Haobo LI

Publications

1. 

   Save It for the" Hot" Day: An LLM-Empowered Visual Analytics System for Heat Risk Management

   Haobo Li, Wong Kam-Kwai, Yan Luo, and 6 more authors

   arXiv preprint arXiv:2406.03317 2024

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   The escalating frequency and intensity of heat-related climate events, particularly heatwaves, emphasize the pressing need for advanced heat risk management strategies. Current approaches, primarily relying on numerical models, face challenges in spatial-temporal resolution and in capturing the dynamic interplay of environmental, social, and behavioral factors affecting heat risks. This has led to difficulties in translating risk assessments into effective mitigation actions. Recognizing these problems, we introduce a novel approach leveraging the burgeoning capabilities of Large Language Models (LLMs) to extract rich and contextual insights from news reports. We hence propose an LLM-empowered visual analytics system, Havior, that integrates the precise, data-driven insights of numerical models with nuanced news report information. This hybrid approach enables a more comprehensive assessment of heat risks and better identification, assessment, and mitigation of heat-related threats. The system incorporates novel visualization designs, such as “thermoglyph” and news glyph, enhancing intuitive understanding and analysis of heat risks. The integration of LLM-based techniques also enables advanced information retrieval and semantic knowledge extraction that can be guided by experts’ analytics needs. We collaborated with six domain experts to conduct a case study on the 2022 China Heatwave and an expert survey & interview, demonstrating the usefulness of our system in providing in-depth and actionable insights for heat risk management.

   @article{li2024save,
     title = {Save It for the" Hot" Day: An LLM-Empowered Visual Analytics System for Heat Risk Management},
     author = {Li, Haobo and Kam-Kwai, Wong and Luo, Yan and Chen, Juntong and Liu, Chengzhong and Zhang, Yaxuan and Lau, Alexis Kai Hon and Qu, Huamin and Liu, Dongyu},
     journal = {arXiv preprint arXiv:2406.03317},
     year = {2024},
     doi = {https://doi.org/10.48550/arXiv.2406.03317},
   }

2. 

   CLLMate: A Multimodal LLM for Weather and Climate Events Forecasting

   Haobo Li, Zhaowei Wang, Jiachen Wang, and 2 more authors

   arXiv preprint arXiv:2409.19058 2024

   Abs arXiv Bib PDF

   Forecasting weather and climate events is crucial for making appropriate measures to mitigate environmental hazards and minimize associated losses. Previous research on environmental forecasting focuses on predicting numerical meteorological variables related to closed-set events rather than forecasting open-set events directly, which limits the comprehensiveness of event forecasting. We propose Weather and Climate Event Forecasting (WCEF), a new task that leverages meteorological raster data and textual event data to predict potential weather and climate events. However, due to difficulties in aligning multimodal data and the lack of sufficient supervised datasets, this task is challenging to accomplish. Therefore, we first propose a framework to align historical meteorological data with past weather and climate events using the large language model (LLM). In this framework, we construct a knowledge graph by using LLM to extract information about weather and climate events from a corpus of over 41k highly environment-focused news articles. Subsequently, we mapped these events with meteorological raster data, creating a supervised dataset, which is the largest and most novel for LLM tuning on the WCEF task. Finally, we introduced our aligned models, CLLMate (LLM for climate), a multimodal LLM to forecast weather and climate events using meteorological raster data. In evaluating CLLMate, we conducted extensive experiments. The results indicate that CLLMate surpasses both the baselines and other multimodal LLMs, showcasing the potential of utilizing LLM to align weather and climate events with meteorological data and highlighting the promising future for research on the WCEF task. As a representative hub in the “My Climate Risk” lighthouse activity initiated by the World Climate Research Programme (WCRP), we contribute CLLMate as a component of our regional solution to help forecast environmental risks and mitigate their loss.

   @article{li2024cllmate,
     title = {CLLMate: A Multimodal LLM for Weather and Climate Events Forecasting},
     author = {Li, Haobo and Wang, Zhaowei and Wang, Jiachen and Lau, Alexis Kai Hon and Qu, Huamin},
     journal = {arXiv preprint arXiv:2409.19058},
     year = {2024},
     doi = {https://doi.org/10.48550/arXiv.2409.19058},
   }

3. 

   Assessing the Impact of Cumulus Convection and Turbulence Parameterizations on Typhoon Precipitation Forecast

   Yueya Wang, Haobo Li, Xiaoming Shi, and 1 more author

   Geophysical Research Letters 2024

   Abs Bib PDF

   Improving typhoon precipitation forecast with convection-permitting models remains challenging. This study investigates the influence of cumulus parameterizations and turbulence models, including the Reconstruction and Nonlinear Anisotropy (RNA) turbulence scheme, on precipitation prediction in multiple typhoon cases. Incorporating the cumulus and RNA schemes increases domain-averaged precipitation, improves recall scores, and lowers relative error across various precipitation thresholds, which is substantial in three out of four studied typhoon cases. Applying appropriate cumulus parameterization schemes alone also contributes to enhancing heavy precipitation forecasts. In Typhoon Hato, the RNA and Grell-3 schemes demonstrated a doubled recall rate for extreme rainfall compared to simulations without any cumulus scheme. The improved forecasting ability is attributed to the RNA’s capacity to model dissipation and backscatter. The RNA scheme can dynamically reinforce typhoon circulation with upgradient momentum transport in the lower troposphere and enhance the buoyancy by favorable heat flux distribution, which is conducive to developing heavy precipitation.

   @article{wang2024assessing,
     title = {Assessing the Impact of Cumulus Convection and Turbulence Parameterizations on Typhoon Precipitation Forecast},
     author = {Wang, Yueya and Li, Haobo and Shi, Xiaoming and Fung, Jimmy},
     journal = {Geophysical Research Letters},
     year = {2024},
   }