HKUST study reveals why tropical cyclones rainfall surges before landfall
Rainfall intensifies by over 20% as early as 60 hours before landfall
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A group photo of Prof. GAN Jianping (right), Chair Professor and Head of the Department of Ocean Science at HKUST, and Director of the Centre for Ocean Research in Hong Kong and Macao and Dr. ZHONG Quanjia (left), Post-doctoral Fellow of the Department of Ocean Science at HKUST.
view moreCredit: HKUST
A research team at The Hong Kong University of Science and Technology (HKUST) has analyzed 40 years of data covering around 1,500 tropical cyclones and discovered that average rain rates surge by more than 20% in the 60 hours before landfall. The study is also the first to clearly identify the physical mechanisms behind this increase, showing that rising humidity over coastal areas and enhanced land-sea frictional contrasts strengthen convection, intensifying rainfall ahead of landfall. The results provide valuable insights for improving coastal disaster preparedness and early‑warning systems.
The research was led by Prof. GAN Jianping, Chair Professor and Head of the Department of Ocean Science at HKUST, and Director of the Centre for Ocean Research in Hong Kong and Macau. The study, titled Global increase in rain rate of tropical cyclones prior to landfall, has been published in the international journal Nature Communications.
While previous studies have focused mainly on long‑term changes in tropical cyclone rainfall driven by global warming, short‑term rainfall changes in the critical hours before landfall—when early warning is most essential—have remained under‑examined. To fill this gap, the HKUST team analyzed global satellite rainfall datasets from 1980 to 2020 to assess how rainfall evolves as storms approach the coast and to uncover the physical processes driving these changes.
The study found that, across all ocean basins, storm intensities and latitude bands, rainfall systematically increases before landfall. Crucially, this surge is not directly caused by sea‑surface warming. Instead, it is driven by land–sea contrasts that emerge as the storm nears the coast. These include an increased low‑level humidity over coastal land; higher surface friction over land than over ocean, enhancing near‑shore convergence and an increased atmospheric instability that strengthens convection. Together, these factors cause tropical cyclones to produce markedly more rainfall in the 60 hours prior to landfall, with the rise exceeding 20% globally. This means coastal regions face elevated flood risk even before the storm actually makes landfall.
Prof. Gan remarked, “This study identifies the key mechanisms behind the sharp increase in rainfall before tropical cyclones reach land. The findings can help meteorological agencies and governments improve forecasts of heavy rain, flooding and landslides. Combined with our team’s immersive digital twin platform of regional earth system, WavyOcean 2.0—which integrates data on ocean currents, marine ecology, atmospheric conditions, and the distribution of rivers and pollutants in terrestrial watersheds—this work will support more comprehensive disaster‑risk assessment and emergency planning in the future.”
The study was supported by the University Grants Committee Research Grants Council’s Areas of Excellence (AoE) Scheme, further demonstrating HKUST’s leadership in ocean and atmospheric science research.
Global evolution of rain rates for landfalling tropical cyclones (Negative time denotes hours prior to landfall).
Schematic illustration of the physical mechanisms driving pre-landfall rainfall intensification, highlighting the influence of land–sea contrasts on near-coastal convection and precipitation.
Credit
HKUST
Journal
Nature Communications
Method of Research
Meta-analysis
Subject of Research
Not applicable
Article Title
Global increase in rain rate of tropical cyclones prior to landfall
Predicting extreme rainfall through novel spatial modeling
Osaka Metropolitan University
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Using weather data from 1981–2020, statistical methods analyzed data to predict extreme rainfall in the next hundred years.
view moreCredit: Osaka Metropolitan University
Japan is an archipelago with diverse climate zones and complex topography that is prone to heavy rain and flooding. Add the growing effects of global warming, these disaster risks are heightened with an increased frequency and intensity of extreme precipitation events. Thus, predicting when and where these events might strike is crucial for future-proofing vulnerable infrastructure, especially in rural areas.
However, current systems for tracking comprehensive weather data are primarily stationed around urban areas, presenting a significant statistical gap across large swathes of Japan. Proper data analysis methods to overcome this and provide accurate predictions to assist in future disaster preparedness further pose a challenge. In related fields, there is debate regarding the traditional kriging method used for spatial predictions, as it causes the underestimation of extreme values, and the high computational load of Bayesian hierarchical models based on the Markov Chain Monte Carlo (MCMC) method. As a plausible alternative, the Integrated Nested Laplace Approximation - Stochastic Partial Differential Equation (INLA-SPDE) method is positioned as an efficient alternative that overcomes these shortcomings and is widely used in environmental and climate research. However, its application to spatiotemporal analysis in complex topographies like Japan is limited.
To determine the most effective approach, Associate Professor Jihui Yuan, Emeritus Professor Kazuo Emura, and Professor Craig Farnham from Osaka Metropolitan University’s Graduate School of Human Life and Ecology, and Visiting Researcher Zhichao Jiao from Yantai University’s Architecture School conducted a study to predict the increasing risk of extreme precipitation across the four main islands of Japan. The researchers divided Japan into four areas based on climate and used hourly precipitation data obtained from 752 meteorological observation stations across Japan from 1981 to 2020. They estimated the Generalized Extreme Value distribution (GEV) at each station using the MCMC method and calculated return value levels for 2-year, 5-year, 10-year, 25-year, 50-year, and 100-year events. The researchers then applied the INLA-SPDE method and kriging methods, ordinary kriging (OK) and kriging with external drift (KED), for spatial prediction, forecasting extreme precipitation in unobserved regions using annual precipitation, distance from the coast, and population as covariates. The Leave-One-Out Cross-Validation (LOOCV) was used to evaluate the model’s performance.
The results showed that the INLA-SPDE method, particularly the SPDE1 model with annual precipitation as a covariate, exhibited higher prediction stability than the kriging method. With a smaller standard deviation during long return periods, spatial variability increased, revealing an expansion of the high-risk zone from south to north.
“This study is significant in that it contributes to improving the quality of disaster prevention plans by identifying the limitations of conventional hazard maps and presenting a framework for scientifically assessing flood risks under climate change,” stated Professor Yuan. “Going forward, we will incorporate dynamic meteorological factors such as typhoon paths into the model and work on expanding it to spatio-temporal models. By resolving these challenges, it will be possible to capture the development process of extreme rainfall more realistically, paving the way for high-resolution forecasting.”
The findings were published in the Journal of Hydrology: Regional Studies.
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About OMU
Established in Osaka as one of the largest public universities in Japan, Osaka Metropolitan University is committed to shaping the future of society through the “Convergence of Knowledge” and the promotion of world-class research. For more research news, visit https://www.omu.ac.jp/en/ and follow us on social media: X, Facebook, Instagram, LinkedIn.
Journal
Journal of Hydrology Regional Studies
Method of Research
Data/statistical analysis
Subject of Research
Not applicable
Article Title
Assessing the risk of extreme precipitation in Japan through GEV distribution and spatial modeling
Sharper weather: Refining 35 years of climate data toward precision forecasting
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SMU Professor Fiona Williamson is part of an expert team working on a high-resolution weather reanalysis between 1990 and 2025 for Singapore and Southeast Asia, with the sharpened data set to aid long-term, AI-driven planning.
view moreCredit: Singapore Management University
By Vince Chong
SMU Office of Research Governance & Administration – Tightening energy security. Boosting aviation and maritime risk management and efficiency. Enhance urban and infrastructure resilience planning.
These are objectives that SMU Professor Fiona Williamson hopes to meet as she works on an ambitious Singapore-based multi-institutional project to reanalyse weather records for Southeast Asia (SEA) across 35 years between 1990 and 2025, under the Weather Science Research Programme (WSRP). This is the first time that a reanalysis study is done for this region, even though such studies are “standard” in other parts of the world, the project’s proposal noted.
As climate and environmental threats continue to rise, the resulting high-quality data set will be used to train artificial intelligence (AI) that will make even more precise weather forecasting in SEA.
“The types of data that we hope to uncover… will be able to feed into all different types of projects that urgently need better and improved data sets for weather modelling or reconstructions,” Professor Williamson told the Office of Research Governance and Administration (ORGA).
The benefits are multi-fold. Improved weather forecasting means SEA governments can plan for, say, a drier-than-usual climate in Laos that could affect hydropower supply to neighbours like Singapore, or a period of cloudier skies that might hit solar energy planning.
The aviation and maritime sectors will also be able to access details such as wind speed and direction in greater precision. This will boost aircraft landing safety, inform on best routes to minimise fuel consumption, or simply reduce adverse weather risks for staff.
“All of these areas are equally important,” Professor Williamson continued.
“Many are linked and cannot be considered in isolation.”
The environmental historian is one of the experts in the Maritime Continent Atmospheric Regional Reanalysis (MCARR) study, which has been awarded a grant by Singapore’s National Environment Agency under the WSRP Funding Initiative. The project, based in Singapore, will also reinforce the country’s National AI Strategy, and its leadership in regional climate resilience and smart weather forecasting.
A major step
One of the most “prominent” effects of the project is “the development of new capabilities and know-how,” the project proposal set out. These include executing and managing proper data quality control, as well as scientific analysis, and amplifying downstream applications for public agencies to, for example, tackle heat and flood resilience.
Current weather forecasting in Singapore, it noted, is largely based on a “deterministic ‘will it rain or won’t it’ approach.”
“However, decision makers require a more risk-based framework, combining a ‘probabilistic’ approach (likelihoods of rain of a certain threshold estimated) with assessment of impact,” the proposal stated.
SMU Research Fellow Praveen Teleti, who is assisting Professor Williamson, noted that the MCARR “is truly a major step forward in regional reanalysis model evolution.”
Historical weather observations in SEA, for one, he explained, were constrained by available technology, with existing forecasting models restricted to coarse mapping resolutions from a distance of nine kilometres or more. By contrast, the MCARR team will attempt to reduce this resolution to four kilometres or less.
“This will pave the way for local and potentially devastating weather systems to be detected in unprecedented detail,” said the climate scientist. Such detail is crucial for understanding “small-scale processes” such as thunderstorms and convective precipitation – rains caused by a surfeit of warm air – which can cause flash floods that kill and destroy property.
The project is not without its challenges, a key one being finding and accessing weather data for SEA compared to other regions. This is a “big problem” exacerbated by a lack of resource in many SEA countries to preserve, construct images, and digitise such historical data that is kept in paper form, Professor Williamson said.
“First, there is the problem of data sharing amongst different SEA countries, which tends to be sporadic and dependent on data sharing policies of the country itself,” she explained.
“Second, and perhaps more importantly, is the level of data that is available in usable formats before the 1990s… Thus, even if a country was happy to share information, it may not be able to.”
Another challenge, as the proposal noted, is that while AI and machine learning (ML) have "emerged as revolutionary forces in weather and climate science," it comes with the risks of "degraded forecast skill" and "unphysical solutions."
As Dr Teleti explained, while ML models are being actively used in climate research, they might not be “learning” properly from expensive climate models whose forecasts typically take weeks or months to generate. ML meanwhile, could produce one in days.
“Hence without a properly reanalysed and refined climate model rooted in physical laws, some of the ML predictions could be outlandish, like negative precipitation, [or] too hot or cold temperatures,” he said.
The MCARR project, the proposal added, aims to “assess the potential hybrid physical-AI modelling systems to provide a better balance of cost and accuracy.”
New opportunities
Other key benefits attached to the project, the document added, include helping financial institutions in SEA such as banks and insurance companies better manage and price climate risks.
The same goes for building professions and government agencies developing urban projects in the region. With the project’s help, they will be able to better design, for instance, drainage or stormwater storage systems, to avoid “catastrophic failure of infrastructure,” while unlocking new applications with the help of AI.
In all, it appears to be an exciting time for weather experts in the region, whose area of work has long caught the imagination of the public, for example, through big-budget disaster movies.
Most of which, Professor Williamson said, make her “irritable” for their unscientific presumptions. She does however, support movies that are “important for better communicating to a wide audience the need to do something about climate change on individual, community, and national levels.”
Finding better data and improving the capabilities of tools to be used to study climate change might not fetch obvious benefits in everyday life, she said, but “they are of utmost importance in the region of planning, mitigation, and sustainability.”
