Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling
研究通过分布式模型-代理耦合，在Met Office统一模型中使用在线强化学习方法，对模型进行动态一致性和数值稳定性校正，以提高天气预报准确性。
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From Licensing to Open Access: Designing a Sustainable Transition in Operational Weather Data
This work proposes a tiered service architecture designed to reconcile compliance with the European Union’s open data policies and long-term operational sustainability. Core weather forecast data are released under a CC BY 4.0 license, while value-added services generate revenue to offset operational costs. The model employs dynamic infrastructure provisioning and an annual iterative evaluation mechanism, progressively reducing revenue targets to facilitate a smooth transition from a licensing-based model to full open access. Within six months of implementation, over 93% of previously paying institutions renewed their service agreements, and downloads of open data increased substantially. These outcomes demonstrate the architecture’s effectiveness in lowering compliance overhead, enhancing global distribution scalability, and ensuring financial sustainability.
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The promising potential of vision language models for the generation of textual weather forecasts
This study addresses the inefficiency and labor-intensive nature of conventional meteorological text forecasting, which relies heavily on manual composition. We propose an end-to-end generative approach based on vision-language models (VLMs), wherein time-series gridded weather data are encoded as video sequences and directly fed into a VLM to generate natural-language maritime weather forecasts. To our knowledge, this is the first work to apply VLMs to meteorological text generation, thereby overcoming the longstanding cross-modal mapping bottleneck from multimodal meteorological data to structured textual forecasts. Experimental results demonstrate that the method preserves forecast accuracy while substantially improving generation efficiency. Moreover, it exhibits strong scalability and operational feasibility. By enabling automated, intelligent meteorological service delivery, this framework establishes a novel paradigm for next-generation weather forecasting systems.
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Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling
研究通过分布式模型-代理耦合，在Met Office统一模型中使用在线强化学习方法，对模型进行动态一致性和数值稳定性校正，以提高天气预报准确性。
0 citationsRead paper
From Licensing to Open Access: Designing a Sustainable Transition in Operational Weather Data
This work proposes a tiered service architecture designed to reconcile compliance with the European Union’s open data policies and long-term operational sustainability. Core weather forecast data are released under a CC BY 4.0 license, while value-added services generate revenue to offset operational costs. The model employs dynamic infrastructure provisioning and an annual iterative evaluation mechanism, progressively reducing revenue targets to facilitate a smooth transition from a licensing-based model to full open access. Within six months of implementation, over 93% of previously paying institutions renewed their service agreements, and downloads of open data increased substantially. These outcomes demonstrate the architecture’s effectiveness in lowering compliance overhead, enhancing global distribution scalability, and ensuring financial sustainability.
0 citationsRead paper
The promising potential of vision language models for the generation of textual weather forecasts
This study addresses the inefficiency and labor-intensive nature of conventional meteorological text forecasting, which relies heavily on manual composition. We propose an end-to-end generative approach based on vision-language models (VLMs), wherein time-series gridded weather data are encoded as video sequences and directly fed into a VLM to generate natural-language maritime weather forecasts. To our knowledge, this is the first work to apply VLMs to meteorological text generation, thereby overcoming the longstanding cross-modal mapping bottleneck from multimodal meteorological data to structured textual forecasts. Experimental results demonstrate that the method preserves forecast accuracy while substantially improving generation efficiency. Moreover, it exhibits strong scalability and operational feasibility. By enabling automated, intelligent meteorological service delivery, this framework establishes a novel paradigm for next-generation weather forecasting systems.
0 citationsRead paper