Kazuki Nanko

This study conducted long-term observations using a volume scanning video disdrometer at five sites representative of the two contrasting climates in Japanese snow-covered areas to identify generalized patterns of solid precipitation particle size and velocity distributions (PSVDs). A novel framework combining the expectation-maximization (EM) algorithm and self-organizing maps successfully approximated complex distributions composed of multiple particle types through the superposition of generalized child distributions. This approach effectively eliminates the need for subjectively preconfiguring the number of child elements and mitigates the overfitting problem in standard EM algorithms. The child distributions were broadly classified into four groups: (S) a small and slow distribution serving as a fundamental component commonly present in all observed PSVDs; (A1) a large and slow distribution primarily corresponding to aggregated plate-type crystals; (A2) an intermediate distribution between A1 and G primarily corresponding to aggregated column-type crystals or their combinations with plate-type crystals; and (G) small and fast distributions corresponding to riming crystals. While the pattern consisting solely of S with low particle concentrations was the most frequent across all sites, superposition patterns of S+A1 and S+A2 (or solely A2) were the next most prevalent. These patterns were site-specific: S+A1 patterns predominated at sites facing the Sea of Japan, where precipitation is primarily driven by the winter monsoon, whereas S+A2 (or solely A2) patterns were more frequent at inland sites dominated by extratropical cyclones. As a generalizable framework, the findings of this study could potentially refine bulk microphysical schemes.

Leading-edge erosion of wind turbine blades caused by repeated raindrop impingement can significantly reduce power output and increase maintenance costs. This study develops a rain erosion atlas for Japan over 11 years from 2006 to 2016 based on the CRIEPI-RCM-Era2 dataset. The NREL 5 MW, DTU 10 MW, and IEA 15 MW wind turbines were employed to evaluate the incubation time (erosion onset time) of commercial polyurethane-based coating at the blade tip. Erosion progression was simulated using an empirical damage model that relates raindrop impingement and impact velocity to the incubation time. The rain erosion atlas reveals a clear correlation between wind turbine size and erosion risk: the NREL 5MW turbine shows an incubation time of 3–12 years, the DTU 10MW turbine 1–4 years, and the IEA 15MW turbine 0.5–2 years. Shorter incubation times are observed on the Pacific Ocean side, where annual precipitation is higher than on the Sea of Japan side. Additionally, the influence of coating degradation due to ultraviolet radiation was assessed using solar radiation data, revealing a further reduction in incubation time on the Pacific Ocean side. Finally, the potential of erosion-safe mode operation was examined, demonstrating its effectiveness in alleviating erosion progression.

Leading-edge erosion of wind turbine blades caused by repeated raindrop impingement can significantly reduce power output and increase maintenance costs. This study develops a rain erosion atlas for Japan over 11 years from 2006 to 2016 based on CRIEPI-RCM-Era2 dataset. The NREL 5 MW, DTU 10 MW, and IEA 15 MW wind turbines were employed to evaluate the incubation time (erosion onset time) of commercial polyurethane-based coating at blade tip. Erosion progression was simulated using an empirical damage model that relates raindrop impingement and impact velocity to the incubation time. The rain erosion atlas reveals a clear correlation between wind turbine size and erosion risk: the NREL 5MW turbine shows the incubation time of 3–12 years, the DTU 10MW turbine 1–4 years, and the IEA 15MW turbine 0.5–2 years. Shorter incubation times are observed on the Pacific Ocean side, where annual precipitation is higher than on the Sea of Japan side. Additionally, the influence of coating degradation due to ultraviolet radiation was assessed using solar radiation data, revealing a further reduction in incubation time on the Pacific Ocean side. Finally, the potential of erosion-safe mode operation was examined, demonstrating its effectiveness in alleviating erosion progression.

Throughfall is a significant majority of the total precipitation reaching the ground in forested areas. This study revealed biotic and abiotic factors influencing the throughfall generation process, with the throughfall partitioning into free throughfall, splash throughfall, and canopy drip created at foliar surface drip points (FSDPs) and occasional woody surface drip points (O‐WSDPs), utilizing machine learning. Using a large‐scale rainfall simulator, throughfall drops were simultaneously measured at 19 locations under a mix of deciduous and coniferous tree species in both foliated and unfoliated states. Random forest modeling showed that biotic factors, such as foliage amount, primarily affected the development and volume fraction of canopy drip in foliated trees. In contrast, for unfoliated trees, canopy drip volume fraction was mainly influenced by abiotic factors, including drop size and kinetic energy of open rainfall. The formation and volume fraction of splash throughfall were primarily influenced by abiotic factors for both foliated and unfoliated trees. From the comparison between the foliated and unfoliated states, the generation process of canopy drip was separately clarified between FSDPs and O‐WSDPs. More and larger canopy drip was generated by more foliage with a more wetted canopy with less fluctuation at the FSDPs, whereas a less wetted canopy and/or higher drop impact energy generated more and larger canopy drip at O‐WSDPs. This study underscores the importance of canopy structure and meteorological conditions in determining throughfall partitioning. The findings contribute to a nuanced understanding of rainwater redistribution in forest ecosystems.

Leaf surface wettability significantly influences rainfall interception and the overall water balance within forest ecosystems. However, the effects of biological factors, particularly fungi, on this critical property have not been extensively documented. In this study, we assessed the impact of Erysiphe castaneigena, a powdery mildew fungus, on the wettability of leaves from Japanese chestnut trees. We compared the wettability of infected and uninfected leaves by measuring the contact angles of water droplets on their surfaces. Our analysis revealed that leaves infected with the powdery mildew fungus displayed significantly higher hydrophobicity compared with uninfected leaves. This increased hydrophobicity correlated with the densification of the mycelium as the fungal coverage of the leaf surface expanded. These findings emphasize the substantial influence of phyllosphere microorganisms such as powdery mildew fungi on leaf wettability. Our results also enhance our understanding of the ecohydrological implications of microbial interactions on leaf surfaces, highlighting the need for further research into how these processes impact broader ecological and hydrological systems.