Noise Filtering Property of Incremental Analysis Update Method in Convective-Scale Ultra-Rapid Updating 3DVAR Analysis Cycle

This study reports preliminary results of noise filtering property of incremental analysis updates (IAU) method in convective-scale ultra-rapid updating 3DVAR analysis cycle, which is suitable for real-time processing. In this study, 3DVAR with IAU (hereafter, 3DVAR+IAU) was calculated for a case of a tornadic storm using 1-km horizontal grid spacing with updates every 10 min for 6 h. Radial velocity observations by eight X-band multi-parameter Doppler radars and three Doppler lidars around the Tokyo Metropolitan area, Japan, were used for the analysis. In this study, three types of analyses were performed between 1800 to 2400 LST (local standard time: UTC + 9 h) 6 September 2015. Analysis increments were multiplied by a damping coefficient (0.2) to avoid excessive modification. To investigate noise filtering property of IAU method, three sensitivity experiments for IAU time window widths (hereafter IAUw; 150, 300, and 600 s) were performed. The noise filtering effect of IAU method was evaluated by the monitoring of area-averaged surface pressure tendency (|ΔP_s/Δt|). As a result, the longer IAUw set, the smaller |ΔP_s/Δt| became. After the model spin up, |ΔPs/Δt|~50 Pa/s for analysis by 3DVAR, while |ΔP_s/Δt|~40, 12, and 3 Pa/s for analysis by 3DVAR+IAU with 150, 300, and 600 s IAUw, respectively. To valid the accuracy of these experiments, analysis surface wind speeds were compared with 36 points surface observation. The root mean square error (RMSE) of 6 h analysis surface wind without 3DVAR, with 3DVAR, and with 3DVAR+IAU (for all IAUw) was 3.07, 2.65, and 2.56 m/s, respectively. In this case, the analysis by 3DVAR+IAU showed the best accuracy, and the accuracy was not sensitive to the IAUw. A real-time analysis needs not only low-noise and high-accuracy analysis but also low computational cost. The longer IAUw well filtered noise but computational cost of the longer IAUw become higher than that of shorter IAUw. This study provides useful information on the most suitable ultra-rapid cycling data assimilation method for the real-time analysis of surface wind fields.