Numerical weather forecast models have biases caused by insufficient grid resolution and incomplete physical processes, especially near the land surface. Therefore, the Japan Meteorological Agency (JMA) has been operationally post-processing the forecast model outputs to correct biases. The operational post-processing method uses a Kalman filter (KF) algorithm for surface temperature prediction. Recent reports have shown that deep convolutional neural networks (CNNs) outperform the JMA operational method in correcting temperature forecast biases. This study combined the CNN-based bias correction scheme with the JMA operational KF algorithm. We expected that the combination of CNNs and a KF would improve the post-processing performance, as the CNNs modify large horizontal structures, and then, the KF corrects minor spatiotemporal deviations. As expected, we confirmed that the combination outperformed both CNNs and the KF alone. This study demonstrated the advantages of the new method in correcting coastal fronts, heat waves, and radiative cooling biases.

 In this study, we investigated the feasibility of rain enhancement by cloud seeding over a target area (Sameura Dam catchment area, Kochi Prefecture) in early summer. The effects of salt micro-powder (MP) and hygroscopic flare (HF) seeding on the initial cloud microphysical structures were investigated using a detailed bin microphysics parcel model with background atmospheric aerosol data collected from ground-based observations conducted on the windward side of the target area and seeding aerosol data collected from the coordinated flights of seeding helicopter and in-situ measurement aircraft. Numerical seeding experiments showed that the size distributions of cloud droplets were broadened, and the onset of raindrop formation was accelerated by MP and HF seeding, although MP seeding showed more notable seeding effects than did HF seeding. MP seeding increased the mean droplet size and decreased the total number concentration of cloud droplets, whereas HF seeding had the opposite effect. Based on the relationship between the increase/decrease ratio of the cloud droplet number concentration and increase/decrease ratio of the surface precipitation by hygroscopic seeding obtained in previous studies, MP seeding had a positive seeding effect, whereas HF seeding had a negative effect. In the numerical seeding experiments, a range of variations in the number concentration and hygroscopicity of background aerosol particles, updraft velocity near the cloud base, the amount of seeding material applied, and the change in the physicochemical properties of the seeding aerosols to improve seeding effects were also considered. However, the outline of the results described above remained unchanged. These results demonstrate the possibility of increasing surface precipitation by MP seeding over the catchment. However, seeding a large amount of MP (NaCl) is necessary to enhance precipitation substantially. Simultaneously, considering the environmental impact is essential, as shown in our study.

 Background (BG) aerosol particles (APs) acting as cloud condensation nuclei (CCN) and/or ice nucleating particles (INPs) influence short-range precipitation forecasts and climate change projections by modulating cloud and precipitation microphysical structures and influence the effects of cloud seeding on precipitation enhancement. However, data on the CCN and INP capabilities of BG APs are limited in terms of geographical locations and time. To investigate the characteristics of BG APs, we conducted ground-based measurements of BG AP and CCN in Kochi City, Japan, in June 2010. Comparisons with previously published data on AP and CCN concentrations in East Asia showed that the mean concentrations of APs and CCN at the observation site were considerably affected by air pollution. Our findings also suggest that during the observation period, even air masses from the Pacific Ocean were considerably affected by air pollution in East Asia, including Japan. Moreover, aircraft-measured AP and CCN concentrations in the boundary layer were comparable to those measured concurrently at the surface observation site, although the horizontal positions of the ground- and aircraft-based measurements were not identical; the size distributions of the APs were similar. These results suggest that ground-based measurements represent APs and CCN in the boundary layer, where the air is ingested by clouds. Numerical simulations with a detailed bin microphysics parcel model showed that cloud droplet number concentrations, based on meteorological conditions and aerosol characteristics expected near the observation site environments, would range from 500 to 1,500 droplets cm⁻³. These concentrations were consistent with aircraft measurements. These values are higher than the threshold concentration of ∼500 droplets cm⁻³ in clouds suitable for hygroscopic seeding, as suggested by previous studies. Therefore, this area is considered to be suitable for rain enhancement by hygroscopic seeding.