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

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.

Initiation and Development of a Squall Line Crossing Hangzhou Bay

“…This 3D objective analysis is then used as the first guess for the assimilation of radar volumetric observations of radial velocity and reflectivity using the 4DVar method in VDRAS. The cloud-model constraining the 4DVar has a microphysical scheme with four categories of hydrometeor (Chang et al, 2016). ZHANG ET AL.…”

“…VDRAS can retrieve mesoscale dynamical and thermodynamic fields with frequent updating of less than 20 min by assimilating radial velocity and reflectivity data from radar networks and surface observations from dense mesonets. It has been used for convective‐scale data assimilation research (Chang et al., 2016; Sun & Crook, 1994; Tai et al., 2017) and for severe convective events studies (Chang et al., 2014; Friedrich et al., 2016; Gochis et al., 2014; Sun & Zhang, 2008). The 3‐dimensional gridded high‐resolution and frequency updated analyses of meteorological fields created by VDRAS can provide valuable information for this study in which meso and convective‐scale dynamical processes play crucial roles.…”

Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case

“…Through the cloud model and the 4DVAR scheme, VDRAS can retrieve the unobserved (by radar) temperature, wind and other microphysical variables by assimilating reflectivity and radial velocity observations from a single or multiple radar networks. Readers are referred to Crook (1997, 2001) for the system's design and cloud model, Chang et al (2015) for the recent ad-dition of an ice physics scheme, and Sun and Crook (2001), Crook and Sun (2004), and Sun et al (2010) for an evaluation of the system's operational performance.…”

Improving Radar Echo Lagrangian Extrapolation Nowcasting by Blending Numerical Model Wind Information: Statistical Performance of 16 Typhoon Cases

“…However, model forecasts face spinup or spindown issues, whereby a certain amount of time (usually 1-3 h) is required after warm-start initialization to reach a stable model state (Shrestha et al 2013;Chung et al 2013;Jacques et al 2017). On the other hand, by assimilating radar observations in different data assimilation systems, as previous studies have shown, the improvement of QPF sometimes could last up to 6 h (Kain et al 2010;Sun et al 2014), and sometimes the impact may remain confined to around 1-2 h (Aksoy et al 2010;Chang et al 2016;Chang et al 2014;Chung et al 2009). Therefore, to forecast precipitation of severe weather in the very short-term effectively, radar echo extrapolation remains a powerful and highly relevant method.…”