AI 中文总结
该研究针对哥伦比亚农业供应链的气候风险问题，提出数据驱动的实时临近预报框架，整合气象预报与供应链风险建模，可生成可操作风险指标支撑前瞻性决策。
AI 中文摘要
本文提出一种采用数据驱动临近预报技术的实时气候风险评估方法框架，以增强哥伦比亚农业领域的供应链韧性。哥伦比亚的气候具有降雨不规则、温度波动大及极端事件频发的特点，直接影响农业生产与物流，尤其对时间敏感型作物影响显著。该方法整合基于历史气象观测的短期气候预报与供应链风险建模，构建概念性预警系统架构。在受控计算环境中开发的原型实现，利用来自官方统计和再分析产品的历史气象及农业时间序列数据验证了框架的可行性，无需依赖卫星图像或计算机视觉组件。该方法通过明确的风险映射、基于阈值的分类及面向利益相关者的风险信号，解决气候临近预报与供应链决策的整合问题。合成数据与历史数据实验结果表明，短期降水临近预报可转化为农业供应链可操作的风险指标，支持与库存、采购及运输相关的前瞻性决策。
英文摘要
This paper presents a methodological framework for real-time climate risk assessment using data-driven nowcasting techniques to enhance supply chain resilience in Colombian agricultural contexts. Climate variability in Colombia, characterized by irregular rainfall, temperature fluctuations, and recurrent extreme events, has a direct impact on agricultural production and logistics, particularly for time sensitive crops. The proposed approach integrates short term climate forecasting based on historical meteorological observations with supply chain risk modeling to establish a conceptual early warning system architecture. A prototype implementation developed in a controlled computational environment demonstrates the feasibility of the framework using historical meteorological and agricultural time series derived from official statistics and reanalysis products, without reliance on satellite imagery or computer vision components. The methodology addresses the integration of climate nowcasting with supply chain decision making through explicit risk mapping, threshold-based categorization, and stakeholder-oriented risk signals. Results from synthetic and historical data experiments indicate that short term precipitation nowcasts can be translated into actionable risk indicators for agricultural supply chains, supporting anticipatory decisions related to inventory, sourcing, and transport.
Comments6 pages, 3 tables
