Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
Tea is an important global commodity,with important tea-growing regions spanning across South America,Africa,and Asia,and burgeoning smaller-scale and artisanal tea production in the UK and Europe.In each of these reg...Tea is an important global commodity,with important tea-growing regions spanning across South America,Africa,and Asia,and burgeoning smaller-scale and artisanal tea production in the UK and Europe.In each of these regions,the quality and quantity of tea production,with their economic and social consequences,are highly sensitive to variations in the climate on both short-term weather,seasonal and climate change timescales.The provision of tailored climate information in a timely and accessible manner through the development,delivery and use of climate services can help tea-farmers and other relevant stakeholders better understand the impacts of climate variability and climate change on decision-making and a range of adaptive actions.This paper presents an overview of the Tea-CUP project(Co-developing Useful Predictions),a joint initiative between UK and Chinese partners,which aims to develop and implement solutions for improving robust decision-making.Co-production principles are core,ensuring that the resultant climate services are usable and useful;users'needs are met through close engagement and joint research and decision-making.The paper also reports on the exchange of knowledge and experiences,such as between tea growers in China and the UK,which has resulted from this collaborative work,fostering global knowledge sharing,enriching understanding,and driving innovation by integrating diverse perspectives and expertise from different countries.This is an unintended but valuable side-effect of the collaborative approach taken and highlights the benefits of a highly relational and multidisciplinary approach to climate services development that will inform future work in the field.展开更多
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
基金funded by the Met Office Climate Science for Service Partnership(CSSP)China project under the International Science Partnerships Fund(ISPF)supported by funds from the National Natural Science Foundation of China(Grant No.42475022).
文摘Tea is an important global commodity,with important tea-growing regions spanning across South America,Africa,and Asia,and burgeoning smaller-scale and artisanal tea production in the UK and Europe.In each of these regions,the quality and quantity of tea production,with their economic and social consequences,are highly sensitive to variations in the climate on both short-term weather,seasonal and climate change timescales.The provision of tailored climate information in a timely and accessible manner through the development,delivery and use of climate services can help tea-farmers and other relevant stakeholders better understand the impacts of climate variability and climate change on decision-making and a range of adaptive actions.This paper presents an overview of the Tea-CUP project(Co-developing Useful Predictions),a joint initiative between UK and Chinese partners,which aims to develop and implement solutions for improving robust decision-making.Co-production principles are core,ensuring that the resultant climate services are usable and useful;users'needs are met through close engagement and joint research and decision-making.The paper also reports on the exchange of knowledge and experiences,such as between tea growers in China and the UK,which has resulted from this collaborative work,fostering global knowledge sharing,enriching understanding,and driving innovation by integrating diverse perspectives and expertise from different countries.This is an unintended but valuable side-effect of the collaborative approach taken and highlights the benefits of a highly relational and multidisciplinary approach to climate services development that will inform future work in the field.
