基于贝叶斯分位数的水文产品校正与合成方法
Bayesian Quantile-Based Correction and Synthesis of Hydrologic Products
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中文总结 AI 辅助
本研究提出基于DQLMs的贝叶斯分位数校正合成框架，结合多源水文数据，实现中期流量预报及多水平不确定性量化，为水文预报提供有效方法。
中文摘要 AI 辅助
河流流量预报需要在常规和极端条件下均保持信息性的预测分布。我们开发了一种基于贝叶斯分位数的校正与合成框架，该框架构建于动态分位数线性模型（Dynamic Quantile Linear Models，DQLMs）之上。该框架通过共享潜在分位数过程，关联美国地质调查局（U.S. Geological Survey，USGS）的观测数据、回顾性产品和集合预报产品，学习每个外部来源的动态偏差，并将分位数特定的后验预测组合为单一预测分布。我们还将变分贝叶斯推理适配到扩展的动态分位数线性模型中，使用拉普拉斯-德尔塔（Laplace--Delta）近似处理非共轭参数。该方法通过圣洛伦佐河的日流量数据，结合欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts，ECMWF）全球洪水预警系统（Global Flood Awareness System，GloFAS）和美国国家海洋和大气管理局（National Oceanic and Atmospheric Administration，NOAA）国家气象局（National Weather Service，NWS）的产品进行研究，重点关注中期预报和多一分位数水平下的不确定性量化。
英文摘要
River-flow forecasting requires predictive distributions that remain informative in both routine and extreme conditions. We develop a Bayesian quantile-based correction-and-synthesis framework built on Dynamic Quantile Linear Models (DQLMs). The framework links U.S. Geological Survey (USGS) observations, retrospective products, and ensemble forecast products through a shared latent quantile process, learns dynamic discrepancies for each external source, and combines quantile-specific posterior predictions into a single predictive distribution. We also adapt variational Bayes inference to the extended dynamic quantile linear model using Laplace--Delta approximations for non-conjugate parameters. The methodology is illustrated using daily flow for the San Lorenzo River together with products from the European Centre for Medium-Range Weather Forecasts (ECMWF) Global Flood Awareness System (GloFAS) and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS), with emphasis on medium-range forecasting and uncertainty quantification across multiple quantile levels.
