Improvement of Two-Hour-Ahead QPF Using Blending Technique with Spatial Maximum Filter for Tolerating Forecast Displacement Errors and Water Vapor Lidar Assimilation

Disasters caused by heavy rainfall associated with quasi-stationary line-shaped mesoscale convective systems (MCSs) frequently occur in Japan. Thus, highly accurate quantitative precipitation forecast (QPF) information that contributes to decision-making by municipalities to issue evacuation orders is necessary. To this end, we developed a blending forecasting system (BFS) for predicting heavy rainfall associated with MCSs. The BFS blends 1-h observed rainfall and forecasts of extrapolation-based nowcasting (EXT) in the first hour and numerical weather prediction (NWP) in the second hour, predicting 3-h accumulated rainfall (P3h) and its return period (RP) of up to 2 h ahead with a higher horizontal resolution (1 km) and higher-frequency updates (every 10 min) compared to the current operational systems. A blending technique with a spatial maximum filter for tolerating forecast displacement errors (BLEDE) was applied to the predicted rainfall of EXT and NWP. To improve the accuracy of the NWP, vertical profiles of water vapor obtained with two water vapor lidars (WVLs) were assimilated into the NWP. This combination predicted rare heavy rainfall with an RP of more than 10 years in the same city where flooding occurred for a heavy rainfall event associated with quasi-stationary line-shaped MCSs in southern Kyushu on 10 July 2021. The BFS yielded such forecast information 40 min earlier than the existing warning information, indicating the potential for providing a longer lead time for evacuation. The improvement in forecast accuracy was due to both BLEDE and WVL data assimilation (WVL-DA); however, the contribution of BLEDE was more than five times that of WVL-DA in terms of predicting the P3h for the threshold of 80 mm. Additionally, the sensitivity of the predicted rainfall to the background error covariance matrix in WVL-DA is also discussed.