The dual-polarization (dual-pol) Doppler radar receives and transmits horizontally and vertically polarized power returns simultaneously. As a result, the measurements from dual-pol radar have been shown to provide more accurate estimates of cloud and precipitation particles compared to traditional Doppler radar. In this study, the horizontal reflectivity (ZH), differential reflectivity (ZDR), specific differential phase (KDP), and radial velocity (VR) collected by the C-band Advanced Radar for Meteorological and Operational Research (ARMOR) are assimilated to improve the initial conditions for two convective storms. A warm rain radar data assimilation scheme is constructed to assimilate ZH, ZDR, andKDP data using the three-dimensional variational data assimilation (3DVAR) system in the advanced research version of the Weather Research and Forecasting (WRF) model. The main goals of this study are to first demonstrate and compare the benefit of the dual-pol radar data assimilation in the initialization of convective storms in the WRF model, and second to test hypotheses on how these dual-pol radar fields may be best incorporated into a 3DVAR system.
The assimilation of dual-pol radar data may be a potential way to improve the performance of radar data assimilation. However, not much effort has been given to dual-pol radar data assimilation in past studies, partly due to data unavailability and the special requirements for data processing. An early study by Wu et al.  attempted to indirectly assimilate ZDR data with a 4DVAR system into a cloud model. Rain and ice mixing ratios derived from ZH and ZDR information were assimilated for an isolated thunderstorm. The results showed that a large forecast error occurred due to either the deficiencies in the simple microphysics scheme used, or the inability of their cloud model to simulate the nonlinear processes in the actual atmospheric systems. This preliminary effort exemplified the difficulty in the assimilation of dual-pol radar observations for real case studies. An observing system simulation experiment (OSSE) was conducted by Jung et al.  to directly assimilate the model-simulated dual-pol variables with an EnKF. Using a sophisticated microphysical scheme, they successfully assimilated ZDR, ZDP, and KDP data into the Advanced Regional Prediction System model. With the OSSE, Jung et al.  demonstrated the significant improvements that the dual-pol variables provided in vertical velocity, water vapor, and rainwater fields.
The above studies revealed both the benefits and challenges in dual-pol radar data assimilation, which motivated our recent study Li and Mecikalski [2010, LM10 hereafter]. To our knowledge, LM10 is among the very few studies that successfully assimilated dual-pol radar variables for real case studies. In LM10, a radar forward operator was built in the WRF 3DVAR system to directly assimilate ZH, ZDR, and VR data collected from the Advanced Radar for Meteorological and Operational Research (ARMOR) in north-central Alabama. The result showed significant positive impacts of assimilating ZH, ZDR, and VR measurement for a mesoscale convective system (MCS) on 15 March 2008. Furthermore, the assimilation of ZDR data provided additional improvement in storm initialization, which subsequently leads to better short-term precipitation forecast/QPF. This result encouraged us to take further steps in studying the assimilation of the dual-pol radar variables. Therefore, in the present paper, a comparison is done to evaluate the impacts of ZH and ZDR data assimilation with KDP and ZDR data assimilation. The main goals of this study are to (1) seek further improvement in storm initialization beyond LM10, with assimilation of additional dual-pol radar variables; and (2) understand further how the dual-pol variables can be used more efficiently in NWP. The findings in this paper should be especially useful for both research purposes and operational implementation when the entire National Weather Service (NWS) WSR-88D “NEXRAD” radar network is upgraded to include the dual-pol characteristics (beginning in 2011).
Research to date has examined two case studies to demonstrate and compare the performances of data assimilation of several dual-pol Doppler radar variables, using the 3DVAR data assimilation technique with a regional WRF mesoscale model, for the short-term forecast of convection and the associated precipitation. Using single dual-pol Doppler radar observations, with cycled data assimilation procedure, four dual-pol variables (ZH, ZDR, VR and KDP) are successfully assimilated into the initial condition of the WRF model. Two convective storms are examined in this study: One MCS in the afternoon of 15 March 2008 is first tested, which indicates that the KDP and ZDR data assimilation provide the best storm initialization. Second, an isolated summer thunderstorm over northern Alabama occurring on 23 June 2008 is studied following similar data assimilation strategies. Our goals are to compare and quantify the benefits associated with the assimilation of four dual-pol radar variables (i.e. ZH, ZDR, VR, and KDP), and to show how the dual-polarimetric information may be best used in a regional mesoscale model for a meso-g scale storm.
In summary, the results from the case studies indicate that:
- The ZH, ZDR, KDP, and VR data are appropriately assimilated into the high-resolution WRF model. Our dual-pol radar data assimilation scheme effectively improves the mesoscale structure in wind (u, v, and w), thermodynamic (qv and q), and microphysics fields (qr, and qc) in the model initial condition, hence resulting in more accurate short-term forecasts for real case events.
- For warm-rain radar data assimilation, the result indicates that KDP and ZDR provide larger improvements in the storm initialization compared to ZH and ZDR assimilation, due to the more accurate observational operator implemented to estimate liquid water content.
- For convective events like weakly organized convective storms, their horizontal scales are usually quite small, especially at the early stage (on the order of 10 km). The initial condition contains substantial errors on the meso-g scale characteristics of these events. The predictability of such storms in NWP models is very limited. Radar data assimilation can produce significant improvements in forecasting the movement, structure and evolution of such events, as shown here.
- The beneficial impact from the radar data assimilation does not remain long in the model simulation; within about 2 hours, the improvement decreases to about 1/2 of the original influence.
In this study, KDP and ZDR data assimilation is superior to ZH and ZDR data assimilation in the initialization of the simulated convective storms. In this light, more case studies are needed to confirm the conclusions of this paper.
Several issues need to be considered prior to performing additional assimilation experiments. Specifically, KDP is not a direct measurement from the dual-pol radar, and hence it carries several shortcomings. Differential propagation measurements have accuracies of a few degrees, and they are filtered in range before the computation of KDP. This filtering process may directly cause bias in KDP values [Gorgucci et al., 1999]. Side-lobe contamination may also cause bias in estimates of KDP [Sachidanda and Zrnić, 1987]. KDP is found being vulnerable to random and artifact errors [Sachidananda and Zrnić, 1987; Smyth et al., 1999; Brandes et al., 2001; Ryzhkov and Zrnić, 1996]. These data artifacts indicate that careful examination and quality control procedures should be applied before the KDP data being assimilated for more real case studies. To reduce the influence of artifacts errors and take full advantage of the dual-pol variables, future studies should focus on constructing more sophisticated radar forward operators in which multiple dual-pol variables are employed to describe different properties of hydrometeors and microphysical processes in clouds (a focus of our ongoing research).
Limited data coverage is the most common issue when using single radar observations. Fortunately, this issue will be solved in the coming years, as the entire NEXRAD radar network will be upgraded with dual-pol capabilities (after the beta tests for several radar sites) starting in January 2011. With the NEXRAD upgrade, the 3D dual-pol products and Level II variables will be constructed, and the dual-pol radar data assimilation can be examined and employed for both research and operational usage. Dual-pol radar data assimilation research is at its early stages. As with most existing radar data assimilation systems [e.g. Xiao et al., 2007; Chung et al., 2009; Sugimoto et al., 2009], this present study also adopts the warm rain microphysical processes. Therefore, the hydrometeor fields in the model only contain rain and cloud only. The lack of robust ice-phased microphysical processes certainly limits our ability for a more accurate prediction of the convective storms. With the successes of radar data assimilation in this study and LM10, we are increasingly curious that if an ice-phased microphysical forward model is developed for dual-pol observations, how much more information can be retrieved and incorporated into the model, which dual-pol variables would be the most informative ones for data assimilation, and to what extent can dual-pol variables be used to improve the NWP modeling of convective storms. The construction of such a radar data assimilation package with more sophisticated, ice-phased microphysical processes is currently a major research focus.