilizing the existing four-dimensional variational (4DVar) minimization framework employed by VDRAS. Experiments are conducted to examine the accuracy of the new 4DVar system with the ice physics scheme implemented and to explore the impact of the ice-phase process on numerical simulations, parameter retrievals, and the model's quantitative precipitation nowcasting (QPN) capability. It is shown that the ice-phase microphysical process can significantly alter the kinematic and thermodynamic structure of deep convection and provide a better description of the contents of the hydrometeors. During the 4DVar minimization, using the VDRAS-predicted freezing level after the previous assimilation cycle to replace the true but unknown 0°C line is found to be a feasible approach for separating the rain and snow and, at the same time, allowing the 4DVar minimization algorithm to converge to an optimal solution. A real case study from intensive observation period 8 of the 2008 Southwest Monsoon Experiment shows that, with the added ice-phase process, VDRAS is more capable of capturing the actual evolution of the reflectivity field than the original scheme. The model's QPN skill is also improved significantly. Thus, the benefits of adding the ice-phase process into a 4DVar radar data assimilation system on the convective-scale weather analysis and forecast are demonstrated.

Original languageEnglish
Pages (from-to)1015-1038
Number of pages24
JournalJournal of the Atmospheric Sciences
Volume73
Issue number3
DOIs
StatePublished - 1 Mar 2016

Keywords

  • Data assimilation
  • Forecasting
  • Models and modeling
  • Nowcasting
  • Numerical weather prediction/forecasting
  • Observational techniques and algorithms
  • Radars/radar observations

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