The mission of the Climate, Weather and Water Forum (CWWF) is to facilitate an annual dialog among scientists, engineers, students, public and private enterprises and government entities on pressing issues related to climate change, weather extremes, water availability, and sustainability. We aim to:
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Accurate prediction of precipitation and its extremes at subseasonal-to-seasonal timescales remains a central challenge in meteorology, with direct implications for flood preparedness, water-resource management, agriculture, and climate-risk reduction. This talk will highlight recent advances in understanding and improving precipitation forecast skill from three complementary perspectives: physical dynamics, atmospheric energetics, and artificial intelligence. First, I will discuss how the stability of tropical and extratropical wave propagation governs subseasonal predictability, creating identifiable “opportunities” when precursor signals propagate coherently and “barriers” when they do not. Second, I will show how emerging atmospheric total-energy signals offer a physically grounded route to extending early-warning capability for extreme precipitation in a warming climate. Third, I will introduce recent progress in AI-based subseasonal forecasting systems, which are beginning to outperform conventional dynamical models for precipitation prediction and provide new tools for identifying precursor signals. Together, these studies suggest a shift from predicting precipitation directly to diagnosing the dynamical, energetic, and data-driven sources of predictability that can support next-generation forecasting and early-warning systems.
Methane (CH₄) is a powerful greenhouse gas, over 20 times more potent than CO₂ over a century. Vast amounts of CH₄ are stored under the ocean floor, slowly leaking at sites called methane or cold seeps. Yet, a critical knowledge gap exists. Scientists lack actual measurements of this global methane flux because 1) seeps in global ocean are largely unmapped — especially in the Global South, 2) unknown total area of ocean seeps which is likely much larger than estimated, and 3) unquantified methane flux from most known seeps. This undermines accurate climate predictions. Furthermore, as future natural gas extraction targets the deep-sea seabed, the impact on the unique chemosynthetic ecosystems and biodiversity in seep field remains poorly understood.
MThe UN Ocean Decade Program “Global Climate Impacts of Methane Seeps (CliMetS)” and the UN Science Decade program “Mysteries of Ocean Cold Seep Interfaces (MOCSI)” are launched to address these blind spots. These are a global, collaborative effort to find, study, and assess methane seeps and their ecosystems. Its work will have profound impacts on 1) Climate Strategies: Quantifying seabed methane flux into the water column and atmosphere is essential for accurate climate models, 2) Ecosystem Safety: The programme will map these deep-sea oases, establish a biodiversity baseline, and create conservation guidelines, and 3) Policy & Management: Findings will be translated into practical tools for ocean governance under frameworks like CBD, UNCLOS, BBNJ, and the work of the IPCC.
MIn this presentation, I will introduce these UN Decade programs and welcome world experts in climate study to join our programs to fill up the knowledge gaps in the impacts of ocean methane seeps on future climate.
The Loess Plateau of China has witnessed a remarkable greening trend due to vegetation restoration in recent decades. However, the precipitation response to greening remains unclear, and the hydrological effect of greening is controversial. Here, we revisited biophysical effects of greening on precipitation over the plateau during 2002–2015 using the state-of-the-art water vapor tracer embedded in a regional coupled model. We find that greening can promote the growing season precipitation (0.45 mm·day−1), with 15% and 85% of the precipitation increment resulting from increases in the local evapotranspiration and the water vapor inflow from outside the plateau, respectively. As a consequence, the enhanced precipitation can compensate for the terrestrial water loss driven by the increased evapotranspiration, leading to a slight increase in the water yield. This study highlights the dominant role of the nonlocal effect in precipitation responses to greening in this region.
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Indian Summer Monsoon forecasts generated by three operational sub-seasonal ensemble prediction systems are calibrated against Indian Meteorological Department gridded observations using a UNET convolutional neural network (CNN). The UNET is trained using ensemble-mean precipitation hindcasts from three GCMs, to calibrate 1-degree resolution tercile probabilities of weekly rainfall at lead times of 1 to 4 weeks for the June-September season. Hindcast skill and real-time forecasts are compared against a baseline of extended logistic regression (ELR). Both the UNET and ELR are evaluated firstly for each of the three GCMs individually using the same cross-validation approach. The UNET is shown have higher Ranked Probability Skill Score (RPSS) than the ELR, although its variance is larger with negative skill at some gridpoints. An equally-weighted multi-model combination of 2-3 models improves the UNET skill further, and reduces the inter-gridbox variance, providing positive RPSS almost uniformly. Finally, we evaluate refinements to the UNET by including additional predictor variables, including univariate antecedent observed MJO and ENSO observed indices.
| No | Theme | Presenting Author | Affliation | Title |
|---|---|---|---|---|
| 1 | I | Dr. Xiaoting Sun | National Climate Centre, China Meteorological Administration | Objective Definition of the Southwest China Vortex and Its Subseasonal Predictability |
| 2 | I | Dr. Minghao Wang | National Climate Centre, China Meteorological Administration | Study on Dynamically computed characteristic adjustment time scale for convective parameterization scheme |
| 3 | I | Dr. Kailun Gao | Aerospace Information Research Institute, Henan Academy of Sciences | A Dynamic Index Linking Tropical and Arctic Forcing Pathways to Main Rainy Season Precipitation over the Beijing–Tianjin–Hebei Region |
| 4 | I | Dr. Yue Zhang | Fudan University | Sequential forcing between the autumn Indian Ocean Dipole and winter Atlantic Niño and their combined influence on Euro–African precipitation and atmospheric teleconnections. |
| 5 | I | Mr. Xuefeng Liu | Nanjing University of Information Science and Technology | North American winter subseasonal temperature variability encoded in the Pacific subtropical jet |
| 6 | I | Mr. Yongmao Peng | Yunnan University | Linkage in the Diversity of Atmospheric Rivers: A Global Perspective on Multi-Framework Classification |
| 7 | I | Dr. Qingyu Cai | Yunnan University | The Silent Expansion of Fire Activity in Continental Tropical Asia shaped by natural and anthropogenic forces |
| 8 | I | Prof. Shizuo Liu | School of Atmospheric Sciences, Nanjing University | A snow-fire bridge mechanism for the 2025 Southern California winter wildfire |
| 9 | II | Prof. Chongbo Zhao | China Meteorological Administration National Climate Center | Combined dynamical downscaling and UNet downscaling in subseasonal precipitation prediction |
| 10 | II | Dr. Siyan Dong | National Climate Centre, China Meteorological Administration | High-Resolution Downscaling and Preliminary Evaluation of Extreme Precipitation in the Beijing–Tianjin–Hebei Region under Climate Change |
| 11 | II | Dr. Yuru Dong | Guangdong University of Technology | A Novel Spatiotemporal Graph Neural Network for Extreme Flood Forecasting via Multi-Basin Spatiotemporal Information Sharing |
| 12 | II | Ms. Yumeng Yang | Beijing Normal University | Regionalization and projection of CLIGEN input parameters based on machine learning technique |
| 13 | II | Ms. Wen Shi | Tsinghua University | A Bayesian Generative Framework for Bridging Dynamical Circulation Forecasts to High-Resolution S2S Ensemble Precipitation Prediction |
| 14 | II | Mr. Can Liu | Beijing Normal University | A Hybrid AI Correction Model for Subseasonal Probabilistic Precipitation Forecast |
| 15 | II | Miss. Jingyu Ma | Fudan University | A Machine Learning Model Reveals an Indian–East Asian Monsoon Teleconnection Precursor for East China Summer Rainfall |
| 16 | III | Prof. Lihui Tian | Qinghai University | Unexpected Life-History Strategies and Ecohydrological Feedbacks of Plantations in an Alpine Desert |
| 17 | III | Miss. Yanni Wang | Chengdu University of Information Technology | Towards Improved Climate Projections: Evaluating Antarctic Reanalysis Data |
| 18 | III | Ms. Xiaoyu LI | Jiaying University | Inter-Site Characteristics of Raindrop Size Distribution and Rain-Type Dependent Z–R Relations for Radar Rainfall Estimation |
| 19 | III | Miss. Ruoxi wang | Qinghai University | Ecological Restoration Reshapes Soil Hydrothermal Coupling in an Alpine Desert |
| 20 | III | Miss. Danting Li | Nanjing University of Information Science and Technology | Interdecadal enhancement in the impact of decaying El Niño on early autumn Southern China extreme high temperature events |
| 21 | III | Mr. Han Li | Nanjing University of Information Science and Technology | Quantifying Solar Drought Impacts on Photovoltaic Generation and Climate-Resilient PV Siting in China |
| 22 | III | Miss. Xinyue Sun | Nanjing University of Information Science & Technology | Adaptation Measures of Air Conditioning in Mitigating Future Vulnerable Exposure to Compound Heat-Humidity Extremes |
| 23 | III | Dr. Pak Wah Chan | Fudan University | Hong Kong Hot Days Caused by Outer Subsiding Air of Tropical Cyclones: Statistical Analyses on Favorable Cyclone Locations and Rapid Onsets |
| 24 | III | Dr. Qiuyun Wang | Ocean University of China | Effect of accumulated energy variation of pelagic tropical cyclones over the Northeast Pacific on El Niño spatial diversity |
| 25 | III | Prof. Mengxin Pan | Simon Fraser University | Contrasting historical trends of atmospheric rivers in the Northern Hemisphere |
| 26 | III | Dr. Zaoying Bi | Nanjing University of Information Science and Technology | A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones |
| 27 | III | Dr. Yansong Guan | Department of Geography and Resource Management, The Chinese University of Hong Kong | Global drought from ocean to land and from surface to subsurface |
| 28 | III | Mr. Jizeng Du | Beijing Normal University | The intensification of precipitation pattern changes has led to a rapid transition of droughts and floods in Poyang Lake |
| 29 | III | Dr. Yunting Xiao | Tianjin University | Ocean Warming Weakens the Sea–Land Breeze in Coastal Megacities |
| 30 | III | Dr. zhaoming LI | Foshan Tornado Research Center, and China Meteorological Administration Tornado Key Laboratory | Fine-scale Phased-array Radar Observations of an EF2 Tornadic Supercell near Mountain Lee |
| 31 | III | Miss. Xianxiang Huang | Foshan Tornado Research Center, and China Meteorological Administration Tornado Key Laboratory | The 28 to 29 September 2025 South China Tornado Outbreak and Its Predictability |
| 32 | III | Ms. Cailing Li | Foshan Tornado Research Center, and China Meteorological Administration Tornado Key Laboratory | A Brief Discussion on the High-impact Cold-season Tornado Outbreak During 10-11 December 2021 in the United States |
| 33 | III | Dr. Aifang Chen | University of Freiburg | Impact of tropical cyclones and socioeconomic exposure on flood risk distribution in the Mekong Basin |
| 34 | Dr. Jingdan Mao | Nanjing University of Information Science and Technology | To Be Updated | |
| 35 | Prof. Yaokui Cui | Peking University | To Be Updated |
Climate, weather, water extremes, among others, are intensifying. The scale of their impact depends on the choices societies make about infrastructure, land and water management, as well as preparedness. Drawing on the work of the Julie Ann Wrigley Global Futures Laboratory at Arizona State University, this talk will examine how universities can serve as key partners in advancing disaster risk reduction in an era of accelerating Earth system change. As institutions that connect knowledge with purpose, universities are uniquely positioned to help transform scientific understanding into risk-informed decision-making that protects lives, sustains development, and expands the options available to future generations. In doing so, they can help reposition disaster risk reduction from a reactive practice to an anticipatory practice.
Assessments of extreme weather events such as droughts and floods have traditionally focused on physical severity, often overlooking human-centered impacts due to limited timely data. This study addresses this gap by developing a socially informed, data-driven framework that integrates physical and societal impacts. Using case studies of droughts in California and Texas and flooding associated with Hurricane Helene, we leverage crowdsourcing, data mining, and Internet-of Things inputs to rapidly generate datasets from social and news media. These datasets capture socialphysical interdependencies and the evolving dynamics of societal impacts. Results demonstrate that reliable insights can be extracted from inherently noisy social mediabased data, enabling more human-centric impact assessments and mitigation strategies. We further develop online diagnostic platforms that provide synthesized insights to stakeholders while enabling affected communities to report experiences and damages
Indian Summer Monsoon forecasts generated by three operational sub-seasonal ensemble prediction systems are calibrated against Indian Meteorological Department gridded observations using a UNET convolutional neural network (CNN). The UNET is trained using ensemble-mean precipitation hindcasts from three GCMs, to calibrate 1-degree resolution tercile probabilities of weekly rainfall at lead times of 1 to 4 weeks for the June-September season. Hindcast skill and real-time forecasts are compared against a baseline of extended logistic regression (ELR). Both the UNET and ELR are evaluated firstly for each of the three GCMs individually using the same cross-validation approach. The UNET is shown have higher Ranked Probability Skill Score (RPSS) than the ELR, although its variance is larger with negative skill at some gridpoints. An equally-weighted multi-model combination of 2-3 models improves the UNET skill further, and reduces the inter-gridbox variance, providing positive RPSS almost uniformly. Finally, we evaluate refinements to the UNET by including additional predictor variables, including univariate antecedent observed MJO and ENSO observed indices.
The sub-seasonal timescale (2 weeks to 2 months) is a critical window for anticipatory action in disaster risk reduction and sustainable urban development. This report presents Shanghai as a pioneering case in transitioning sub-seasonal prediction from research to operations for managing summer hazards including heatwaves, heavy rainfall, etc. As a megacity highly vulnerable to extreme weather, Shanghai has built a user-driven forecasting system bridging climate science and decision-making. Based on understanding of sub-seasonal predictability sources, the system employs physical-statistical, dynamical downscaling, and hybrid dynamical-machine learning models, applied across disaster prevention, agriculture, energy, and environmental sectors. These models support sector-specific early warnings. Case studies show reliable signals for heatwaves and heavy rainfall, enabling proactive response. The system also contributes to sustainable development by enhancing agricultural contingency planning, optimizing energy use, and strengthening social resilience. Looking ahead, Shanghai's MAZU Urban Multi-hazard Early Warning Agent—an AI-driven system trialed since January 2025—aims to incorporate sub-seasonal prediction for maritime Silk Road disaster prevention, and urban resilience.
Building resilience across borders in the Hindu Kush Himalaya (HKH) requires more than technical evidence; it requires a science–policy–diplomacy nexus approach that connects local risk knowledge, basin-scale cooperation, and global water action. The HKH and neighbouring mountain regions of Central Asia form the wider Third Pole system, the largest store of ice outside the polar regions and a water source for nearly two billion people. Yet climate change, cascading hazards, infrastructure expansion, groundwater stress, ecosystem degradation, and fragmented governance are reshaping risks from mountain slopes to downstream deltas. This presentation examines how ICIMOD and partners are translating science into shared resilience through three linked scales. At the community level, participatory digital mapping in Nepal’s Himalayas shows how locally adapted tools can improve hazard visualization, land-use decisions, and learning, while revealing barriers of migration, digital divides, and social inclusion. At the regional level, the Lower Mahakali WEFE assessment demonstrates that water, energy, food, ecosystems, disaster risk, and livelihoods are already connected on the ground, demanding integrated basin planning rather than siloed management. Cross-border DRR research and hydropower risk work further show how scientific cooperation can support monitoring, early warning, and resilient infrastructure in complex landscapes. At the basin scale, regional basin networks provide neutral, science-based platforms that co-produce assessments, build trust among riparian experts, and channel evidence into SDGs, high-level forums, and the post-2030 water agenda. The presentation argues that “what happens in the mountains does not stay in the mountains”: it travels through rivers, food systems, energy systems, ecosystems, economies, and communities. Resilience across borders therefore begins at the source, recognizes mountains as core water infrastructure, and anchors regional institutions as implementation partners for sustainable development. It offers practical pathways for evidence, cooperation, finance, and implementation together in the HKH and beyond.
Recent AI weather models such as Pangu-Weather, Aurora, GraphCast, GenCast, AIFS, FuXi, FourCastNet3, and SFNO now rival operational numerical prediction, yet no single architecture dominates across all variables and lead times, and continued scaling of any one model yields diminishing returns. We present FTAE-Weather, a deep reinforcement learning framework that coordinates these pretrained models rather than training yet another foundation model. A tactical Weight-Agent reads the atmospheric state and emits continuous, state-conditioned weights over the expert pool; a strategic Evolve-Agent uses the Weight-Agent's revealed preferences to prune redundant models and absorb newly released architectures. Across ten variables and lead times from 24 to 360 hours, FTAE-Weather lowers RMSE by up to 15% over the best individual model and 22% over equal-weight averaging, while adding fewer than 0.1% parameters. The result is a forecasting system that improves automatically as the field releases new models — with direct implications for early-warning systems and operational disaster preparedness.
The Madden-Julian Oscillation (MJO) is the dominant source of subseasonal-to-seasonal (S2S) predictability and underpins early warning of extreme weather and disaster risk reduction. Traditional S2S models such as IAP-CAS v1.3 suffer from inadequate ensemble spread, which fails to fully capture atmospheric uncertainty and constrains MJO prediction capability. This study integrates the Second-Order Exact Sampling (SOES) scheme into ensemble initialization to refine perturbation generation using large historical samples at low computational cost. Sensitivity experiments based on winter MJO cases from 2019 to 2023 optimize the configuration, lifting the real-time MJO forecast skill by up to 6 days and exceeding 30 forecast days in operational predictions. The upgraded IAP-CAS system better reproduces MJO moisture structures and thermal stratification, improving convection representation and substantially advancing precipitation forecasts across China. Beyond MJO prediction, the SOES-optimized platform supports practical S2S applications, including 2026 Asian monsoon outlook and decadal-scale projection of future westerly jet variations.
The Hong Kong Observatory (HKO) launched the climate prediction services in 2001. Leveraging decades of advancements in climate modeling and improvement in sub-seasonal to seasonal (S2S) forecasting techniques, HKO has expanded the climate prediction services over the past two decades, now spanning monthly forecasts and seasonal forecasts on temperature and rainfall; an annual outlook covering annual mean temperature, total rainfall, and the number of tropical cyclones affecting Hong Kong; as well as long-term climate projections. This presentation will cover the latest progress in climate prediction services in Hong Kong and highlight some potential future challenges, including climate prediction in a warming world with increasing extreme weather. Moreover, recent research collaborations with academia to enhance climate prediction services and examples of stakeholder engagements to support climate risk assessment, infrastructure design, urban planning, and water resources management will be mentioned.
Land surface processes provide an important source of weather predictability from days to weeks, because soil moisture anomalies can persistently influence the lower atmosphere. Exploiting this predictability requires accurate land surface states, but current land data assimilation systems are computationally expensive and depend heavily on expert-designed workflows. Here we present AI-Land-DA, a fully data-driven framework that integrates remote sensing observations with a neural land surface model to predict land states across diverse hydroclimatic regimes. AI-Land-DA suppresses long-term error growth and improves estimates of soil moisture and surface energy fluxes, especially at longer lead times. Compared with the operational GEFS, it substantially improves flash drought predictability, including more accurate detection of drought onset and intensification.
Climate analytics create value only when they are rigorously integrated into financial decision-making. In this talk, I focus on financial institutions and the translation of climate projections into asset valuations, capital allocation, and credit decisions. I argue that even when the underlying science is sound, its implementation is often simplistic or poorly calibrated. Drawing on evidence from regulatory climate stress tests of real estate and sovereign bond portfolios — covering both transition and physical risks — I show that upstream modelling choices can alter estimates of asset-level risk by margins comparable to the climate signal itself. Robustness to these choices is therefore not a refinement but a prerequisite for credible climate risk translation. I conclude by illustrating how this translation can be operationalised through weather-indexed insurance and structured de-risking solutions.
Climate and weather extremes — storms, heat waves, floods, droughts, and compound events — have become the defining natural hazard challenge of the 21st century. Their growing frequency and intensity are overwhelming engineered infrastructure, disrupting global supply chains, and propagating risks across societies through teleconnections that no single country can insulate itself against. While climate change mitigation through decarbonization remains an urgent priority, even optimistic emissions trajectories leave us facing decades of increasing exposure. Climate adaptation efforts — improved infrastructure design, financial instruments, early warning systems — are essential but are constrained by limited data, deep uncertainty in future projections, and the diffuse question of who bears responsibility for action.
This talk argues that a third pillar is emerging and demands serious scientific and institutional attention: Climate Stabilization, or the deliberate modification of developing weather and climate extremes to reduce their societal impact. Rather than waiting for disasters to unfold and recovering afterward, this paradigm asks whether the physical dynamics of the atmosphere offer leverage points — windows in time and space — where strategically placed, small perturbations could redirect the trajectory of an extreme event. This is the core idea of Weather Jiu-Jitsu: exploiting the inherent instabilities and nonlinear sensitivities of atmospheric circulation to achieve large-scale redirection of an extreme using energy borrowed from the circulation itself, not brute-force external forcing. Related efforts, such as Japan's Moonshot Goal 8 program, are exploring similar scientific and technological frontiers.
The talk will address the foundational questions this agenda raises for a forecasting and Earth science community: What physical mechanisms enable or constrain atmospheric steering? How can ensemble prediction systems, adjoint methods, and emerging AI tools be harnessed to identify intervention points and compute impact outcomes with spatial specificity? What are the data and modeling gaps? How do we frame the ethical and governance dimensions as this moves from laboratory curiosity to potential operational deployment and commercial application? In the context of CWWF's themes of seamless prediction, physical modeling, and AI for Earth science, I will sketch a research roadmap integrating chaos-informed perturbation theory to AI enabled adaptive control optimization that builds on AI-accelerated impact forecasting to provide the foundation for Climate Stabilization as a rigorous scientific enterprise and, within a decade, a viable business with measurable returns to investors and societies alike.
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This talk addresses the challenge of translating subseasonal-to-seasonal (S2S) predictions into actionable services for diverse sectors. Despite inherent low forecast skill, we emphasize three key steps for bridging the last mile: defining industry-specific prediction targets, developing tailored evaluation metrics with economic value, and providing decision-making guidelines based on forecast confidence. Three case studies illustrate this framework: (1) a hybrid climate‑physiological‑AI model for maize yield prediction and growth‑stage management in North China, reducing net losses by ~7,000 CNY/ha during heatwaves; (2) S2S forecasts for renewable energy, where improved model skill cuts wind‑supply bias by 40% and cooling‑demand bias by 6%, saving ~250 million CNY in dispatch costs; (3) a multi‑factor disaster risk chain (hazard, exposure, vulnerability, recovery) for population‑centric flood warnings in Shanghai, integrating dynamic population mobility and infrastructure. We conclude by inviting collaborations under a UN sustainable development programme to co‑develop practical, impact‑oriented S2S services
Climate services are scientifically based information and products that support improved decision-making by enhancing understanding of how climate variability and change influence risks and impacts. As a rapidly growing field, climate services sit at the interface between scientific research and user needs, with increasing recognition of their critical role in disaster risk reduction and sustainable development.
Effective climate services can help societies anticipate and manage climate-related hazards, such as droughts, floods, and extreme weather, thereby reducing vulnerability and strengthening resilience in climate-sensitive sectors. However, ensuring that climate information is accessible, relevant, and actionable remains a significant challenge. Evidence shows that climate services are most effective when they are co-developed and co-produced with users. This talk will explore how collaborative approaches and sustained user engagement can enhance the uptake and impact of climate information, drawing on experiences from the Climate Science for Service Partnership (CSSP China). Focusing on the agricultural sector in the UK and China, it will highlight how co-produced climate services can support risk-informed decision-making, improve preparedness for climate-related hazards, and contribute to more resilient and sustainable development pathways.
Flash floods are among the most critical climate-related hazards in arid and semi-arid regions, especially in Egypt where climate variability, urban expansion, and water scarcity increase disaster risks. Although rainfall is generally limited, extreme storms have become more frequent and intense in recent decades, causing severe damage to infrastructure, communities, and economic activities. At the same time, floodwaters represent an important non-conventional water resource for a country highly dependent on the Nile River.
Egypt has adopted integrated approaches that transform flash floods from destructive hazards into valuable water resources within disaster risk reduction and sustainable development frameworks. These approaches combine structural and non-structural measures, including protection dams, storage lakes, artificial channels, hydrometeorological monitoring networks, flood forecasting and early warning systems, and updated hydrological risk assessments under changing climate conditions.
Successful rainwater harvesting experiences in South Sinai, particularly in Saint Catherine, show how low-cost mountainous lakes and groundwater recharge can support flood mitigation, groundwater sustainability, agricultural development, and community resilience. Egypt’s experience demonstrates that integrating flood management, water harvesting, scientific research, and stakeholder participation can strengthen climate resilience, water security, and sustainable development in arid regions.
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Founder of the Otto Poon Charitable Foundation, Hong Kong
Professor, Provost of HKUST, & CAE Foreign Academician, Hong Kong
Professor & Vice-President, HKUST, Hong Kong
Professor & CAS Foreign Academician, Tsinghua University, China
Professor, Arizona State University, United States
Professor, Arizona State University, United States
Professor, HKUST, Hong Kong
Senior Research Scientist, Columbia University, United States
Professor, University of Illinois at Urbana-Champaign, United States
Professor, Imperial College London, United Kingdom
Senior Scientific Officer, HKO, Hong Kong
Professor, Yunnan University, China
Professor & Vice Dean of School of Atmospheric Sciences, Nanjing University, China
Professor, Institute of Atmospheric Physics-CAS, China
Director, the Water Resources Research Institute, Egypt
Senior Scientist, Met Office, United Kingdom
Water Policy Specialist, ICIMOD, Nepal
Professor, Dalian Marine University, China
Professor, Tianjin University, China
HKUST, Hong Kong
Associate Research Fellow, Lanzhou University, China
Forum Chair / Director of CCRS, HKUST
Forum Co-Chair / BNU
Forum Co-Chair / Shanghai Meteorological Service
Event Coordinator / HKUST
Secretariat / HKUST
Secretariat / HKUST
Website / HKUST
Registration / HKUST
Scheduling / HKUST
Scheduling / HKUST
Onsite Helper / HKUST
Onsite Helper / HKUST
Onsite Helper / BNU
Onsite Helper / BNU
Onsite Helper / BNU
