Hydrometeorological Disasters Engineering
To safeguard the smiles of future generations from torrential rain disasters, we are dedicated to elucidating heavy rainfall mechanisms and advancing disaster prevention technologies through the lens of hydrometeorology. Our research pursues a multi-layered vision of the 'Future of Extreme Rainfall': predicting the immediate future through short-term forecasting, understanding the future under climate change, and shaping a future through weather intervention and control.
Academic Staff
Kosei YAMAGUCHI
Professor (Disaster Prevention Research Institute)
Research Topics
I am working on developing hydrometeorological strategies for heavy rainfall disaster prevention from numerical models and observations. My goal is to establish a conviviality between heavy rainfall, human society, and monitoring, forecasting, and control technologies to protect the smiles of future generations.
Contacts
E-528D, Disaster Prevention Research Institute, Uji Campus
TEL: +81-774-38-4262
FAX: +81-774-38-4265
E-mail: yamaguchi.kosei.5r
kyoto-u.ac.jp
Yukari (OSAKADA) NAKA
Assistant Professor (Disaster Prevention Research Institute)
Research Topics
My research is on the mechanisms of extreme rainfall and its future changes due to global warming based on climate/ meteorological modelling and observation. The linkage of meteorology, climatology, and engineering is an important key of my research to achieve the disaster prevention and mitigation.
Contacts
E-531D, Disaster Prevention Research Institute, Uji Campus
TEL: +81-774-38-4266
FAX: +81-774-38-4266
E-mail: osakadahmd.dpri.kyoto-u.ac.jp
Research Topics
Elucidating the Mechanisms of Heavy Rainfall
"Observing" heavy rainfall is the foundational pillar of our laboratory’s work. To clarify the initiation and development processes of cumulonimbus clouds that cause torrential rain, we conduct large-scale field observations using sensor technologies such as weather radar and particle sondes. Our approach integrates kinematic analysis focusing on airflow vortices with cloud microphysical analysis focusing on precipitation particles. Through this synergy, we investigate the fundamental mechanisms of how the "seeds" and "embryos" of heavy rain are conceived and nurtured. Furthermore, we have developed a proprietary Urban Weather LES (Large Eddy Simulation) model to reproduce complex airflow fields, revealing how small-scale phenomena evolve into large-scale systems through the merging of vortices. A key strength of our research is the integration of high-precision observations with numerical modeling to achieve insights that neither could reach alone. In recent years, we have also focused on the interplay between stochasticity (chance) and determinism (necessity) in the formation of heavy rain. From the perspective of chaotic dynamics, we are conducting fundamental research to identify the "tipping points" where phenomena transition from a stochastic state to a deterministic one. Through these multifaceted approaches, we strive to reach the very essence of heavy rainfall.
Large-scale field observations capturing the initiation and development of cumulonimbus clouds
Enhancing Precision and Practical Application of Precipitation and Flood Forecasting
We are dedicated to improving the accuracy of real-time precipitation and flood forecasting—an enduring challenge in disaster prevention. In short-term precipitation forecasting, the precision of initial conditions significantly dictates the final results; therefore, we are advancing research into data assimilation to integrate observational data into numerical models. Specifically, we have developed a system that assimilates high-fidelity data from polarimetric radars using the Ensemble Kalman Filter (EnKF). This system not only reflects the precise state of rain clouds in the initial fields but also improves the representation of surrounding temperature and humidity. We have demonstrated that this approach enables highly accurate rainfall predictions with longer lead times than previously possible, significantly increasing the value of radar assimilation in disaster management and contributing to improved storm-scale forecasting. Furthermore, with a view toward application in flood forecasting, we are focusing on the utilization of ensemble weather forecasting. Beyond traditional probabilistic information on rainfall amounts and risks, we are developing a novel perspective in disaster communication: "predicting the predictability" (forecasting the miss-forecast). By creating these more practical and actionable forecasting methods, we aim to support more effective decision-making in the face of disasters.
Improving the prediction accuracy of back-building convective systems through data assimilation
Future Changes and Adaptation to Heavy Rainfall Driven by Climate Change
We analyze how various types of extreme precipitation—including Baiu frontal rain, linear convective systems, and localized "guerrilla" rainstorms—will evolve in the future. By utilizing large-scale ensemble climate model simulations, we aim to provide high-reliability assessments by presenting probabilistic information on the frequency and intensity of heavy rainfall in a warming climate. Our goal is to transcend conventional regional-scale risk assessments and provide actionable heavy rain risk information at the river basin scale. To achieve this, we employ unique approaches, such as high-resolution simulations and the analysis of future changes in water vapor transport—the fundamental process itself—to predict the future nature of localized heavy rain, evaluating phenomena that are difficult for standard climate models to represent. Furthermore, as the impacts of climate change are already manifesting in recent storms, studying "today’s heavy rain" is increasingly becoming synonymous with studying "heavy rain under global warming." By integrating accumulated observational data with climate simulations, we are clarifying whether long-term trends in heavy rainfall are linear, non-linear, or characterized by step-like shifts. This research bridges the gap between meteorology and climatology, linking the elucidation of warming mechanisms to near-future predictions. Additionally, we evaluate the accuracy of real-time short-term precipitation forecasting under future climate scenarios, aiming to generate risk information that supports "soft" disaster prevention measures, such as timely evacuation orders.
Future change of heavy rainfall in Baiu season
Heavy Rainfall Control
Based on the understanding that human activities, such as the urban heat island effect and global warming, are intensifying heavy rainfall, we are researching ways to appropriately suppress these events from the perspective of "humanity caring for nature." This is not an attempt to forcibly turn storms into clear skies; rather, it is carefully positioned as a "last resort" to protect human lives from disasters. With a profound sense of awe and respect for nature, we are exploring a new approach: "Weather control to calm heavy rainfall." Our research has focused on innovative methods to modify airflow, validated through numerical simulations. To date, our results have shown that the strategic installation of wind turbines could potentially suppress rainfall intensity by approximately 15%, while the use of offshore "curtains" could reduce peak rainfall by about 30%. These findings are also contributing to a deeper understanding of heavy rainfall mechanisms. Moving forward, we aim to validate these suppression techniques across diverse meteorological conditions to establish them as a universal theory. Simultaneously, we will develop analytical methods to bridge the scale gap between localized phenomena around control devices and broad-scale rainfall, evolving this into a feedback theory for robust, real-time control. We are also advancing hardware development through wind tunnel experiments and collaboration with private-sector partners. Following field verification, we aim for the dawn of the era of heavy rainfall control by 2050. Furthermore, we aspire to propose new disaster prevention and urban designs to realize "cities inherently resilient to heavy rainfall."
Heavy rain control simulation