###### Abstract  
Automated and accurate radar dealiasing algorithms are very important for their assimilation into operational numerical weather forecasting models. A radar radial velocity dealiasing algorithm aimed at radar data assimilation is introduced and assessed using from several S-band and C-band radar observations under the severe weather conditions of hurricanes, typhoons, and deep continental convection in this paper. This dealiasing algorithm, named automated dealiasing for data assimilation (ADDA), is a further development of the dealiasing algorithm named the China radar network (CINRAD) improved dealiasing algorithm (CIDA), originally developed for China's CINRAD (China Next Generation Weather Radar) radar network. The improved scheme contains five modules employed to remove noisy data, select the suitable first radial, preserve the convective regions, execute multipass dealiasing in both azimuthal and radial directions and conduct the final local dealiasing with an error check. This new dealiasing algorithm was applied to two hurricane cases, two typhoon cases, and three intense-convection cases that were observed from the CINRAD of China, Taiwan's radar network, and NEXRAD (Next Generation Weather Radar) of the U.S. with a continuous period of more than 12 h for each case. The dealiasing results demonstrated that ADDA performed better than CIDA for all selected cases. This algorithm not only produced a high success rate for the S-band radar, but also a reasonable performance for the C-band radar. dealiasing; typhoon; radial velocity; data assimilation 2019 11 2457 2019 11.2457 10.3390/rs11202457 10.3390/rs11202457 10.  
and a powerful quality control scheme for radar observations. An existing challenge of data assimilation with radial velocity observations is velocity de-aliasing. The maximum radial velocity or Nyquist radial velocity [7] of each radar observation is related to the radar pulse repetition frequency (PRF) and transmitted radar wavelength where:  
\[v_{\text{max}}=(\text{PRF})\lambda/4 \tag{1}\]  
The interval \([-v_{\text{max}},v_{\text{max}}]\) is an unambiguous velocity interval. True radial velocity observations exceeding the spacing of \([-v_{\text{max}},v_{\text{max}}]\) are aliased and will show between \(\pm v_{\text{max}}\). The relation between the observed radial velocity \(v_{0}\) and true radial velocity \(v_{\text{T}}\) is:  
\[v_{\text{T}}=v_{\text{o}}+2\text{n}*v_{\text{max}}\text{n}=0,\pm 1,\pm 2,\cdots \tag{2}\]  
By choosing a correct n, the true velocity \(v_{\text{T}}\) can be recovered from the original radar radial velocity \(v_{0}\) from an accurate dealiasing algorithm. The existing dealiasing algorithms include two kinds of general techniques, known as the reference wind check scheme and continuity check scheme, and are meant to correct the aliased velocities. Accurate and robust radial velocity dealiasing is still a difficult challenge although some success has been demonstrated. In particular, it is essential to have a radial velocity dealiasing algorithm that is highly reliable and fully automated when the observed radar radial velocities are assimilated into a numerical weather forecasting model. Forecasting model analysis errors of initial conditions which then pollute the consecutive forecasts can be caused by only a small percentage of erroneously or mistakenly dealiased radial velocities. When a data assimilation system is cycled, the problem can become more serious because of the error propagation with time. The reference wind check dealiasing scheme applies a referred wind velocity field to dealiase the radar radial velocity observations in each grid point [8; 9]. [PERSON] and [PERSON] [8] developed a reference wind check dealiasing scheme in which the reference velocity was frequently updated through the data assimilation of radar observations, creating an \"online\" two-way interactive data assimilation/velocity dealiasing procedure. The dealiasing process was thus embedded in the cycled radar data assimilation system.